HSL Harpur Hill Buxton Derbyshire SK 17 9JN United Kingdom Tel: 01298 218000 Fax 01298 218050

Investigation of the factors

influencing pesticide distribution in naturally-ventilated buildings

HSL/2005/49

Project Leader: Nathalie GobeauAuthor(s): Nathalie Gobeau, Chris Lea, Adrian Kelsey

and John Saunders Science Groups: Fire and Explosion Group and

Environmental Sciences Group © Crown copyright (2005)

ACKNOWLEDGEMENTS

The authors are grateful to Dr Yehuda Sinai, Ansys CFX, who provided some technical support during the course of the project and in particular undertook some CFD simulations.

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CONTENTS

1 INTRODUCTION ...................................................................................................1

2 OVERVIEW OF FACTORS INFLUENCING PESTICIDE DISPERSION................3

3 VALIDATION OF COMPUTATIONAL FLUID DYNAMICS....................................5

3.1 Experimental work.......................................................................................................... 5

3.2 CFD modelling................................................................................................................. 8

3.3 Conclusions .................................................................................................................... 13

4 ASSESSMENT OF THE EFFECTS OF THERMAL STRATIFICATION AND VENTILATION RATE ON PESTICIDE DISPERSION BY CFD .................................. 15

4.1 Methodology .................................................................................................................. 15

4.2 Thermal stratification................................................................................................... 15

4.3 Air change rate .............................................................................................................. 19

4.4 Classification.................................................................................................................. 22

4.5 Conclusion...................................................................................................................... 26

4.6 Implications for aerosols............................................................................................... 27

5 REVIEW OF THE LITERATURE: MAIN FINDINGS ............................................ 28

5.1 Relative importance of the factors ............................................................................... 28

5.2 Gaps in knowledge ........................................................................................................ 29

6 STRATEGY FOR A WAY FORWARD ................................................................ 30

6.1 Introduction ................................................................................................................... 30

6.2 Methods for estimating pesticide dispersion: pros and cons ..................................... 30 6.2.1 ‘Rules of thumb’...................................................................................................... 30 6.2.2 Dimensional analysis............................................................................................... 30 6.2.3 Empirical models..................................................................................................... 31 6.2.4 Simple deterministic models ................................................................................... 32 6.2.5 CFD modelling........................................................................................................ 33 6.2.6 Probabilistic models ................................................................................................ 33 6.2.7 Small-scale experiments.......................................................................................... 33 6.2.8 Field measurements................................................................................................. 34

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6.2.9 Summary ................................................................................................................. 34

6.3 The recommended strategy .......................................................................................... 34

7 REFERENCES .................................................................................................... 36

8 APPENDICES ..................................................................................................... 37

APPENDIX A – PAPER PRESENTED AT THE CONFERENCE ROOMVENT 2002 .38

APPENDIX B – PAPER PRESENTED AT ROOMVENT 2004 ................................... 39

APPENDIX C – DETAILS OF THE LITERATURE REVIEW....................................... 40

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EXECUTIVE SUMMARY

Objectives To validate Computational Fluid Dynamics (CFD) for the prediction of gas dispersion in naturally-ventilated buildings including the design of an experiment to obtain relevant data; To identify and quantify by CFD the effects of the main factors influencing gas dispersion in naturally-ventilated buildings; To devise a simple methodology that can be used by HSE Inspectors to evaluate the risk of exposure to a pesticide that has been applied in a naturally-ventilated building and estimate when it is safe to re-enter. Main findings A full-scale experiment was conducted to provide data for the validation of CFD. Assumptions were made to allow a simplified system to be examined as a first step towards understanding pesticide dispersion in naturally-ventilated buildings: thermal effects resulting for example from solar radiation were ignored and a tracer gas was employed to mimic the pesticide which is in reality often sprayed as a fog or mist. However, it was not possible to eliminate completely the differences in temperature in the experiment and although the temperature difference in the cell was just a few degrees, the effect on gas dispersion was found to be more profound than anticipated. Other parameters were difficult to control at full scale and obtaining experimental data for CFD validation proved difficult because of the low velocities in the test cell for the range of ventilation rates of interest (from 0.5 to 3 air changes per hour) that were lower than the sensitivity of the probes. As a result, the only data available for CFD validation were the gas concentration decay from an initial uniform distribution at four locations. Still, these indicated that the gas distribution in the cell was not uniform and showed that the time when it is safe to re-enter a building after application of a hazardous gas can potentially be under-estimated when the gas is assumed to be fully mixed within the building A more sophisticated approach is needed. Confidence in CFD to predict the non-uniform distribution of the tracer gas was gained through comparison with the four measured gas concentration decay curves and through an extensive sensitivity study. CFD was then employed to investigate the effect of two important factors on the gas dispersion in the test cell: the presence/absence of a thermal stratification and the ventilation rate. A statistical analysis of the thousands of CFD-predicted concentration values obtained across the cell was undertaken to identify the median, first and third quartiles and maximum values in order to characterise the gas distribution over time. This information was employed to devise a simple methodology to allow HSE Inspectors to take into account the departure of the gas decay from a well-mixed assumption, in order to estimate more accurately when it is safe to re-enter a building in which pesticide has been applied. A literature survey was also undertaken to identify the other factors likely to influence the gas distribution and the possible means to quantify their effects to refine the simple methodology in order to include these factors, or at least the most influential ones. v

Recommendations It is recommended to develop further the methodology designed in this project in order to take into account not only the two main factors studied in detail here but also other important factors identified in the literature survey: droplet size distribution; number of inlets/outlets; respective position of the inlets/outlets. It is also recommended to validate the methodology to cover the range of applications of interest to HSE. Hence, the suggested strategy is: 1. Undertake a dimensional analysis to identify the main parameters influencing pesticide

dispersion;

2. Review with HSE the range of configurations needed to be covered and determine the ranges of the parameters identified above that correspond to the scenarios of interest to HSE; this is to downsize the problem to a more manageable task and spend the efforts on the important issues;

3. Find out in the literature any data that is relevant to the scenarios of interest identified

above; 4. Employ simple techniques (such as empirical modelling, rules of thumb) to sketch a simple

methodology, similar to the one suggested in Table 2 of Section 3 from the data available. 5. Employ a probabilistic model to refine the methodology, evaluate its uncertainties and

identify the gaps that need to be addressed to improve its reliability; 6. Employ simple models to fill the gaps; 7. If needs be, employ advanced modelling techniques such as CFD to study in more detail

and with more accuracy a few key scenarios; 8. Ideally, if possible and practical, undertake at least one small-scale experiment to validate

the models employed – simple and advanced – for one of the key scenarios; 9. Refine the methodology by incorporating the new data obtained at the steps 6 to 8 with the

probabilistic method 10. Undertake field measurements for a number of real scenarios; 11. Evaluate the reliability of the methodology to predict the behaviour of pesticide by

comparison with the measurements.

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1 INTRODUCTION

Pesticides are often used to treat indoor spaces. Their application can range from treatment of domestic premises to fumigation of agricultural stores. Once they have been applied, re-entry to the space should not occur until airborne concentrations have fallen to safe levels. The safe re-entry time is usually determined by assuming that the pesticide is uniformly mixed throughout the space. If the rate of air exchange with the surrounding atmosphere is known, or can be estimated, then it is possible to calculate the time needed for concentrations to have fallen to a safe level. In general the time-scale is of the order of hours, rather than minutes. In some circumstances the pesticide will not be uniformly mixed. This departure from well-mixed conditions can occur for a variety of reasons. However, the effects are to decrease pesticide concentrations below the well-mixed value at some locations, whilst increasing concentrations at other locations. It is the latter effect which is of most concern here, since it means that the safe re-entry time will be increased. Since Computational Fluid Dynamics (CFD) proved successful in predicting the transport of pesticide released by a pyrotechnic device (Rimmer et al, 2000), it was decided to employ the technique to investigate the influence of a range of factors on the dispersion of pesticide in a naturally-ventilated building. CFD is a three-dimensional modelling technique which can reproduce the complex air flow patterns in a room and predict the subsequent transport of a substance. The technique provides the concentration of the substance at a large number of locations throughout the space; so, in principle, it is able to predict any distribution including when it is not uniformly mixed. The objectives of this work were three-fold:

a) design an experiment and validate CFD for this application; b) investigate the influence of a range of factors on the pesticide distribution and how this may affect the safe re-entry time;

c) design a simple methodology to enable HSE Inspectors to evaluate a safe re-entry time.

The assumptions made allowed a simplified system to be examined as a first step towards understanding pesticide dispersion in naturally-ventilated buildings. However, during the CFD validation work, one of the factors initially neglected – thermal effects - was found to have a more profound effect than anticipated. Its influence was thus investigated by CFD as well as the influence of the air change rate. It was not possible, within the scope of this project, to investigate by CFD the influence of other factors such as the shape of the building, the location of the inlets/outlets. Instead, a literature review was undertaken with a view to identifying all the factors which influence the internal dispersion of pesticide; understanding their relative importance; establishing the present level of knowledge and identifying gaps; producing a strategy for a way forward. These objectives were rather ambitious for the time available for this study. However it was possible to meet each of them, although obviously not in great depth. This report begins with an overview of the factors that influence pesticide dispersion in Section 2. It is followed by the CFD validation work with a description of the experiment and a comparison of the CFD predictions with the measurements. The difficulties encountered both for the experimental and modelling work are outlined and discussed. Section 4 presents the 1

influence of the presence of a thermal stratification and the influence of the air change rate as identified by CFD. It includes a statistical analysis of the CFD predictions which provides a simple means to Inspectors to take into account these effects when evaluating a safe re-entry time. The main findings of the literature review on the relative influence of other factors are summarised in Section 5. A strategy for increasing the understanding of the many factors that influence pesticide dispersion and improving the evaluation of safe re-entry times is presented in Section 6.

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2 OVERVIEW OF FACTORS INFLUENCING PESTICIDE DISPERSION

Dispersion is the means by which a contaminant (gas, vapour or aerosol) is gradually mixed, diluted and transported in a flow. Transport of a contaminant, such as a pesticide, occurs as a result of bulk air motions. These can be induced by air movement within the building driven by wind-induced external pressure differences. The resulting air motion could be a general drift from the high to the low-pressure side of the building, or, could be a more complex flow pattern resulting from local air jets at cracks and other openings in the building envelope. In addition, temperature differences can result in buoyancy-driven flows which drive bulk air motions within the building. These thermal effects can be very significant, even when the temperature differences are small. For example, Gobeau et al (2004) uses Computational Fluid Dynamics (CFD) simulations to illustrate how a temperature difference of just 2.3oC between the incoming air and the floor of a test chamber ventilated at three air changes per hour can completely change the flow patterns from those established in isothermal conditions. The effect was to make the distribution of a contaminant in the chamber far less homogenous. Turbulence is also a factor that enhances mixing, thus dilution. Turbulence is generated and sustained by shear stresses within a fluid. Shear is produced, or can be induced, by a variety of means. For example, an air jet, as might occur at an opening in a building envelope subject to external wind conditions, will result in shear forces being generated due to the difference in velocity between the jet and the surrounding fluid. This is in fact a very effective means of generating turbulence. The turbulence induced by a jet reduces contaminant concentrations as the contaminant is mixed into the outside air introduced by the jet. In addition, the jet sets up air motions within the building which also generate turbulence. Finally, these air motions can transport the turbulence created by the jet. Turbulence can also be created and transported by buoyancy-driven flows in a building. For example, heated, or cooled, items of equipment in a building will create plumes that will generate and transport turbulence. Other sources of buoyancy-driven turbulence could be convection currents from surfaces heated by solar radiation, or, cooled by contact with the ground. In addition, temperature differentials between the incoming air and that in the building can also lead to turbulence generation due to the resulting bulk air motion. The release of pesticide may also generate turbulence and set-up bulk air motion within the building. Thus a pyrotechnic pesticide will create a plume which will be effective at mixing and transporting pesticide around the building (Rimmer et al, 2000). However the resulting air motion and turbulence will decay relatively rapidly once the release is complete. The natural ventilation flow in the building will then further dilute and transport the pesticide. For a pyrotechnic pesticide release it is reasonable to assume that the pesticide will, initially, be well-mixed in the building air. However, this may not be a valid assumption for other types of more passive release. These could include pesticides which settle rapidly onto surfaces, are absorbed onto these surfaces, then desorb from the surfaces over time. If the release falls into this latter category then this adds a further level of complexity to the problem: the subsequent

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distribution of pesticide will depend on the nature of the source of the release as well as its location within the building. In this study, pesticide is initially assumed to be uniformly mixed throughout the building. It is then important to know how the subsequent decay of pesticide concentration with time varies throughout the building. For well-mixed conditions the decay will be the same at all locations. However well-mixed conditions cannot simply be assumed to exist, since pesticide dispersion is influenced by a wide range of factors. The factors which influence pesticide dispersion are numerous. Table 1 lists some of the main factors.

Table 1 Factors affecting the dispersion of pesticide Factors Variables Atmospheric conditions Wind speed

Wind direction Surrounding buildings Air temperature with respect to that of air in the building Solar radiation (The latter two are dependent on the time of year)

Building Single or multi-storey Single or multi-compartment Size Aspect ratios (i.e. deep or short room, Vs. narrow or broad room) Roof line Obstructions within the building, such as partitions, or storage. Construction material and radiation properties.

Openings in the building envelope Number, type (i.e. crevice flows around windows, cracks around door frames, vent flows, etc.) and location

Thermal effects Thermal stratification Heat sources or sinks within the building Envelope temperature Temperature differentials with respect to the incoming air

Pesticide Vapour or aerosol source Aerosol size distribution Characteristics of the source: size (point, surface, etc..); generation of momentum?, etc.. Surface absorption and desorption Release location in the room

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3 VALIDATION OF COMPUTATIONAL FLUID DYNAMICS

The objective of this phase of the project was to establish the capabilities and limitations of CFD for prediction of pesticide dispersion in naturally-ventilated buildings. To do so, an experiment was carried out and measurements were undertaken. The experimental set-up was reproduced using a CFD model and the predictions were compared against the measurements in order to evaluate the reliability of CFD. The results and conclusions of this work have been presented at the two international conferences ‘Roomvent’ in 2002 and 2004, the papers of which are in Appendices A and B respectively. The readers are referred to these papers for details of the work. This section presents the main findings. 3.1 EXPERIMENTAL WORK

A detailed description of the experiment can be found in Appendix A (paper by Gobeau and Saunders, 2002). The experiment was designed to be representative of the application of interest:

− The size of the test cell, 4 m x 4 m and 2.7 m high, was typical of agricultural buildings;

− The air change rate in the test cell was controlled by a fan and was set within the

range of relatively low values (between 0.5 and 3 air changes per hour) covering the range of interest;

− The thin long inlet on one side of the test cell represented a gap above a door;

Simplifications and assumptions were agreed for the design of the experiment, in particular:

− The pesticide was assumed to be initially fully-mixed within the cellt; this was to make the present work relevant to any scenario where the method of applying pesticide eventually leads to a well-mixed distribution (such as for example a fogging operation). It avoided the work being specific to a particular spraying method.

− The pesticide was assumed to behave like a gas whilst in fact it is often applied as a

mist or fog and will thus consist of a large number of droplets, some of them reaching up to 100 µm in diameter. This assumption allowed to simplify both the experimental and CFD work and was seen as a first step in understanding the behaviour of the air flow in a space and its influence on the dispersion of a substance;

− Thermal effects were neglected although it is likely that in reality the presence of

metallic doors, for example, will induce radiative heat that can influence the air flow and thus pesticide distribution inside the building.

Figure 1 presents a schema of the test cell showing the location of the gas samplers. A tracer gas was introduced into the test cell and mixed with the air of the room by additional fans to ensure full mixing. These were then turned off when the gas was well mixed within the space. The gas

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continued to be supplied through the inlet for a period of time, allowing the flow to re-establish itself after the perturbations induced by the use of the additional fans while ensuring it remained fully mixed. The gas supply was then turned off and the gas concentration was measured by the gas samplers over one to two hours. This procedure was repeated at three different ventilation rates: 0.5, 1 and 3 air changes per hour.

Figure 1 – Experimental set-up

Figure 2 presents the observed tracer gas concentration decay with time for all three air change rates compared with the theoretical gas decay curve obtained by assuming the gas is fully mixed within the room. The time ‘t = 0 second’ corresponds to the time when the gas supply is stopped. As expected, the higher the ventilation rate, the faster the gas concentration decay is. This is the case at each of the four locations. However, for a given ventilation rate, the rates of decay vary from one location to another and also vary with time. The lowest air change rate shows least variation between all locations which seems to indicate that the gas is relatively well mixed within the test cell. However, the measured decay curves do not match the theoretical decay curve obtained assuming the gas is fully-mixed within the test cell. It could be that the gas distribution is in fact not unifom but that the number of samplers is too limited to capture it. For the other two air change rates, the measured gas concentration decay curves are distributed around the theoretical curve based on a fully-mixed assumption and indicate a non-uniform gas distribution of the gas in the test cell. The fully-mixed assumption initially over-estimates the concentration at two positions. Using this assumption to evaluate if a person is safe to enter the enclosure is thus a conservative approach for these two locations. However, at the other two spots the measured concentration is higher than the one estimated with a fully-mixed assumption for the whole duration of the measurements. Note that one of these locations is the centre of the room, thus at breathing height, where the concentration is underestimated by a factor 1.6 for 1 air change per hour and a factor 3.8 for 3 air changes per hour after 45 minutes. 6

a) 0.5 air change per hour

b) 1 air change per hour

c) 3 air changes per hour

Figure 2 – Measured decay of gas concentration at four locations compared with the theoretical decay based on a fully-mixed assumption

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Thus, evaluating a re-entry time based on a fully-mixed assumption may not be conservative. It could potentially lead to re-entry times that are unsafe depending on the toxicity of the substance. Note that for all three ventilation rates, while the short-term gas decay is in some cases similar to the decay assuming the pesticide is fully mixed, over the long-term (typically after an hour) the gas concentration decay is actually slower than the one predicted with a fully-mixed assumption. The difference could be significant when dealing with highly toxic products. In conclusion, this experiment shows that a fully-mixed assumption, presently used to evaluate re-entry times, may not be conservative for all cases. 3.2 CFD MODELLING

The conditions of the experiment were reproduced in a CFD model. Initially, a number of assumptions were made to keep the model simple: for example, the geometry was limited to half of the room, making the assumption that the results in the other half would be symmetric to the symmetry plane of the geometry. More details were then included in the CFD model to represent the details of the physics and geometry until satisfactory results were achieved. The different models employed and the sensitivity of the results to these different modelling approaches were presented at the two International ‘Roomvent’ Conferences in 2002 and 2004. The papers and conclusions drawn from these sensitivity tests can be found respectively in Appendices A and B. To summarise, the list below describes the sensitivity studies that have been carried out and reference is made to the paper in which they are described:

− Computational domains: o limited to half of the test cell (Appendix A); o including the full volume of the test cell (Appendix A); o extended to parts outside the test cell (Apendix B).

− Computational grids:

Ranged from a grid of 148,500 elements for the full volume of the test cell up to 1,200,000 elements for half of the volume of the test cell. (Appendices A and B)

− Boundary conditions: o A constant velocity profile imposed at the inlet (Appendix A); o The measured velocity profile imposed at the inlet (Appendix A); o A constant fix mass flow rate imposed at the outlet (Appendix B) to

reproduce the experimental set up which consisted of extracting the air from the room with a mechanical fan.

− Turbulence models:

o Standard k-epsilon turbulence model (Appendix A); o Low Reynolds Launder-Sharma turbulence model (Appendix A); o Reynolds Stress turbulence model (Appendix B);

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− Presence/absence of thermal stratification (Appendix B). Although the experiment was designed to limit thermal effects, small temperature differences were unavoidable. The temperature was measured in the centre of the room at four different heights. The lowest temperature was found to be 16.6oC 5 cm above the floor and the highest was 18.9oC at a height of 1.8 m. The temperature of the floor was fixed at 16.5oC in the CFD model whilst the temperature of the incoming air was set at 19oC.

All these tests were undertaken for the ventilation rate of 3 air changes per hour. Of the sensitivity studies listed above, the inclusion in the model of thermal stratification in the cell had the greatest effect on the predictions. This is despite the fact that the temperature difference was only 2.5oC. The predictions then successfully reproduced the extent of the non-uniform distribution of pesticide measured by the four samplers. Figure 3 compares the predictions of gas concentration and air velocities in the vertical symmetry plane of the room between the cases with and without thermal effects after six and a half minutes. Without thermal effects, a large eddy of the size of the room is created by the incoming jet. The momentum of the jet from the inlet to the outlet is sufficient to entrain the air of the whole room. The gas is transported by this eddy and mixed with clean air quite efficiently before eventually leaving the room. The gas tends to concentrate in the region of lowest velocities, i.e. in the centre of the eddy located in the region of the centre of the room. With thermal effects, the air in the lower half of the room is nearly stagnant: the momentum of the jet is no longer sufficient to entrain the cooler and hence heavier air near the floor. As a result, the gas decay in the upper part of the room is relatively fast as it is mixed by the jet. However, the cool mixture of air and gas in the bottom part of the room is not mixed efficiently by the clean air and so the gas stays at a high concentration for much longer. The highest concentration reached at the bottom of the room is greater than the one at the centre of the eddy in the isothermal case. As a consequence, the gas concentration has a wider distribution in the presence of thermal effects, which is in better agreement with the measurements. This can be seen in Figure 4 which presents the CFD predictions of gas decay with and without a thermal stratification at four locations compared with the measured values. Although the range of gas concentration is successfully predicted by the CFD model when the thermal effects are taken into account, the agreement between the CFD results and measurements varies greatly from one location to another: it is very good for the sampler located in the exhaust; acceptable for the sampler beneath the inlet; not so good for the central location and bad for the sampler in one of the corners of the room. In fact, both the CFD and the experiments undertaken showed that a small change in the physics of the flow can have a significant effect. on the gas concentration. The thermal plume generated by the operator holding the probe while measuring the velocity profile across the inlet was found to disturb the flow too much for the data to be reliable for the lowest ventilation rate. In the CFD model, the introduction of a temperature difference of 2.5oC leads to a very different behaviour of the gas. It is quite likely that the physics included in the CFD model does not match exactly the conditions of the experiment. For example, not enough information was available to determine the heat transfer boundary conditions. The temperature was only measured in the centre of the room at four different heights. The temperature of the floor in the CFD model was fixed at 16.5oC which was the temperature measured nearest the floor, at 5 cm above it, in order to reproduce a similar temperature gradient (see Figure 5). In reality, the floor may have been at a lower temperature. Also, in the absence of measurements near the walls, 9

these were considered adiabatic. This is an ideal condition which assumes there is no heat transfer. Reality is necessarily different and, even if the heat transfer at the walls is only slightly different, it may still lead to a significant discrepancy in the predicted gas decay.

Isothermal conditions Non-isothermal conditions

Air

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Rel

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c) d) Figure 3 - CFD predictions of the air flow field and distribution of relative gas concentration

390 seconds after the decay started.

a) Isothermal model b) Non-isothermal model

Figure 4 – Comparison of the measured and CFD-predicted gas concentration decay curves at the four sampling locations for a ventilation rate of 3 air changes per hour

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The large discrepancy between the CFD and measurements could indicate that there may have been a leak in the test cell. Ensuring a complete seal of a large space such as the test cell is not easy and even a small hole could allow a small flow of clean air to enter the room and contribute to change the flow pattern inside the room. Unfortunately it was not possible to determine whether or not this was the case. Air velocity measurements were undertaken across the room but unfortunately, the readings were very low and close to the limits of detection of the measuring equipment. They were thus not reliable enough to identify the overall pattern of the air flow and help detect the presence of leaks - if any.

Figure 5 – Measured and predicted temperature profiles in the centre of the room

(Note that the temperature measurements showed a similar gradient for all three air change rates.)

Despite the remaining uncertainties, the model including the thermal effects was then applied to the other two air change rates: 0.5 and 1 air changes per hour. Further sensitivity tests of the results to a few model parameters were undertaken to increase confidence in the results for the very low air velocities encountered at these two ventilation rates: the use of laminar/turbulent models; modification of the values of the gas diffusivity coefficient. In CFD, an a priori decision has to be made whether the flow is laminar or turbulent since the physical behaviour of the fluid and hence the set of equations solved are different. The decision is based on the value of the Reynolds number. For the three cases studied, the Reynolds number based on the inlet flow conditions is 2,200; 4,400 and 13,300 respectively for 0.5; 1 and 3 air changes per hour. Although these values are relatively low, they still indicate the flow is turbulent. However, they are based on the flow characteristics at the inlet where the velocities are highest. In other parts of the room, where velocities are lower, the Reynolds number will be smaller. It is therefore possible that the flow will be laminar or transitional in some parts of the room. One simulation was undertaken with a laminar model for the lowest ventilation rate. The solution showed a transient behaviour more characteristic of a turbulent flow. A turbulence model was thus applied for all ventilation rates. A range of turbulence models were compared including the Reynolds Stress turbulence model which was applied to all ventilation rates. In fact, the predictions did not show a strong sensitivity to the turbulence model and the Reynolds Stress model was retained as it was believed to suffer fewer limitations, at least in theory, for this type of flow. However, it must be stressed that none of the turbulence models applied was designed for low turbulent flows nor transitional flows that may be expected when dealing with low ventilated spaces. The only model available in the CFD code CFX5 that can in principle deal with low turbulent flows is a Large Eddy Simulation. It is, however, very demanding in computing resources and thus not appropriate to obtain information on gas decay over a long period of time.

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A sensitivity test to the value of the diffusivity of the gas was also undertaken. When velocities are low, the transport by diffusion may prevail over the transport by convection. It may thus be important to evaluate exactly the diffusivity coefficient of the tracer gas, a mixture of SF6 and helium, to be closer to the experimental conditions. Two values 10-5 and 10-6 m2/s were tested. No significant difference in the gas distribution was observed. A value of 10-6 m2/s was employed for all the simulations presented in this report. Figure 6 presents a comparison of the CFD predictions and the measured gas decay curves at the rates of 0.5 and 1 air change per hour.

0.5 AIR CHANGE PER HOUR

1 AIR CHANGE PER HOUR

Figure 6 – Comparison of the measured and CFD-predicted gas concentration decay curves at the four sampling locations for a ventilation rate of 0.5 and of 1

air change per hour

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In both scenarios, the CFD predictions show a relatively wide distribution of concentration that spans the decay curve based on a fully-mixed assumption. This is not in agreement with the measurements which show a very narrow distribution for the lowest air change rate. Although the CFD predictions for all three ventilation rates and the measurements for the two higher ventilation rates show some consistency, the measured values for the lowest air change rate are not consistent with the other measurements. Again, this could be due to the presence of leaks in the test cell. The measurements were undertaken on different days for the different ventilation rates, with the test cell being resealed each time. If leaks are the result of the doors not being properly taped, they could be at different locations and at different sizes. Also, the higher the ventilation rate, the more likely air is to be forced into the room through the leaks due to the relatively large pressure difference created by the fan. However, for a low ventilation rate, the pressure difference at the leaks may not just be driven by the fan but also by what is happening outside the test cell. Hence the air could be coming into or going outside the test cell. These two last possibilities could explain the inconsistent results of gas decay measurements for the lowest air change rate compared to the two other rates. 3.3 CONCLUSIONS

An experiment was designed to validate CFD for the prediction of pesticide dispersion in naturally-ventilated buildings. Several difficulties were encountered. Although it was initially decided to neglect thermal effects in this project as a first step towards understanding the dispersion of pesticide in buildings, the CFD sensitivity study indicated that the small temperature difference of just a few degrees found to be present along the vertical centreline of the test cell had a significant influence on the distribution of pesticide. Not enough measurements were taken, however, to be able to fix the boundary conditions accurately; so, the difference between the CFD results and the gas decay measurements could come from a specification of the boundary conditions that differ from the real conditions. In addition, the flow may be sensitive to leaks in the test cell that have not been identified and are not included in the CFD model but may explain the ‘inconsistent’ values of gas decays obtained for three different ventilation rates: 0.5, 1 and 3 air changes per hour. The air velocities were measured at twenty-four locations across the room using ultrasonic probes. However, the velocities were too low for the data to be reliable. Hence it was difficult to identify the presence and location of possible leaks. This also meant that only the concentrations of tracer gas measured over time were available to compare the CFD results against and evaluate their accuracy. These were taken at just four locations. It is thus difficult to be sure of the performance of CFD and impossible to quantify its accuracy. Nevertheless, the results were employed to investigate the influence of two factors since CFD has two main advantages: the set-up conditions are easily controlled which makes it possible to isolate and investigate the effect of a particular factor; also, a wealth of information is obtained – typically the values of all variables playing a role (air velocities, temperature and turbulence; gas concentration) are calculated at hundred thousands locations across the space. An extensive sensitivity study of the CFD predictions to a wide range of parameters was undertaken to gain confidence in the CFD results in order to compensate for the shortage of

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experimental data. It is believed that CFD is able at least to give an indication of the behaviour of gas dispersion. The main uncertainty of the present model lies in the turbulence model employed which is not, in principle, designed for the type of flow of interest here, namely low turbulent and perhaps transional, flows. The only turbulence model available in the CFD code CFX5 that are meant for these flows is the Large Eddy Simulation (LES) which is very demanding in computer resources. An LES simulation is presently being undertaken for this scenario as part of an HSE project to further develop modelling expertise. Because it is computer-intensive, the simulation is only expected to cover a period of a few minutes. This will not provide the information sought - gas decay over an hour or so - but it will be sufficient to compare with the results obtained with other turbulence models and check how sensitive the results are. In order to determine which model is more appropriate and evaluate its reliability, ideally, measurements should be made. Alternatives to full-scale experiments are discussed in Section 6 to reach this objective. It has to be acknowledged, though, that gathering such data is not easy. Compromises may have to be made.

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4 ASSESSMENT OF THE EFFECTS OF THERMAL STRATIFICATION AND VENTILATION RATE ON PESTICIDE

DISPERSION BY CFD

4.1 METHODOLOGY

The use of just four sampling locations is insufficient to represent and characterise the gas distribution across the room. However, an advantage of CFD is the large number of values of gas concentration that are predicted throughout the space - in this case at 156,282 locations. A statistical analysis of the CFD results was undertaken to characterise the distribution of pesticide throughout the room. It was obtained with a set of 9,261 values of concentration at locations spread uniformly across the room. A comparison of the statistics obtained with sampling sets containing fewer and more concentration values showed this number of data was sufficient to obtain reliable statistics. The sets compared contained 1,728; 9,261 and 17,556 and a further set containing the 156,282 values obtained at the CFD grid cells – which were not evenly spaced as the grid was refined near the floor, ceiling and walls and in the vicinity of the outlet. The statistical data extracted were:

− The maximum value in order to determine the maximum risk of exposure. − The median value which provides the middle value of the gas concentration

distribution: half of the values of the sampling set, and since the sampling locations are evenly distributed, half of the volume of the room will have a concentration above the median value and half below.

− The first quartile: 25% of the volume of the room will have a concentration below

the first quartile and 75% above.

− The third quartile: 75 % of the volume of the room will have a concentration below the third quartile and 25% above.

− The interquartile range: it is the difference between the third and first quartiles. It is

a measure of the spread of the gas distribution.

The statistical data of the different CFD simulations were compared in order to investigate the effect of thermal stratification and ventilation rate. 4.2 THERMAL STRATIFICATION

Two simulations, one with and one without thermal stratification, were undertaken for the ventilation rates of 0.5 and 3 air changes per hour. Figure 7 presents a comparison of the time-dependent statistical characteristics, namely the median and the first and third quartiles, with a gas decay curve obtained assuming a fully mixed assumption.

15

a) Isothermal b) Thermal stratification

0.5

air c

hang

e pe

r hou

r 3

air c

hang

es p

er h

our

Figure 7 – Comparison of the time-dependent statistics with a fully-mixed gas decay curve.

For the isothermal case, the median curve is in good agreement with the gas decay curve based on a fully-mixed assumption. Also, the distribution of the concentration, showed by the first and third quartiles, is quite narrow. This is the case for both ventilation rates. This indicates that the flow is relatively well mixed within the room due to the large eddy created by the air entrained by the inlet jet identified in Figure 3. The fully-mixed assumption describes reasonably well the gas decay in most parts of the room although it under-estimates the maximum value. This would have an importance only if the concentration was close to the exposure limit of the substance. However, with the presence of a thermal stratification of just a few degrees, the median deviates from the fully-mixed gas decay curve as time goes by and the distribution of gas concentration is wide. The fully-mixed assumption provides estimations of gas concentration consistently lower than the median and even lower than the first quartile after an hour and a quarter for the lower ventilation rate and fifteen minutes for the higher rate. This means that after this time, the fully-mixed estimations under-estimate the values of concentration in more than three quarters of the room. Figure 8 compares the interquartile ranges which provide a measure of the spread of the distributions. In the absence of thermal stratification, the maximum values are 0.130 and 0.142 for 0.5 and 3 air changes per hour respectively. The time at which it is reached depends on the ventilation rate and is 10 minutes 40 seconds (640 seconds) for 0.5 air change per hour and 5 minutes 50 seconds (350 seconds), for 3 air changes per hour. In the presence of a thermal stratification, the interquartile ranges are always higher than in the absence of a stratification.

16

They reach peak values which are again of the same order for both ventilation rates and which are nearly three times higher than in the absence of stratification. The times at which they are reached again depends on the ventilation rate: one hour 35 minutes for 0.5 air change per hour and 12 minutes for 3 air changes per hour. The interquartile range then diminishes but in the case of 3 air changes per hour, it is only after more than 50 minutes that it becomes smaller than the maximum value reached in the absence of thermal stratification. It takes much longer for the lower ventilation rate and is beyond the simulated period of nearly 3 hours.

Figure 8 – Comparison of the interquartile ranges

in the absence and presence of thermal stratification Figure 3 showed that the regions of high gas concentration were where the velocities were low: in the centre of the large eddy in the vicinity of the centre of the room for the isothermal case and in the bottom part of the room filled with relatively cold and heavy air less prone to entrainment by the inlet jet for the case with a thermal stratification. Figures 9 to 11 present the quantitative vertical profiles of respectively temperature, lateral velocity and gas concentration in the centre of the room. Comparison of Figures 9 and 10 highlight again that a small temperature gradient across the room results in a different air flow pattern. In the presence of a thermal stratification, the air is nearly stagnant within a zone up to 75 cm above the floor. Figure 11 shows that in the early stages, until the distribution reaches its maximum, the gas concentration is close to 1 everywhere in this part of the room. Above, there is a linear decrease of concentration with height. The concentration gradient then becomes linear from the floor to the ceiling and decreases with time. For the isothermal case, the maximum concentration is found at a height of about one metre. The maximum value decreases with time but the height at which it is reached does not vary. Note that at adult breathing height, the concentration is higher for the isothermal case than for the case of a thermal stratification at least until 660 seconds. However, at a child breathing height, the reverse is true. So the importance of taking into account the thermal stratification and the spatial variation of the concentration will depend, at least for this scenario, on the context of the risk assessment. To better estimate the risk of exposure of an adult, a child or a crawling child, the statistical data could be calculated for the corresponding breathing zone instead of the full room. 17

0.5 air change per hour 3 air changes per hour

Figure 9 – Vertical profile of temperature in the centre of the room

0.5 air change per hour 3 air changes per hour

Figure 10 – Vertical profile of lateral velocities in the centre of the room

0.5 air change per hour 3 air changes per hour

T=33

0 se

cond

s

T=72

0 se

cond

s

Figure 11 – Vertical profile of gas concentration in the centre of the room at different times

18

4.3 AIR CHANGE RATE

a) 0.5 air change per hour

b) 1 air change per hour

c) 3 air changes per hour

Figure 12 - Comparison of the time-dependent statistics with a fully-mixed gas decay

curve. 19

Figure 12 shows a comparison of the statistics for the three different ventilation rates: 0.5, 1 and 3 air changes per hour. The CFD simulations the statistics are based on all include the presence of a thermal stratification which results from a relatively cold floor (16.5oC) compared to the temperature of the ambient air (19oC). As expected, the slower the air change rate, the slower the gas decay. For all three ventilation rates, the fully-mixed assumption predicts a gas decay closer to the first quartile than the median. The fully-mixed prediction even becomes lower than the first quartile after a certain time that decreases with the ventilation rate: 1 hour 15 minutes for 0.5 air change rate; 40 minutes for 1 air change rate and 15 minutes for 3 air change rates. After these times, the prediction based on a fully-mixed assumption underpredicts the concentration in all but a small proportion (less than a quarter) of the room. Figure 13 presents the interquartile ranges for the three ventilation rates. The three time-dependent curves are similar: they increase sharply to a maximum value and then decrease initially nearly symmetrically to the rise to their peak; they then decrease inversally to an exponential. For the three ventilation rates, the maximum values are close to each other. They are between 0.41 and 0.44. However, the times at which the maximum is reached decreases with the ventilation rate: 1 hour 35 minutes for 0.5 air change rate per hour; 52 minutes for 1 air change rate per hour; 12 minutes for 3 air change rates. Since there is a steep gradient of the interquartile range towards the maximum value, this means that the lower the ventilation rate, the longer the distribution stays wide around the median. This can be seen in Figure 12a): even at the end of the simulations, nearly 3 hours, the distribution of gas decay remains significant and may have to be taken into account depending on the circumstances, for example on the toxicity of the substance.

Figure 13 - Comparison of the interquartile ranges for the three ventilation rates,

in the presence of a thermal stratification 20

Figures 14 presents a comparison for the three ventilation rates of the vertical profiles in the centre of the room of the temperature, lateral velocity and gas concentration. The temperature gradient is linear and similar for 0.5 and 1 air change per hour but slightly different for 3 air changes per hour. This is a result of the differences in the air flow velocities: the velocity profiles show that the entrainment of air for the highest rate is significantly greater than for the other two rates: the zone of stagnant air is limited to a height of 75 cm above the floor whilst it reaches a height of 2 metres for the other rates. Heat is thus transferred by convection more effectively and the temperature in the room is more uniform, close to the temperature of the inlet jet, with a steep gradient near the floor. Gas concentration profiles show similarities: there is a zone with a nearly constant and high concentration close to the floor, the height of which decreases with the ventilation rate and with time; above, there is a zone of decreasing gradient. At least for the highest ventilation rate, the region of constant and high concentration eventually disappears and there is a linear gradient with a slope decreasing with time. This may happen as well for the other two ventilation rates. However, the CFD simulations may have been stopped before this behaviour could be seen: Figure 13 shows that the distribution range was still high (at 75 % or more of their maximum value) at this time.

a) 330 seconds b) 720 seconds

c) 3090 seconds d) 5690 seconds

Figure 14 – Vertical profile of gas concentration in the centre of the room at different times

21

4.4 CLASSIFICATION

The purpose of this Section is to provide a simple methodology to estimate the behaviour of pesticide dispersion in enclosures. It has been shown that in the absence of thermal stratification, the gas is relatively well mixed within the room and the decay is reasonably predicted by a fully-mixed assumption. However, in the presence of a thermal stratification, the gas decay deviates significantly from the fully-mixed assumption. We make an attempt here to characterise this deviation. Three parameters were considered for this:

− The decay rate of the median; − The interquartile maximum; − The time at which the interquartile maximum is reached.

Although the gas decay does not follow the fully-mixed assumption, after some time, and typically after the distribution range has reached its maximum, the gas decay can be expressed as a relation similar to the fully-mixed gas decay, i.e. a relation of the form:

��

���

�−= t3600ach'expmaxCC (1)

where C is the gas concentration, t is the time in seconds, Cmax and ach’ are parameters to be chosen so as to best fit the gas decay. For a fully mixed distribution, Cmax=1 and ach’=ach where ach is the ventilation rate expressed in air changes per hour. Figure 15 shows the median, first and third quartiles fitted with a curve of type (1). The values of Cmax and ach’ are specified in the Figure. The deviation of the long-term decay rate from the fully-mixed assumption is characterised by the parameter ach’/ach where ach’ is the fitted parameter for the median decay curve that we will call equivalent air change rate. The maximum interquartile range, i.e. the spread of distribution around the median, does not seem to depend on the air change rate but on the absence or presence of thermal stratification. It is respectively around 0.14 and 0.42. However, the time Tmax at which it is reached does depend on the ventilation rate. This time also approximately corresponds to the time from which the median decay can be described by Equation (1). Tmax is compared with the nominal time (see Appendix C) Tn defined by Tn=3600/ach. Let us now consider the Richardson number to characterise the flow since it is the ratio of the buoyancy to the momentum forces which are the main forces in competition to determine the gas decay. Its definition is given in Appendix B and its value for the CFD simulations undertaken are provided in Table 2. The values of the Reynolds number, based on the characteristics of the flow at the inlet, are also given for reference. They indicate the level of turbulence of the flow.

Table 2 – Richardson and Reynolds numbers for the cases studied Air change per hour Temperature

gradient in the room (oC and K)

Richardson number Reynolds number

0.5 0 0 2,200 0.5 2.5 29 2,200 1 2.5 7.3 4,400 3 0 0 13,000 3 2.5 0.8 13,000

22

Isothermal Thermal stratification

a) 0

.5 a

ir ch

ange

per

hou

r b)

1 a

ir ch

ange

per

hou

r

c) 3

air

chan

ges p

er h

our

Figure 15 - Comparison of the time-dependent statistics fitted with exponential decay curves. The parameters ach’/ach and Tmax/Tn, expressed in percentages, which represent the deviation of the gas decay from the fully-mixed behaviour are plotted against the Richardson number respectively in Figures 16 and 17. From these figures, there seems to be three regimes: low Richardson numbers below 0.01 for which the momentum forces are dominant and which result in a nearly fully-mixed distribution of the pesticide. For intermediate values of the Richardson numbers, typically between 0.01 and 8, momentum and buoyancy forces are in competition. The gas dispersion deviates from the fully-mixed behaviour and the deviation depends on both the ventilation rate and strength of the thermal forces compared to the momentum of the ventilation flow. For larger values of the Richardson numbers, the buoyancy forces become dominant. The maximum effect of thermal stratification on departure from a fully-mixed behaviour seems to have been reached. The deviation still depends on the ventilation rate but not so much on the degree of the thermal stratification.

23

Figure 16 – Equivalent air change rate Vs Richardson number

Figure 17 – Time of maximum gas concentration interquartile range Vs Richardson

number Table 3 summarises the rules and relations that can be used to predict the gas dispersion more accurately than with a fully-mixed assumption. Note that to achieve these relations, Cmax for the median was assumed to be 1. In fact it was found to be close to 1, except for the rate of 3 air 24

changes per hour in the presence of thermal stratification (see Figure 15). Its value was 0.88 for this specific case. However, a value of 1 will over-estimate the concentration and thus is a conservative assumption as far as evaluating a safe re-entry time is concerned.

Table 3 – Summary of gas concentration evaluation For , the gas concentration range C can be estimated by: max t T>

( )ach 'exp t3600

0.5 IR

median initial

median

C C

C C

⎧⎪= −⎪

⎪⎨⎪⎪ = ±⎪⎩

where Cintial is the initial concentration; ach’, IR and Tmax are parameters that depend on the Richardson number Ri and ventilation rate ach (expressed in air change per hour).

2UL

TTgRi ∆=

where g is the gravitational acceleration (=9.81 m2/s); ∆T is the temperature gradient; T is the ambient temperature (in Kelvin); L is the characteristic length of the room; U is the inlet velocity.

0< Ri < 0.01 Regime where the

momentum forces are dominant.

14.0IR0max

ach'ach

==

=T

0.01<Ri<8

Mixed regime where momentum and

buoyancy forces are in competition.

42.0IR100

ln*1063ach

3600max

100ln*955achach'

=

��

���

� +=

��

���

� −=

RiT

Ri

8<Ri<30 Regime where

buoyancy forces become dominant.

42.0IRach

3600*8.0max

ach*4.0ach'

=

=

=

T

25

4.5 CONCLUSION

The influence of two factors, the absence/presence of a thermal stratification and the ventilation rate, on the gas distribution across the room was investigated usin CFD. Statisics, namely the median, first and third quartiles, were employed to characterise the spatial distribution within the full space. The deviation of this distribution from a fully-mixed behaviour was quantified with three parameters. Relationships between these and the influencing factors were studied. Three different regimes that depended on the Richardson number were identified. For each regime, a relationship was deduced to evaluate the gas concentration range. These relationships involved two parameters related to the two factors investigated: the ventilation rate and the Richardson number which provides a measure of the ‘strength’ of the thermal stratification relative to the momentum of the ventilation flow. However, too few scenarios were investigated to guarantee the extrapolation of this gas dispersion classification and of the relationships to other cases. In particular, a few questions remain: Is the classification based on the Richardson number appropriate? The categorisation of the gas decay behaviour using the Richardson number suggested above is relevant if, for example, for the same Richardson number but different values of the air change rate and temperature gradient, the characteristics of the gas dispersion are the same. There were only two simulations fulfilling this condition: it was the isothermal simulations undertaken for 0.5 and 3 air changes per hour. The Richardson number was zero for both of them. Both showed the same behaviour similar to a fully mixed assumption. However, would a ventilation rate of 4.2 air change per hour and a temperature gradient of 5oC or a rate of 6 air change per hour and a temperature gradient of 10oC lead to the same deviation from the fully-mixed assumption as 3 air changes per hour and 2.5oC temperature differences. All three cases have the same Richardson number. Are the two parameters, Richardson number and air change rate, sufficient? The relationships characterising the departure from well-mixed conditions may depend on other factors that were kept identical in the relatively limited number of simulations: for example, the location of the inlet and outlet; their size and shape; their number; the size and shape of the room. These are not reflected in any of the parameters considered – i.e. Richardson number and air change rate. Are the relationships providing the gas concentration range reliable? At best, only three points were available to fit a curve for the different regimes. There is thus an uncertainty in the relationships provided in Table 2. Also, the relationships validity beyond a Richardson number value of 30 is uncertain as there may be another regime when the buoyancy forces overcome the momentum forces. The cut-off values of 0.01 and 8 of the Richardson number for the regimes have a relatively large uncertainty due to the limited number of cases studied. In conclusion, more simulations – or data obtained through other means - would be needed to ensure the methodology suggested is relevant. These simulations should ideally cover the range of configurations of interest to HSE. A strategy to achieve this is discussed in Section 6. 26

4.6 IMPLICATIONS FOR AEROSOLS

The approach followed in this project assumed the pesticide behaved as a passive scalar. However, in reality, the pesticide is in the form of airborne droplets the size of which range from a few micrometres up to 30 µm. There are two main differences between the behaviour of a gas and droplets. First, if the droplets are heavy, their gravity will play a non negligible role and will deflect their trajectories from those of molecules of gas. The inclination of a droplet to follow the behaviour of a gas is characterised by the relaxation time or response time of the droplet to the air flow: it represents the time it takes for the droplet to adjust to the local air flow. The heavier the droplet, the longer the relaxation time is and the more different the droplet trajectory is from the trajectory of a gas molecule. Secondly, droplets can deposit on walls unlike gases. As a result of deposition, the passive scalar assumption should be conservative as it will over-estimate the airborne concentration. However, deposited pesticide may cause another risk, such as a risk of dermal exposure through contact with contaminated surfaces, or it may also be re-suspended in the air as droplets or evaporate into the air in gaseous form. These risks are not taken into account when making a passive scalar assumption. Table 4 presents the characteristics of the droplets within the range of interest. The settling velocity corresponds to the equilibrium velocity a particle reaches in quiescent air under the influence of its own gravity. The time in the right column is the time it takes for a droplet to fall by one metre in still air. It is typically the time it would take for a droplet in the centre of the room to reach and deposit onto the floor. The characteristics are calculated assuming that the density of the droplets is 1000 kg/m3 – i.e. the same as water - and that the surrounding air is at a temperature of 20oC and at a pressure of 1 atmosphere.

Table 4 – Characteristics of pesticide droplets Particle diameter

(µm) Relaxation time

(ms) Settling velocity

(m/s) Time to fall by

one metre 1 3.54 10-3 3.48 10-5 8 hours 10 0.312 3.6 10-3 4.5 minutes 30 2.78 2.72 10-2 37 seconds

Aerosols of 1 µm would be expected to have a similar behaviour as a gas. However, for aerosols with a diameter of 10 µm and more, the gravity force is sufficient to change their trajectories. Also, the time taken by a 1 µm droplet to fall by 1 metre is 8 hours. The maximum period over which the CFD simulations were undertaken were less than a third of this time, hence only a few 1µm aerosols would be expected to have deposited. Hence the conclusions drawn with the passive scalar approximation can be applied. However, the time required for aerosols to fall by one metre decreases rapidly with the diameter. It is only half a minute for 30 µm droplets. So it is very likely that a significant number will have deposited over the period of time of interest. The conclusions obtained with a passive scalar assumption are unlikely to be valid for these larger droplets. Further work is thus recommended to understand the influence of the aerosols size distribution on the internal distribution and also on the patterns of deposition onto the floor and walls.

27

5 REVIEW OF THE LITERATURE: MAIN FINDINGS

A limited review of the literature was undertaken with the objective of identifying the relative importance of the many factors identified in Section 2 as influencing the dispersion of pesticides in naturally-ventilated buildings and to identify the possible gaps in knowledge. The review included general books and course notes on ventilation (Aubertin, 1993; Etheridge and Sandberg, 1996; Nielsen, 1998; Goodfellow and Tahti, 2001; ASHRAE Handbook, 2003) and about fifteen papers from journals and conference proceedings. Most of the material, with one exception, dealt with mechanical rather then natural ventilation. The main differences are:

− the size and shape of the openings: For mechanically-ventilated buildings, the openings are of the size and shape of ventilation ducts whilst in naturally-ventilated buildings, the openings can be wider such as, for example, open windows and doors, or much smaller and of a different shape, such as gaps around windows or doors.

− the ventilation rates tend to be higher for mechanically-ventilated buildings; − the temperature and velocity of the ventilation air flow created by a mechanical

system are relatively constant compared to the wind. Studies on natural ventilation in the literature were usually interested in large openings and were therefore more remote from the concern here, not well–sealed buildings, than the studies dedicated to mechanical ventilation. Although not directly relevant to HSE’s area of interest, useful lessons can still be learnt. Although none of the work was directly related to pesticide, and not all of it was studying the dispersion of a substance from an initial uniform distribution, it was possible to draw conclusions when information such as the air exchange efficiency was provided. This Section summarises the main findings. Details of the review can be found in Appendix C. 5.1 RELATIVE IMPORTANCE OF THE FACTORS

The main factors identified from the literature review were:

− Relative location of the supply and extract air devices; − Momentum of the jet; − Heat load in the room (magnitude and location); − Supply of positive or negative buoyant air;

The effects these factors have are inter-related. Figure 18 summarises the effects the main parameters have.

28

5.2 GAPS IN KNOWLEDGE

Most of the studies are limited to gases. Too few related to particulates are available on which to draw conclusions. This is believed to be an important gap to fill for the application to the dispersion of pesticides. Other factors were found to have an effect on the gas dispersion such as the shape of the room, and particularly its length. However, conclusions from the literature were not in agreement. This may reflect the fact that too limited data were available to reach these conclusions. Other factors may play a role in agricultural buildings such as the content of the building, for example boxes stacked in the space; presence of partitions, etc... This may create a complex internal space remote from a box-shape type room. Since the presence of a thermal stratification was found to be an important factor in this limited review, whether data is available to evaluate the influence of any fluctuation of this stratification needs to be checked. This is likely to happen in naturally-ventilated buildings due to the variations in temperature of the wind. Similarly, the effect wind speed variations has need to be considered.

29

6 STRATEGY FOR A WAY FORWARD

6.1 INTRODUCTION

The literature review in Section 5 has identified some of what is known about pesticide dispersion in buildings. This knowledge ideally needs to be encapsulated in a methodology which can be readily applied to determine how safe re-entry times may be affected by the absence of uniform pesticide concentrations throughout the building, for a range of configurations and variables. To advise the form this methodology might take, it is useful to briefly examine the methods available for estimating pesticide dispersion. This will provide a useful background and foundation for the strategy and methodology proposed in Section 6.3. There are, in fact, several different approaches that can be used to provide estimates or measures of the dispersion of pesticide in a building. These are discussed, briefly, below. 6.2 METHODS FOR ESTIMATING PESTICIDE DISPERSION: PROS AND

CONS

6.2.1 ‘Rules of thumb’

‘Rules of thumb’ could be used to provide a crude first estimate of the distribution of pesticide concentration, or, which indicate when well-mixed conditions are not likely to be found. Some possible examples are listed below: If the air temperature outside the building is similar to that inside, then if openings are at the same level and near the roof or ceiling, much of the flow may bypass the occupied level and be ineffective in diluting contaminants there (adapted from ASHRAE, 2003, F.26.11). If the inside air temperature is hotter than outside (due to space heating, solar radiation, etc.), then for openings at high level well-mixed conditions can be expected. However, if openings are at low level, short circuiting could occur at low level. In this latter case the pesticide concentration will not be uniform and will tend to increase with height.

6.2.2 Dimensional analysis

One widely used method for simplifying a complex problem governed by multiple variables is dimensional analysis. In this approach the governing non-dimensional groups are identified. For example, these would include the Reynolds number, which characterises the ratio of inertial to viscous forces in a flow. At small Reynolds number the flow will be laminar, at large Reynolds number it will be turbulent. Rates of mixing are far greater for turbulent flow, which will mean that the concentration of pesticide in a building will be more uniform than if the flow were laminar. In practice the flow will tend to be turbulent in most buildings, even at relatively low air change rates.

30

Once the non-dimensional groups have been identified the problem is immediately reduced in complexity, because the number of variables essentially reduces to the number of dimensionless groups. In principle it is then possible to express the flow parameter of interest in terms of these dimensionless groups, without knowledge of the exact functional relationship between individual variables. However, for the present case, the number of dimensionless groups will still be large. They will include: Reynolds number, as discussed above; Archimedes number, characterising the ratio of buoyancy to inertial forces in the flow (depending on the fields of application the Froude number or Richardson number could be used); Nusselt number, characterising heat transfer from a solid surface to the air; Cp, characterising the external, wind-imposed, surface pressure distribution; building aspect ratios; non-dimensional parameters characterising openings in the building envelope; etc. The flow parameter of interest is the spatial distribution of pesticide concentration. However dimensional analysis is primarily of benefit for calculation of global flow parameters, such as an air change rate, rather than detailed spatial information. This fact, coupled with the large amount of data which would be required to determine the relationship with the governing non-dimensional groups, means that dimensional analysis is, by itself, not a viable approach. However dimensional analysis can be of value for indicating the presence or absence of a particular flow regime. For example, a jet of air issuing from an opening in a building will be deflected after a certain distance if the inflow is at a higher, or lower, temperature than the air within the building. This is due to buoyancy. Correlations based on Archimedes number can be used to calculate the distance before the jet will be deflected (Etheridge & Sandberg, 1996, page 444, Goodfellow & Tahti, 2003, page 490-491). This information could be of value for establishing the range of conditions for which a region of a building above, or below, the level of the primary openings is likely to be purged by fresh air. 6.2.3 Empirical models

This modelling approach is based on data. The data may be obtained from field measurements, small-scale models, or even deterministic models such as CFD. The model may be presented in the form of correlations, often in non-dimensional form. Alternatively the model may consist of no more than ‘rules of thumb’ for certain configurations and range of variables. Often these approaches are supplemented by physical reasoning and dimensional analysis. The main challenge for the present application would be to find sufficient data on which to build empirical models for the full range of configurations and variable range of interest. Such data will simply not exist. However this class of model could still make a valuable contribution, although for a more limited set of configurations and range of variables. 31

A further challenge would be the classification of the range of configurations and variables of interest, such that the appropriate empirical model would be applied by the end user. However this task would be eased considerably if attention was focused on a limited set of configurations and variable range. 6.2.4 Simple deterministic models

Gobeau (2004) reviews such approaches, which generally fall into three categories; diffusion models, box models and multi-zone models. The only relevant approach for the present study would be zonal models, since they alone provide information on the spatial distribution of a pesticide. In this approach the indoor space is split into a small number of zones, possibly as few as two, and flow variables such as concentration or temperature are assumed to be uniform in a zone. Fluxes of heat, mass and momentum are modelled across the boundary of the zones. One significant drawback of zonal models is that they sometimes require the user to define the zones in advance of running the model. This requires prior knowledge of the flow behaviour. In addition, they can only provide limited information on the spatial distribution of a pesticide, equivalent to the number of zones. Furthermore, they can prove difficult to use. A possible alternative which is still under development and being tested at HSL (Deevy, 2005) is the application of CFD on a coarse grid. The solution depends less on the prior knowledge of the flow behaviour as fewer input parameters are required compared to zonal model. However, the solution will still depend on the grid which will have to be chosen carefully. Figure 18 shows a comparison of the statistics obtained with a standard and a coarse CFD simulations for the ventilation rate of 3 air changes per hour in the presence of a thermal stratification – presented in Section 2.

Figure 18 – Comparison of the statistics obtained with a standard and coarse CFD

simulations for a ventilation rate of 3 air changes per hour

32

in the presence of thermal startification The median and first quartile are similar between the standard and coarse CFD simulations. The third quartile is, however, under-estimated by the coarse CFD model. This is due to the very coarse cells employed near the floor which is the location where the gas is highly concentrated. Relatively fine cells were employed near the ceiling, where the concentration was low, in order to be able to predict the jet. As a result, the low concentrated areas are better predicted and so the first quartile is more accurate. Nevertheless, the coarse CFD simulation reproduces the trend of the gas dispersion quite well and is much less demanding than a standard simulation: typically half a day is sufficient to undertake a simulation over an hour whilst a couple of weeks were needed for the fine grid simulation. The coarse CFD model could thus be employed to identify the relative importance of factors although it may not provide a quantitative measure. 6.2.5 CFD modelling

In principle, CFD modelling could supply much of the information required. However, it should be remembered that CFD is a model with its attendant uncertainties. In addition, CFD modelling is not simple to undertake and can be costly, especially if a very wide range of configurations are to be simulated. Gobeau & Lea (1999) discuss the benefits and drawbacks of CFD for pesticide applications. CFD modelling could nevertheless be useful in the present context for providing data which is not available by other means. Such data could be used to refine a simpler model for everyday use. 6.2.6 Probabilistic models

Probabilistic tools such as Bayesian modelling allow the combination of data from different types and sources to express overall probabilities and uncertainties. Such tools could be employed here to combine the results from the literature that may provide different information (for example the age of air or the gas concentration decay) and that may not be in complete agreement with each other. They can also help interpret and structure the information obtained from the various different techniques presented in this section. 6.2.7 Small-scale experiments

Scale-model experiments have been used extensively for examining flow in buildings. The key requirement is that dynamic similarity be maintained. That is, the value of the governing non-dimensional groups should be the same at small-scale as at full-scale. This ensures that the ratio of the driving forces, whether inertial, viscous, buoyant, etc. is the same at both scales. In reality, scaling involves approximations, since it can be difficult, impractical, or even impossible, to maintain dynamic similarity for all the governing non-dimensional groups. This is particularly the case for building flows in which heat transfer is important. Usually the approach adopted is to assume that one non-dimensional group is dominant, and scale using that group. However, this can introduce uncertainty into the value of measurements at small-scale.

33

In addition, scale model experiments may be expensive to set-up, instrument and undertake, especially if a large range of configurations are of interest. It is proposed that, for pesticide dispersion in buildings the main value of scale-model experiments would be in helping devise and validate other modelling approaches. 6.2.8 Field measurements

Field measurements supply the information required. Unfortunately it is often impractical to make measurements for all the configurations and range of variables of interest. Nor is it possible to control these parameters. In addition, measurements in the field can be difficult and costly to undertake, especially if many measurement locations are required. The main value of field measurements in the context of the present work would be in providing data for validation, and, possibly, calibration, of other modelling approaches. 6.2.9 Summary

The requirement for a methodology which covers the very wide range of factors and variables that could affect pesticide dispersion in buildings, means that it is not practicable to base a methodology entirely on modelling techniques such as CFD or small-scale experiments. However these techniques are of value for providing data for specific configurations of interest. Field measurements are of most use, however these are limited to particular configurations and then generally for a narrow range of variables, i.e. external wind speed/direction. Empirical models, based on all of the above sources of data, could provide a practical basis for a simple methodology to estimate pesticide dispersion in buildings. These models can be used in conjunction with dimensional analysis and physical reasoning to extrapolate and interpolate from specific datasets. Statistical methods could also be helpful for refining an empirically–based methodology, particularly where there is uncertainty in the empiricism. However it still remains a formidable task to devise a methodology which will address all the possible configurations of interest. 6.3 THE RECOMMENDED STRATEGY

It is recommended to further develop the simple methodology designed in Section 4 which takes into account the effect of two factors (thermal effects and ventilation rate) in order to include the other important parameters identified in Section 5. Following the experience gained through this work and the review of the pros and cons of the methodologies available in this Section, it is recommended that a combination of methods is employed for the development and validation of the methodology. The following steps are recommended: 1. Undertake a dimensional analysis to identify the main parameters influencing pesticide

dispersion;

34

2. Review with HSE the range of configurations needed to be covered and determine the ranges of the parameters identified above that correspond to the scenarios of interest to HSE; this is to downsize the problem to a more manageable task and spend the efforts on the important issues;

3. Find out in the literature any data that is relevant to the scenarios of interest identified

above; 4. Employ simple techniques (such as empirical modelling, rules of thumb) to sketch a simple

methodology as the one suggested in Table 2 of Section 3 from the data available. 5. Employ a probabilistic model to refine the methodology, evaluate its uncertainties and

identify the gaps that need to be addressed to improve its reliability; 6. Employ simple models to fill the gaps; 7. If needs be, employ advanced modelling techniques such as CFD to study in more details

and with more accuracy a few key scenarios; 8. Ideally, if possible and practical, undertake at least one small-scale experiment to validate

the models employed – simple and advanced – for one of the key scenarios; 9. Refine the methodology by incorporating the new data obtained at the steps 6 to 8 with the

probabilistic method 10. Undertake field measurements for a number of real scenarios; 11. Evaluate the reliability of the methodology to predict the behaviour of pesticide by

comparison with the measurements.

35

7 REFERENCES

ASHRAE HandbookCD, 2003 Deevy, M., 2005, Developing expertise for modelling internal dispersion using simple models, HSL report CM/05/09. Etheridge, D and Sandberg, M, 1996, Building Ventilation, John Wiley & Sons. Gobeau, N, 2003, Review of simple deterministic models predicting the indoor concentration of a contaminant, HSL report CM/03/02. Gobeau, N et al, 2004, Thermal effects on the dispersion of a gaseous contaminant in a naturally-ventilated room, Roomvent 2004, Portugal. Gobeau, N and Lea, C J, 1999, Feasibility study into the contribution of Computational Fluid Dynamics modelling for pesticide applications, HSL report CM/98/03. Goodfellow, H and Tahti, E, 2001, Industrial Ventilation, Academic Press Rimmer D, Johnson P, Gobeau N, Roff M, Pickering D, Saunders J and Wheeler J (2000). Application of pesticides by pyrotechnic devices. HSL report OMS/2000/06. http://www.hse.gov.uk/research/hsl_pdf/2000/hsl00-05.pdf

36

8 APPENDICES

37

APPENDIX A – Paper presented at the conference Roomvent 2002

38

Modelling techniques 1

COMPUTATIONAL FLUID DYNAMICS (CFD) VALIDATION OF THE PREDICTION OF PESTICIDE DISPERSION IN A

NATURALLY-VENTILATED BUILDING N Gobeau, C J Saunders

Health & Safety Laboratory, Buxton, Derbyshire SK17 9JN, UK Phone:+44 114 289 2038 Fax:+44 114 289 2045 Nathalie_Gobeau@hsl.gov.uk

http://www.hsl.gov.uk/

Summary A combination of full-scale measurements and numerical simulations was undertaken to evaluate CFD capability to predict the dispersion of a pesticide applied in a naturally ventilated building. Different CFD models were set up to investigate the effect of inlet conditions, geometries and turbulence models. The predictions of the time-dependent decay of pesticide concentration due to the natural ventilation were compared with measurements at four locations. CFD successfully reproduced the trends. However, quantitatively, CFD over-estimates the mixing inside the building. Introduction The Health and Safety Executive (HSE) have responsibility in the UK for ensuring that risks to people and the environment from pesticides are properly controlled. As part of this HSE is required to carry out risk assessments for situations where pesticides are applied in an indoor environment. The use of pesticides in an indoor environment presents a health hazard to people inside the building and potentially to people outside due to the pesticide escaping through gaps in the building fabric e.g. leakage through gaps around doors and windows. Information on the concentration and distribution of pesticide following application in an indoor environment is therefore essential in assessing the potential exposure of people in and around the building and to establish the most effective methods for controlling exposure. Simple models assess the risks by assuming the pesticide is fully mixed with the air inside the room. In reality, internal wind-induced flows can be complex and lead to uneven pesticide distribution.

CFD has been applied extensively to investigate various aspects of indoor airflows. For example, the technique has been employed to investigate mechanical ventilation effectiveness in removing contaminants or in increasing thermal comfort. A number of studies have also focused on natural ventilation. However, the accuracy of the results have not always been assessed. There are a number of

parameters set by the CFD user which can affect the results, for example the turbulence model (Nielsen, 1998).

In this study, the objective was to validate an industrial CFD model, implemented in the commercial code CFX, for the prediction of pesticide distribution inside poorly sealed buildings for relatively low ventilation rates, leading to typical indoor wind-induced flows (Baldwin and Maynard, 1998). Experimental Procedure Measurements were carried out in a test room measuring 4m by 4m by 2.7m high. The room was configured with one inlet and one outlet. The inlet was 15 mm high and positioned along the top edge of one wall. It was designed to represent a gap above a door of a poorly sealed building typical of the situation encountered by HSE. The outlet was located in the roof and measured 200 mm by 200 mm. Air was extracted mechanically from the outlet at three different rates, which equated to 0.5, 1 and 3 air changes per hour (ach) – see Figure 1. Only the results obtained for a ventilation rate of 3 ach are presented in this paper. The mechanical ventilation had two purposes: first, to repeat the measurements for different ventilation rates, covering the range likely to be caused by external winds; second, to eliminate the uncertainties in the specification of the inlet conditions of the CFD model related to the time-dependent behaviour of the wind.

Figure 1 – Experimental configuration including tracer gas samplers positions.

© British Crown copyright (2002) 81

Modelling techniques 1

The velocity profile across the inlet was measured using a hot wire anemometer – TSI VelociCalc Plus Model 8345 – for the 1 and 3 ach. Figure 2 shows the non-dimensional measured data for a ventilation rate of 3 ach. It was not possible to accurately measure the velocity profile for the lowest rate of 0.5 ach as the inlet velocity was the order of 0.1 m/s and readings were affected by the presence of the operator.

Figure 2 – Non-dimensional inlet velocity profile Volume flow rates were measured using a Wilson flow grid installed in the exhaust ductwork.

To aid validation of the model, velocity measurements were made in the room. An assumption was made that the flow patterns in the room would be symmetrical about the vertical plane dissecting the outlet and the inlet, therefore all measurements were confined to one half of the room. Measurements of the three-components of velocities were made using ultrasonic anemometers at twenty-four positions at three heights: 0.3 m.; 1.8 m.; and 2.5 m.

Tracer gas tests were carried out using a neutrally buoyant gas (16% Sulphur Hexafluoride (SF6), remainder Helium). The tracer gas was released through a perforated tube positioned just upstream of the inlet. The concentration in the room was allowed to build up to a steady state before the tracer gas was switched off. The concentration of SF6 was then measured at the three locations in the room shown in Figure 1. The concentration was also measured in the exhaust to quantify the amount released into the atmosphere. Figure 3 presents the four decay curves.

Figure 3 – Measured time-dependent decay of tracer gas concentration at four locations

Numerical simulations The validation is presented here for the rate of 3 ach. The experiment was reproduced numerically using the commercial code CFX of AEA Technology. The two versions of the code CFX, CFX4 and CFX5, were employed as they offer different turbulence modelling approaches. Optimised respectively for structured meshes (i.e. cells of regular shapes) and unstructured meshes (i.e. cells of complex shapes), they encompass different numerical techniques for the resolution of the equations. The same structured mesh was employed to eliminate the discrepancies of the CFD predictions linked to the mesh and isolate the influence of the turbulence modelling approaches. It is believed that the discrepancies are mainly due to the difference in the turbulence models, although the numerical techniques could affect the result to a lesser extent.

Equations were solved for the three air velocity components, gas concentration and turbulence quantities (using a k-ε turbulence model) with a second-order differencing scheme. The air velocities inside the room induced by a prescribed inlet condition were calculated by a steady-state simulation. A Neumann boundary condition was applied at the exhaust. A transient simulation was then undertaken until the target residuals were achieved. A transient simulation was then started with the computed velocities and a uniform gas concentration as initial conditions. The decay of gas concentration in the room was predicted over a few minutes. Four models, summarised in Table 1, were employed.

Computational domains Initially, the experiment was designed to be symmetrical in order to be able to restrict the numerical study to half of the room. However, the outlet was found to be located 55 mm to

82

Modelling techniques 1

one side of the symmetry plane, towards the negative X. Two computational domains were thus created: one representing the full room; another limited to half of the room - assuming the outlet was ideally located on the symmetry plane. Whilst the error in its position was found to affect the predictions at the corner and in the centre, the extent to which the results were affected was not significant (see Figure 4). The reduced computational domain was thus used to investigate the performance of different turbulence models.

Computational grids Two structured grids were generated with 600,000 and 1,200,000 cells. The latter grid was designed to resolve the turbulent boundary layer with 2 mm wide cells next to the walls. It was applied to only half of the room to limit the requirement in computing resources.

Inlet conditions Two inlet boundary conditions were used: a constant profile of 0.6 m/s (corresponding to 3 ach) and a linear interpolation of the measured profile.

Turbulence models Two types of k-ε turbulence models were used: a standard model combined with scalable wall functions available in CFX5 (Grotjans and Menter, 1998) and the low Reynolds model proposed by Launder and Sharma (1974), available in CFX4. The scalable wall functions are meant to eliminate the constraints of standard wall functions on the grid size. Results and discussion Figure 4 presents the comparison between the time-dependent decay of gas concentration for the CFD predictions and the measurements. Qualitatively CFD reproduces the trends, although all models overestimate the mixing inside the room - especially in the exhaust and beneath the inlet where the decays are quite similar to those predicted by a fully mixed assumption. a)

b) c) d)

Figure 4 – Comparison of the time-dependent gas concentration decay

between CFD, measurements (points) and fully-mixed predictions (straight line) – see

legend in Table 1.

The predicted velocities (Figure 5) show that the clean air coming through the narrow inlet creates a large eddy in the room. As a result, the tracer tends to decay more slowly in the centre where the low velocities are found and hence where the decay is due predominantly to diffusion rather than convection processes. In planes across the outlet, the strength of the fresh air jet is lowered due to the extraction of mass and momentum through the outlet. This leads to a slower decay at the three measurement positions across the plane through the outlet compared to that in the corner. The presence of the outlet, small compared to the inlet length, produces three-

Modelling techniques 1

dimensional effects, noticeable especially downstream the outlet.

Run 2

Run 3

Run 4

Figure 5 - CFD computed velocities across the symmetrical plane of the inlet

Performance of the turbulence models (Runs 3 and 4) was assessed for a less computationally demanding domain; limited to one half of the room. The differences between the models are believed to be mainly due to the near wall treatment. The boundary layer is resolved with the low Reynolds number treatment and this

ultimately impacts on flow velocities in the core of the room.

Unfortunately, the measured velocities were quite low, only a few cm/s, and are just at the limit of the sensitivity of the anemometers (10 cm/s). CFD predicted velocities below this limit in most of the room. Their reliability being questioned, these measurements could not be used to help assess the performance of CFD. Conclusion CFD could qualitatively represent the trends observed in the experiment. However, quantitatively, the use of more advanced techniques did not improve significantly the results.

It is intended to validate CFD for two other ventilation rates and to investigate numerically a range of configurations: ventilation locations, room shapes, roof shapes. The results will be used to help improve the current evaluations made by HSE based on a fully-mixed assumption.

Acknowledgments The authors would like to thank Mr A Garrod (HSE) for advice and support in conducting this study. References Baldwin P.E.J., Maynard A.D. (1998) ‘A Survey of Wind Speeds in Indoor Workplaces’. Ann. Occ. Hyg., Vol. 42, No 5, pp.303-313.

Grotjans H., Menter F.M. (1998) ‘Wall Functions for General Application CFD codes.’ Eccomas.

Launder B.E., Sharma B.I. (1974) ‘Application of the energy-dissipation model of turbulence to the calculation of flow near a spinning disc’ Letters in Heat and Mass Transfer. Vol 1.1, pp.131-137.

Nielsen(1998) ‘The Selection of Turbulence Models for Prediction of Room Airflow.’ ASHRAE Transactions: Symposia. SF-98-10-1.pp.1119-1127.

Simulation Code Geometry Grid Type of k-ε

turbulence model Inlet conditions Legend of

Figure 1 Run 1 CFX5 Full room 600,000 High Reynolds Measured profile Run 2 CFX5 Full room 600,000 High Reynolds Constant profile Run 3 CFX5 Half room 1,200,000 High Reynolds Constant profile Run 4 CFX4 Half room 1,200,000 Low Reynolds Constant profile

Table 1 – Summary of the numerical simulations

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APPENDIX B – Paper presented at Roomvent 2004

39

Thermal effects on the dispersion of a gaseous contaminant in a naturally-ventilated room

N. Gobeau1, A. Kelsey1, C J. Saunders1 and Y. Sinai2

1Health and Safety Laboratory, Harpur Hill, Buxton, Derbyshire, SK17 9JN, United Kingdom

email: Nathalie_Gobeau@hsl.gov.uk http://www.hsl.gov.uk 2Ansys CFX, The Gemini Building, Fermi Avenue, Harwell International Business Centre, Didcot, Oxfordshire,

OX11 0QR, United Kingdom

Summary: The influence of thermal effects on the dispersion of a gas in a naturally-ventilated room is investigated using CFD in conjunction with measurements. The gas dispersion inside the room, with and without thermal effects, is characterised by a statistical analysis of the CFD-predicted gas concentrations at a large number of points across the room with a view to quantifying the thermal effects. It is concluded that even small temperature differences can lead to significantly different gross flow behaviour and rates of gas concentration decay at the relatively low air change rate considered. It is found that for the same ventilation rate, the presence of temperature stratification in the room can lead to a higher level of exposure to a gas contaminant in the lower parts of the room. Keywords: Thermal effects, dispersion, CFD, natural ventilation Category: Natural and hybrid ventilation

1 Introduction Application of pesticides in buildings can cause health hazards. Even when pesticides are being released in an empty building, establishing when it is safe to re-enter can prove difficult. Generally a fully-mixed assumption is made to predict dispersion and consequently the safe re-entry time. However, many factors can influence the internal dispersion, such as the ventilation rate and building shape. Not all of these factors are accounted for by simple models based on a fully mixed assumption. This work aims to employ a more sophisticated modelling technique, Computational Fluid Dynamics (CFD), to investigate the thermal effects on the dispersion of a tracer gas in a naturally-ventilated room. The approach was validated by comparing the predictions with measurements.

2 Test case Measurements were carried out in a test room measuring 4m by 4m by 2.7 m high, see Figure 1. The room was configured with one inlet and one outlet. The inlet was 15 mm high and positioned along the top edge of one wall to represent a typical leak above a double door. The outlet was located in the roof and measured 200 mm by 200 mm. Air was extracted mechanically from the outlet at three different ventilation rates, which equated to 0.5, 1 and 3 air changes per hour (ach). The lateral air velocity profile along the inlet slot was measured for the two highest ventilation rates. Three

component velocity measurements were made at twenty-four strategic locations in the room using ultrasonic probes. A neutrally buoyant tracer gas, representing the pesticide, was released in the inlet. Mixing fans were employed to ensure a uniform concentration in the room. The mixing fans were stopped and over an hour was allowed for the disturbances to the flow created by the fans to disappear. The concentration in the room was believed to be fully mixed by then. The supply of the tracer was then stopped and the time-dependent decay of gas concentration was measured at four locations in the room, including the outlet. Details of the experiment can be found in [1].

Fig 1. Experimental set-up The test room was enclosed in a building and therefore air being drawn into it originated from indoors. Hence only small differences in the temperature of the air throughout the room were expected. The air temperature was measured at four

© British Crown copyright (2004)

different heights at the centre of the test room – see Table 1.

Table 1. Air temperature measurements

at the centre of the room

Height from the floor (m)

T (oC)

0.05 16.6 0.87 18.1 1.79 18.9 2.6 18.4

3 Modelling technique A CFD model of the test room was created using the commercial code CFX5.5.1 of Ansys-CFX. A comparison of the predictions against the measurements for the ventilation rate of 3 ach was presented in [1] for a range of turbulence models, computational meshes and specifications of the boundary conditions. All simulations were carried out assuming isothermal conditions. In this paper we present a comparison of predictions obtained with an isothermal CFD model and with a CFD model taking into account the small temperature differences measured for the rate of 3 ach. A simple analysis was undertaken to determine if these thermal effects could influence the gas dispersion. The Richardson number Ri was evaluated. This number represents the ratio of buoyancy to momentum forces. In general, buoyancy effects prevail if Ri is much smaller than 1 and momentum effects are dominant if Ri is much greater than 1. For values of Ri around 1, the buoyancy and momentum forces both influence the flow. The Richardson number is defined as:

2T L

Ri=gT U∆

where g is the gravitational acceleration (9.8 m/s2); ∆T is the temperature difference in the room (3oC); T is the ambient temperature (19oC); L is the characteristic length of the room (3.5 m - taken as the cubic root of the volume of the room); U is the characteristic velocity (0.6 m/s – based on the air velocity entering the room through the inlet). Based on these values, the Richardon number is 1. This indicates that the buoyant effects from the air temperature differences measured in the room and the momentum effects from the ventilation air flow could be as equally important. The results presented here are based on a CFD model which includes part of the air ducts before the inlet and behind the outlet – see Figure 2.

Fig 2. CFD geometry The computational mesh in the room is structured and consists of 148,500 hexahedrons. The cells are refined near the walls and in the vicinity of the outlet. The mesh in those regions external to the room is unstructured. Details of the type and number of cells in the different parts of the geometry are given in Table 2. The mesh in a vertical plane is presented in Figure 3.

Table 2. Type and number of cells of the CFD mesh

Part of the

domain Number of

cells Type of cells

Pipe 12,334 Tetrahedrons Room 148,500 Hexahedrons Duct and expansion

149,478 Tetrahedrons

Fig 3. Computational mesh in a vertical plane

The Reynolds Stress turbulence model of Speziale et al. [2] and implemented in CFX5.5.1 was used. Initial results were obtained for isothermal conditions. Temperature effects were then included by drawing on the temperature profile measured in the centre of the room which ranged from 16.6oC near the floor to 18.9oC in the centre. An energy equation was solved. A relatively low temperature of 16.5oC was imposed in the CFD model at the floor compared to a temperature of 18.4oC for the incoming air. The walls and ceiling were assumed adiabiatic, i.e. no temperature transfer was allowed

© British Crown copyright (2004)

between the air and these surfaces. The Boussinesq approximation was employed – i.e. the density of the air was assumed constant and the air movement due to the thermal effect is taken into account by an additional term in the momentum equation. This assumption is justified by the small temperature differences involved. The equation for a passive scalar quantity was solved to predict the tracer gas concentration. The air flow field was first calculated by a steady-state simulation, followed by a transient simulation with time steps of 0.5 second to capture the transient behaviour of the flow. The concentration of the passive scalar was set to one across the room and the time was re-set to zero. A transient simulation, again using time steps of 0.5 second, was undertaken from these initial conditions to predict the decay of the passive scalar across the room. A high level of convergence was achieved: mass residuals were below 0.002% of the incoming air flow rate.

4 CFD results The behaviour of the predicted air flow is quite different depending on whether the thermal effects are taken into account or not – see Figure 4. For isothermal conditions, a large eddy the size of the room can be seen in a central plane – see Figure 4a. It is created by the momentum of the ceiling jet from the inlet to the outlet and which entrains air from the room. With thermal effects included, the air in the lower half of the room is nearly stagnant – see Figure 4b. The momentum of the jet is no longer sufficient to entrain the air near the floor which is colder and hence heavier than in the rest of the room, i.e. the flow is stably-stratified. As a result, the distribution of the tracer gas is different too. In both cases the gas concentrates in the regions of low air velocity where the mixing is less efficient: that is in the centre of the large eddy corresponding approximately to the centre of the room for isothermal conditions (Figure 4c); and, in the lower part of the room when thermal effects are taken into account (Figure 4d). The predictions of gas decay were compared with the measured values obtained at four different

positions in the room: at the centre of the room, in a corner, beneath the inlet near the ground (positions shown in Figure 1) and at the outlet. This comparison is shown in Figure 5. For this purpose, the non-dimensionalised CFD values of gas concentration were multiplied by the concentration of gas measured at the start of monitoring the decay, i.e. 68 ppm. For isothermal conditions, the four predicted decay curves qualitatively show the same trends as the measurements. In particular, they show that fully-mixed conditions do not occur. However, quantitatively, they show a narrower distribution than the measurements. With thermal effects included, the distribution of the four predicted gas decay curves is wider and quantitatively corresponds better to the measurements. In particular, the values at the exhaust and beneath the inlet match the measurements relatively well. However, the value predicted at the corner is not in agreement at all with the measurement. The flow inside the room is three-dimensional and complex. Hence, a small variation in position can lead to quite a different gas concentration. CFD cannot be expected to predict the exact concentration at the exact location. Nevertheless, the fact that the gas concentration at the outlet for the non-isothermal simulation is in good agreement with the measurement seems to indicate that the main gas transport mechanisms are being taken into account. The gas concentrations predicted at the centre of the room and beneath the inlet are not in such a good agreement but are still reasonable considering the complex three-dimentional nature of the flow. Gas concentration in the corner is, however, not satisfactory: this may be due to an air leakage from the test room which, even if small, could lead to a significant effect on the gas transport. Because of the three-dimensional and complex nature of the gas distribution leading to a sensitiviy to small variations in measurement location, it was decided to undertake a statistical analysis of the predictions to better characterise the gas distribution inside the room and compare the cases with and without thermal effects.

© British Crown copyright (2004)

Isothermal conditions Non-isothermal conditions

Air

velo

citie

s and

rela

tive

gas c

once

ntra

tion

cont

ours

in a

ver

tical

pla

ne in

the

cent

re o

f th

e ro

om

a) b)

Rel

ativ

e ga

s con

cent

ratio

n in

a v

ertic

al p

lane

in

the

cent

re o

f the

room

c) d) Fig 4. CFD predictions of the air flow field and distribution of relative gas concentration

390 seconds after the decay started.

a) Isothermal conditions b) Non-isothermal conditions

Fig 5. Comparison of the time-dependent gas decay between the CFD predictions and the measurements

5 Statistical analysis of the CFD predictions

a) Isothermal conditions b) Non-isothermal conditions

Fig 6. Statistical values (median, maximum, 25th and 75th percentiles) obtained from a large number of CFD predictions compared with the four measured gas decay curves.

Statistical information was obtained from a large number of CFD values across the room every 30 seconds: the maximum; the minimum; the median or 50th percentile; the 25th and 75th percentiles; the interquartile range, i.e. the difference between the 25th and 75th percentiles. The median represents the halfway concentration, i.e. for any point in the room the concentration is as likely to fall below the median as it is to fall above. The interquartile range is a measure of the variability of the gas concentration, i.e. the likely range in which the concentration at any point in the room is most likely to fall. A sensitivity study of the statistical data to the number of CFD values considered in the sample was first undertaken. Three sets of CFD concentrations were employed: a) the 148,500 values obtained at the CFD grid. These were not evenly spread across the room as the grid was refined near the walls, ceiling and floor and in the vicinity of the outlet – see Figure 3; b) 1,728 values; c) 9,261 values. For the last two sets, the values were taken at locations that were evenly spaced for each direction between the minimum and maximum grid points. The spacings for each direction for the two sets were respectively (in the x, y and z directions): b) ∆x=32.5 cm; ∆y=32.8cm; ∆z=22.2 cm. c) ∆x=18.6 cm; ∆y=18.8 cm; ∆z=12.7 cm. A comparison of the statistical data between the three sets for both isothermal and non-isothermal conditions showed that there was no difference between the minimum and maximum values – probably because these were reached at locations closest to the ceiling and to the walls that were included in all the samples. The median values

were within 8% for the isothermal case and were nearly identical for the case with thermal effects. The interquartile ranges showed the greatest discrepancies between the three sets with a maximum difference of 20% in the isothermal case and 30% in the case with thermal effects. These maximum differences were seen at the peak values of the interquartile ranges which were found at the same moment in time for all sets. Elsewhere, the differences were less important. It is thus believed that all three samples are suitable for a statistical analysis. The intermediate set of 9,261 values was selected for the analysis presented here. Figure 6 shows the values of maximum, median, 25th and 75th percentiles plotted against time. These were dimensionalised with the initial value of gas concentration measured when the gas decay started to be monitored. This allowed comparison of the statistical data with the measured gas decay values obtained at four locations in the room. Note that the minimum was found to be zero, except at the initial time when the room was uniformly filled with gas. The maximum concentration obtained for isothermal conditions is not greater than all the measured values, unlike the maximum obtained when accounting for thermal effects. Hence, this last simulation is believed to be closer to reality, although thermal effects are unlikely to be represented accurately in the model and possible leaks from the test room are not accounted for. The two simulations, however, highlight the influence of the presence of temperature stratification in the room. In the isothermal case, the gas is relatively well-mixed within the room. The presence of thermal effects leads to a stably-

© British Crown copyright (2004)

stratified flow which reduces mixing in the vertical direction. Hence, two zones appear in the room: one near the ground which is poorly ventilated and in which the gas concentration decays slowly; one at a higher level under the influence of the inlet jet which is well ventilated (similar to the isothermal case) and in which the gas decays significantly faster. As a consequence, the range of gas concentrations is greater in the case with thermal effects. This is evidenced by the range between the 25th and 75th percentiles – which contain by definition 50% of the concentration values across the room – which is only a narrow band around the median curve in the isothermal case. The interquartile range in Figure 7 gives an indication of the most likely range of concentrations across the room. This range initially increases with time, reaches a peak and then decreases. The peak is reached at a time of 400 seconds for the isothermal case and 660 seconds for the non-isothermal case. The peak of the interquartile range is larger in the non-isothermal

case than in the isothermal case by nearly a factor of four.

6 Conclusion The influence of the presence of a temperature stratification in a room on the internal gas dispersion was investigated by means of CFD. Temperature differences of only just over 2oC were investigated for an air change rate per hour of 3. A comparison with measurements was made to evaluate the methodology. A statistical analysis of the CFD predictions across the room was then undertaken to compare the median, minimum and maximum concentrations in the cases with and without thermal effects. The median gas decay was slower in the case with thermal effects than for isothermal conditions. The most likely range of concentrations, evaluated by the interquartile range, was also wider in the former than in the latter. Hence for the same ventilation rate, the presence of temperature stratification in the room could lead to a higher level of exposure to a gas contaminant in the lower parts of the room.

Fig 7. Interquartile range

7 References [1] N. Gobeau and C.J. Saunders. Computational Fluid Dynamics validation of the prediction of pesticide dispersion in a naturally-ventilated building. In Proc. 8th Int. Air Distribution in

Rooms, Roomvent 2002, pp. 697–700, Copenhagen, Denmark, 8–12 September 2002. [2] C.G. Speziale, S. Sarkar, T.B. Gatski, Modelling the pressure-strain correlation of turbulence: an invariant dynamical systems approach, Journal of Fluid Mechanics, Vol. 277, pp. 245-272, 1991.

© British Crown copyright (2004)

APPENDIX C – Details of the literature review C.1 MONOGRAPHS C.1.1 Etheridge & Sandberg (1996) There is little material here which is specific to the problem of pesticide dispersion in buildings. However this reference does provide some potentially useful rules of thumb. In addition, some quantitative data is presented which is relevant if the pesticide can be treated as a passive scalar initially uniformly mixed throughout the building. (Page 267/268) For a building in which no contaminant enters from outside, a contaminant removal effectiveness can be defined as;

CCe=ε x 100%

Cε is the concentration in the air which leaves the building. C is the average concentration inside the building. If complete mixing occurs, ε is 100%. For poorly-ventilated spaces, in which there are regions of the building in which the contaminant concentration is higher than that leaving the building (for example, in a stagnant area), then ε falls below 100%. The air exchange efficiency, εa, is a measure of the effectiveness of the ventilation, defined as;

exc

na τ

τε = x 100%

τn is a nominal time constant, defined as the building volume divided by the ventilation flow rate (i.e. the reciprocal of the air change rate). τn is therefore the shortest possible time it might take to replace the air in the building. τexc is the average time it actually takes to replace the air in the building. If complete mixing occurs τexc is equal to 2τn, and εa is therefore 50%. It is more common to find measures of εa in the literature, than ε. (Page 268) For the special case in which the contaminant is passive and released uniformly throughout the entire air space, ε is linearly-related to εa. In general this is not the case, since ε will depend on the nature of the contaminant source and its location. However, if we assume that the pesticide is passive and is initial uniformly mixed throughout the building, literature data for εa can be used in place of that for ε to indicate the effectiveness of the ventilation in removing the contaminant. (Page 268-271) For example, Etheridge & Sandberg present figures showing how contaminant removal effectiveness and air exchange efficiency are affected by the location of openings in a simple room and, importantly, the temperature difference between the internal air and that entering the

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room. In this case the contaminant is not passive and is released at a point. This means that the data presented for contaminant removal effectiveness is only valid for this particular contaminant source, which is of limited use. However, using the above principle, the data for air exchange efficiency can be used to infer the contaminant removal effectiveness. Thus this data shows that for an inlet and outlet at high level, and on opposite walls, the air exchange efficiency is approximately 25% under iso-thermal conditions. This implies that the contaminant removal effectiveness would be 50% for a passive contaminant initially released uniformly throughout the room. When the temperature of the air entering the room is lower than that of air already in the room, the air exchange efficiency rises. This is because the relatively cooler air which enters the room tends to fall towards the floor before leaving at high level. This promotes mixing in the room. The contaminant removal effectiveness would follow the same trend as the air exchange efficiency for a passive contaminant initially released uniformly throughout the room. When the temperature of air entering the room is higher than that in the room, short-circuiting tends to occur at high level and the air exchange efficiency falls. The contaminant removal effectiveness would also fall, for a passive contaminant initially released uniformly throughout the room. This data also shows that for an inlet and outlet at high level, on the same wall, the air exchange efficiency is 50% under iso-thermal conditions. This means that complete mixing is occurring. This implies that the contaminant removal effectiveness would be 100% for a passive contaminant initially released uniformly throughout the room. Again, when the temperature of air entering the room is higher than that of air already in the room, the air exchange efficiency, and hence contaminant removal effectiveness, falls. Although this particular dataset does not provide information on local concentrations, it does nevertheless give an indication of the overall effectiveness of the ventilation for dispersion of a contaminant under certain conditions. The data could be used to infer the overall behaviour of pesticide dispersion, provided that it can be treated as passive. In addition, the data provides a rough ‘rule of thumb’ (see also page 266):

• For inlets and outlets located at high level: when the incoming air is warmer than the average room air temperature then short-circuiting is likely, with a reduction in air exchange efficiency. When the incoming air temperature is less than the average room air temperature, the incoming air drops down and complete mixing is promoted.

A similar effect occurs for inlets and outlets both at low level, although in this case short-circuiting occurs when the incoming air is cooler than the average room air temperature. This can provide a further ‘rule of thumb’:

• For inlets and outlets located at low level: when the incoming air is less than the average room air temperature then short-circuiting is likely, with a reduction in air exchange efficiency. When the incoming air is warmer than the average room air temperature, the incoming air rises and complete mixing is promoted.

Note that these ‘rules of thumb’ are only valid if thermal stratification does not occur in the room. It may be possible to obtain a criterion, based on dimensional analysis, which indicates whether or not thermal stratification is likely.

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(Page 396) A simple expression is given which can be used to indicate whether heat sources in a building are likely to give rise to typical flow velocities which are comparable to those created from a jet of air through a leakage at the building envelope. If this is the case, then thermal effects could have an important influence on pesticide dispersion. The expression is as follows:

)0()0(3.3 3/1

2/1

UE

AUU

B

r =

Ur is a room velocity generated by a leakage jet, UB is a buoyancy-induced air velocity, A(0) is the area of the opening in the building envelope which gives rise to the leakage jet, U(0) is the initial velocity of the air jet, E is the convective heat output of the buoyant source. As an example, if A(0)1/2 is 0.1 m, and U(0) is 5 m/s, then E only needs to be a few Watts for the buoyancy-induced flow velocity to be comparable to that induced from a leakage jet. This indicates that heat sources in a building are likely to be a very significant contributor to air movement. (Page 397) Similar, simple, relationships can be used to indicate when a leakage flow which is either hotter or cooler than the internal air temperature in the building will undergo transition from momentum to buoyancy-dominated. If the distance to transition is far greater than the building dimension in the direction of the flow, then the buoyancy from the leakage flow can be expected to only have a minor influence on flow in the building. (Page 412) CFD simulations are presented for isothermal flow in a room with the same height but different lengths. The inflow is at high level. The outflow is at low level, either on the same, or opposite wall, from the inflow. The results show, for the inflow and outflow on the same wall, that if the inflow is at relatively low velocity it may not penetrate to the opposite wall. Instead, primary and secondary regions of recirculating flow are created. Etheridge & Sandberg state that there is good mass and momentum transfer between the recirculation cells. This tends to imply that the distribution of pesticide in such circumstances could be more homogeneous than at first expected. However this conclusion may be particular to the configurations investigated. Its relevance to larger buildings, rather than simple rooms, is uncertain. This study also indicates the value of CFD for detailed investigation of particular configurations. (Page 418-419) Etheridge & Sandberg state that; “with supply of isothermal air the location of the supply and extract have little direct influence on the degree of mixing……….” The authors then go on to consider the gross flow patterns created by differing supply and extract points for supply of positive or negatively buoyant air, as well as flow controlled by the buoyancy of a internal source.

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This particular analysis is aimed at flows in rooms, with a presumption that the flow is forced ventilated. Whilst these conditions are different to those of naturally-ventilated building flows, some potentially useful ‘rules of thumb’ could nevertheless be extracted. (Page 451) The authors point out that in thermally-stratified conditions the distribution of contaminant may be more stratified than the temperature field. This is because radiation also acts to re-distribute heat, a process which has no analogy in mass transfer. The relevance to dispersion of pesticides is that the thermal stratification may not need to be particularly strong for there to be a marked stratification in pesticide distribution. In thermally-stratified conditions there is a marked vertical temperature gradient, with air temperature increasing with height. There tends to be a horizontal interface which separates air at relatively high temperature from that at relatively low temperature. Mixing is poor across the interface. For openings at low level in a building with thermal stratification the higher temperature region will not be well-ventilated, since the air will short-circuit at low level. Whether this presents a problem for dispersion of pesticide depends on the height of the interface with respect to the breathing zone. Conversely, for openings at high level in a building with thermal stratification it is possible that the air will tend to circulate in the upper regions only, leaving the lower regions poorly-ventilated. (Page 471) Unfortunately, the accurate prediction of the vertical temperature gradient is difficult. It requires a model for air flow in the room, coupled to a thermal model for heat transfer at the building envelope. However, simplified modelling approaches are available (Page 472). These simpler modelling approaches could, possibly, be used to indicate the interface height in a thermally-stratified building air flow. Simple rules of thumb could then be used. For example, for openings at low level, if the interface height is above the breathing zone then pesticide concentrations in the lower region could be expected to be no higher than those produced from an assumption of complete mixing. (Page 475-477) Etheridge & Sandberg summarise the factors affecting air exchange efficiency. We recall that contaminant removal effectiveness behaves in the same way as air exchange efficiency provided that the contaminant is passive and initially uniformly mixed throughout the building. The factors identified are:

• relative location of the supply and extract air devices; • momentum of the jet; • heat load in the room (magnitude and location); • supply of positive or negatively-buoyant air; • discharge Archimedes number. (The Archimedes number is a dimensionless parameter

which expresses the ratio of buoyancy to inertial forces in the flow.) These factors are aimed primarily at forced-ventilated rooms. However they can also expect to be relevant to naturally-ventilated buildings. Thus the main openings in the building envelope are analogous to the supply and extract devices, the wind or buoyancy-driven air exchange

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through these openings is analogous to the ‘jet’, etc. The relative influence of these factors is presented in the form of two figures (Page 477) showing the effect on air exchange efficiency for a simple room. The main findings are that the air exchange efficiency can show a marked decrease when relatively warmer air is supplied and extracted at high level (due to short circuiting), however when the air change rate is increased, from two to four room volumes per hour, all configurations show the same air exchange efficiency of ~50% - equivalent to complete mixing. C.1.2 Goodfellow & Tahti (2001) Again, there is little material which is specific to the problem of pesticide dispersion in buildings. However this reference does provide a few potentially useful rules of thumb. (Page 476-478) Goodfellow & Tahti provide an overview of experimental studies of isothermal horizontal jets in confined spaces. This may be relevant to pesticide dispersion in naturally-ventilated buildings when air movement is driven by an external wind creating flows through the building envelope. The case of a jet setting-up primary, secondary and possibly other recirculation cells, is covered. Interestingly, Goodfellow & Tahti suggest that ventilation may be poor in the secondary and subsequent recirculation cells, a finding which appears to be at odds with Etheridge & Sandberg (page 412). The difference may arise from the different source material. Goodfellow & Tahti’s material is based on experimental studies, whilst Etheridge & Sandberg is based on CFD modelling. Using this review by Goodfellow & Tahti as a basis, and by following-up the original references, it may be possible to devise some simple rules of thumb, based on building aspect ratio and the type of opening in the building, to indicate those parts of a building which could be expected to be well-ventilated by an isothermal jet, and those parts which could be expected to be poorly-ventilated. (Page 488- 491) Goodfellow & Tahti present a number of relationships, all based on Archimedes number, which can be used to indicate when flow in a building will behave as though it were isothermal, even though the supply air temperature is different to that inside. It is possible that these relationships could be used to indicate when the flow will depart from behaving as isothermal, i.e. could become thermally-stratified. This could be useful from the viewpoint of pesticide dispersion, since, dependent on the location of openings, the presence of thermal stratification seems likely to be a good indicator of flow conditions which could be far removed from being fully-mixed. (Page 629 – 657) In Sections 8.6 & 8.7, Goodfellow and Tahti present a number of different strategies for controlling contaminants in industrial buildings. Although the emphasis is quite different to that of pesticide dispersion in naturally-ventilated buildings, there nevertheless appears to be some material here which could be used to devise rough rules of thumb. For example, a building configuration is illustrated (page 649) in which air enters at low level and exhausts through a roof vent. The flow is buoyancy driven, with cool air entering at low level and warmer air exhausting from a stratified region at high level. The authors indicate that contaminant concentrations in the lower region can be about one-third of those found in the upper, stratified zone, even with relatively weak thermal stratification. Information of this sort could be used to devise rough rules of thumb for pesticide dispersion and re-entry times. (Page 657 – 661) 44

In Section 8.8 Goodfellow & Tahti discuss the influence of the location of the exhaust from an indoor space. Again, the emphasis is from the standpoint of ventilation strategies to remove contaminants from industrial spaces, but there is some material which could be useful for the present application of pesticide dispersion, provided that the position of exhaust openings can be identified. Also in Section 8.8, the authors state that; “Thermal stratification in the room air greatly influences the spreading and dispersion of contaminants” It seems very likely that the presence or absence of thermal stratification will be a key factor in determining dispersion of pesticide in buildings. C.1.3 ASHRAE HandbookCD (2003) This handbook contains little of additional relevance for the current application. C.2 ARCHIVE JOURNALS AND CONFERENCE PROCEEDINGS 1. Etheridge, D W, 2000, Unsteady flow effects due to fluctuating wind pressures in natural ventilation design. This paper compares the performance of two models for the prediction of the effects of unsteady wind pressures on the instantaneous flow rates in a limited class of naturally-ventilated buildings. The models are based on ordinary differential equations in which time is the independent variable. The types of buildings to which the models apply are restricted by a number of modelling assumptions. This work could be of interest if simple models for thermal comfort were needed to design a means to damp transient internal effects due to unsteady external flows. However the work has no relevance for the dispersion of pesticides in buildings, which occurs over a long time frame. 2. Hurnik, M et al, Date? , Problem of air flow pattern reproduction in scale models of ventilated rooms. This paper presents the results of experimental tests which investigate the mean velocity field in a simple room, for three different model scales. It demonstrates that, provided that dynamic similarity is maintained, scale models can replicate the flow patterns seen at larger scale. The paper points out that similarity in thermal boundary conditions must be achieved, otherwise the gross flow patterns can be quite different. The paper is of no direct relevance to pesticide dispersion in buildings. However it could be of some use, as background material, if small-scale experiments were to be considered as a means to address the problem of pesticide dispersion in buildings. 3. Feigley, C E et al, 2002, Performance of deterministic workplace exposure assessment models for various contaminant source, air inlet and exhaust locations. This paper compares the performance of three simple models for workplace exposure assessment against the results of CFD (FLUENT). The simple models are one and two-zone completely mixed models, referred to as CM-1 and CM-2, and a uniform diffusivity (UD) model – which is based on simple diffusion principles. The models are applied to the release of a passive contaminant from a 1 m high table in a 10 x 3 x 7 m room at 4 ACH. Two types of inlet were investigated: a wall jet and a ceiling diffuser. Nine different exhaust arrangements were examined. Only isothermal conditions were investigated. 45

The CFD simulations are claimed to be validated by comparison against a 1/10th scale model, but little information is given. The reader is instead referred to the following papers: Bennett, J S et al, 2000, Evaluation of mathematical models for workplace exposure assessment using computational fluid dynamics, Appl. Occup. Environ. Hyg. 15: 131 – 144; Feigley, C E et al, 2002, Improving the use of mixing factors for dilution ventilation design, Appl. Occup. Environ. Hyg. 17: 333 – 343. It may be useful to obtain these papers, but not for the present project. Some of the simple model parameters are extracted from the CFD results. Even so the UD model performed poorly. In the far-field CM-1 and CM-2 perform similarly and are in reasonably close agreement with the CFD. In the near-field CM-2 overestimates concentrations, whilst CM-1 underestimates. The need to prescribe the air exchange between the two zones in CM-2 is highlighted as a limit to the applicability of the model. There is little to be gained from this paper which is relevant to the dispersion of pesticides in buildings. The results are very specific to this room, its boundary conditions, and in particular the contaminant source. 4. Kindangen, J et al, 1997, Effects of roof shapes on wind-induced air motion inside buildings. The effect of roof size and shape on the ventilation velocity and its distribution inside a simple building, is investigated using CFD. Ten different roof types are investigated, ranging from flat roofed, to dome-shaped. The simple building is ventilated via large openings on opposite faces, representative of open windows. The effect of wind direction is also examined, for a fixed wind speed. It is important to note that the volume of air contained in this simple building is constant with roof shape. This is because the roof space is sealed from the air volume contained in the simple room below. This work is of primary relevance to the natural ventilation of simple buildings with open windows in tropical environments. It has no direct relevance to pesticide dispersion in buildings. 5. Nielsen, P V, 1998, Ventilation in commercial and residential buildings, VKI Lecture Series 1998-07: Ventilation systems and air quality. These are lecture notes. They contain general principles of indoor air and contaminant movement, similar to those in Etheridge & Sandberg (1996). However they contain little additional material of relevance to pesticide dispersion in buildings, beyond that already noted from Etheridge & Sandberg (1996) and Goodfellow & Tahti (2001). They do, however, reinforce the message in Goodfellow & Tahti (2001) (page 649), that displacement ventilation – in which plumes from hot objects generate an upward movement of air and contaminants, subsequently exhausted at roof level, with supply of fresh air at low level – can be effective in confining high levels of contaminant concentration to the upper regions of a room. In this arrangement the flow is thermally stratified. The concentration field is also stratified, usually to a far greater extent than the thermal field. The concentration of contaminants below the stratification height is quoted as typically being between 0.1 and 0.3 of that found above the stratification height. The effectiveness of this arrangement relies on the stratification height being above the breathing zone. If certain types of building or space in which pesticide is dispersed can be categorised as being likely to lead to displacement ventilation, then provided that the stratification height is above the breathing zone it would be conservative to base estimates of exposure on a fully-mixed 46

assumption, since in reality pesticide concentrations would be lower than this below the stratification height. The stratification height would, of course, need to be estimated. Design methods by Nielsen could probably be used (Nielsen, P V, Displacement ventilation, theory and design, Aalborg University, ISSN 0902-8002 U9306, 1993). 6. Zhao et al, 2004, Comparison of indoor aerosol particle concentration and deposition in different ventilated rooms by numerical method. Building and Environment, Vol 39, pp 1- 8. Zhao et al compare airborne and deposited aerosol concentrations for two different ventilation regimes in a ~ 5.2 x 2.4 x 3.7 m room, using CFD. The ventilation regimes are ‘displacement’ and ‘mixing’. The former has an inlet at low level and a roof outlet, that latter has the inlet at high level and the outlet at low level. The room is ventilated at 5 ACH and contains heat sources of ~ 0.7 kw. The aerosol is assumed to be of the same density as an oil mist, with the particle mass evenly distributed into four sizes: 1, 2.5, 5 and 10 µm. Initially the aerosol is uniformily distributed. The CFD code is first compared against a test problem, in which an aerosol is gradually purged from an indoor space. The CFD tends to under-predict the rate at which aerosol leaves the space. Significant differences are seen between the two ventilation regimes with respect to the mass of aerosol which is transported from the room, and that deposited in the room. Displacement ventilation is more effective at clearing the room of aerosol, and results in very much less deposition than the mixing arrangement. This work could be used to provide a rough indication of the relative proportions of aerosol which escape, remain suspended, or are deposited, by aerosol size and for the two differing ventilation arrangements. However, for this data to be applicable to pesticide dispersion, the room in which pesticide is applied, as well as the ventilation arrangements, would have to be comparable to that simulated. This restricts the wider applicability of the findings. The work does illustrate how CFD can be used to predict the time-dependent behaviour of flow and aerosol behaviour in a room. 7. Lu et al, 1999, CFD modelling and measurement of aerosol particle distributions in ventilated multi-zone rooms. Lu et al describe the application of CFD to the transport of an aerosol from one room to another. Their results are compared with measurements of the average particle concentration, expressed in µg/m3. In fact this work is based in the same test chamber as the validation data selected by Zhao et al, 2004, above. The main variable is the area of the interconnection between the two rooms, which varies between 2 and 9% of the wall area, i.e. is relatively large. Initially the aerosol is fully-mixed in one room, then the two rooms are connected together. It is not clear that the work would be useful for advising pesticide dispersion in buildings, partly because the air change rate is relatively high (9 to 14 ACH) but also because there is uncertainty in the aerosol size: the authors state that this is in the range 1 to 5 mm - but this seems unreasonably large. In addition, the results are, again, applicable only to the particular geometry

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of the two rooms investigated and their interconnection – which as already noted is relatively large. Possibly the main value of the work is in the information it provides on the validity of CFD modelling. Again, the rate at which aerosol leaves the treated room is under-predicted. From the viewpoint of exposure prediction, these CFD simulations would over-estimate exposure in the treated room and under-estimate exposure in the second room. 8. Chow, W K, 2001, Numerical studies of airflows induced by mechanical ventilation and air-conditioning (MVAC) systems. This paper reports the application of CFD to the simulation of a variety of mechanically ventilated spaces. The air change rates are, in the main, far larger than would be experienced in pesticide dispersion, i.e. 20 to 86 ACH. In addition the CFD is modelling is very crude, with, in some cases, three-dimensional mesh employed with less than 4000 control volumes. This work is of no value for the present application. 9. Demmers, T G M et al, 2000, Assessment of techniques for measuring the ventilation rate, using an experimental building section. This paper primarily concentrates on an assessment of measurement techniques to determine ventilation rate in a naturally-ventilated livestock building. A tracer gas is released from a point source. Measurements of steady-state concentration of tracer were obtained, as well as a mapping of the velocity field. This paper is of little relevance to pesticide dispersion in buildings. It is specific to air and contaminant movement in livestock buildings. For example, in these experiments a uniform heat load of 350 W/m2 of floor area was imposed to represent the heat loss from animals. Such a heat load is most unlikely in pesticide applications. Some CFD modelling was also undertaken, but it is clear that the authors are far from expert in the use of the technique. Ultimately CFD modelling was abandoned in favour of visualisation using artificial smoke. 10. Weathers J W & Spitler, J D, 1993, A comparative study of room airflow: Numerical prediction using computational fluid dynamics and full-scale experimental measurements. Measurements and CFD modelling are presented for the flow in a simple room, at high air change rates: 15 up to 100 ACH. The incoming flow is cooler than that already in the room – the walls being maintained at 30oC compared to an inflow at 21oC. The inlet is located part way up a wall, which means that the inlet jet bends towards the floor due to its negative buoyancy. The CFD captures this behaviour, but appears to overpredict the lateral spreading rate. Overall the CFD work is dated and conclusions should be treated with some suspicion. For instance, it is claimed that the results are grid-independent for a 3-D mesh of just 23040 cells. The paper is of very little relevance to the problem of pesticide dispersion in buildings. 11. D Tommaso, R M et al, 1999, Influence of the boundary thermal conditions on the air change efficiency indexes, Indoor Air, Vol. 9, pp 63-69. This paper presents the results of measurements in a simple room ventilated at either 1 or 3 ACH. The room is 2.4 x 2.4 x 4.0 m, with one inlet and outlet - each of dimensions 0.96 x 0.18 48

m. Two ventilation arrangements are investigated: inlet at low level and outlet at high level on the opposite wall; or outlet at high level on the same wall as the inlet. The effect of small temperature differences, from 0 to 5oC, between the inlet air, and the room air and walls, was investigated. The decay of a tracer was measured at numerous points in the room, initially starting from a uniform distribution. The results are presented in terms of air exchange efficiency, εa. This is useful because it means that the data can be re-interpreted in terms of contaminant removal effectiveness, ε, on the assumption that the contaminant is passive and initially uniformily distributed. The air exchange efficiency is correlated against Archimedes number using a log-law. The resulting variation of air exchange efficiency with Archimedes number is similar for both ventilation arrangements. These correlations could provide a means to predict contaminant removal effectiveness if an Archimedes number can be defined. The results would, however, be specific to the geometry and arrangement of boundary conditions in this simple room. This paper could be useful for the problem of pesticide dispersion in buildings, provided that the space of interest is of similar dimensions to the simple room in these tests and also has similar ventilation arrangements. If this is not the case, then extrapolation could cautiously be undertaken for other room sizes and configurations, possibly substantiated by limited CFD simulations. However the authors do state that the conclusions should not be regarded as generally valid, but instead should be used as evidence that temperature differences play an important role on the performance of ventilation systems. The authors also conclude that the Archimedes number can be used as a tool to predict air exchange efficiency. There are some similarities in this work, to that presented in Etheridge & Sandberg (1996), p268 – 271. 12. Yeoh, G H and Li, Y, 1998, A comparison study of the effectiveness of three ventilation systems in purging pollutant using CFD. This paper contains the results of CFD simulations for a 4.2 x 3.6 x 2.5 m room. The flow and contaminant behaviour is investigated under three differing ventilation systems: mixing (high inlet, low outlet), displacement (low inlet, high outlet), vortex (four low inlets, ceiling outlet). The vortex system is unlikely to be encountered in pesticide applications and is not considered any further here. The flow is at 5 ACH with a centrally-located source of heat (0.4 kW) and passive contaminant. The CFD is undertaken on coarse meshes of no more than 26000 control volumes. The flow and contaminant distribution is largely as expected, for example, with the displacement ventilation system the lower levels of the room are largely free from contaminant – which is confined to the upper region of the room. For the mixing ventilation system the contaminant distribution is much more uniform. This paper adds nothing in addition to those already discussed. However it is useful for graphically illustrating the effect of mixing and displacement ventilation systems on contaminant distribution. 13. Gadgil et al, 2003, Indoor pollutant mixing time in an isothermal closed room: an investigation using CFD.

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This paper addresses the time to complete mixing for a point source released in a sealed room which is stirred by fans. The study comprises experiments and CFD modelling. The main findings are that CFD predictions for mixing time, using a standard k-ε model, are within 30% of those measured; mixing time is primarily dependent on the mean airflow in the room, rather than source location; turbulent intensity at the source location is poorly correlated with mixing time. The work is not useful for the problem of pesticide dispersion in buildings. 14. Khedari, J et al, 2002, Field comparative analysis of roof solar chimney design on indoor conditions. This paper reports the results of small-scale experimental studies investigating how different design of roof-mounted ‘chimneys’ affects the natural ventilation in a room below. The context of this work is the drive to reduce power consumption from air conditioning systems in tropical countries, such as Thailand – which is where the experiments were conducted. The work has no relevance to pesticide dispersion in buildings. 15. Beghein, C et al, 1994, Numerical study of the influence of inlet velocity and thermal and solutal diffusivities on air flow pattern in a ventilated enclosure, Roomvent ’94. The influence of pollutant concentration gradients on the flow in a 2-D room is examined using CFD. The pollutant is benzene or methanol, emitted from one wall of the room. The walls are maintained at a 6oC temperature difference. The air change rate is varied between 2 and 24 ACH. The results show that the pollutant gives rise to buoyancy forces which can modify the flow field. The value of this work is that it demonstrates that it may not always be appropriate to assume that a gaseous contaminant acts as a passive scalar. The authors conclude by saying that more work is required in this field. This paper does illustrate that when ventilation rates are low, gaseous contaminants with a higher molecular weight than that of air can influence the flow, provided that their emission rate is sufficiently high. It is possible that this effect could be relevant to the dispersion of pesticides in buildings. Simple hand calculations should indicate whether this is the case or not. These calculations could be based on a comparison of a buoyancy flux generated from a range of emission rates of differing gaseous contaminant, against those generated by temperature differences, with both compared to a momentum flux from forced or natural ventilation. This effect could also be of relevance to exposure modelling in manufacturing, for instance styrene emissions in boat building. 16. Fletcher, B & Johnson, A E, 1992, Ventilation of small factory units, J wind Engineering and Industrial Aerodynamics, Vol 40, pp 293 – 305. This paper describes the use of tracer gas techniques to measure air change rates in small industrial buildings. The resulting air change rates are found to be proportional to the wind speed, when the temperature inside is similar to that outside. However, when the inside temperature is substantially different from that outside, the air change rate is seen to depend on both the wind speed and the temperature difference, i.e. the stack effect. The work has little direct relevance to pesticide dispersion in buildings, except to indicate that temperature differences between the inside and outside of a building can have as great an effect on air change rate as wind speed. The paper could be used to support general arguments that thermal effects can be very influential on pesticide dispersion. 50

17. Lee, E et al, 2002, An investigation of air inlet velocity in simulating the dispersion of indoor contaminants via computational fluid dynamics, Ann Occup Hygiene, Vol 46, No 8, pp 701 – 712. This paper compares the results of CFD simulations of tracer gas concentration to measurements made in a small test chamber, for steady-state conditions. The tracer is released from a central location. The outlet from the chamber is at low level, in one corner. The inlet is a slot at high level. The main objective of this work is to compare the performance of the CFD model for two differing treatments of the inlet velocity profile: uniform profile; velocity profile taken from measurements. Qualitatively, the results for both inlet treatments are similar to the measurements. However there are substantial quantitative differences. For example, the mean of the % difference between simulation and measurement over all sample locations is in the range 42% to 76% for the uniform profile, but just 8% for the imposed measured profile. This work demonstrates that CFD modelling of indoor air movement and contaminant behaviour can be very sensitive to small details in the modelling of boundary conditions. The paper is of little direct relevance to the problem of pesticide dispersion in buildings. However it does serve as a caution that, unless boundary conditions are known and specified with certainty, CFD models for indoor air movement and contaminant behaviour can produce relatively poor quantitative results. Nevertheless, the overall qualitative flow behaviour may still be adequately captured. 18. Aubertin, G, 1993, Introduction to the design of ventilation systems, in, Advanced design of ventilation systems, VKI Lecture Series 1993 -07. These lecture notes provide an overview of predictive methods for the design of ventilation systems. There is little they add to that material already covered by Etheridge & Sandberg (1996). C.3 SUMMARY This is a limited review of the literature, but it provides an indication of the type of information that can be obtained. It also highlights which factors have been studied in details and the effect of which are relatively well known and which ones need further investigation. The key findings are summarised in the main body of the report, in Section 5.

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