A Numerical Approach to Support an Effective Design ofRegenerative Chambers in Glass Industry

Davide Bassoa,∗, Carlo Craveroa,∗, Alessandro Molab

aUniversity of Genoa, DIME, Via all’Opera Pia, 15 16145, GenoabStaraglass S.p.A., P.zza Rossetti 3 A/1 16129, Genoa

Abstract

The overall efficiency of a regenerative chamber for glass furnace mostly relies on the thermo-fluid dynamics of airand waste gas alternatively flowing through stacks of refractory bricks (checkers) that fulfils the heat recovery. Anumerical approach could effectively support the design strategies in order to achieve a deeper understanding of thecurrent technology and hopefully suggest new perspectives of improvement. A CFD approach to the regenerator isproposed here by means of Ansys’ commercial software. The need for a porous domain instead of the real geometryof the checkers, and the set up of such a model, is discussed comprehensively. Results confirm that the experimentaldata can be properly fitted to the calculations and applications are presented to show how CFD could be efficientlyused to improve the overall performances of the chamber.

Keywords: CFD, regenerator, porous, glass

INTRODUCTION

The heat recovery in regenerative chambers for glassfurnaces is obtained switching the fluid flowing throughhoneycomb refractory structures called checkers. Theheat of waste gas is absorbed by the refractory bricks(hot period) and then released to the combustion airwhen the cycle swaps (cold period) about every 20minutes. Despite few major improvements due tomaterial technology evolution “this regenerator modelis still the heritage of 1850 Martin-Siemens’ openheart furnace, as it guarantees an high heat recoveryefficiency level (60 ÷ 68%, against a theoretical limitvalue of 75 ÷ 78%) and its ceramic structure easilyallows the reaching and the duration in time of veryhigh working temperatures”[1]. Nevertheless the grow-ing concern about energy consumption and pollutantemissions, demands a more strategic utilization of thesetechnologies and likely their integration with otherdevices. In this perspective CFD could bring a preciouscontribution to the design process, providing in-depthanalysis of the well established traditional solutions andhelping decision making for the future.

∗Corresp6onding authorsEmail addresses: davide.basso.cfd@gmail.com (Davide

Basso), cravero@unige.it (Carlo Cravero)

Several works already tackled the problem of modelthe thermodynamics of a, so called fixed bed regener-ator (the kind used in glass furnaces), see for exampleKoshelnik [2] or Zarrinehkafsh and Sadrameli [3]. An-other interesting work was conducted by Reboussin etal. [4] since the disposal of refractory bricks inside thecheckers is arranged in such a way to form a dense arrayof vertical channels, Reboussin proposes a CFD-basedprocedure to estimate a global thermal coefficient for thecheckers with calculations for a single channel and val-idation with a experimental regenerator Saint Gobain.Despite the goodness of this approach it also would betoo demanding if applied to the entire regenerator (asexplained in section b), thus the aim of the present workis to extend the fluidynamical analysis to the whole re-generator in such a way that could give quick feedbacksto the designers while maintaining a good level of ac-curacy. The basic idea is to substitute a model wherethe direct calculation would be too time and resourceconsuming namely the checkers zone. The typical re-generative chamber for glass industry (showed in figure6) consists of three zones: the top chamber, the bot-tom chamber and the checkers in between. Each zonehas a double role depending on the cycle, namely, thetop chamber redistributes the flow from the checkersto the melting tank (cold period) and vice versa (hotperiod), similarly the bottom chamber redistributes the

Preprint submitted to Applied Thermal Engineering April 8, 2015

Figure 1: Overview of the regenerative chamber model withboundary conditions for both air (light grey) and waste gas(dark grey) cycle.

flow from the waste gas port to the checkers and viceversa. In addition to the heat recovery the checkerscreate a dynamic effect due to buoyancy forces (stackeffect). This fact is clearly visible from experimentaldata reported in figure 2: during the cold period the top-bottom pressure profile inside the chamber has the typi-cal chimney behaviour with larger values at the top andlower values at the bottom.

CFD APPROACH

The first consideration, which arises from the obser-vation of typical working conditions for regenerativechambers, is that temperature dependence of thermo-fluid dynamical properties of fluids cannot be neglected,and proper functions should be considered instead. Sec-ondly the typical checkers zone fills a volume of about150 ÷ 200 [m3] with dense stacks of bricks (in numberof about 15 thousand) and meshing the real geometry ofsuch a complex and wide structure would require hugecomputational resources and it is, in any sense, not af-fordable. A well established solution for similar prob-lems (see for example Yakinthos et al. [5]) is to substi-tute to the real geometry of the checkers with a porousdomain correctly tuned. Whenever a model is intro-duced in place of the direct calculation, special atten-tion must be paid to ensure a physical equivalence be-tween the two. In the case of checkers three governingeffects can be identified: head loss due to the obstruc-tion of bricks, thermal energy transfer (from checkers to

air or from waste gas to checkers) and buoyancy forces(due to solid-fluid temperature gradients). The introduc-tion of all these effects will be covered in the follow-ing paragraphs. All the numerical workflow has beenperformed by means of ANSYS’ commercial software:namely ICEM-CFD has been used for CAD repairingand meshing while Fluent has been chose for the calcu-lations.

MESH

In order to perform a comparison between the ef-ficiency of the chamber during the different cycles, amesh that could represent well the flow of both air andwaste gas has been set up. The approximation of check-ers with a porous medium considerably simplifies thecomputational domain, nevertheless the bottom cham-ber geometry still remains quite complex and becomesindeed the major bottleneck for the meshing process. Infigure 6 is possible to see the cluster of arches whichhold up the checkers and the top chamber: each one ofthese arches is about 150[mm] wide and in order to keepa decent number of elements along the width, the localmesh size must be kept below 15[mm], which is verysmall compared to the characteristic dimensions of thewhole regenerator.Two different approaches have been tested to gener-ate different meshes: a mixed Octree-Delaunay proce-dure for a tetrahedral mesh with prism layer on the wallboundaries and a blocking strategy for a fully hexahe-dral mesh. Each zone of the chamber (see figure 1)has been meshed separately and then connected to theneighbours through interfaces. Since the mesh localsize must necessarily be quite small inside each arch,and there are many of them, a considerable percent-age of the mesh elements (see tab 1) is concentratedin the 6bottom chamber, regardless the meshing strat-egy adopted. Satisfactory results were obtained withboth meshes; a tighter convergence of the residuals wasachieved with the hexa mesh but in both cases the ex-perimental data have been matched.In the aim of the present work, in order to develop aCFD procedure that could effectively support the futuredesign of regenerative chambers, the hexahedral meshis probably the best choice anyway since, once a properblocking strategy has been defined, it guarantees highquality meshes for almost any given chamber geometry.

EVOLVING FLUIDS

Both air and waste gas have been defined as fixedcomposition mixtures of semi-perfect gases; this

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(a) Temperatures [K] (b) Pressures [Pa]

Figure 2: Experimental data provided by StaraGlass averaged over several sampling campains performed on site. Thisconfiguration is quite typical among end-port furnaces

DOMAIN TET [Mcells] HEX [Mcells]Bott. Ch. 7.1 4.2Checkers 2.0 2.1Top Ch. 1.0 1.3

Table 1: Characteristic sizes of tested meshes

Kairperm [m2] Kair

loss [m−1] Kw.g.perm [m2] Kw.g.

loss [m−1]Top 0.0007 0.29 0.00061 0.08Bott. 0.00009 0.52 0.00105 0.12

Table 2: Porous resistance coefficients in the streamwisedirection under top and bottom temperature conditions

enhances the flexibility of the model especially forwaste gas calculations as the chemical compositionusually varies from one furnace to another. The fluidsexperience large temperature gradients through thechamber (up to 1200 degrees in the case of cold period);under similar conditions temperature dependency ofgas properties cannot be neglected. Let us consider forexample Nitrogen (see figure ??): in the temperaturerange of interest the heat coefficient variation is about25%, clearly a constant value for such a calculationwould introduce unacceptable errors for heat exchangeproblems. Keeping the assumption of ideal gas forthe equation of state, in order to evaluate density, allrelevant properties have been defined as functions oftemperature as following:

Heat Capacity at constant pressure: NASA poly-nomial format for cp is used. Two distinct ranges oftemperature and five coefficients for each range arerequired to write specific heat at constant pressure inthe following form:

cp

R= a1 + a2 T + a3 T 2 + a4 T 3 + a5 T 4 (1)

The coefficients were extrapolated from a polynomialregression of NIST database for air [6]; compared withthe experimental data the fitting is very good over theentire temperature range of interest.

Dynamic Viscosity: Sutherland’s formulation for

dynamic viscosity “closely represents the variation ofµ with temperature for several gases over fairly wideranges of temperature” [7]. It can be written as:

µ

µ′=

T ′ + ST + S

( TT ′

) 32

(2)

where T ′ = 273 [K], and µ′ = µ(T ′) and S is theSutherland constant, specific for each gas. Values forS were taken from [7] and [8]. The fitting betweenNIST database and Sutherland’s formula shows anunderestimation of viscosity at high temperatures.The reason for this discrepancy is that the values ofSutherland’s law can only correctly predict viscosityvalues over a temperature range more narrow than thetypical operating conditions of regenerative chambers(19.8 ÷ 825◦C for Nitrogen [7]). Nevertheless, alsoconsidering the simpleness of such formulation, theerror is still acceptable for the purpose of the presentwork.

Thermal Conductivity: Once cp and µ have beendetermined, the evaluation of thermal conductivity λbecomes straightforward applying the Eucken ModifiedApproximation. It is based on the kinetic theory ofgases and its common formulation [9] is:

λ

µ cv= 1.32 +

1.77(cp/R − 1)

(3)

where cv and cp are the heat capacities at constant pres-sure and volume respectively and R is the universal gas

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Figure 3: Fluid domain of one checkers’ tube sample. Boundary conditions are reported for air (dark grey) and waste gas(light grey) flows

0 1 20

1

2

Velocity [m s−1]

Pres

sure

Dro

p[P

a] bottomtop

0 1 20

0,1

0,2

0,3

Velocity [m s−1]

Pres

sure

Dro

p[P

a] bottomtop

Figure 4: Resulting head losses along the tube sample for air (on the left) and waste gas (on the right) underdifferent temperature conditions.

constant. Since such formulation includes viscosity, anunderestimation of thermal conductivity is introducedalso for the thermal conductivity.From the numerical point of view, the introduction ofa multicomponent model requires N − 1 conservationequations to be solved where N is the total number ofchemical species of the mixture. Such equations takethe standard advection-diffusion form for the mass frac-tion of i-th specie without the addition of source terms(there is no production nor creation of species).

∂

∂tYi + ∇ · (ρvYi) = −∇ · Ji (4)

POROUS DOMAIN

As previously discussed in the mesh section of thispaper, a full mesh for thousands of bricks is not possi-ble and the real geometry of the checkers is substituteby a porous domain. The purpose of such modeliza-tion is to avoid the computational efforts while keepingthe relevant physics aspects involved in the regeneratornamely: the buoyancy forces, the obstruction of the re-fractory bricks to the gas flow and the heat exchangebetween the solid part and the fluid part.Porous media are modelled by the addition of a sink

term to the standard governing momentum equations,the volume blockage that is physically present is notrepresented in the model, and instead of the physi-cal velocity (or true velocity) a common approach inCFD codes is to consider a superficial velocity definedas vsup = γvreal where γ is the volumetric porosity.This notation is very handy since superficial velocityis conserved across the computational domain regard-less porosity changes (including fluid-porous interfacesections), on the other hand it also “limits the accuracyof the porous model where there should be an increasein velocity throughout the porous region” [10]. Thus,since buoyancy-driven velocity gradients are expectedalong the checkers, it becomes necessary to solve forthe physical velocity. The momentum sink term is com-posed of two parts: a viscous loss term (first term on ther.h.s. of eqn. 5), and an inertial loss term (second termon the r.h.s. of eqn. 5)

S M,i = −

3∑j=1

Di j µv j +

3∑j=1

Ci jρ

2|v|v j

(5)

where Di j and Ci j are prescribed diagonal matrices.

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(a) Cold period pressure-coloured streamlines (b) Hot period temperature-coloured streamlines

Figure 5: Experimental data provided by StaraGlass averaged over several sampling campains performed on site. Thisconfiguration is quite typical among end-port furnaces

Porous Resistance CoefficientsThe disposal of refractory bricks inside the checkers

is such to form kind of a tube bundle of variable crosssection (depending on the brick shape) with a typicalhydraulic diameter of about 150÷200[mm]; the flow in-side each tube is pushed (or pulled) vertically by buoy-ancy and the openings between adjacent tubes are lim-ited if not completely absent. These facts suggest ananisotropic formulation of the porous source:

S M,x = −µ

KTperm− KT

lossρ

2|v| vx

S M,y = −µ

KSperm− KS

lossρ

2|v| vy (6)

S M,z = −µ

KTperm− KT

lossρ

2|v| vz

identifying a streamwise direction for the porous do-main (y-coordinate in the present case), under the as-sumption of straight vertical flow, the motion along theother directions is inhibited setting the correspondingporous coefficients (KT

perm and KTless) as the streamwise

coefficients (KSperm and KS

less) multiplied by a factor of102 [10]. The coefficients for the streamwise directionhave been extrapolated from experimental data in theform of pressure drop against mean velocity througha 1500 [mm] high sample of tube (figure 3). In thiscase the exact geometry of bricks have been taken into

account but not for the entire height of the checkerssince the computational costs would have become toohigh. With the boundary conditions set as in figure 3the changes in pressure drop along the tube sample havebeen recorded with respect to different inlet bulk veloci-ties and reported on a cartesian plane as shown in figure(4). These points can be fitted by means of a curve withthe following form:

∆p = cv2 + dv (7)

Since the pressure drop inside a porous media of thick-ness ∆n can be expressed as:

∆pi = −S i∆n (8)

comparing equations 7 and 6 it is now possible to eval-uate KS

perm and KSloss. It is important to note, however,

that these coefficients do not remain constant along thechamber since the properties of gases (viscosity, den-sity) vary with temperature and so does the porous re-sistance. To take into account this fact, the above pro-cedure has been applied with bottom and top chambertemperature conditions both for air and waste gas (seefigure 4). For each cycle the variation of porous resis-tance coefficients with height has been assumed linearbetween the corresponding extreme conditions. The re-sults showed a different behaviour for the two regimes:while for air the resistance is higher at the top of the

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checkers, for waste gas is the opposite. The reasonfor that could rely on the different heat transfer regime(cooling or heating) and it will be subject of furtherstudies.

Thermal Aspects

Apart from porous resistance the physics of check-ers is governed by the heat transfer between bricks andfluids that fulfils the heat recovery and causes the den-sity gradients responsible for buoyancy forces. The netamount of heat power absorbed by the air or released bythe waste gas is derived from integration of the corre-sponding law for heat capacity at constant pressure overthe measured temperature range (figure 2a), multipliedby the mass flow rate, namely:

S M,therm = m∫ Ttop

Tbot

cp dT (9)

Also buoyancy effects are taken into account adding asource term to the momentum equation as follows:

S M,buoy = (ρ − ρre f ) g (10)

where ρ is the actual temperature dependent density ofthe fluid, ρre f is a reference value (density evaluated bot-tom chamber temperature in the present case) and g isgravity. The density difference is directly evaluated inthe fluid domain value since the temperature gradientsare large and a Boussinesq approach wouldn’t be real-istic for this case. This is another reason for the impor-tance of defining the gas properties (density in this case)as function of temperature. It is worth noting that for airthe heat exchange occurs by mixed natural/forced con-vection, while for waste gas radiation dominates due tothe presence of H2O and CO2. On the other hand in bothcases the heat exchange is achieved mainly inside thecheckers, where (from a numerical point of view) thesource term for energy equation come into play. Hencea radiative model wouldn’t bring, as a counterpart of thecomplications, substantial benefits to the simulation ofthe hot period and it has not been considered. The dif-ferent definition of boundary conditions for the cham-ber walls (see figure 1) is based on the fact that air iseverywhere cooler than the walls and vice versa for thewaste gas; for cold period calculations a fixed tempera-ture value (estimated for the checkers and and measuredwith a pyrometer for the top and the bottom chamber)has been set, while for the hot period calculations wallheat fluxes were estimated through thermal properties ofthe materials and averaged expected temperatures (seefigure 2).

Figure 6: Streamlines of waste gas flow recirculating inside the cham-ber. Highlighted in orange the arches which hold up the entire struc-ture of the checkers.

RESULTS AND APPLICATIONS

Calculations were performed both for cold andhot period, the numerical convergence for the scaledresiduals during the cold period is around 10−6 with2nd order discretisation schemes, while less tight butstill satisfactory during the hot period (around 10−5

with identical numerics). Such a discrepancy might bedue to the different transition of the flow through thefluid-porous top interface: while in the cold period theflow leaves the porous domain perpendicularly to theinterface, the waste gas flow tends to approach the topinterface in a tangential direction before being abruptlyforced in the vertical direction by the high anisotropyof the porous model. The complexity of the flow insidethe top chamber during the hot period is likely thereason of the loser convergence.Results show that the experimental data presented infigure 2 can effectively be matched with this model.Streamlines in figure 5b for example, show the typicalchimney distribution occurring during the cold period,with pressure values increasing in the downstreamdirection. The net pressure drop between top andbottom chamber is around 60[Pa] which is in fairlygood agreement with the 70[Pa] measured on site (seefig 2). Temperatures values also correctly match theexperimental data as seen in figure 5a but since thethermal volume source was calibrated upon these same

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Figure 7: Results of the waste gas duct optimization. For the original case (on the left) and the optimized case (on theright) the velocity contours and mass flows in the middle section of each duct are reported in the 2D view, while the 3Dview presents velocity streamline and pressure contours at the bottom interface.

values (see equation 9) this result was largely expected.Much more interesting in terms of validation of themodel has been the results from a recent application ofwhich will be discussed at the end of this section.As already mentioned the transition of waste gasflowing from the top chamber to the checkers isparticularly critical. An interesting application of thismodel, currently under study, is the parametric analysisof the chamber geometry in order to establish somecorrelations (which is not the case yet) between thecharacteristic dimensions of the chamber and the distri-bution of the velocity field in the fluid domain. Manycalculations were performed on the same chamberobserving how the homogeneity of the flow at the topinterface (defined as the standard deviation of verticalvelocity from its mean value) is affected by the aspectratio of the top interface and pitch of the waste gasport. Figure 8 reports some of these results: the bestconfigurations are those which leave more room to thewaste gas flow coming from the port to expand insidethe chamber without impinging on the opposite wall.In other words, better results were achieved with loweraspect ratio values (with more space between the portand the opposite wall) but also the longitudinal pitchangle of the waste gas port seems to affect positivelythe homogeneity of the flow.

Figure 7 present the results of another analysis madeto estimate the impact of ducts design on the waste gasflow inside the bottom chamber. On the left hand sidethe original design is presented: the 2D view at thebottom shows the velocity contours on the transversalmiddle plane of the two smaller ducts while the 3D

view on top shows the pressure contours at the interfacewith the checkers. Some streamlines are also reportedto give an idea of the flow structure. The mass flowvalue for each duct is also reported in the 2D viewand, clearly, the original design presented an imbalancebetween the two. The right hand side of the same figureshow for comparison the optimized configuration:modifying the cross section of each duct as shown inthe 2D view, an almost even repartition of the mass flowwas achieved. As a side effect the pressure distributionat the interface with the checkers (right 3D view offigure 7 is more balanced than the original case andwith a lower mean value. This enhances the efficiencyof the bottom chamber and can help a better distributionof the waste gas flow upstream inside the checkers.The most recent application of the discussed CFDprocedure has been, as part of the ongoing EuropeanLife Project “Primeglass”, the development of a waste-gas recirculation system, in order to reduce the NOx

emissions by lowering the oxygen concentration in thecombustion air. Without going into details which arenow beyond the purpose of this article it is enough tosay that a certain percentage of waste gas is extractedfrom the waste gas bottom chamber and injected intothe air bottom chamber (see fig...). The waste gas,mixing with the air, follows a similar path upstream thecheckers and is discharged through the port into themelting tank.The regenerator model (with the appropriate modifica-tions in order to consider the piping system linking onechamber to the other) has been used to establish a fewfundamental design guidelines. Once the system has

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been installed on a pilot furnace several campaigns ofexperimental measurements have been performed andthis offered the perfect chance to validate the model.In order to achieve a better understanding of how thewaste gas is distributed inside the top chamber, themolar concentration of chemical species were taken onnine points more or less equally spaced to form a 3 by3 grid on a ideal plane right above the top interface.These nine points cover an area of about 20 [m2] so theresolution of the data is quite coarse, but enough to setup a comparison with the CFD results. On (approx-imately) the same plane of the measures, the surfacewas split into nine equal patches (arranged in a 3 by 3matrix) and for each one an averaged value between allthe nodes contained in the patch was calculated (seefigure 10 right and 9 right). In the end thousands ofnodes were approximated with nine values that couldbe compared with the experimental data. A similarprocedure was applied for the transversal middle planeof the port (see figure 11 right and 9 left).Figures 10 and 11 show the comparison for a 10%waste gas mass flow rate (relative to the air). Despitesome evident discrepancies it has to be said that takinginto account all the difficulties of retrieving data on site(lack of access to the inside of the chamber, very hightemperatures, discontinuous working conditions etc.)and the approximation done to compare them with theCFD model, the validation seems satisfactory. Above

all these results show that the strongest assumptionmade during the set up of the porous domain (con-dition of straight vertical flow inside the checkers)is realistic and while reducing the complexity of themodel it does not compromises the quality of the results.

CONCLUSIONS

This CFD model for regenerative chambers showed agood potential in term of flexibility and realism of re-sults. As far as efficiency is concerned, this calculationscould be of great importance in the future plant designs.The porous domain assumption is realistic and the han-dling of heat recovery through addition of source termsto the energy equation simplifies the definition of theproblem. Nevertheless the major drawback of this ap-proach is the necessity, a priori, of experimental tem-perature data to calculate the integral in equation 9. Thenext step in the development of this model would bethe introduction of a non-equilibrium thermal model in-side the porous domain. In this way the fluid and thesolid part of each cell would be handled separately bythe solver, and once the solid part together with the heattransfer mode have been properly defined the modelwould be capable of giving reliable results for new de-signs of regenerative chambers without the need of over-all temperature data for calibration. Another interesting

Figure 8: Example of how the aspect ratio of the top interface section affects the homogeneity of the flow defined as thestandard deviation of vertical velocity component from the mean value Wavg = 0.75 [m s−1]

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goal could be the definition of reliable efficiency indi-cators for improving the future designs and offers termsfor comparisons between different choices in terms ofgeometry of the chamber, type of checkers etc.

ACKNOWLEDGEMENTS

The numerical simulations presented have been per-formed using the hardware and software platformsavailable at Consorzio SI.RE. in Savona. The assistanceof the staff from Consorzio SI.RE. is greatly acknowl-edged. All the experimental data presented have beenkindly provided by Staraglass. For the support through-out the development of the model, their R&D depart-ment must be also greatly acknowledged.

References

[1] A. Mola, C. Cravero, D. Basso, E. Cattaneo and G. Minestrini,2013. “Hybrid exchangers for glass furnace: experience, perfor-mance and prospective”. Glass Online Journal.

[2] A.V., Koshelnik, 2008. “Modelling Operation of System of Re-cuperative Heat Exchangers for Aero Engine with CombinedUse of Porosity Model and Thermo-Mechanical Model”. Glassand Ceramics. 65, 9-10, 301-304

[3] M.T. Zarrinehkafsh, S.M. Sadrameli, 2004. “Simulation of fixedbed regenerative heat exchangers for flue gas heat recovery”.Applied Thermal Engineering. 24, 373-382

[4] Y. Reboussin a , J.F. Fourmigue, Ph. Marty, O. Citti, 2005 “Anumerical approach for the study of glass furnace regenerators”.Applied Thermal Engineering. 25, 2299-2320

[5] K. Yakinthos, D. Missirlis, A. Sideridis, Z. Vlahostergios, O.Seite and A. Goulas, 2012. “Modeling of thermal processes inthe packing of regenerative heat exchangers in industrial glass-melting furnaces”. Engineering Applications of ComputationalFluid Mechanics. 6:4, 608-621

[6] “Nist Standard Reference Database number 69”.http://webbook.nist.gov/chemistry/.

[7] S. Chapman, T. G. Cowling, 1995. “The mathematical theory ofnon-uniform gases”. Cambridge University Press.

[8] Z.K. Morvay, D.D. Gvozdenac, 2008. “Applied Industrial En-ergy and Environmental Management”. Wiley-IEEE Press.

[9] B.E. Poling, J.M. Prausnitz, J.M. O’Connell, 2004. “The Prop-erties of Gases and Liquids”. McGraw-Hill.

[10] “ANSYS FLUENT Theory Guide”. ANSYS, Inc.

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Figure 9: Extrapolated nodes distributions for the trasversal middle plane of the port (on the left) and top interface plane(on the right).

% O2 exp % O2 CFD % CO2 exp % CO2 CFDP9 20 18 2.4 2.02P8 18.7 18.07 1.8 1.84P7 18.29 17.62 2.2 2.18P6 20.34 20.36 0.4 0.4P5 20.47 20.41 0.3 0.37P4 20.68 20.23 0.22 0.5P3 20.66 20.95 0.1 0.04P2 20.64 20.93 0.1 0.04P1 20.59 20.99 0.2 0.01

Figure 10: Experimental and numerical data comparison, in terms of molar concentration of oxygen and carbon dioxide,for each of the nine patches in which the top interface has been split into.

% O2 exp % O2 CFD % CO2 exp % CO2 CFDTOP 20.36 20.95 0.3 0.05MID 19.62 19.85 1 0.75LOW 18.06 17.95 2.3 1.93

Figure 11: Experimental and numerical data comparison, in terms of molar concentration of oxygen and carbon dioxide,for three different heights on the trasversal cross plane of the port.

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