POLITECNICO DI MILANO

Facoltà di Ingegneria Industriale

Corso di Laurea in

Ingegneria Energetica

Dipartimento di Elettronica e Informazione

Object-Oriented scalable-detail with building simulation: a model

library and some comparisons with state-of-the-art tools.

Relatore: Prof. Francesco CASELLA

Co-relatore: Ing. Alberto LEVA

Ing. Marco BONVINI

Tesi di Laurea di:

Rosalia SCIORTINO

Matr. 740769

Anno Accademico 2010 - 2011

2

All’amico

don Fabio Coppini

Contents

1. INTRODUCTION ......................................................................................... 13

1.1 The energy problem ...................................................................................... 13

1.2 European legislation ...................................................................................... 15

1.3 The Italian scenario ....................................................................................... 16

1.4 Limits of the Italian legislation ..................................................................... 18

1.5 Advantages and disadvantages of “simplified” procedures .......................... 20

1.6 Usefulness of dynamic simulation ................................................................ 22

2. BUILDING SIMULATION TOOLS........................................................... 25

2.1 Classification of calculation codes ................................................................ 25

2.1.1 Clarke and Maver’s classification .................................................... 25

2.1.2 Sahlin’s classification ....................................................................... 26

2.1.3 CIRIAF’s classification .................................................................... 27

2.2 Thermal simulation methods for buildings ................................................... 29

2.2.1 The finite – difference method ......................................................... 30

2.2.2 The finite element method ................................................................ 32

2.3 A literature review ......................................................................................... 34

2.4 Peculiar difficulties of dynamic modeling .................................................... 37

2.4.1 The complexity of energy exchanges ............................................... 37

2.4.2 Difficulty in writing the equation for building simulation ............... 40

2.4.3 Different physical phenomena .......................................................... 42

2.4.4 Different disciplines.......................................................................... 43

2.4.5 Different time scales and spatial sizes .............................................. 43

2.5 Current dynamic tools ................................................................................... 44

3. AN OBJECT-ORIENTED SOLUTION BASED ON MODELICA ........ 45

3.1 Model library structuring .............................................................................. 45

3.1.1 Structuring step 1 .............................................................................. 45

3.1.2 Structuring step 2 .............................................................................. 47

3.2 Object Oriented Modeling (and Simulation) ................................................ 49

3.2.1 Physically meaningful connections .................................................. 49

3.2.2 Interface abstraction and a-causal approach ..................................... 50

4

3.3 Modelica ........................................................................................................ 51

3.4 A Modelica library ........................................................................................ 56

3.4.1 Overview........................................................................................... 56

3.4.2 An example model ............................................................................ 59

4. VALIDATION AND CASE STUDIES ....................................................... 65

4.1 Test 1: dynamic simulation and (static) certification .................................... 66

4.2 Test 2: comparison with other simulation programs ..................................... 72

4.2.1 Simulation in winter conditions ........................................................ 74

4.2.2 Simulation in summer conditions ..................................................... 87

4.3 Test 3: a complete simulation model at various detail level ......................... 95

4.4 Test 4: including sub-zonal models1 ........................................................... 114

5. CONCLUSIONS AND FUTURE WORK ................................................ 121

6. ACRONYMS ............................................................................................... 122

7. BIBLIOGRAPHY ....................................................................................... 123

5

List of figures

1.1 Distribution of European consumption for end-use................................. 14

1.2 Distribution of consumption in the European Community for final

uses .......................................................................................................... 14

1.3 Distribution of European consumption for end-use................................. 15

1.4 Effects of inertia on the wave thermal ..................................................... 19

1.5 A good way to design using dynamic simulation.................................... 23

2.1 Heat transfer simulation methods ............................................................ 30

2.2 Discretization of a multi-component ....................................................... 33

2.3 Relative distance between nodes ............................................................. 33

2.4 Energy flows in a building ...................................................................... 39

2.5 Factors that influence the air flow distribution in buildings ................... 40

3.1 Connection of three fluid subsystems ...................................................... 50

3.2 Simulation of model Example1 in listing 1 ............................................. 55

3.3 Organization of the BUILD Modelica library ......................................... 58

3.4 An example of room model ..................................................................... 59

4.1 A room model for test 1 ........................................................................... 66

4.2 Test 1: temperature trade of the North wall ............................................ 71

4.3 Test 1: temperature trade of the air-conditioned environment ................ 71

4.4 Test 1: plant and section of the building ................................................. 73

4.5 Test 2: the building model implemented in Modelica ............................. 75

4.6 Test 2: comparison of the primary specific energy demands

(kWh/m2y) for heating of the containment ............................................. 78

4.7 The Sun variable position through zenith and azimuth angles ................ 79

4.8 Example of the Sun path in January ........................................................ 79

4.9 Example of the Sun path in June ............................................................. 80

4.10 Changing of external temperature during the coldest month in 2010 ..... 81

4.11 Azimuth and zenith trades ....................................................................... 82

4.12 Sunrise and sunset hour trades in 31 days ............................................... 83

4.13 External temperature trade during the coldest month in 2010 ................ 83

4.14 Test 2: winter settings for Milan (second method) .................................. 84

4.15 Mean external temperature trade (°C) during the entire heating

season ...................................................................................................... 85

4.16 Sunrise and sunset hour trades in 183 days ............................................. 86

4.17 External temperature trade during the winter season .............................. 87

4.18 Test 2: comparison of the primary specific energy demands

(kWh/m2y) for cooling of the containment ............................................. 89

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4.19 Changing of external temperature during the hottest month in 2010 ...... 91

4.20 External temperature trade during the hottest month in 2010 ................. 91

4.21 Test 2: summer settings for Milan (third method) .................................. 92

4.22 Mean external temperature trade (°C) during the entire summer

season ...................................................................................................... 93

4.23 External temperature trade during the summer season ........................... 94

4.24 Test 2: total specific energy need (kWh/m²y) of the containment on

annual basis ............................................................................................. 95

4.25 Test 3: the building model ....................................................................... 96

4.26 Test 3: lost energy of walls by transmission at different exposures ....... 97

4.27 Test 3: dynamic study - Level 0 ............................................................ 100

4.28 AWPI analogue control ......................................................................... 102

4.29 Test 3: dynamic analysis of the room only and control system

for each local - Level 1a ....................................................................... 102

4.30 Test 3: temperature trades in adjacent rooms - Level 2 ........................ 103

4.31 Test 3: power supplied by the control system - Level 1a ..................... 103

4.32 Test 3: studies with decoupling control - Level 1b ............................... 104

4.33 Block system of a general decoupling ................................................... 105

4.34 Representation of a generic compensator .............................................. 105

4.35 “Backwards" decoupling for a 2×2 system ........................................... 108

4.36 Test 3: action and correction of the multivariable control system -

Level 1b ................................................................................................. 108

4.37 Test 3: building model in open loop ...................................................... 109

4.38 Test 3: temperature trends in open loop ................................................ 109

4.39 Test 3: simplified analysis of the heater only - Level 2 ........................ 110

4.40 Test 3: temperature trades in the rooms - Level 2 ................................. 111

4.41 Test 3: analysis of the heater only with control system - Level 3 ......... 112

4.42 Test 3: temperature trades in adjacent rooms - Level 3 ........................ 112

4.43 Test 3: power supplied by the control system - Level 3 ........................ 113

4.44 Test 3: comparison between level 1(a) and level 3 ............................... 113

4.45 Test 4: Modelica elements for the application example models ........... 114

4.46 Possible configurations of all the model elements ................................ 115

4.47 Test 4: control of the mean room temperature (K), levels L1- 4 ........... 116

4.48 Test 4: control of the power (W), levels L1 - 4 ..................................... 116

4.49 Test 4: the total computed consumption (J), levels L1 - 4 .................... 117

4.50 Test 4: temperature at different heights, levels L2 and L4 .................... 118

4.51 Different positions for a temperature sensor ......................................... 118

4.52 Test 4: temperature and power control, and the total consumption

with different sensor positions at L2 ..................................................... 119

7

List of tables

1.1 Using the methodology of calculation of energy building

performance ............................................................................................. 17

2.1 Calculation codes of the first level for evaluating building energy ........ 28

2.2 Calculation codes of the second level for evaluating building energy .... 28

2.3 Comparison of E/E with ESP-r/DOE-2/BLAST Weather Data

Formats .................................................................................................... 36

4.1 Test 1: settings ......................................................................................... 67

4.2 Test 1: physical characteristics of walls .................................................. 68

4.3 Test 1: physical characteristics of the roof .............................................. 68

4.4 Test 1: physical characteristics of the floor ............................................. 69

4.5 The exposure factors ............................................................................... 69

4.6 Test 1: comparison in terms of energy lost by transmission ................... 70

4.7 Test 1: thermal transmittance .................................................................. 70

4.8 Test 1: energy performance index ........................................................... 70

4.9 Physical characteristics of the building ................................................... 74

4.10 Heating and cooling seasons ................................................................... 74

4.11 Test 2: transmission lost power (W) of the building located

in Milan ................................................................................................... 76

4.12 Test 2: energy performance index in winter conditions for

the building located in Milan.................................................................. 76

4.13 Test 2: transmission lost power (W) of the building located

in Rome .................................................................................................. 76

4.14 Test 2: energy performance index in winter conditions for

the building located in Rome................................................................... 76

4.15 Test 2: software results ............................................................................ 77

4.16 Test 2: physical proprieties in stationary conditions (first method) ........ 80

4.17 Test 2: physical proprieties for monthly calculation

(second method) ...................................................................................... 81

4.18 Mean monthly external temperatures (°C) during

the winter season ..................................................................................... 85

4.19 Test 2: physical proprieties for annual calculation (third method) ......... 86

4.20 Test 2: different energy performance index for the building

containment situated in the same location (Milan) ................................. 87

4.21 Test 2: energy performance index in summer conditions for

the building located in Milan.................................................................. 88

8

4.22 Test 2: energy performance index in summer conditions for

the building located in Rome................................................................... 88

4.23 Software results in the summer season .................................................... 89

4.24 Test 2: stationary conditions in the summer season ................................ 90

4.25 Test 2: mean monthly external temperatures during the summer

season (°C) .............................................................................................. 93

4.26 Test 2: different energy performance index of the building situated

in the same location (Milan) .................................................................... 94

4.27 Test 3: settings ......................................................................................... 97

4.28 Test 3: physical characteristics of walls .................................................. 97

4.29 Test 3: physical characteristics of the roof .............................................. 97

4.30 Test 3: physical characteristics of internal walls ..................................... 97

4.31 Test 3: physical characteristics of the floor ............................................. 98

4.32 Test 3: physical characteristics of building components ......................... 98

4.33 Test 3: energy lost by transmission and ventilation for

each exposure ......................................................................................... 99

4.34 Test 3: comparison between static and dynamic calculation ................ 101

9

Ringraziamenti

Vorrei ringraziare il Professore Francesco Casella, per la grande possibilità di

lavorare a questa Tesi. E ancor di più desidero ringraziare il Professore Alberto

Leva e Marco Bonvini per il loro sostegno ed aiuto durante l’intero periodo di

lavoro. La loro grande esperienza nel campo è stata di grande valore per i

risultati presentati in questa Tesi, ma è stata soprattutto la passione, attenzione,

dedizione e cura nel lavoro svolto insieme a stupirmi e ad incoraggiarmi.

E così ringrazio tutti quei professori a cui ho guardato con grande ammirazione

per l’amore all’insegnamento e l’interesse sincero mostratomi durante questi

anni, durante i quali la strada che portava all’università e poi al lavoro si faceva

sempre più chiara. Ricordo in particolare l’insegnante di italiano del liceo, che,

terminata la maturità, mi ragalò una tartarughina fatta da lei all’uncinetto.

Questo piccolo peluche porta al collo un foglio arrotolato con la seguente

citazione: «Il professore è uno che parla nel sonno altrui» (W. H. Auden).

E ora i ringraziamenti più difficili, non perché non riesca a trovare le parole

adatte, ma per l’impossibilità di poter arrivare - come io vorrei - a tutte le

persone incontrate lungo il cammino. Prima di tutto ringrazio i miei genitori,

Salvo e Laura, per il sostegno e l’amore ricevuto, ma soprattutto la pazienza.

E i miei più cari amici senza i quali non avrei potuto fare un passo. Il primo sei

tu, don Fabio, che mi hai insegnato ad amare, a saper guardare con il cuore e a

giudicare tutte le circostanze che vivevo affinchè potessi diventare una donna.

E il mio pensiero va ai più piccoli amici dell’oratorio, mi avete regalato la

possibilià di vedervi crescere nella vostra semplicità d’animo, e diventare grandi

e curiosi della vita. E a voi, amici incontrati in università o meglio compagni di

viaggio, a voi rivolgo tutto il mio affetto. Vivere l’università in vostra

compagnia ha dato un significato alla scelta dei miei studi. Così lo studio non

era più una fatica ma un modo attraverso il quale poter scoprire e conoscere di

più me stessa. E così tutto il tempo passato insieme portava a un “di più”.

Ho davvero davanti a me molti volti di cui vorrei scrivere per ciascuno il nome

in modo tale che rimanga inciso in questo lavoro che per me rappresenta il

completamento di un incredibile percorso umano. Decido di lasciarvi un breve

passo di Oscar Milosz tratto dal libro “Miguel Manara” che mi permette di poter

abbracciare e voler bene fino in fondo ciascuno di voi.

"Verrà forse un giorno in cui Dio ti permetterà di entrare brutalmente, come

una scure, nella carne dell'albero, di cadere pazzamente, come una pietra, nella

notte dell'acqua e di scivolare cantando, come il fuoco, nel cuore del metallo.

E quel giorno saprai di quale carne è fatto il mondo, e parlerai liberamente

all'anima del mondo dell'Albero, dell'Acqua e del Metallo, e gli parlerai con la

voce del vento e della pioggia e della donna innamorata"

10

Abstract

The main idea behind this thesis is to show that dynamic simulation can help

achieve better energy efficiency in buildings. To this end, moreover, it is very

important to be able of conducting system-level studies with a scalable detail

level. This work presents the motivations for the statements above, and explains

how simulation can be a decision aid tool along the entire life of a project, be it

a new design or a refurbishing. Finally, OOMS (Object-Oriented Modeling and

Simulation) is proposed as the formalism for the required modeling and

simulation tasks, and the reasons for adopting that formalism in system-level

building simulation are illustrated by means of convenient examples.

The work also acknowledges the relevant problems involved in such a matter,

especially the necessity for dynamic simulation models to maintain steady-state

consistence with design-oriented ones (in the classical sense). In so doing, we

define the role and expected outcome of the major types of tools, for example

showing what one can (and cannot) expect from certification systems, sizing-

oriented models, and so forth.

The goal of the long-term research to which this thesis belongs is constructing a

set of dynamic simulation models and procedures capable of tackling the whole-

building problem in a unified framework, and with fully scalable complexity. In

order to do that in practice, a model library is thus introduced, and some

examples demonstrate the improvements yielded by OOMS to both the ease and

the usefulness of simulation in building design. By means of some case studies,

show the usefulness on two different fronts:

The same model can be used to conduct studies at different levels of detail, allowing the designer to base his/her decisions on simulation

outputs right from the beginning of a design, and maintaining coherence

along that design process;

Models conceived in this way allow to synthesize and compare different control systems, both at a high level (correctness of a strategy and

suitability for the specifications at hand) and with arbitrarily fine detail

(capability of the installed devices to actually realize that strategy).

Finally, this work preliminarily shows how OOMS allows to model relevant

facts such as the behavior of air movers, and sub-zonal airflow descriptions.

In doing so, OOMS allows to capture energy-relevant phenomena at a level that

is surely coarse with respect to fine-scale 3D CFD codes. However, this in

OOMS is done together with all other modeling task, in a single framework and

tool.

11

Specifically, the contribution of this thesis consists in implementing some

library models, and especially in defining and conducting comparative tests to

relate the proposed approach to well assessed engineering techniques, thereby

evidencing the proposal’s advantages.

Keywords: Building simulation, object-oriented modelling, and scalable detail.

Sommario

L’idea principale che sta alla base di questa tesi è mostrare come la simulazione

dinamica possa aiutare a raggiungere una migliore efficienza energetica negli

edifici. A tal proposito, è molto importante che essa, inoltre, sia in grado di

condurre le analisi a livello di sistema di controllo con un livello di dettaglio

scalabile. Questo lavoro presenterà le motivazioni di quanto appena dichiarato, e

spiegherà come la simulazione possa essere uno strumento di aiuto decisionale

lungo l’intera vita di un progetto, sia che si tratti di nuova costruzione o di

rimessa a nuovo. Infine, il linguaggio orientato agli oggetti definito OOMS

(Object-Oriented Modeling and Simulation) viene proposto come formalismo

per la modellazione e simulazione, e alcuni esempi saranno presentati per

illustrare le ragioni per le quali si è scelto tale formalismo per la simulazione

degli edifici a livello di sistema di regolazione. Il lavoro riconosce anche i

problemi rilevanti coinvolti in tale materia, in particolare la necessità dei

modelli di simulazione dinamica di mantenere la consistenza dello stato

stazionario con quello orientato (nel senso classico). Così facendo, si definisce il

ruolo e i risultati attesi dei principali tipi di strumenti, mostrando per esempio

ciò che uno può (e non può) aspettarsi dai programmi di certificazione,

dimensionamento degli modelli orientati, e così via.

L’obiettivo della ricerca a lungo termine a cui appartiene questa tesi è di

formulare una serie di modelli di simulazione dinamica e procedure in grado di

affrontare la progettazione dell’intera costruzione attraverso un quadro unitario e

una complessità completamente scalabile. A tale scopo, nella pratica saranno

presentati un modello di libreria e alcuni esempi che dimostreranno i

miglioramenti apportati dal linguaggio OOMS sia per la facilità sia per l’utilità

12

della simulazione nella progettazione edilizia. Per mezzo di alcuni casi di studio,

si dimostra tale utilità su due fronti differenti:

Lo stesso modello può essere utilizzato per condurre studi a diversi livelli di dettaglio, consentendo in tal modo al progettista di basare le

proprie decisioni progettuali sulle uscite di simulazione fin dall’inizio

della costruzione dell’edificio, e di mantenere la coerenza lungo quel

processo di progettazione;

Modelli concepiti in questo modo permettono di sintetizzare e confrontare i diversi sistemi di controllo, sia ad alto livello (correttezza

di una strategia e idoneità per le specifiche a portata di mano) sia con

dettagli arbitrariamente sottili (capacità dei dispositivi installati per

realizzare effettivamente la strategia).

Infine, questo lavoro mostra preliminarmente come il linguaggio OOMS

permetta di modellare alcune situazioni rilevanti come il comportamento dei

moti dell’aria (compreso il caso che si tratti di una descrizione sub-zonale). In

tal modo, la tecnica agli oggetti orientati permette di catturare fenomeni

energetici rilevanti ad un livello che è sicuramente grossolano rispetto alla scala 3D dei codici eleborati da CFD. Tuttavia, nel linguaggio di programmazione

scelto questo viene fatto insieme a tutti gli altri compiti di modellazione

attraverso un unico strumento. In particolare, il contributo di questa tesi consiste

nell’implementare alcuni modelli della libreria, e soprattutto nel definire e

condurre alcune prove comparative per mettere in relazione l’approccio

proposto con le tecniche di ingegneria ben consolidate, evidenziando così i

vantaggi della proposta.

Parole chiave: simulazione di edificio, modellazione object-oriented e studio di

dettaglio scalabile

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Chapter 1

Introduction

1.1 The energy problem Since 1997 the global climate change debate has focused largely around the

Kyoto Protocol that requires industrialized countries to reduce their emissions of

greenhouse gases. The identification of effective strategies to control climate

change is therefore a key challenge for the research undertaken on sustainability

issues.

Nowadays, energy savings and improved efficiency in end uses seem to be the

way forward to be able to resolve, at least partially, the serious energy problems

that are affecting all countries.

Since 1973, when the first oil crisis happened, the state of energy began to be

analyzed and assessed. Especially in the Anglo-Saxon world professions such as

Energy Managers were created with the task of analyzing and solving all the

problems related to improper use of energy resources. In support of this, a series

of regulations was subsequently launched that took into account energy saving

and provided guidance to improve end-use efficiency.

The work of the World Climate Conference (particularly the COP 3 in 1997 that

defined the Kyoto Protocol) has gradually promoted programs, strategies and

actions aimed at reducing air pollution and consumption of non-renewable

energy sources, the promotion of renewable energy and energy-saving incentive

[1]. The need to rethink the way we produce and use energy is inescapable: first,

the inadequacy of the current energy production in meeting the demands of

consumption growth and, secondly, the impact on the environment and quality

of life that an increase in production and consumption of fossil fuels would

bring.

The energy consumption of cities is particularly significant: according to recent

estimates, in fact, half the world's population lives in urban settlements. For

example, in Italy, one third of the population and most activities are

concentrated in a seventeenth of the national territory.

In terms of energy, in 2003 the Europe Union has spent a total of 1,505 million

tons of oil equivalent: a breakdown in end uses can see a large part of

consumption attributable to the residential and tertiary sector (see fig. 1.1).

Energy efficiency is the most important, fast and effective tool identified by the

European Union to ensure global competitiveness, security and quality of our

environment, reducing dependence on foreign supply of raw materials and

14

energy. It has been estimated that the EU would be able to save, with

appropriate interventions, at least 20% of the current consumption of around 60

billion euro per year: on a smaller scale, an average family could save from 200

to 1,000 euro per year.

Figure 1.1. Distribution of European consumption for end-use (The Green Paper, 2000)

In the energy balance of EU countries an important role is played by the civil

sector, which includes energy consumption for the use and management of

residential and tertiary buildings. According to data presented by the EU itself,

this sector uses more than 40% of the total EU final energy demand (see fig.

1.2).

Figure 1.2. Distribution of consumption in the European Community for final uses (The

Green Paper, 2005)

The end-uses in a building are numerous and include the air-conditioned

(heating and cooling), the production of hot water, ventilation and air handling,

lighting, use of appliances and electronic equipment [2].

42%

28%

30%

Residential and tertiary Transport Industry

15

Figure 1.3 - Final consumption of energy in residential use category (ENEA data

processing MAP – 2004)

The air conditioning in buildings in winter and summer season is the most

energy-consuming component, even considering the differences between

buildings due to different types of construction and intended use (see fig. 1.3).

In addition, the increased use of cooling systems in residential construction has

contributed in recent years, the significant increase in energy consumption for

air conditioning in summer.

Based on these evaluations and considering that existing building stocks are

many cases ancient and inefficient in terms of energy1, the EU and member

countries have identified a strategic sector for achieving the overall energy

efficiency.

Thus, in recent years, both at European and national level, a new legislative

framework has established, able to develop the necessary regulatory

requirements, financial, technological and cultural challenge for adequate

response to the energy efficiency of buildings.

1.2 European legislation In 2002 the European Parliament adopted Directive 2002/91/EC (Energy

Performance of Buildings Directive) on energy efficiency in buildings with the

aim of improving the energy performance of buildings within the Community.

This Directive [3] dictates, in fact, that each State shall provide an energy

performance certificate at the time of construction, sale and leasing of new or

existing building. This certificate shall be obtained based on a methodology for

1 in Italy there are 13 million of existing buildings, of which about 11 million prior to the Act 373/73 - Source: White Paper on Energy, Building, Environment

16

calculating the energy performance of buildings. Standards are therefore needed

to define, quantify, and assess energy performance.

The objective of Energy Performance of Building Directive is to “promote the

improvement of the energy performance of buildings within the European

Community, taking into account outdoor climatic and local conditions, as well

as indoor climate requirements and cost-effectiveness” (Art.1).

This is to be achieved through five main actions:

The creation of a single methodology that can be used to calculate the energy performance of buildings.

The application of minimum requirements, to all new residential and tertiary (generally public and commercial) buildings and to the major

refurbishment of existing buildings with floor areas greater than 1,000

square meters.

The introduction of an energy performance certificate to be produced whenever a building is constructed, rented or sold.

Regular inspection of boilers with outputs of more than 20 kW and inspection every two years for boilers of more than 100 kW.

Regular inspection of air conditioning systems with outputs of more than 12 kW.

Confirming the increased focus on more effective integration of building into

the environment surrounding, Art.8 of the initial considerations provides that

“Member States shall set minimum energy performance requirements for

technical building systems that are installed in buildings”.

1.3 The Italian scenario

In Italy, Law 373 of 30 April 1976 concerns, in particular, limitations in energy

consumption for heating. It requires that the building envelope ensure the least

possible loss of heat to the outside.

By Act 10 of 9 January 1991 the approach to the problem of energy saving was

of a different nature, although in the analogous purpose of inducing the user to

reduce energy consumption for its own needs for environmental heating. In

practice, the observations should be concentrated on the energy needs of the user

17

over an entire year in question. It is necessary to consider not only the effects of

isolation, but also the free contributions (people, lights, solar radiation, etc.) that

contribute to environmental heating.

Following the European Directive, we have seen a renewal of legislation [4],

that has led to the promulgation of national Legislative Decree 192 of 19 August

2005, which was, later, supplemented and corrected by legislative decree of 29

December 2006, n. 311 and more recently by the implementing decrees, D.P.R.

59/09 and D.M 29/06/2009 containing the "National guidelines for energy

certification of buildings".

These certifications are based on precise calculation methods of national

reference, divided by new and existing buildings, by type of building, size and

complexity of the same (see tab. 1.1).

In particular, new buildings are referred to the “method of calculation of

project” (paragraph 5.1 of Annex A of 06/26/2009 Decree). It refers to UNI/TS

11300-1 and UNI/TS 11300–2 [5] for index calculation of energy performance

for heating (EPi and EPi invol) and cooling (EPe,invol). This procedure is applicable

to all types of buildings, whatever their size.

Table 1.1. Using the methodology of calculation of energy building performance (Annex 3

of Annex A of 26/06/2009 Decree)

18

For existing buildings, however, one has to refer to the "method of calculation to

relief on the building" (paragraph 5.2 of Annex A of 06/26/2009 Decree). It

refers to UNI/TS 11300-1 and UNI/TS 11300-2 for index calculation of energy

performance for heating (EPi and EPi,invol) and cooling (EPe,invol) and it provides

the following three levels of detail, depending on the type and size of the

building:

1. For all building types, regardless of their size, the technical standards UNI/TS 11300-1 and UNI/TS 11300-2 (and their simplifications provided for

existing buildings) are the national reference.

2. Only for residential buildings with floor area up to 3000 m2 it refers to the method of DOCET calculation, prepared by CNR and ENEA on the basis of

the technical standards UNI/TS 11300-1 and UNI/TS 11300-2. This method

meets the requirements of simplification, aimed at minimizing the burden on

applicants.

3. Only for residential buildings with floor area up to 1000 m2, the simplified method set out in Annex 2 of Annex A of 06/26/2009 Decree is used as a

reference for the calculation of building energy performance indices for

winter heating (EPi and EPiinvol).

1.4 Limits of the Italian legislation Designing second a proper approach due to the principles of sustainable

architecture, the building envelope will need to: release little heat and capture

solar energy from sunlight in the winter season, and reject the solar radiation

and release heat when necessary, if summer season.

In this regard, countries with a temperate climate (Southern Europe) will solve a

more difficult task: to design solutions that can deal with the cooling as well as

with the heating.

Much research [6] has been conducted to evaluate the performance of buildings

and their materials with the change in thermal inertia. The thermal inertia is

simply the ability of materials to store heat and release it gradually over time:

the energy received during the hottest hours is stored in the mass of the building

and then gradually released. Materials have the task to mitigate (damping) and

delay (time lag) entry in the environment of a heat wave (see fig 1.4) due to

incident solar radiation on the building envelope. This ability depends on the

thickness of the material, the thermal capacity and conductivity. This will

19

determine, within the building, a lag and a reduction of fluctuations and peaks

that characterize the outside temperature.

Figure 1.4. Effects of inertia on the wave thermal (TERMOBUILD)

The conscious use of the mass has a significant positive effect on comfort

conditions, energy consumption and cooling loads, particularly those peak,

which is one of the reasons for the summer blackout.

In assessing the energy costs, one must therefore consider both the total

consumption and the maximum loads, which determine the sizing of air

conditioning. The mass is not in itself a solution applied indiscriminately to

automatically improve the energy performance. The use of a heavy containment

implies a deep understanding of the dynamic properties of the closures. It is a

solution that fits nicely with the passive cooling strategies also mentioned in the

initial note (n.18) by EPBD. The European Union, in this note, remember that in

recent years in countries of southern Europe, there has been an increased use of

facilities for air conditioning thus posing serious problems at peak load2.

Therefore, it states that priority with respect to energy consumption for cooling

rather than heating should be given in some European countries.

With reference to air conditioning in summer, the Legislative Decree 192/05 and

311/06 require (for some climate zones and destinations of use) the adoption of

certain solutions of containment without also requiring any calculations [7].

In particular, establishing a single limit, equal for all locations to the surface

mass of opaque component, is a simplistic prescription. It does not properly

consider some effects of various parameters (thermal, solar, user and

environmental) on summer loads and energy needs. Finally, the only indicator of

energy performance introduced by these regulations refers to the winter heating

(EPi). This seems at odds with the Directive 2002/91/EC which comprises the

2 it was summer-time record of power equal to 56,589 MW (source TERNA - July 20, 2007)

20

total energy consumption of the building: winter heating, summer air

conditioning, domestic hot water, lighting, ventilation.

1.5 Advantages and disadvantages of “simplified” procedures When one plans to proceed with the simulation for the design of buildings, it is

convenient to wonder if it is advantageous to opt for a stationary rather than a

dynamic evaluation. To overcome the complexity of data collection and a real

dynamic simulation of energy behavior of the building, the standardization

organizations have called for simplified procedures.

Some software tools (CENED, DOCET, BESTClass, MC Impianto and so forth)

quantify the energy performance of a building [8]. They have a broad consensus

among experts thanks to several advantages:

- their immediacy of use,

- simplicity,

- repeatability,

- understandability for user,

- transparency to all actors involved (designer, project manager, tester),

- reduced expensiveness.

The implemented computational procedures (called “quasi-stationary”) were

translated into rules that require methodological simplifications, such as those

derived from CEN (Comitato Europeo di Normazione). The decrees transposing

EU Directive on energy efficiency in buildings are based on these procedures.

Currently, commercial software for the energy certification, as well as those

provided by ITC-CNR or by individual regions, returns a set of data and

indicators on energy performance on a monthly basis.

However, steady-state simulations can only partially investigate the actual

performance of a building, because they start from the assumption that the

periodic changes in temperature and the contribution of solar radiation can be

neglected or zeroed out by averaging. It is therefore possible to use highly

aggregated climate data. As such, these tools are not able to properly appreciate

the effects of climate change detectable within 24 hours. Therefore, static

21

procedures are not sufficient to calculate the real thermal-dynamic behavior of

the constructive system.

Simulations carried out in dynamic conditions, instead, allow a much more

realistic and comprehensive analysis. They assess in detail the response of the

building affected by various external factors such as the outdoor temperature,

solar radiation, natural ventilation, the behavior of the occupants, the air

conditioning system.

In professional terms, it is therefore important to deepen as much as possible the

energy analysis, establishing means and skills to use the tools that operate in

dynamic regime, which can give particularly efficient and tangible support to

the design.

To understand how the energy behavior of the building changes with the change

in the methods of calculation, we advise the reader to analyze the search result

[9] of Simone Ferrari - Environmental Technical Assistant Professor of Physics

at the Department of the Polytechnic BEST Milan. The study reveals that the

simplified procedures are insensitive to appreciate the effects of heat capacity

building to respond to the variability of weather conditions. The change in

climate, especially on the contribution of solar radiation, measured with

sophisticated instruments, not only plays a key role in determining the energy

requirements for buildings, but allows a designer to appreciate the advantages

given by the heat capacity of the building.

The study [10] made in the University of Technology in Finland could be also

interesting. Researchers have analyzed the effects of thermal mass on heating

and cooling energy in Nordic climate and for modern, well-insulated Nordic

buildings. The effect of thermal mass was analyzed by calculations made by

seven different calculation programs. Six of these programs are simulation

programs (Consolis Energy, IDA-ICE, SciaQPro, TASE, VIP, VTT House

model) and one monthly energy balance method based on the standard EN 832,

which is the predecessor of ISO DIS 13790. The study purpose was to evaluate

the reliability of the monthly energy calculation method and especially its gain

utilization factor compared with the simulation programs. The results showed

that the simplified standard methods of EN 832 and of ISO DIS 13790 generally

give accurate results in calculating the annual heating energy, e.g., in the context

of energy design and energy certification. However, the gain utilization factor of

these standards was too low for very light buildings having no massive surfaces

resulting in too high energy consumption. The study showed that the differences

in input data cause often greater differences in calculation results than the

differences between various calculation and simulation methods.

22

1.6 Usefulness of dynamic simulation The need to improve energy efficiency has also influenced the construction

sector. Improving the energy performance of buildings is, in fact, the basis of

new legislation (2010/31/EC) which aims to contribute to the efficiency-related

energy use. Specifically, in the context of production processes and buildings,

whether for residential, commercial or industrial, we can identify four main

types of intervention: energy savings associated with devices and/or good

design/restructuring, rationalization in use of energy, cogeneration, and

integration of different sources.

The fundamental problems of classical design/modeling are related to:

Ineffectiveness of the stationary approaches when the designed systems assume a substantial degree of complexity;

A lack of technology that allows simultaneous analysis of the interaction between buildings, variable weather conditions, presence of renewable

resources, issues of performance limits.

A significant advantage of a good simulation method is the ability to investigate

the sensitivity of individual parameters, which allows designers to efficiently

compare different designs. In fact, during the design or refurbishing of a

building, a designer has to take some complex decisions and dynamic simulation

is an optimal tool to do that as all the problems can be identified and solve in

advance. It can give a full and tangible support to the design (fig. 1.5),

simulating the project at any time, irrespective of what was already fully

designed. It makes also possible to move back and forth among the complexity

levels implicitly defined above, in the case some past decision needs re-

discussing.

Using a dynamic model of the system, one can evaluate the behavior of the

generating section while the heat and electrical load are not constant. This will

give a designer the possibility to assess the integration of more energy-efficient

technologies (renewables, CHP, solar-cooling, etc.) according to the weather

characteristics of the site and the demands of the territory.

23

Figure 1.5. A good way to design using dynamic simulation

In some sense, the availability of a dynamic simulator “joins” the existing

instruments, and can provide additional information with respect to that

available in the planning, design or (semi) static evaluation. In fact, dynamic

simulation is a necessary tool in the design phase of thermal plants, especially

when it comes to test the responses of innovative systems. It plays a crucial role

in the early stages of a design, since the control strategies and the necessary

equipment are evaluated, to the certification of the validity of the control

system.

Dynamic modeling and transient analysis are however frequently thought to

require considerable effort and investment. Such investments are most often

paid off by savings to be gained by optimizing the configuration of the system

or by the discovery of potential vulnerabilities, instead of having to perform

timely and more costly in the future. Nonetheless, simulation models must be

efficient (in terms of machine time), modular (the model is made by assembling

appropriate models of individual components, each relating to a portion of

physical plant), transparent (the code must be legible and reflect the original

equations); said models also need to enforce integrity (the model is to grasp the

essential dynamics and be able to match the model of the control system,

demonstrating the ability of the system to work properly).

A complete building simulation tool has three main classes of potential users

with different requirements:

- building designers,

- government policy makers, and

- research scientists.

24

These users can apply a simulation tool to pre-construction testing, indoor air

quality prediction, energy efficient heating and ventilation design, and design

validation (Kendrick 1993).

In so complex a panorama, this thesis belongs to a long-term research proposing

OOMS (Object-Oriented Modeling and Simulation) as a possible solution in

terms of a unified framework where to cast the overall problem.

25

Chapter 2

Building simulation tools

After a brief introduction on the current legislation and the usefulness of

dynamic simulation, this chapter presents different ways to classify simulation

tools and shows some relevant problems in dynamic modeling.

2.1 Classification of calculation codes In building simulation the first software tools arose from the implementation of

“handbook” procedures, characterized by a simplified outline, and operating in

steady state. Therefore, such procedures provide only first-cut results.

Later on models appeared that took into account part of the energy dynamics in

buildings. These applications were however difficult to use, in particular

because of the lack of a graphical interface, and of limited usefulness because

they were aimed at resolving specific problems such as the sizing of air ducts, or

the determination of thermal loads.

In the current generation of software tools, the behavior of the entire building-

plant complex can in principle be simulated, matching both analytical and

numerical procedures. In particular the modeling of heat flows, electrical,

lighting, sound and behavior of the occupants can be solved simultaneously.

Although they present easier and more intuitive graphical interface and various

functions have been introduced to help the process of data entry, these software

however involve non trivial mechanism (up to co-simulation) and as a

consequence may often require considerable experience on the part of the user.

2.1.1 Clarke and Maver’s classification

A way to classify building simulation tools was proposed by Clarke and Maver

(1991), who suggest the following classification [11]:

1st generation: such tools are handbook oriented computer implementations, analytical in formulation, and biased towards

26

simplicity. They often lack rigorous approach, and thus provide only

indicative results within constrained solution domains.

2nd generation: such tools are characterized by the introduction of the dynamics coming from the containment, but are decoupled in relation to

the treatment of air movement, systems and control. Early tools were

decoupled from the design process by limited interfaces and

computational requirements which were demanding for their time. Later

implementations are often still marketed owing to their ease of use and

speed of solution.

3rd generation: such tools are characterized by treating the entire building as a coupled field problem and employing a mix of numerical

and analytical techniques. These tools require considerable experience

and resources to go beyond simple problems. Interfaces are able to

reduce some barriers to their use. Modeling integrity is enhanced but the

tools are often used to derive information to be incorporated in

simplified techniques.

4th generation: such tools are characterized by full computer-aided building design integration and advanced numerical methods which

allow integrated performance assessments across analysis domains.

Interfaces and underlying data models are evolved to present and operate

on simulation entities as objects in the user’s domain. One common

evolution is the incorporation of knowledge bases within the tool

infrastructure.

The first and second generations refer to as simplified methods because of their

constrained treatment of the underlying physics, and the third and fourth

generations refer to as simulation or dynamic methods (Hand 1998).

It is worth noticing that nowadays, commercial tools have more or less reached

the performance of generation 3; the rest is still mostly research.

2.1.2 Sahlin’s classification

Another classification criterion was given by Sahlin (1996), who suggest

classifying simulation program by “modular” vs. “traditional” tools [11]. In

order to clarify the concept of modular software, two conditions are reported

that must be observed to fall into this category:

27

Models are treated as data. The key characteristic of an MSE is that the mathematical models are exchangeable. The environment allows

radically different models to be used for the same physical device.

Software models for modeling and solution are separated. The software architecture allows exchange of solvers. Although only a few MSEs

really offer a selection of different solvers, they are flexible in this

respect.

2.1.3 CIRIAF’s classification

According to [12] the institution CIRIAF (Centro Interuniversitario di Ricerca

sull’Inquinamento da Agenti Fisici), classified computer codes in two levels,

based on the temporal scheme of calculation:

1st level: codes that work in steady regime.

2nd level: codes that work in dynamic regime.

Within each level, the computer codes can then be further subdivided in other

macro-categories based on:

The interface, dividing the codes in instruments backed by a computer graphics interface (input graph) and tools with no graphical interface;

The evaluated building systems, basically codes that allow the evaluation of energy performance in winter conditions and those that analyze the

summer conditions.

Table 2.1 and 2.2 show, for the first and second level respectively, the

classifications of tools to evaluate building energy more available and widely

used in the construction sector, accompanied by information concerning the

origin, the implementation team and possible Internet sites.

28

Table 2.1. Calculation codes of the first level for evaluating building energy (CIRIAF)

Table 2.2. Calculation codes of the second level for evaluating building energy (CIRIAF)

29

2.2 Thermal simulation methods for buildings Models can be either identified from data, or derived from first principles. To

describe something that does not exist yet, such as a building that is being

designed, the second way is the only possible, and thus this work focuses on it.

Dynamic first principle models are based on the (dynamic) balances of mass,

energy and momentum:

0)(

w

t

(Mass)

)()()( TTcwet

p

(Energy)

fpwwwt

)()( (Momentum)

The scalars p, T, cp and are respectively the fluid pressure, temperature, specific energy and density; the vectors w and f are the fluid velocity and the

possible motion driving forces, and the scalar parameters and cp are the fluid thermal conductivity and constant-pressure specific heat capacity. In this case

we consider air as a mixture of ideal gases, so it allows expressing the specific

energy e as cv*T, where cv is the constant - volume specific heat capacity.

Solving partial differential equations that are obtained from said balances means

finding the motion of the state variables that characterize it. The entire

resolution is reported in [13].

The solutions can be obtained by analytical or numerical way. In the first case

one can find the exact solution, limited to problems where the geometry and

boundary conditions are very simple; in the latter case, appropriate

approximations are used to help a simulator resolve any problems on computers

through the calculation programs and to identify the solution with reasonable

accuracy. Analytical solutions allow for the calculation of variables at any point

in the model, but with very long calculation time. Therefore, they are suitable

only to solve simple problems. With numerical methods, conversely, one can

easily solve problems of any degree of complexity: they have general

applicability, but refer only to points (segments, areas or volumes) by default.

The selected (or discrete) points will be characterized by the properties of a

small region which they belong. Such a discrete point is frequently termed a

nodal point (or simply a node), and the aggregate of points is termed a nodal

network, grid or mesh.

According to [11], Kallblad (1983) grouped building heat transfer simulation methods in time-dependent methods and simplified methods. The simplified

30

methods can further be categorized ad steady-state heat balance, degree-day, and

other methods (see fig. 2.1).

Figure 2.1. Heat transfer simulation methods (Kallblad 1983)

Dynamic thermal simulation methods can be classified as heat balance and

weighting factor methods, with the heat balance method giving more detail

output data than the weighting factor method.

When the heat balance method is used, the solution of the time-dependent

temperature distribution within a solid during transient process is often difficult

to obtain. Therefore, where possible, a simple approach is preferred. One such

approach is termed the lumped capacitance method, where the temperature of

the solid is assumed spatially uniform at any instant during the transient process.

This assumption implies that the temperature gradient within the solid is

negligible (Incropera and De Witt 1990).

The numerical methods used are various: we report below those that are more

suited to solving the problem of heat transfer, in particular the technique of

finite differences and finite volume. Notice, with a view to evidencing the

peculiarity of this work, which OOMS tools allow to use either of them freely.

2.2.1 The finite – difference method

An approximate solution to solve a problem of heat conduction through a wall in

the direction of one-dimensional is given by the finite difference method

31

(FDM). In the field of iterative methods it is the easiest to treat in terms of

writing equations.

The equations (2.1) and (2.2) that characterize the finite difference method

derived from the discretization of the derivative of a generic function in a

predefined point, made by Taylor series.

)(!3

)(2

)()()(32

xfh

xfh

xfhxfhxf (2.1)

)(!3

)(2

)()()(32

xfh

xfh

xfhxfhxf (2.2)

By the relations written above, it is possible to approximate the first derivative.

This can be done in two different ways (2.3) (2.4), “forward” and “backward”:

)(!3

)(2

)()()('

2

xfh

xfh

h

xfhxfxf

(2.3)

)(!3

)(2

)()()(

2

xfh

xfh

h

hxfxfxf

(2.4)

Then subtracting the former from the latter we get “central” discretization (2.5):

)(!32

)()()(

2

xfh

h

hxfhxfxf

(2.5)

Similarly, for the second derivative,

)(2 2

2

21 hoh

fffif iii

(forward)

)(2 2

2

12 hoh

fffif

iii

(backward)

)(2 2

2

11 hoh

fffif iii

(central)

Dividing the spatial and temporal domains with an arbitrary number of nodes

separated by a step h, it is possible, through direct or iterative methods, solve the

system of equations thus created (in which the unknown function f for us is the

temperature T) and obtain a solution to the problem. However, the solution is

approximated by its truncation in the Taylor series, and the error introduced is

unavoidable, as is inherent to the logic of discretization. Since the temporal and

32

spatial coordinates of the problem switching from continuous domain to an

approximate one, a good solution will be given by a computational grid dense

enough to reduce the pace and to approach the real solution to the approximate

one. Of course, the program structure must be such as to ensure convergence

and stability of the calculation.

2.2.2 The finite element method

The finite volume method (FVM) consists in splitting the domain into “control

volumes” adjacent to one another and applying to them the balance equations in

their integral form. Nodes are placed within each volume, usually in the middle.

Unlike the previous method, the unknown quantities are not related to the nodes

but it is the grid to define the faces of control volumes. Because the variable

refers to each node, even in this case, a system composed of many algebraic

equations as there are volumes of discretization is obtained.

In order to numerically solve the problem of heat conduction in a solid, for

example, is necessary to introduce the concept of spatial discretization, in order

to define control volumes. The volume control (VC) are defined as the

application of a Cartesian grid computing with a pitch not necessarily constant:

if we consider, for example, the case of a multilayer wall, the procedure

involves first identifying of each layer (possibly divided into sub volumes), after

the awarding of a central node at each volume considered (using the "cell

centered" method). Figures 2.2 and 2.3 show the location of the nodes for given

control volume: the distance between them is a fundamental fact about writing

of the equations that solve the problem.

It is assumed that heat transfer occurs between node and node, and that:

the physical and thermal properties of the body are uniform and are not a function of temperature;

the heat capacities are concentrated in the nodes;

changes in temperature between the nodes are linear;

heat transfer takes place only between adjacent nodes.

33

Figure 2.2. Discretization of a multi-component

Figure 2.3. Relative distance between nodes (a)

Figure 2.3. Relative distance between nodes (b)

Unlike the problems in steady state, where it is sufficient to refer only to the

spatial discretization, for dynamic problems the need for time integration arises:

the solution should be calculated referring to the individual intervals where the

34

time domain is divided. Among the temporal discretization methods that require

only two moments of time for each equation we refer to the Euler method:

1. explicit: ttf nnnn ),(1 (2.6)

2. implicit: ttf nnnn

),( 111 (2.7)

There too, notice that OOMS tools natively support the time derivative as a

language primitive, so that only the spatial one remains as a burden for the

analyst.

2.3 A literature review

The US Department of Energy (DOE) has compiled an extensive summary of

building simulation tools [14], which describes more than 200 energy-related

software tools for buildings, with an emphasis on using renewable energy and

achieving energy efficiency and sustainability in buildings. In the following

paragraphs, a brief description of five of the most commonly used building

simulation tools is provided to show typical features of tools. A complete

comparison can be seen in [15] resumed in table 2.3.

DOE-2 is a tool that uses hourly weather data to simulate a building’s energy

use and energy cost for a given description of the building’s indoor climate,

architecture, materials, operating schedules, and HVAC equipment. Its

development has been funded by the U.S. Department of Energy. It is used for

building science research, teaching, designing energy-efficient buildings,

analyzing the impact of new technologies and developing energy conservation

standards [16].

EnergyPlus is a building simulation program that is currently being developed

by the Simulation Research Group at Lawrence Berkeley National Laboratory,

the Building Systems Laboratory at the University of Illinois, the U.S. Army

Construction Engineering Research Laboratory, and U.S. Department of Energy.

This program combines the best capabilities and features from BLAST and

DOE-2 along with new capabilities. It models heating, cooling, lighting,

ventilation, other energy flows, and water use. EnergyPlus includes many

innovative simulation capabilities: time-steps less than an hour, modular

systems and plant integrated with heat balance-based zone simulation, multi-

zone air flow, thermal comfort, water use, natural ventilation and photovoltaic

systems [16].

35

ESP-r is a comprehensive simulation environment which can address problems

related to several domains. It has been developed at the Strathclyde University

in Scotland since 1974. ESP-r allows researchers and designers to assess the

manner in which actual weather patterns, occupant interactions, design

parameter changes and control systems affect energy requirements and

environmental states. It is used in many European universities and research

institutes, and in some private companies. Within ESP-r, it is possible to select

different approaches to domain solution – one, two or three dimensional

calculation, a mix of scheduled air flow, network or computational fluid

dynamics (CFD) for flow assessments, and a mix of ideal or explicit

representation of plant and control systems [17].

IDA is an advanced simulation environment for building and energy system

simulation. It has originally been developed at the Swedish Institute of Applied

mathematics in co-operation with the department of Building Services

Engineering at Royal university of Stockholm. This simulation package consists

of IDA Modeller, IDA Solver, and a Neutral Model Format (NMF) library. The

key idea is to separate the models, which are defined by free combinations of

algebraic and differential equations in NMF format, and the solver. By adopting

this approach, several practical problems with traditional monolithic simulation

tools can be avoided (Sahlin 1996). Namely, new building component models

can be described in NMF format, and they still can be solved by a differential-

algebraic equation (DAE) solver without any need to rewrite simulation and

solution source code [18].

TRNSYS (TRaNsient SYstem Simulation Program) was developed during the

early seventies at the Solar Energy Laboratory at the University of Wisconsin.

The primary application was initially solar energy systems. An important feature

of TRNSYS is that component models are pre-compiled. This means that end

users may compose system models with fixed components without access to a

compiler (Sahlin 1996). Historically, TRNSYS has been used for simulating

solar thermal systems, modern renewable energy systems including PV and

wind power, general HVAC systems, and buildings [19].

Each simulation tool has special features and some limitations. For example,

DOE-2 is based on a simplified modeling approach which makes it difficult to

include new systems and devices in the model. This had led to a whole new

development effort (i.e. EnergyPlus), which is still going on (Crawley 2000).

ESP-r is a very comprehensive simulation environment, but this simulation tool

is available only in special mainframe computers using UNIX operating system

(Hand 1998). IDA is a modern and promising simulation environment. It is

36

becoming gradually more popular, but it has been reported some problems with

long execution times. The latest version of TRNSYS features many

improvements, and it has been utilized successfully in many cases. However,

TRNSYS also has some limitations due to adopted modeling methods.

Therefore, despite the fact that a great effort has been put in developing all the

existing building simulation tools, additional work is needed to rectify their

deficiencies.

Table 2.3. Comparison of E/E with ESP-r/DOE-2/BLAST Weather Data Formats

37

2.4 Peculiar difficulties of dynamic modeling

Building simulation is an interdisciplinary subject, with elements from

numerical analysis, information technology, signal processing, as well as the

building sciences (Sahlin 1996). This makes it a fascinating field with the

endless challenge to estimate interacting energy flow paths encountered within

buildings with a meaningful level of accuracy. Further complexity comes from

the behavior of the heat and mass transfer mechanisms themselves, because they

are often highly non-linear, coupled and are dependent on design parameters

which, in turn, change with time (Clarke and Maver 1991).

A simulation of a building is a mathematical representation of the physical

behavior of each of its parts. However, it cannot precisely replicate a real

construction because all the simulations are based on a number of key

assumptions that affect the accuracy. The dynamic modeling approach tries to

preserve the integrity of the entire building-plant system, simultaneously

analyzing all the energy flows with a level of detail appropriate for the

objectives of the problem and the amount of data available. In this regard, a

building must be seen as systemic (entire system consists of many separate

parts), dynamic (the parts interact, have some memory of the past, and may

evolve at different rates), nonlinear (thermodynamic parameters depend on the

state) and, above all, complex (there are a myriad of interconnections and

iterations between the parties).

Assuming that the simulation has a theoretically perfect representation of the

operation of a building, it cannot perfectly replicate the real dynamics that

govern the behavior of energy. For example, the climate can drift apart from the

available meteorological data; the systems never work exactly as expected from

the curves of partial load operation; the performance may also change with the

age of the plant and the actual number of hours worked since the last cleaning or

maintenance. Consequently, particular care when interpreting the results, as they

constitute a representation on how it works, or can work, a building-plant

system.

2.4.1 The complexity of energy exchanges

As shown in figure 2.4, the internal environment conditions in buildings are

determined by different energy sources that evolve with different speeds and

characteristics. The main sources can be identified as:

external climate, whose main variables are: air temperature, radiant temperature, humidity, solar radiation, speed and direction of wind;

38

occupants, causing an unpredictable energy supply because of their metabolism, the use electrical equipment and the adjustment of the

settings regulation;

auxiliary systems, which can provide heating, air conditioning or ventilation of the indoor environment.

In buildings, the air flows that result in an increase of heat transfer by

convection are infiltration, flows with the neighboring areas and forced

ventilation.

Infiltration means all air inlets from the outside; they can be divided into two

categories:

uncontrollable air infiltration through the seals of the windows and through the building envelope itself;

desired air inputs, implemented with the opening of doors and windows (natural ventilation).

Figure 2.5 shows the main factors influencing the distribution of air flow.

Random events, such as opening doors and windows, intermittent use of

ventilation systems have a strong influence on the assessment of the air flows

because they affect not only the areas directly affected but also the adjacent

rooms. So, modeling air is not easy and needs a particular accuracy.

Air movement is often computed on a mesh, where nodes represent volumes of

fluid, characterized by thermodynamic parameters such as temperature,

pressure, humidity; while nodal connections represent pathways, including loss,

which connect these volumes and through which air can flow. To determine the

parameters of each node in the network, techniques of numerical analysis are

commonly used. Many examples exist for the solution of the Navier-Stokes

equations with (continuity) ones of mass and energy balance.

Every air flow simulation model that uses the network approach must model

these phenomena.

The model of the air could be described by a three-dimensional model, using

advanced methods of calculation (i.e. CFD). Through an approach based on the

principle of finite elements, it could be possible to fully describe the motion of

the air in all conditions and provide information on speed, pressure and

temperature at each point in the simulated environment.

39

Figure 2.4. Energy flows in a building (Hand 1998)

40

Figure 2.5. Factors that influence the air flow distribution in buildings (Feustel and Dieris

1992)

2.4.2 Difficulty in writing the equation for building simulation

The problems associated with the thermodynamics of a building are complex for

a variety of reasons: for example the heat transfer processes are simultaneous

and have different characteristics; the heat input from air plants and solar

radiation are a function of time and space; the control volumes defined in the

discretization of the system may not always be homogeneous and isotropic; then

41

the system geometry is multidimensional: the solution of these problems, therefore, can only be numeric. If the system is discretized with a grid arbitrarily

thick and conservation laws are written for each volume, unless discretization

error the results may be sufficiently "good".

The partial differential equations that govern the conductive heat transfer in

solids can be obtained from the energy conservation principle, which expresses

an energy balance for a volume V:

A VV

p dVqdAnqdVt

c 0

(2.8)

where the first term represents the change in internal energy, the second the heat

flux entering and the third generation of internal heat on the volume V, q is the

specific heat flux vector, n

is the unit vector normal to the surface and directed

outwards; finally, q is the internal heat generation over time.

The general equation of conduction in isotropic solids is:

qkt

cp

)(

(2.9)

Where the scalars θ, cp, and k are respectively the fluid temperature, constant-pressure specific heat capacity, density and thermal conductivity.

Fourier's postulate is obtained by replacing (2.9) in (2.8):

tkq (2.10)

We consider a control volume in thermodynamic equilibrium with the regions

surrounding it. The heat fluxes for this volume can result from energy

(mechanisms of convection, conduction, radiation) or mass exchanges. The

general problem must be placed in a dynamic context: assuming the presence in

Volume I of an internal heat generation, assumed, for convenience, independent

of temperature in the region, we obtain the numerical formulation corresponding

to (2.8):

ttIj

n

j

IjII

IIpI qkt

Vc )()(

)()()(1

,

0

, (2.11)

42

Where I is the control volume, j adjacent regions; the term )(I is

characteristic density of volume I in ξ time [kgm-3

], )(, Ipc is characteristic

constant-pressure specific heat capacity [Jkg-1

°C-1

], )(V is cell volume [m3],

finally, n is the number of regions that exchange heat with I.

This type of equations refer to a time step, or two distinct moments in time: the

apex 0 indicates that the value is relative to known reference time. The

assessment of heat flow and the heat generation [W] referring to instant of

known time tt provides an explicit formulation, while at instant of

unknown time t an implicit formulation. Equation (2.8) written for each

node, provides a set of differential equations that can describe the exchange of

energy (in this case conductive) of the building-plant system. These equations

can then be collected to build the system and, therefore, the matrix for

computers.

So, the dynamic simulation of a building is not possible without a proper

modeling of systems installed in it. To make the mass and energy balances

describing the system, the components of a system are modeled by nodes and

inter-linked. The balance equations, again picked by a system, are written

through the matrix and solved simultaneously.

2.4.3 Different physical phenomena

The dynamic behavior of any "building-system" is influenced by:

- one or more walls (structure, roofing and cladding);

- fixed or mobile openings (such as windows, doors, gates, etc.);

- one or more energy conversion facilities that provides the necessary thermal energy for air conditioning of the building;

- one or more of the thermal energy distribution systems;

- one or more regulation systems of temperature, humidity, light;

- the influx of people;

- the usage factor.

43

It is easy to see that the complexity of each part of the building - due to the

nonlinearity, the variability of the boundary conditions, etc. - means that its

dynamic behavior is difficult to predict. This is even more evident when

considering the overall system, where interactions between a component and the

others are not negligible.

2.4.4 Different disciplines

It is worth noticing that the contexts in which control problems arise, are very

varied, as well as technological realizations of controllers may be different. It is,

therefore, natural to ask how to deal with so heterogeneous equipment, ranging

from physics to hydraulics and electronics, in a unified way.

First, it is very convenient to recast the problem in purely mathematical terms.

Descriptions of the items that appear in the control system should be expressed

in mathematical form. As well as for the process, transducers and actuators,

appropriate mathematical models should be formulated. Facing the modeling of

a physical system is generally complex and the complexity grows exponentially

with the size of the initial problem.

2.4.5 Different time scales and spatial sizes

Model components are heterogeneous, and quite often their temporal dynamics

are different. Just think of the energy flow through a wall and along a conduit.

Another difficulty is represented by the fact that it is not easy to find a suitable

method to model a given physical situation. A resolution procedure, such as

finite difference approximation to solve a differential equation, might be perfect

(this includes mathematics and its computer coding to solve a particular model).

However, the model may be inadequate. For example, a method to model the

heat transfer through a wall is accomplished by using simplifying assumptions

such as the one-dimensional conduction. However, it can happen that one make

a mistake using a one-dimensional thermal conduction model to represent a

situation where the two-dimensional conduction is dominant.

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2.5 Current dynamic tools

There are different tools currently available (including MATLAB Simulink,

Energy Plus, CFD – related ones such as Fluent or Code-Saturne, and so forth).

Consequently, the approach of simulation is different among different language

codes, which create an ad-hoc model of a specific subsystem. The optimal

solution would be to exchange simulation models from different domains and

create collaboration among the various simulation languages. However, the lack

of separation between models, data and solvers makes it hard to integrate

models from different disciplines for co-simulation. In addition, any code

conducts specific system-level studies with a scalable detail level, based on the

particular simulation purpose. From an engineer’s perspective, it could be hard

to manage components in a modular manner and not convenient in terms of

timing of the simulation process. In addition, many essential elements of

building models are described in a distributed-parameter way, in one dimension

(piping) or even in three (air volume). It is the reason why simulation is difficult

to manage.

There is, therefore, a need to develop appropriate tools to carry out the

simulation of the entire system. A unique language is needed that is not aimed at

any particular branch of engineering (such as mechanics, thermodynamics,

electronics) but allows modeling of various systems, provided that described by

differential algebraic equations. In this way, reports from different backgrounds

can be treated in the same way. As a result, it will be immediate to combine

models coming from different fields of engineering, characterized by control

systems with continuous or discrete time. It is against this background that the

object-oriented modeling places.

45

Chapter 3

An object-oriented solution based on

Modelica

After evidencing the major problems to be solved for efficient building

simulation, this chapter sketches out a solution based on OOMS.

3.1 Model library structuring

As anticipated, project-oriented system-level simulations need conducting at

different levels of detail. In the opinion of the author said levels have to play a

key role in structuring a model library, and for that purpose, adopting the

Object-Oriented Modeling and Simulation (OOMS) paradigm is highly

beneficial, allowing to cast the entire set of addressed problems into a single,

unitary framework. Doing so however requires a specific effort in a view to

suitably structuring the library, so that the inherent OOMS advantages are

exploited. Notice that such a structuring methodology, discussed in the

following, is novel with respect to similar works in the literature. The proposed

library structuring is composed of three steps [20].

3.1.1 Structuring step 1

The first step consists of defining and qualifying the already mentioned detail

levels. In this work four levels, are defined, corresponding to the basic questions

encountered along a building project. Of course the matter is more articulated,

and one could consider defining more levels, or further customizing them based

on the needs of some particular class of applications.

For each defined level, we point out (a) the purpose, i.e., what type of analysis it

is conceived for; (b) the hypotheses under which its models are valid; (c) the

analysis protocol, i.e., how the intended analysis is to be performed; (d) the

structural limitations, i.e., what facts the models are by construction unable of

capturing, and thus are implicitly considered negligible in the intended analysis;

(e) the practice-based limitations, i.e., for example, what the models could in

p