Advanced Building Simulation

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performance assessment methods 109–14 performance as behavior 162–3 performance indicators (PIs) 18–20, 112–13 performance limits 54 performance simulation 4, 9, 87, 89, 100, 115, 159, 162, 178, 187, 201, 221 “ping-pong” model 99–102 PMV values 209 PREJUDICE method 48 pressure simulation 77 “proactive” control strategies 159 probabilistic inversion 48–9 probabilistic structural availability theory 54 probability, concept of 29 Probability Density Function (PDF) 65–7 probability distributions 45, 48 Product Data Technology (PDT) 189–90, 193–6, 202 product models 162, 165, 211 project models 16 project windows 199–201 propagation techniques 31–2, 39–40, 49 prototyping 220 Quality Assurance (QA) 15, 87, 107–9, 247 questionnaires, use of 44 radiation, solar 72–3 Radio Frequency IDentification (RFID) technology 171 random number generators 69–71 rationality, axioms of 50–1 Rayleigh number 127, 149–50, 153 “reactive” control strategies 159 real time building simulation 20–1 real-time model updating 170–1 registration requirements (for immersive simulation) 228–33 Reynolds Averaged Navier Stokes (RANS) modeling 121–5, 135–7, 148 Reynolds number 121–3, 148–53 risk aversion 52 robotics 220 scenarios 29, 39–40, 206–8 schema-to-schema mapping 193 scientific visualization 222 seed variables 46 self-organizing models 159–87 SEMPER project 162 sensitivity analysis 32–4, 40–1, 48, 113–14, 224 sensor tracking 228–9 sentient buildings 159–61 sequential coupling 99 sick building syndrome 119 similarity analysis 152–3 simplified simulation 178 simulation engines 222 simulation models, nature of 60 simulation tools 6–12, 25–6, 212; designer-friendly and design-integrated types 12–16 single-constraint-at-a-time tracking 233 Smagorinsky model 122, 125, 135 solar radiation models 72–3 specification uncertainty 29 standard deviation, estimation of 67–8 Statements Of Requirements (SORs) 18 Steffensen iteration 98 STEP standards 193–4, 198–9 stochastic models 65–8 “stripping” of models 199 structural reliability problems 54 structured idealizations 202 structured and unstructured grids 226–7 subschemas 197, 199, 211 substitutes for simulation 178 Swifib software 100 target probabilities for failure 54–5 target values for performance 113 tele-immersion 220 telepresence 219 temperature modeling 64–72 temperature stratification 38–9, 42 terrorism 119 thermal comfort 212 thermal control of test spaces 183–7 thermal environment 120–1 thermodynamic integrity 100 three-dimensional visualization 225, 244 TO performance indicator 28, 50–4 training of experts 44–5 tristrips technique 237 TRNSYS package 100 turbulence models 121–4, 133, 137, 148–50 Index 251 uncertainty: in building simulation 25–56; and decision-making 50–5; in indoor air temperature distribution 47–9; in model parameters 34–9; in physical properties of materials and components 35–6; propagation of 31–2, 39–40, 49; in scenarios 39–40; in wind pressure coefficients 36–7, 43–7 uncertainty analysis 29–42; principles of 30–4; refinement of 42–9 utility functions 51–3
252 Index validation of simulation models 80–4 Validation and Verification (V&V) 151–3 verification methods 55 Virtual Environments (VE) 217–21, 228, 236, 239 Virtual Reality (VR) 217 Virtual Reality Modeling Language (VRML) 234, 238–42 visual capture 228 visualization processes 217, 222–6; dynamic and static 223–4 weather models: correlation of variables 78; input data for 84; use in forecasting 60–2 web-based simulation services 17–18, 21, 248 wind pressure coefficients, uncertainty in 36–7, 43–7 wind profiles 108 wind speed simulation 77 wind tunnel technology 220 workbench approach 205–7, 212–13 workflow modeling 207–9, 212 World Meteorological Organization 84 XML tools 194–6, 211 zonal modeling of airflow 89–100
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Advanced Building Simulation
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First published 2003 by Spon Press
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vi Contents 8 Developments in inter
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viii Figures 3.12 Relation between
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x Figures 8.14 Workflow Modeling Wi
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Contributors D. Michelle Addington,
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Prologue Introduction and overview
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Prologue 3 two topics that represen
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Trends in building simulation 5 com
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Reality Space averaged treatment of
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Pervasive/invisible Web-enabled Des
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Trends in building simulation 11 in
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Trends in building simulation 13 ea
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Trends in building simulation 15 ba
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Trends in building simulation 17 1.
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Trends in building simulation 19 th
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and predictive simulation-based con
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Trends in building simulation 23 Fu
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Chapter 2 Uncertainty in building s
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Uncertainty in building simulation
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This completes the outline of the c
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and inputs may be a formidable task
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Kleijnen (1997), Reedijk (2000), an
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Table 2.1 Categories of uncertain m
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∆C p (-) 1.6 1.2 0.8 0.4 0.0 -0.4
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S d (h) 80 60 40 20 0 -80 2 Uncerta
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2.4.2 Uncertainty in wind pressure
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an experiment involving structured
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with their uncertainties, in terms
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Table 2.5 Expected utilities for de
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y a performance gain on another asp
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Simulation and uncertainty: weather
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makes every month normalized to a z
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Dry-bulb temperature (°F) 60 50 40
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(b) Compute today’s average tempe
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ackward. First, the average daily h
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a��sin(�)* ln[I SC * � D] (
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H = Total daily horizontal insolati
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Temperature (°C) or wind speed (m/
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Horizontal insolation (MJ/m 2 /day)
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References Simulation and uncertain
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Chapter 4 Integrated building airfl
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(a) (b) zone 6 5 4 3 2 1 3 1 Pre-he
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a different physical state variable
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West Zone n Zone m Figure 4.5 Examp
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Node n Figure 4.6 An example two zo
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The nodal pressures are then iterat
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when employing Newton-Raphson solut
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Integrated building airflow simulat
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25.9 15.9 13.7 10.2 7.2 4.2 0.0 10.
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Air temperature (°C) 40.0 35.0 30.
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It was found that the differences a
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of the problem. In any event, calib
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Resolution CFD Decision Reduced Dec
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Although most of the basic physical
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Chapter 5 The use of Computational
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CFD tools for indoor environmental
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the Reynolds average rules can be s
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3m Control room Enclosure 5.5 m Out
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y the room conditions. In fact, the
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Y/H Y/H 1 0.8 0.6 0.4 0.2 Plot-1 Pl
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magnitude computing time than the R
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Chapter 6 New perspectives on Compu
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New perspectives on CFD simulation
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law must be invariant to a transfor
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References New perspectives on CFD
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Chapter 7 Self-organizing models fo
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Self-organizing models for sentient
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SOM topo- SOM site 1 graphy 1 1 1 1
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DC EL1 DC EL2 MC EL_1 DC EL3 E 1 MC
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technologies (Wouters 1998; Mahdavi
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and photometric properties, as well
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exploratory implementations. The fi
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the electric light component of ill
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Chapter 8 Developments in interoper
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This chapter introduces the technol
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Developments in interoperability 19
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Developments in interoperability 19
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Instances subset A Building Model S
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Import DT data Export DT data DT sc
Page 216 and 217: Data-centric approach no explicit p
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Page 222 and 223: Glazing system? Window area? Monday
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Page 236 and 237: Immersive building simulation 221 E
Page 238 and 239: Data generation Data preparation Ma
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Page 244 and 245: etter than the others, they suffer
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Page 260 and 261: Immersive building simulation 245 H
Page 262 and 263: Epilogue Godfried Augenbroe and Ali
Page 264 and 265: Index accountability 13 accreditati
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