Advanced Building Simulation

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Contents List of figures vii List of tables xi List of contributors xii Acknowledgement xiii Prologue: introduction and overview of field 1 ALI M. MALKAWI AND GODFRIED AUGENBROE 1 Trends in building simulation 4 GODFRIED AUGENBROE 2 Uncertainty in building simulation 25 STEN DE WIT 3 <strong>Simulation</strong> and uncertainty: weather predictions 60 LARRY DEGELMAN 4 Integrated building airflow simulation 87 JAN HENSEN 5 The use of Computational Fluid Dynamics tools for indoor environmental design 119 QINGYAN (YAN) CHEN AND ZHIQIANG (JOHN) ZHAI 6 New perspectives on Computational Fluid Dynamics simulation 141 D. MICHELLE ADDINGTON 7 Self-organizing models for sentient buildings 159 ARDESHIR MAHDAVI
vi Contents 8 Developments in interoperability 189 GODFRIED AUGENBROE 9 Immersive building simulation 217 ALI M. MALKAWI Epilogue 247 GODFRIED AUGENBROE AND ALI M. MALKAWI Index 249
Page 2: Advanced Building Simulation
Page 5: First published 2003 by Spon Press
Page 9 and 10: viii Figures 3.12 Relation between
Page 11 and 12: x Figures 8.14 Workflow Modeling Wi
Page 13 and 14: Contributors D. Michelle Addington,
Page 16 and 17: Prologue Introduction and overview
Page 18 and 19: Prologue 3 two topics that represen
Page 20 and 21: Trends in building simulation 5 com
Page 22 and 23: Reality Space averaged treatment of
Page 24 and 25: Pervasive/invisible Web-enabled Des
Page 26 and 27: Trends in building simulation 11 in
Page 28 and 29: Trends in building simulation 13 ea
Page 30 and 31: Trends in building simulation 15 ba
Page 32 and 33: Trends in building simulation 17 1.
Page 34 and 35: Trends in building simulation 19 th
Page 36 and 37: and predictive simulation-based con
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Page 40 and 41: Chapter 2 Uncertainty in building s
Page 42 and 43: Uncertainty in building simulation
Page 44 and 45: This completes the outline of the c
Page 46 and 47: and inputs may be a formidable task
Page 48 and 49: Kleijnen (1997), Reedijk (2000), an
Page 50 and 51: Table 2.1 Categories of uncertain m
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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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Uncertainty in building simulation
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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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Instances subset A Building Model S
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Import DT data Export DT data DT sc
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Data-centric approach no explicit p
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Glazing system? Window area? Monday
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Chapter 9 Immersive building simula
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Immersive building simulation 219 T
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Immersive building simulation 221 E
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Data generation Data preparation Ma
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Shear Velocity Acceleration Curvatu
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Immersive building simulation 227 F
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etter than the others, they suffer
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quality CRT screens, wide field of
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Graphical representation Matrix (4D
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Figure 9.22 Test room—section. Wa
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(a) (b) Immersive building simulati
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Immersive building simulation 239 m
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Calibrated tracker data Figure 9.30
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Immersive building simulation 243 g
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Immersive building simulation 245 H
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Epilogue Godfried Augenbroe and Ali
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Index accountability 13 accreditati
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performance assessment methods 109-
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Advanced Building Simulation

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