Advanced Building Simulation

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Tables 2.1 Categories of uncertain model parameters 35 2.2 Parameters that emerge from the parameter screening as most important 41 2.3 Quantile values of the wind pressure difference coefficients 44 2.4 Expected utilities for the example decision-maker 52 2.5 Expected utilities for decision-maker 2 53 3.1 The 31 values of deviations 71 4.1 Summary of prediction potential 92 4.2 Typical fluid flow component types in zonal modeling 94 4.3 Statistical summary of airflow and temperature results 106 4.4 Example of performance indicators and (case dependent) minimum approach 112 6.1 The constitutive components of basic buoyant flows 145 6.2 Interdependence of the characteristic length and the flow phenomenon 151 7.1 Terms, definitions, and instances in building control 164 7.2 Application of rules 1, 2, and 3 169 7.3 Application of rules 2 and 3 170 7.4 Schematic illustration of the simulation-based control process 173 7.5 Initial state as inputs to simulation 182 7.6 Performance indices and the utility values for each optional louver position 182 7.7 Selected control option with the corresponding performance indices and utility 183 7.8 Implementation parameters 183 7.9 Rules used for implementation 184 8.1 A set of IFC-offered material properties and user-added extended properties for energy calculation purposes 195 9.1 Development of virtual environments 218 9.2 CFD data types, visualization techniques and spatial domain 225 9.3 Sensor technologies and associated errors 230
Contributors D. Michelle Addington, Harvard University, USA Godfried Augenbroe, Georgia Tech, USA Qingyan (Yan) Chen, PhD, Purdue University, USA Larry Degelman, Texas A&M University, USA Jan Hensen, Technische Universiteit Eindhoven, The Netherlands Ardeshir Mahdavi, Vienna University of Technology, Austria Ali M. Malkawi, University of Pennsylvania, USA Sten de Wit, TNO <strong>Building</strong> and Construction Research Delft, The Netherlands Zhiqiang (John) Zhai, University of Colorado, Boulder, USA
Page 2: Advanced Building Simulation
Page 5 and 6: First published 2003 by Spon Press
Page 7 and 8: vi Contents 8 Developments in inter
Page 9 and 10: viii Figures 3.12 Relation between
Page 11: x Figures 8.14 Workflow Modeling Wi
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
Page 38 and 39: Trends in building simulation 23 Fu
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
Page 52 and 53: ∆C p (-) 1.6 1.2 0.8 0.4 0.0 -0.4
Page 54 and 55: Uncertainty in building simulation
Page 56 and 57: S d (h) 80 60 40 20 0 -80 2 Uncerta
Page 58 and 59: 2.4.2 Uncertainty in wind pressure
Page 60 and 61: 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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