Advanced Building Simulation

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Figures ix 5.9 Velocity and temperature distributions for the displacement ventilation case 133 5.10 The comparison of the velocity profiles at five positions in the room between the calculated and measured data for the displacement ventilation case 134 5.11 The comparison of the temperature profiles at five positions in the room between the calculated and measured data for the displacement ventilation case 135 5.12 The comparison of the tracer-gas concentration profiles at five positions in the room between the calculated and measured data for the displacement ventilation case 136 6.1 Schematic representation of typical buoyant boundary conditions 148 6.2 Vertical transition from laminar to turbulent regimes in no-slip buoyant flow 149 6.3 Temperature profile comparisons 155 6.4 Velocity profiles comparing micro-source placement 156 7.1 Scheme of the constitutive ingredients of a sentient building 161 7.2 SEMPER’s shared object model 163 7.3 A general control scheme 164 7.4 A high-level building product and control process scheme 165 7.5 Meta-controller for individually controllable identical devices for different devices addressing the same control parameter 166 7.6 Schematic floor plan of the test spaces 168 7.7 Association between sensors and devices 168 7.8 An automatically generated control model 169 7.9 Application of rules 4 and 5 170 7.10 Illustrative preference functions for selected performance variables 180 7.11 Measurements for interior illuminance rule 184 7.12 Heat output of DC-Va as a function of supply temperature 185 7.13 The relation between measured and simulated illuminance levels 185 7.14 Simulated valve positions and space temperatures 186 7.15 Simulated illuminance levels 186 8.1 From non-scalable to scalable interoperability solutions 190 8.2 Data exchange through a central <strong>Building</strong> Model 192 8.3 File based exchange 196 8.4 Definition of <strong>Building</strong> Model subschemas 197 8.5 Process-driven interoperability 198 8.6 Data exchange with off-line applications 199 8.7 Sample ExEx startup screen 200 8.8 The Project Window concept 201 8.9 Design analysis interaction defined at specific interaction moments 203 8.10 Analysis tasks with multiple interaction links with design activities 204 8.11 Different types of interaction and information exchange 204 8.12 Four-layered workbench 206 8.13 Start of the analysis process 207
x Figures 8.14 Workflow Modeling Window 208 8.15 Structured format for AF description 210 8.16 Analysis function as central driver in the data exchange topology 211 9.1 Immersive building simulation—hardware and software dependencies 221 9.2 Immersive simulation—visualization process 223 9.3 Dynamic and static data visualization 223 9.4 Representation of Scalar data 224 9.5 Representation of Vector data 224 9.6 Tensor—local flow field visualization 225 9.7 Structured grid showing regular connectivity 226 9.8 Unstructured grid showing irregular connectivity 226 9.9 Multi-block grid showing subdomains or blocks 226 9.10 Hybrid grid—a combination of structured and unstructured grids 227 9.11 Cartesian grid 227 9.12 Outdoor AR-battlefield augmented reality system (inertial GPS technology) 228 9.13 Motion-capture of an actor performing in the liveActor (Optical technology) 229 9.14 Sensors adorn the actor’s body 229 9.15 Generic HMD 230 9.16 A diagram of the CAVE environment 231 9.17 Vector limits of HMD breakaway force 232 9.18 Human vision system’s binocular FOV 232 9.19 Transformation from hardware data to programming data 233 9.20 Latency due to system lag 233 9.21 Test room—plan 234 9.22 Test room—section 235 9.23 Test room—sensor locations 235 9.24 Data computing procedure 236 9.25 CAVE display of CFD thermal data as seen by participants 237 9.26 Participants inside the CAVE environment 238 9.27 Viewer with HMD 240 9.28 Registration of room in real-space 240 9.29 Integration of multimodal interface with AR environment 240 9.30 VRML scaling and Java3D 241 9.31 Interfacing with CFD datasets 241 9.32 VRML calibration 242 9.33 Tracker data smoothing 242
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: viii Figures 3.12 Relation between
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
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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
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
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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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