Advanced Building Simulation

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Figure 9.22 Test room—section. Wall surface temperature Floor surface temperature Figure 9.23 Test room—sensor locations. 8�0� Opening 1/2� Plywood Styrofoam 1/16� Particle board Glazing Wall surface temperature Immersive building simulation 235 The room was designed to provide flexibility for supply air diffuser location, amount and location of thermal mass, interior finish material as well as glass type. The room is equipped with a ducted fan coil air condition unit with a hot water booster heating coil. Electric water heaters and chillers are connected with heating and cooling coils respectively. All the mechanical system components can be controlled either manually or through the Building Modular Controller. Five thermocouples were used to measure the room surface temperatures with 0.5�F accuracy. These sensors were placed on the walls and window of the test room (Figure 9.23). One thermal comfort transducer—connected to a thermal comfort meter—was placed in the middle of the room. The information from these sensors was 6�0� Ceiling surface temperature Comfort meter Wall surface temperature Comfort meter
236 Malkawi collected in real-time and channeled in two directions: back to the interface for the user to view the results in a graphical mode and to a database. Information from the database was then used to generate historical trends that can also be displayed. This thermal chamber was also modeled and analyzed using CFD Software. The data output was then customized for the visualization phase. In order to construct a VR model, the space was first built using 3D Studio Max and then the data was exported into VRML format. The VRML model was then transformed into Inventor 2.0 format. Inventor is a computer program that allows objects to be displayed in a 3D format that the CAVE can display. Once the model became CAVE compatible, an engine was created to operate the CAVE to allow the interaction between the space and data visualization to be displayed. The computer language used to program the CAVE was the Performer. It is a computer graphics language that allows real-time communication among several programming languages, such as C�� and OpenGL. The engine designed relies on several files that behave as the source files for it to be executed in the CAVE. These source files are connected to the CAVE libraries. Hence, they provide the specifications for some of the basic configurations of the CAVE such as the speed of navigation, the buttons functions, and the interaction with the space. When executing the engine, two different calls occur at the same time. The thermal data plotting (from sensors and from simulation) and the model of the space, which is functioning as the background of the thermal analysis. These data are then mapped onto the space as one object using the performer functions in the CAVE. This allows the user to move around both the data and the room at the same time, Figure 9.24. Sensors (actual space) CFD analysis VRML generation Database Sensor data Postprocessed data 3D space Figure 9.24 Data computing procedure. Data enhancement Real-time Data reduction Immersive CAVE environment Display geometry Data display Historical trends Existing conditions Simulation verification Simulation visualization
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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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Page 200 and 201: Self-organizing models for sentient
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Page 204 and 205: Chapter 8 Developments in interoper
Page 206 and 207: This chapter introduces the technol
Page 208 and 209: Developments in interoperability 19
Page 212 and 213: Instances subset A Building Model S
Page 214 and 215: Import DT data Export DT data DT sc
Page 216 and 217: Data-centric approach no explicit p
Page 222 and 223: Glazing system? Window area? Monday
Page 232 and 233: Chapter 9 Immersive building simula
Page 234 and 235: Immersive building simulation 219 T
Page 236 and 237: Immersive building simulation 221 E
Page 238 and 239: Data generation Data preparation Ma
Page 240 and 241: Shear Velocity Acceleration Curvatu
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Page 244 and 245: etter than the others, they suffer
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Page 248 and 249: Graphical representation Matrix (4D
Page 252 and 253: (a) (b) Immersive building simulati
Page 254 and 255: Immersive building simulation 239 m
Page 256 and 257: Calibrated tracker data Figure 9.30
Page 258 and 259: Immersive building simulation 243 g
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
Page 266 and 267: performance assessment methods 109-
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Advanced Building Simulation

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