Advanced Building Simulation

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Graphical representation Matrix (4D) representation X 000 0 Y 0 0 Z Motionstar xyz coordinates –Y X Transformation Figure 9.19 Transformation from hardware data to programming data. <strong>Simulation</strong> & graphics 0 0 Z 0 Figure 9.20 Latency due to system lag. 0 0 0 0 Transformation data Sensor New virtual image Based on sensor data Immersive building simulation 233 X 000 0 –Z 00 Y 0 0 Y 0 0 0 0 0 Sun Java3D xyz coordinates and process the new view immediately while the computer must generate images with new sets of transformation matrices from the sensors (Figure 9.20). Most of the latency issues are tied to the computer system delay (Holloway 1995). Moreover, the development platform, C�� or Java, is also vital to the processing of such complex programs (Marner 2002). Predicting the future viewpoint (and thereby the viewing transformation) could resolve latency issues. One such predicting algorithm is the “Kalman filter”, which implements a predictor–corrector type estimator that minimizes the estimated errorcovariance when some presumed conditions are met. The use of linear accelerometers and angular rate gyroscopes to sense the rate of motion with the aid of Kalman filter (Kalman 1960) enables accurate immersive environments such as AR (Chai et al. 1999). Single-Constraint-at-a-Time (SCAAT) tracking integrated with Kalman filtering has shown improved accuracy and estimates the pose of HiBall tracker in real-time (Welch et al. 1999). HMD Z View of real scene X
234 Malkawi 9.3 Example cases To illustrate immersive building simulation, this chapter introduces two example cases, one fully immersive and the other augmented using post-processed CFD data. The goal of these investigations is to visualize building thermal behavior using accurate data representation. As described earlier, some of the main differences between fully immersive and augmented simulation are the issues related registration and latency. In fully immersive systems, the issues of registration and latency are not of importance. On the other hand, the structure of both systems from the point of view of data visualization and simulation integration is the same. This implies that the development of the augmented system can begin with the fully immersive system. In addition, the augmented system can be easily reduced to a fully immersive system. Testing the applicability and visualization of both systems can begin by using Virtual Reality Modeling Language (VRML) models. Once the models are tested, additional functionality can be added and translated into the immersive or augmented hardware. 9.3.1 Fully immersive CFD visualization For a fully immersive environment, the aim of this study was to generate a prototype technique that will allow users to visualize various building thermal analysis data in a virtual 3D environment that can facilitate multi-user interaction, such as the CAVE. For visualization, two data collections were used: data detected from the environment (sensors) and simulation results (CFD output). Data detected from sensors was enhanced to provide better data visualization. Data from the simulation was further processed and reduced in order to be visualized in real-time. The space modeled was a thermal chamber that was designed to investigate the dynamic thermal behavior within spaces. The chamber dimensions are 8��8��8�— the approximate size of a one-person office. Its south face was exposed to the outside. The other surfaces are under typical indoor conditions (Figures 9.21 and 9.22). Figure 9.21 Test room—plan. Existing wall Roller support steel plate 8�0� 1/2� Plywood 6�6� Styrofoam 1/16� Particle board
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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 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
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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 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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