Advanced Building Simulation

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of the problem. In any event, calibration should not be taken lightly and sufficient resources should be reserved for this activity. 4.5 Performance assessment methodology Integrated building airflow simulation 109 Simulation quality can only be assured through an appropriate performance assessment methodology. This should always include selection of the correct model resolution/complexity level and calibration as indicated earlier. Obviously simulations should be performed with relevant inside and ambient boundary conditions during a suitable length of time. The results should be thoroughly analyzed and reported. Next the model should be changed to reflect another design option, and the procedure of simulation, results analysis and reporting should be repeated until the predictions are satisfactory. It is very important to convey to clients that simulation is much better for performance-based relative rank-ordering of design options, than for predicting the future performance of a final design in absolute terms. It is “interesting” that this is more likely to be “forgotten” in higher resolution modeling exercises. A good performance assessment methodology should also take into account the limitations of the approach, for instance by a min–max approach or by sensitivity analysis. Too low resolution and/or too simplified approaches might not reliably solve a particular problem. On the other hand, approaches with too high resolution or too much complexity might lead to inaccuracy as well (although this statement cannot be substantiated yet). Obviously, too high resolution or too complex approaches will require excessive amount of resources in terms of computing capacity, manpower and time. How to select the appropriate approach to solve the problem at hand remains the challenge. Slater and Cartmell (2003) developed what they called “early assessment design strategies” (Figure 4.13). From early design brief, the required complexity of the modeling can be assessed. Starting from the design standard, a building design can be assessed whether it falls safely within the Building Regulations criteria, or in the borderline area where compliance might fail, or in a new innovative design altogether. Based on this initial assessment, and with the proposed HVAC strategy, several decision points in Figure 4.13 will help the engineer to decide which level of complexity should be used for simulation. There is a need to go further than what Slater and Cartmell (2003) propose because of the following reasons: 1 Coupled approaches (between energy simulation and CFD) will soon be a viable option that is not addressed in Figure 4.13. 2 As Hensen et al. (1996) point out, we need to use different levels of complexity and resolution at different stages of building design. Figure 4.14 shows a prototype performance-based airflow modeling selection strategy (AMS). This prototype was initially developed as part of a research on coupled simulation between CFD and building energy simulation (BES) (Djunaedy et al. 2003). The fact that coupled CFD and BES is computationally expensive requires that the simulation is justified by a sound rationale. AMS was proposed to identify the need for coupled simulation. However, AMS can be applied more generally to assess the need for a certain level of complexity and resolution for simulation.
Establish initial level of complexity Standard design—well within Building Regulations HVAC Strategy informed from the design brief Full A/c CIBSE/ASHRAE Guides, Standard Calculations Comfort cooling Are exacting/ well defined comfort conditions required ? No Are dynamics calculations required ? No Zonal modeling— steady state Design standards/intentions Borderline compliance under Building Regulations—potential for uncomfortable conditions Mechanical ventilation Are conditions unknown due to innovative systems/ strategies Yes ? Yes Yes No Zonal modeling— dynamic thermal simulation (e.g. IES, TAS, ESP-r) Mixed mode ventilation Does airflow rely on stratification within large spaces or stacks ? No Is there an innovative internal/ external airflow regime ? No Design standards/intentions CFD (e.g. Star-CD, CFD, Flovent, etc) Innovative— unknown conditions Natural ventilation Yes Yes Physical modeling Design cost Number of variables considered Risk Figure 4.13 Early assessment design strategies (adapted from Slater and Cartmell 2003).
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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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Page 172 and 173: 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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