Advanced Building Simulation

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References New perspectives on CFD simulation 157 Addington, D.M. (1997). “Boundary layer control of heat transfer in buildings.” Harvard University Dissertation. Addington, D.M. (1998). “Discrete control of interior environments in buildings.” In Proceedings of the 1998 ASME Fluids Engineering Division. American Society of Mechanical Engineers, New York. Addington, D.M. (2001). “The history and future of ventilation.” The Indoor Air Quality Handbook. Mc-Graw-Hill, New York. Addington, D.M. (2003). “Your breath is your enemy.” Living with the Genie: Governing Technological Change in the 21st Century. Island Press, Washington, DC. ASHRAE Technical Committee 4.10 Activities (1997). ASHRAE Insights. September 1997. Baker, A.J. and Kelso, R.M. (1990). “On validation of computational fluid mechanics procedures for room air motion prediction.” ASHRAE Transactions, Vol. 96, No. 1, pp. 760–774. Banks, J. (ed.) (1998). Handbook of <strong>Simulation</strong>. John Wiley & Sons, New York. Barker, T., Sedgewick, A., and Yau, R. (1992). “From intelligent buildings to intelligent planning.” The Arup Journal, Vol. 27, No. 3, pp. 16–19. Beausoleil-Morrison, I. (2001). “Flow responsive Modelling of Internal Surface Convection.” In Seventh International IBPSA Conference. Rio de Janeiro, Brazil. August 2001. Chen, Q. (2001). Indoor Air Quality Handbook. Mc-Graw-Hill, New York. Chen, Q., Moser, A., and Suter, P. (1992). A Database for Assessing Indoor Airflow, Air Quality and Draught Risk. International Energy Agency, Zurich. Chung, T.J. (2002). Computational Fluid Dynamics. Cambridge University Press, Cambridge, UK. Dubois, T., Jauberteau, F., and Temam, R. (1999). Dynamic Multilevel Methods and the Numerical <strong>Simulation</strong> of Turbulence. Cambridge University Press, Cambridge, UK. Elliot, C.D. (1992) Technics and Architecture. The MIT Press, Cambridge, MA. Emmerich, S.J. (1997). Use of Computational Fluid Dynamics to Analyze Indoor Air Issues. US Department of Commerce. Foias, C., Rosa, R., Manley, O., and Temam, R. (2001). Navier–Stokes Equations and Turbulence. Cambridge University Press, Cambridge, UK. Gebhart, B., Jaluria, Y., Mahajan, R.L., and Sammakia, B. (1988). Buoyancy Induced Flows and Transport. Hemisphere Publishing Corporation, New York. Incropera, F.P. (1988). “Convection Heat Transfer in Electronic Equipment cooling.” Journal of Heat Transfer, Vol. 10, pp. 1097–1111. Leal, L.G. (1992). Laminar Flow and Convective Transport Processes. Butterworth- Heinemann, Boston. Mendenhall, M.R., Childs, R.E., and Morrison, J.H. (2003). “Best practices for reduction of uncertainty in CFD results.” In 41st AIAA Aerospace Sciences Meeting. Reno, Nevada. January 2003. NPARC Alliance. (2003). Overview of CFD Verification and Validation. http://www.grc.nasa.gov/WWW/wind/valid/tutorial/overview.html Oberkampf, W.L., Trucano, T.G., and Hirsch, C. (2002). Foundations for Verification and Validation in the 21st Century Workshop. October, 2002. Oden, J.T. (2003). “Benchmarks.” American Society of Mechanical Engineers Council on Codes and Standards. http://www.usacm.org/vnvcsm Pitts, D.R. and Sissom, L.E. (1977). Outline of Theory and Problems of Heat Transfer. Mc-Graw-Hill Book Company, New York. Popiolek, Z. (1993). “Buoyant plume in the process of ventilation—heat and momentum turbulent diffusion.” In Proceedings of Annex-26 Expert Meeting. Poitiers, France. Roache, P.J. (1998). Verification and Validation in Computational Science and Engineering. Hermosa publishers, Albuquerque, NM.
158 Addington Roache, P.J. (2002). “Code verification by the method of manufactured solutions.” ASME Journal of Fluids Engineering, Vol. 124, No. 1, pp. 4–10. Stribling, D. (2000). The State of the Art in CFD for Ventilation Design. Vent, Helsinki. Tritton, D.J. (1988). Physical Fluid Dynamics. Clarendon Press, Oxford, UK. Wegeng, R.S. and Drost, K. (1994). “Developing new miniature energy systems.” Mechanical Engineering, Vol. 16, No. 9, pp. 82–85. Wesseling, P. (2001). Principles of Computational Fluid Dynamics. Springer, Berlin.
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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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Page 126 and 127: Resolution CFD Decision Reduced Dec
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Page 130 and 131: Although most of the basic physical
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Page 134 and 135: Chapter 5 The use of Computational
Page 136 and 137: CFD tools for indoor environmental
Page 138 and 139: the Reynolds average rules can be s
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Page 146 and 147: y the room conditions. In fact, the
Page 150 and 151: Y/H Y/H 1 0.8 0.6 0.4 0.2 Plot-1 Pl
Page 152 and 153: magnitude computing time than the R
Page 156 and 157: Chapter 6 New perspectives on Compu
Page 158 and 159: New perspectives on CFD simulation
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Page 174 and 175: Chapter 7 Self-organizing models fo
Page 176 and 177: Self-organizing models for sentient
Page 178 and 179: SOM topo- SOM site 1 graphy 1 1 1 1
Page 184 and 185: DC EL1 DC EL2 MC EL_1 DC EL3 E 1 MC
Page 186 and 187: technologies (Wouters 1998; Mahdavi
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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
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Glazing system? Window area? Monday
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Developments in interoperability 20
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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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