Advanced Building Simulation

More documents

Recommendations

Info

Pervasive/invisible Web-enabled Design integrated Code shared Interoperable Function complete R&D applied Energy, light, acoustics, CFD EnergyPlus, ESP, … DAE OOP, EKS, SPARK, IDA, NMF, ... STEP, COMBINE, IAI, ... Trends in building simulation 9 “Invisible” e-Simulation, controls, remote, DAI+ ESP+, BLIS, DAI, SEMPER 1970 1980 1990 2000 2010 Figure 1.3 Trends in technical building performance simulation tools. a matter that needs more study. It can be argued that such seamless transition can in general not be automated as every translation between design and analysis requires intervention of human judgment and expert modeling skills, strongly influenced by design context and analysis purpose. In an attempt to put the observations of this section in a broad historic perspective, Figure 1.3 identifies building simulation trends between 1970 and 2010. The foundation for building simulation as a distinct class of software applications came with the advent of first-principles-based formulation of transport phenomena in buildings, leading to DAE formulations that were amenable to standard computational methods. The next step was towards broader coverage of other aspects of technical building behavior. This movement towards function complete tools led to large software applications that are being used today by a growing user base, albeit that this user base is still composed of a relatively small expert guild. The next two major movements started in parallel in the 1990s and had similar goals in mind on different levels of granularity. Interoperability targets data sharing among (legacy) applications whereas code sharing targets reuse and inter-application exchange of program modules. Whereas the first tries to remove inefficiencies in data exchange, the latter is aiming for functionally transparent kits of parts to support the rapid building (or rather configuration) of simulation models and their rapid deployment. Design integration adds an additional set of process coordination issues to its predecessor movements. Ongoing trials in this category approach different slices of a very complex picture. It is as yet unclear what approach may eventually gain acceptance as the best framework for integration. The two most recent trends in Figure 1.3 have in common that they are Internet driven. The Web enables a new breed of simulation services that is offered at an
10 Augenbroe increasing pace, mostly in conjunction with other project team services. Ultimately, simulation will be available “anywhere and anytime,” and may in many cases go unnoticed, such as in the case of intelligent control devices that, based on known user preferences, take action while running a simulation in the background. At the moment such a simulation would need a dedicated simulation model specifically handmade for this purpose, but eventually it will be driven by a generic, complete “asbuilt” representation of the artifact, metaphorically referred to as “electronic building signature” (Dupagne 1991). Future simulations may have a “hybrid” nature, as they deal with both physical objects as well as occupants that may be regarded as “simulation agents” that interact with building systems. Occupant behavior, for now usually limited to a set of responses to environmental changes, may be codified in a personalized knowledge map of the building together with a set of individual comfort preferences. Recent proceedings of the IBPSA (International Building Performance Simulation Association) conferences (IBPSA 2003) contain publications that address these and other topics as evidence of the increasing palette of functions offered by current building simulation applications. Progress has been significant in areas such as performance prediction, optimization of system parameters, controls, sensitivity studies, nonlinear HVAC components, etc. The recent progress in the treatment of coupled problems is also significant, as reported in (Clarke 1999; Clarke and Hensen 2000; Mahdavi 2001). In spite of tremendous progress in robustness and fidelity there is a set of tool functionalities that have received relatively little attention, maybe because they are very hard to realize. Some of them are discussed below. Rapid evaluation of alternative designs by tools that facilitate quick, accurate and complete analysis of candidate designs. This capability requires easy pre- and postprocessing capabilities and translation of results in performance indicators that can easily be communicated with other members of the design team. For rapid evaluation of alternatives, tools need mechanisms for multidisciplinary analyses and offer performance-based comparison procedures that support rational design decisions. Design as a (rational) decision-making process enabled by tools that support decision-making under risk and uncertainty. Tools should be based on a theory for rigorous evaluation and comparison of design alternatives under uncertainty. Such a theory should be based on an ontology of unambiguously defined performance requirements and their assessments through quantifiable indicators. The underlying theory should be based on modern axioms of rationality and apply them to make decisions with respect to overall measures of building utility. Incremental design strategies supported by tools that recognize repeated evaluations with slight variations. These tools should respond to an explicitly defined design parameter space and offer a mechanism for trend analysis within that space, also providing “memory” between repeated evaluations, so that each step in a design refinement cycle requires only marginal efforts on the part of the tool user. Explicit well-posedness guarantees offered by tools that explicitly check embedded “application validity” rules and are thus able to detect when the application is being used outside its validity range. Robust solvers for nonlinear, mixed and hybrid simulations, going beyond the classical solving of a set of DAEs. The generation of current DAE solvers has limitations
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 and 10: viii Figures 3.12 Relation between
Page 11 and 12: x Figures 8.14 Workflow Modeling Wi
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 26 and 27: Trends in building simulation 11 in
Page 28 and 29: Trends in building simulation 13 ea
Page 30 and 31: Trends in building simulation 15 ba
Page 32 and 33: Trends in building simulation 17 1.
Page 34 and 35: Trends in building simulation 19 th
Page 36 and 37: and predictive simulation-based con
Page 38 and 39: Trends in building simulation 23 Fu
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 56 and 57: S d (h) 80 60 40 20 0 -80 2 Uncerta
Page 58 and 59: 2.4.2 Uncertainty in wind pressure
Page 60 and 61: an experiment involving structured
Page 66 and 67: with their uncertainties, in terms
Page 68 and 69: Table 2.5 Expected utilities for de
Page 70 and 71: y a performance gain on another asp
Page 74 and 75:
Uncertainty in building simulation
Page 76 and 77:
Simulation and uncertainty: weather
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
makes every month normalized to a z
Page 84 and 85:
Dry-bulb temperature (°F) 60 50 40
Page 86 and 87:
(b) Compute today’s average tempe
Page 88 and 89:
Page 90 and 91:
ackward. First, the average daily h
Page 92 and 93:
a��sin(�)* ln[I SC * � D] (
Page 94 and 95:
H = Total daily horizontal insolati
Page 96 and 97:
Temperature (°C) or wind speed (m/
Page 98 and 99:
Horizontal insolation (MJ/m 2 /day)
Page 100 and 101:
References Simulation and uncertain
Page 102 and 103:
Chapter 4 Integrated building airfl
Page 104 and 105:
(a) (b) zone 6 5 4 3 2 1 3 1 Pre-he
Page 106 and 107:
a different physical state variable
Page 108 and 109:
West Zone n Zone m Figure 4.5 Examp
Page 110 and 111:
Node n Figure 4.6 An example two zo
Page 112 and 113:
The nodal pressures are then iterat
Page 114 and 115:
when employing Newton-Raphson solut
Page 116 and 117:
Integrated building airflow simulat
Page 118 and 119:
25.9 15.9 13.7 10.2 7.2 4.2 0.0 10.
Page 120 and 121:
Air temperature (°C) 40.0 35.0 30.
Page 122 and 123:
It was found that the differences a
Page 124 and 125:
of the problem. In any event, calib
Page 126 and 127:
Resolution CFD Decision Reduced Dec
Page 128 and 129:
Page 130 and 131:
Although most of the basic physical
Page 132 and 133:
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
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
3m Control room Enclosure 5.5 m Out
Page 146 and 147:
y the room conditions. In fact, the
Page 148 and 149:
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 154 and 155:
Page 156 and 157:
Chapter 6 New perspectives on Compu
Page 158 and 159:
New perspectives on CFD simulation
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
law must be invariant to a transfor
Page 170 and 171:
Page 172 and 173:
References New perspectives on CFD
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 180 and 181:
Page 182 and 183:
Page 184 and 185:
DC EL1 DC EL2 MC EL_1 DC EL3 E 1 MC
Page 186 and 187:
technologies (Wouters 1998; Mahdavi
Page 188 and 189:
Page 190 and 191:
and photometric properties, as well
Page 192 and 193:
Page 194 and 195:
exploratory implementations. The fi
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
the electric light component of ill
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 210 and 211:
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 218 and 219:
Page 220 and 221:
Page 222 and 223:
Glazing system? Window area? Monday
Page 224 and 225:
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
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
Page 242 and 243:
Immersive building simulation 227 F
Page 244 and 245:
etter than the others, they suffer
Page 246 and 247:
quality CRT screens, wide field of
Page 248 and 249:
Graphical representation Matrix (4D
Page 250 and 251:
Figure 9.22 Test room—section. Wa
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-
show all

Advanced Building Simulation

Create successful ePaper yourself

Delete template?

Save as template?