Advanced Building Simulation

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New perspectives on CFD simulation 145 analytical description but empirically validated. The tautology emerges when one recognizes that the greatest utility of CFD is for the investigation of problems that can’t be empirically tested. As such, many CFD simulations are at best extrapolations— more than sufficient for the investigation of phenomena, insufficient for predicting actual performance. One could argue that, unlike the science and engineering disciplines in which technologies are contingent on and are developed in response to the identification of new phenomena, the field of building systems has been dominated by a single technological type that has persisted for over a century. This technological type is based on the dilution of heat and mass generation. The impact of modeling the response of this existing technology, however, brings two problems—the approach to CFD limits the exploration of phenomena, and the privileging of the dilution-based system constrains the modeling type such that anything other than high velocity systems can’t easily be examined. By basing the building’s performance criteria on the HVAC norm, the resulting simulation models tend toward forced convection—pressure differential is the driving factor—rather than natural convection, or buoyancy, in which density is the driving factor. Yet, buoyant flows predominate in building interiors if one steps back to examine the extant behaviors rather than automatically include the technological response (Table 6.1). Buoyancy-induced air movement occurs when gravity interacts with a density difference. Within buildings, this density difference is generally caused either by thermal energy diffusion or by moisture diffusion. Surface temperatures—walls, windows, roofs, floors—are almost always different from the ambient temperature such that buoyant flow takes place in the boundary layer along the surfaces. All of the Table 6.1 The constitutive components of basic buoyant flows Buoyant flow type Source geometry Source type Architectural examples Conventional Vertical surface (infinite) Isothermal Interior wall Constant flux Exterior wall Vertical surface (finite) Isothermal Radiant panel Constant flux Window Point (on surface) Constant flux Material joint, heat exchanger Unstable Horizontal surface (infinite) Isothermal Interior floor, ceiling Constant flux Heated floor, ceiling below unheated attic Horizontal surface (finite) Isothermal Radiant/chilled panels Constant flux Skylights (winter) Point (on surface) Constant flux Heat exchanger, mounted equipment Point (free) Constant flux Person, small equipment Stable Horizontal surface (finite) Isothermal Radiant/chilled panels—reverse orientation Constant flux Skylights (summer) Point (free) Constant flux Luminaires, heat exchanger Source: Addington (1997).
146 Addington building’s electrical equipments, including computers, lighting and refrigeration, produce heat in proportion to conversion efficiency, inducing an “unbounded” or free buoyant flow. Processes such as cooking and bathing as well as the metabolic exchanges of human bodies produce both thermal energy and moisture. In general, these types of flows are found near entities surrounded by a thermally stratified environment. The thermal processes taking place in a building that are not density driven result from HVAC systems or wind ventilation, but neither of these are extant—both are technologies or responses intended to mitigate the heat and mass transfer of the density-driven processes. Buoyancy flows have only begun to be understood within the last 30 years, as they are particularly difficult to investigate in physical models. Much of the research was initiated to study atmospheric processes, which are very large scale flows, and only recently has buoyancy at a small scale—that of a particle—begun to be investigated. Furthermore, buoyant air movement at the small scale was of little interest to the major industries that were employing CFD modeling. It was not until the surge in microelectronics during the last two decades that small-scale buoyancy began to receive the same-attention as compressible hypersonic flows and nuclear cooling. As processor chips became faster, and electronics packages became smaller, heat shedding within and from the package became the limiting factor. Although forced convection with fans had been the standard for package cooling for many years, fans were no longer compatible with the smaller and higher performance electronics. Researchers looked toward the manipulation of buoyant behavior to increase the heat transfer rate, leading to comprehensive investigation of cavity convection, core flows, and the impact of angles on the gravity density interface. As a result, the study of thermal phenomena—in particular, buoyant behavior—rather than the optimization of the technology, has been responsible for revolutionizing the microelectronics industry. If we increase our focus on the simulation of buoyant behavior, we may begin to be able to characterize the discrete thermal and inertial behavior of interior environments. Rather than undermining the current efforts on performance prediction, this approach would expand the knowledge base of simulation modeling, while opening up new possibilities for technology development. But it requires rethinking the approach toward simulation modeling: the privileging of the HVAC system has affected many aspects of the CFD model—not the least of which is the problem definition. One must also ask fundamental questions about boundaries, scale, and similitude. 6.3 Determining the appropriate modeling criteria (how to model) The most significant difference between modeling to predict performance and modeling to investigate phenomena is the definition of the problem domain. For many indoor air modelers, the problem domain is coincident with the building’s different extents— a room, a group of contiguous rooms, or the building envelope. The geometry of the solid surfaces that enclose air volumes serves as the geometry of the problem. If one accepts that the dilute interior environment is also a well mixed environment, then it is not unreasonable for the domain of the problem to be defined by the building’s surfaces. If the environment is not well mixed, and even more so if the overarching
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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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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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Advanced Building Simulation

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