Advanced Building Simulation

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Shear Velocity Acceleration Curvature Rotation Figure 9.6 Tensor—local flow field visualization. (See Plate IX.) Immersive building simulation 225 Reference Convergence Divergence Table 9.2 CFD data types, visualization techniques and spatial domain Order of data Spatial domain Visualization technique Scalar Volume Volume ray casting Scalar Surface Iso-surface Vector Point Arrow plot Vector Surface Stream surface Vector Point (space–time) Particle animation Tensor Line (space–time) Hyperstreamlines height maps and for the vector display are arrow plots and streamlines or particle paths. In addition, these data are classified further based on the spatial domain dimensionality of the visual objects (such as points, lines, surfaces and volumes) and their association with visualization techniques (Hesselink et al. 1994) (Table 9.2). In three-dimensional visualization of nonimmersive environments, the techniques known from the two-dimensional visualization of fluids are not applicable and can be misleading. However, in immersive environments, three-dimensional visualization of fluid data can be confusing. The use of iso-contours and pseudo-coloring provides a good way to visualize surfaces in the three-dimensional domain. This requires the application of some data reduction processes, such as the techniques described earlier in order to create surfaces in three-dimensional space for which two-dimensional visualization can be useful. Besides the data type, the structure of how the data is being organized plays an important role for visualization. In fluid visualization, data is organized using computational meshes. These meshes define certain ordering of the location in space and time where the governing partial differential equations of the problem are solved numerically. All flow data is stored at these discrete locations. The data may be stored at the node of the mesh or the center of the cell of the mesh. The quality of mesh and the choice of mesh arrangement impact the accuracy of the simulation. For example,
226 Malkawi Figure 9.7 Structured grid showing regular connectivity (Courtesy: Sun 2003). (See Plate X.) Figure 9.8 Unstructured grid showing irregular connectivity (Courtesy: Sun 2003). (See Plate XI.) Figure 9.9 Multi-block grid showing subdomains or blocks (Courtesy: Sun 2003). (See Plate XII.) the higher the mesh density, the higher the level of simulation accuracy, which leads to expensive computation. Five major data structures of meshes or grids are typically used: (1) structured, (2) unstructured, (3) multi-block or block-structured, (4) hybrid, and (5) Cartesian. Structured grids consist of regular connectivity where the points of the grid can be indexed by two indices in 2D and three indices in 3D, etc. Regular connectivity is provided by identification of adjacent nodes, Figure 9.7. Unstructured grids consist of irregular connectivity wherein each point has different neighbors and their connectivity is not trivial. In unstructured grids, the nodes and their complex connectivity matrix define the irregular forms, Figure 9.8. Multi-block approach can break complicated geometry into subdomains or blocks; structured grids are then generated and governing equations can be solved within each block independently. These grids can handle complex geometrical shapes and simultaneously undertake a wide range of numerical analysis (Figure 9.9). Some of the advantages of multi-block grids are as follows: (a) geometry complexity can be greatly reduced by breaking the physical domain into blocks; (b) since the gridline across the blocks are discontinuous, more freedom in local grid refinement is possible; (c) standard structured flow solvers can be used within each block obviating the need for complicated data structure, book keeping and complex algorithms; and (d) provides a natural routine for parallel computing, thereby accelerating the simulation. Hybrid grids are a combination of structured and unstructured grids and are used for better results and greater accuracy. High grid density can be designed at locations where there are sharp features of the boundary (Figure 9.10). Cartesian grids are mostly hexahedral (right parallelopipeds) and the description of the surface is no longer needed to resolve both the flow and the local geometry
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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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Page 212 and 213: Instances subset A Building Model S
Page 214 and 215: Import DT data Export DT data DT sc
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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 256 and 257: Calibrated tracker data Figure 9.30
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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-
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