Advanced Building Simulation

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Self-organizing models for sentient buildings 173 Table 7.4 Schematic illustration of the simulation-based control process Step 1 Control state at time t i Step 2 Identification of candidate control states at time t i�1 Step 3 <strong>Simulation</strong>-based determination and evaluation of the performance implications of the control options identified in step 2 Step 4 Transition to control state at time t i�1 (see Figure 7.6). About 60% of the external wall of this space consists of glazing. The facade system includes a set of three parallel external moveable louvers, which can be used for shading and—to a certain degree—for light redirection. These motorized louvers can be rotated anti-clockwise from a vertical position up to an angle of 105�. We installed an array of 12 illuminance sensors in the central axis of this space at a height of about 0.8 m above the floor to monitor the spatial distribution of the interior illuminance. Outdoor light conditions were monitored using 11 illuminance and
174 Mahdavi irradiance sensors that were installed on the daylight monitoring station on the roof of the IW. As an initial feasibility test of the proposed simulation-based control approach, we considered the problem of determining the “optimal” louver position. Step 1. In this simple case, the control state space has just one dimension, that is, the position of the louver. We further reduced the size of this space, by allowing only four discrete louver positions, namely 0� (vertical), 30�, 60�, and 90� (horizontal). Step 2. Given the small size of the control state space in this case, we considered all four possible louver positions as potential candidates to be compared. Step 3. LUMINA (Pal and Mahdavi 1999), the lighting simulation application in SEMPER (Mahdavi 1999), was used for the prediction of light levels in the test space. LUMINA utilizes the three-component procedure (i.e. the direct, the externally reflected, and the internally reflected component), to obtain the resultant illuminance distribution in buildings. The direct component is computed by numerical integration of the contributions from all of those discretized patches of the sky dome that are “visible” as viewed from reference receiver points in the space. Either computed or measured irradiance values (both global horizontal and diffuse horizontal irradiance) can be used to generate the sky luminance distribution according to the Perez model (Perez et al. 1993). External obstruction (i.e. light redirection louvers) are treated by the projection of their outline from each reference point on to the sky dome and the replacement of the relative luminance values of the occupied sky patches with those of the obstruction. A radiosity-based approach is adopted for computing the internally reflected component. The results generated by LUMINA have shown to compare favorably with measurements in several rooms (Pal and Mahdavi 1999). In the present case, measured irradiance values were used at every time-step to generate the sky model in LUMINA for the subsequent time-step. However, trend-forecasting algorithms could be used to predict outdoor conditions for future time-steps. For each time-step the simulation results (mean illuminance and uniformity levels on a horizontal plane approximately 1 m above the floor) were ordered in a table, which was used to rank and select the most desirable control scenario based on the applicable objective functions. Two illustrative objective functions were considered. The first function aims at minimizing the deviation of the average (daylight-based) illuminance level E m in the test space from a user-defined target illuminance level E t (say 500 lx): Minimize (|E t – E m|) (7.1) The second objective function aims at maximizing the uniformity of the illuminance distribution in the test space as per the following definition (Mahdavi and Pal 1999): Maximize U, where U � E m · (E m � E sd) �1 (7.2) Here E m and E sd are the mean and standard deviation of the illuminance levels measured at various locations in the test space. At time interval t i, the simulation tool predicted for four candidate louver positions the expected interior illuminance levels for the time interval t i�1 (test space 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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Page 146 and 147: y the room conditions. In fact, the
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Page 168 and 169: law must be invariant to a transfor
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 184 and 185: DC EL1 DC EL2 MC EL_1 DC EL3 E 1 MC
Page 186 and 187: technologies (Wouters 1998; Mahdavi
Page 190 and 191: and photometric properties, as well
Page 194 and 195: exploratory implementations. The fi
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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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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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