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A Framework for Optimal Sensor Placement in Full-Scale Studies of Wind Around Buildings Maria Papadopoulou1, 2, Benny Raphael3, Ian F.C. Smith4, Chandra Sekhar2 1 Future Cities Laboratory, Singapore-ETH Centre, Singapore 2 Department of Building, National University of Singapore, Singapore 3 Civil Engineering Department, Indian Institute of Technology Madras, Chennai, India 4 Applied Computing and Mechanics Laboratory, EPFL, Lausanne 1015, Switzerland email:
[email protected],
[email protected],
[email protected],
[email protected] ABSTRACT: Knowledge of wind environments around buildings can help efforts to improve natural ventilation, energy efficiency and occupant comfort. Traditionally, computational fluid dynamics (CFD) simulations have been used to predict wind behavior around buildings. However, the high degree of flow variability usually leads to large discrepancies between simulation predictions and field measurements and this is indicative of high levels of uncertainties in modeling and measurement. Field measurements are necessary in order to ensure that simulation predictions are sound, yet the limited number of sensors and their location remain challenges. While sensor placement in wind studies has been performed mostly through subjective evaluation and engineering judgment, several systematic methodologies have been used in the field of infrastructure diagnosis. Among the available optimal sensor placement methodologies, multiple model-based model falsification approaches are more robust in cases with unknown uncertainties and correlation values. This work proposes a framework for optimal sensor placement in wind studies around buildings when knowledge is limited and when no prior measurement data are available. Sensor placement algorithms and selection criteria are compared according to their ability to identify optimal sensor configurations that improve wind-speed predictions at unmeasured locations. The framework has been applied to a full-scale case study, employing a limited number of optimally configured sensors. KEY WORDS: Computational Fluid Dynamics (CFD); Wind Environment; Sensor Placement; System Identification; Model Falsification. 1
INTRODUCTION
Computational-Fluid-Dynamics (CFD) simulations are increasingly used for predicting wind environment around buildings. The main advantage is that CFD allows the study of complex geometries, while providing detailed information on flow behavior. Yet, CFD predictions often differ significantly from measurement data, especially when steady-state analysis is performed [1, 2]. This is mainly due to uncertainties in mathematical models and input-parameter values, as well as effects caused by low frequency variations of wind flow [3, 4]. Values of these uncertainties and their correlations are difficult to quantify and usually no information is available on their influence on predictions. Inverse modeling is used in system identification to deal with parameter uncertainties and infer parameter values. Among the approaches proposed to date, model falsification is the most robust when values of uncertainty correlations are not known [5]. Model falsification involves multiple models, which can be used to accommodate parameter uncertainty and to account for the possible wind conditions with less risk of parametric-value compensation [6, 7]. Regardless of the inference approach that is employed, field measurements are necessary in order to ensure that CFD predictions are sound [8]. This is particularly the case in wind engineering problems, where physical phenomena cannot be fully represented at reduced-scale in the laboratory, such as buoyancy-driven natural ventilation [9]. Good sensor placement is therefore an important task that can be used to ensure that predictions are improved in the most efficient manner [3]. So far in wind studies, sensors have been placed mostly using educated guess and experience. Some researchers have proposed strategies for optimal sensor placement based on data-driven approaches that employed Gaussian Processes (GPs) to predict indoor and outdoor environmental variables [10-13]. Data-driven approaches, however, are costly and time-intensive, since they require a dense pre-deployment of sensors in order to collect sufficient information on flow distribution and spatial correlations. A recent study [14] has proposed a GP-based strategy using information entropy for selecting optimal sensor locations, similar to [14]. Although both measurement data and CFD simulations were employed, data were assumed to be free of errors and the study was performed outside the urban canopy. A challenge in wind studies within the canopy is that flow properties vary considerably with space and time and as a result the number of sensors, their position, as well as the sampling
14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
2 frequency can significantly affect the value of sensor information [15]. In addition, the number of feasible measurement locations can be limited [8]. While no placement methodologies are available for wind studies, sensor placement methodologies have been proposed for structural diagnosis. Earlier studies have demonstrated that (information) entropy can be successfully used as a criterion to select sensor locations prior to field measurement [16, 17]. In these studies, sensors were placed sequentially at positions that provided high values of entropy in model predictions. Although during sequential sensor placement redundant sensor locations may be selected, such strategies are preferred over global search strategies [18], such as genetic algorithms, because of lower computational cost [16]. This advantage becomes increasingly important when the number of sensors exceeds ten. This research proposes an optimal sensor placement framework for full-scale studies of wind around buildings. The framework is based on a multiple-model CFD approach employed to generate a discrete population of plausible wind predictions. Optimal sensor configurations are selected prior to field measurement, using the population of predictions, assuming limited knowledge of wind behavior around buildings. During sensor selection uncertainties of both modeling and measurement data are considered. The framework is applied to a full-scale case study at the CREATE Tower in order to improve predictions of wind speed at unmeasured locations near buildings. Optimal configurations are then evaluated for their ability to support multiple-model falsification and reduce the number of models and prediction range, and increase the accuracy of predictions. 2
OPTIMAL SENSOR PLACEMENT FRAMEWORK
2.1
Multiple-model CFD simulation
An optimal sensor placement framework is developed based on a multiple-model approach [6] that is adapted to CFD simulations. The approach is adapted from [19] in order to deal with parameter-value uncertainties in CFD models. Plausible ranges of input parameters that are not precisely known are defined by engineering judgment and multiple CFD simulations are performed through varying values of these parameters within the defined ranges. A discrete population of possible wind predictions is then generated at possible sensor locations around buildings. One combination of input parameter-values and wind predictions at specific locations is one model instance, m. The generated population of model instances is called the initial model set, M. Figure 1 illustrates an example of 50 model instances using a parallel axis plot, which is a common way of visualizing highdimensional data. Each line on the plot represents one model instance that consists of input and output values of one simulation. In total the values of 50 simulations are represented on the plot. The simulations were carried out using a combination of parameter-value variations of 13 parameters and wind-speed predictions were obtained at 10 potential sensor locations.
Figure 1. Illustrative example of 50 model instances using a parallel axis plot, of 13 input parameters and 10 potential sensor locations. Optimal sensor locations are selected prior to performing measurements, thereby sensor placement algorithms and selection criteria are evaluated using the initial model set; these terms are explained below.
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3 2.2
Sensor placement algorithms and selection criteria
A histogram is built at each possible sensor location, by dividing the range of predictions to intervals equal to modeling, , and measurement, , errors (Figure 2). Model instances are distributed into intervals according to their prediction values, thereby creating subsets of model instances that, given a potential measurement, will not be possible to separate further. When measurements are taken, after model falsification remaining model instances are called candidate models.
Figure 2. Constructing histogram intervals of simulation predictions at potential sensor locations using modeling and measurement errors The histogram of model instances varies from location to location and in order to select the optimal sensor locations, two selection criteria are compared: the subset-size, which is the maximum number of candidate models at each location and information entropy (also known as Shannon entropy), which represents the disorder in model instances at each location. Entropy is defined as: (1)
where is the entropy of a random output variable at a sensor location , is the probability of the interval of a variable’s distribution with and the maximum number of intervals at the location. The entropy at each location is computed through first counting the number of model instances that lie within each interval and then calculating the probability of the interval as . According to the hypothesis that measurements are best used for falsifying model instances, optimal locations are selected either to minimize the subset-size criterion or to maximize the entropy criterion. Sensor selection is sequential and two strategies are evaluated: incrementally adding sensors from an initial condition of no sensors and incrementally removing sensors from an initial condition of sensors at all possible locations. Two sequential sensor placement algorithms are adapted to incrementally add sensors to an optimal configuration, called forward (inspired by [5]) and forward-max (inspired by [6]), as well as one to incrementally remove sensors, called backward (inspired by [9]) [20]. None of the algorithms evaluates the common information between sensors, since this requires all combinations of subsets to be evaluated that is computationally inefficient when multi-dimensional grid is used to structure the data [21]. However, the forward-max algorithm differs since it incrementally selects sensors based on the subset of model instances, of the previously selected sensors, that maximize the chosen criterion. Therefore the algorithm achieves linear complexity with respect to the number of model instances that is independent from the number of combinations of sensor locations. During sensor placement and following every update, all algorithms store the maximum number of candidate models that is expected with the current optimal sensor configuration. The performance of optimal configurations is then evaluated for their ability to improve simulation predictions (Section 3.3). 3 3.1
APPLICATION Case study description
The optimal sensor placement framework is applied to CREATE Tower, a 60-meter high building on NUS campus in Singapore. Multiple CFD simulations are performed using the commercial software ANSYS FLUENT in order to approximate the equations of flow motion. The following are the required simplifications and assumptions made during simulation: 1. The geometries of the CREATE Tower and the surrounding buildings are modeled with their main shape (Figure 3, left). The size of the computational domain is 2233 m x 1144 m x 368 m and is defined according to recommendations available in literature [22, 23].
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4 2.
The CutCell Cartesian meshing is used as a discretization method to generate a predominantly hexahedral mesh with minimum user input [24]. 3. The SIMPLE algorithm is employed to achieve pressure-velocity coupling and second-order discretization is used as a pressure interpolation scheme. 4. The single-precision solver is considered sufficiently accurate for this study The wind behavior around the CREATE Tower and neighboring buildings is characterized by a set of mathematical models, parameters and variables. The application assumptions are as follows: 1. Steady-RANS analysis is employed to describe wind behavior, the realizable k-ε equations to represent turbulence and the standard wall-functions to treat near-wall turbulence; this is one of the most computationally economical approaches for approximating turbulent wind flows. 2. Three input parameters are found based on engineering judgment: wind speed, horizontal wind direction and Turbulence Kinetic Energy (TKE). Values of the input parameters are selected with simple grid sampling within ranges shown in Table 1. Inlet boundary conditions of wind speed are described by Equation (2) and Equations (3) (4) are used to calculate the TKE and TDE as functions of the varying wind speed profile at the inlet. The sand-grain roughness of the computational domain is calculated using Equation (5). The pressure at the outlet boundary is set to zero Gauge pressure. (2) where is the wind speed at height , is the atmospheric-boundary-layer friction (or shear) velocity, roughness and the von Kármán constant.
the surface
(3) where
is the turbulence kinetic energy and
a model constant. (4)
where
is the turbulence eddy dissipation at height . (5)
where is the roughness constant, set to satisfy the constraint , and is the grid resolution (the distance of the centroid of the wall-adjacent cell to the wall). 3. Wind speed is the output variable and simulation predictions are obtained at 187 potential sensor locations, which are fixed uniformly at 1.5 m height near the balconies (east and west) and the north terrace of CREATE Tower (Figure 3 right). In total, 768 CFD simulations are performed through varying values of the three input parameters (Table 1). A discrete population of wind speed predictions is obtained at 187 potential sensor locations (Figure 3, right). This population of predictions and the corresponding input parameter values comprises the population of model instances that forms the initial model set. The initial model set is used to evaluate the sensor placement algorithms and selection criteria, prior to performing field measurements. Results are presented in the following section. Table 1.Parameters and ranges of values used in the CFD simulations. Minimal bound
Maximal bound
Horizontal wind direction [deg]
1
360
Wind speed [m/s]
0
8.7
8E-3
0.12
Parameter
Surface roughness of neighboring buildings [m]
Comments The wind direction varied from 1 to 360 degrees in order to account for all possible values. Lower and upper bounds were set according to meteorological data obtained from the Changi WMO, Singapore. Lower and upper bounds were set according to [22, 23].
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Figure 3. 3D views of the CREATE Tower and neighboring buildings (left) and plan view (right) of the potential sensor locations 3.2
Comparison of algorithms and criteria
Three sensor placement algorithms, forward, backward and forward-max, using entropy as a selection criterion, were compared according to their ability to reduce the maximum number of candidate models remaining from the initial set of model instances of wind speed. Modeling and measurement errors were explicitly incorporated in the sensor placement algorithms: modeling errors were defined spatially uniform and set to ±0.7 m/s [25] and measurement errors to ±0.05 m/s, according to the characteristics of typical wind-speed sensors. Figure 4 (a) shows a comparison of the three sensor placement algorithms using entropy as a selection criterion. Independent of the sensor placement algorithm, the decrease in the maximum number of candidate models levels off with a small number of sensors. However, sensor configurations identified using the forward-max algorithm estimate a significantly lower number of models than using the forward and backward algorithms, providing on average a 10% higher reduction to the maximum candidate models. For a configuration of 6 sensors, the difference in the expected maximum number of candidate models between the forward-max and the forward and backward algorithms is 53 models, and this remains constant for more sensors. Overall, the forward-max algorithm displays a better performance and estimates a smaller number of candidate models, for the same number of sensors. In Figure 4 (b), the two selection criteria, entropy and subset-size, are compared for their ability to falsify candidate models, using the forward-max sensor placement algorithm. A significant difference in performance is observed between the two criteria. The maximum number of candidate models of the entropy-based configuration of 6 sensors is 1.6 times less than the subset-size based configuration, is expected to achieve a reduction of at least 70% from the initial model set. Figure 5 the optimal configurations of the first six sensors for predicting wind speed that are identified using the forward-max algorithm with the entropy and subset-size criteria. Except the first three sensor locations, L139, L177 and L186 (Table 2), the two criteria construct different optimal sensor configurations. Although using the entropy criterion sensor locations are selected uniformly near the building balconies and terraces and at different heights, with the subset-size criterion all locations, except the first, are selected near the north terrace (Figure 5). Overall, the optimal configurations are sensitive to the sensor selection criterion; no common configurations are found.
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Figure 4. Comparison of (a) three sensor placement algorithms using the entropy criterion and (b) two selection criteria using the forward-max algorithm, for wind-speed predictions; only the first 10 sensors are displayed.
Figure 5. Plan view of optimal sensor configurations of first six sensors for predicting wind speed near CREATE Tower; locations are selected using the forward-max algorithm with the entropy and the subset-size criteria.
14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
7 Table 2.Selection order of first six sensors for predicting wind speed near CREATE Tower; locations are selected using the forward-max algorithm with the entropy and the subset-size criteria. Selection order 1st 2nd 3rd 4th 5th 6th 3.3
Sensor location Entropy Subset size L139 L139 L177 L177 L186 L186 L64 L185 L95 L184 L160 L178
Evaluation of optimal sensor configurations
Optimal sensor configurations are evaluated prior to measurement, and therefore field data at these sensor locations are not currently available. Furthermore, an objective of this study is to evaluate and compare the performance of several sensor configurations at the same instant in time, with the limited number of available weather stations. As discussed in the introduction, this could require costly deployment of a large number of sensors. To deal with this issue, it is necessary to generate simulated measurements. Instead of using random values for simulated measurements, realistic distributions are created at possible sensor locations through combining model predictions with modeling and measurement errors, and sampling by following the probability distribution of historically measured data at other locations. A measurement campaign was carried out near the CREATE Tower from February 14 to March 21, 2014 using eight weather stations (Wireless Vantage Pro2™ and Onset® HOBO®) that were available for testing. The eight weather stations were placed at random locations near the CREATE Tower balconies and terraces during the measurement campaign. In order to capture short-term variations in atmospheric boundary conditions and support the premise of negligible convective effects and isothermal conditions during modeling, one of the highest records of wind speed (observed on March 11, 2014) were selected for evaluation. The probability distribution of the measurement data over 24 hours was used to update the sample distribution of simulated measurements. Optimal sensor configurations are compared as follows: 1. Simulation predictions are compared with the simulated measurements at the optimal locations and inconsistent model instances are falsified. 2. An independent set of candidate models is obtained at each time step, representing variable wind-speed and boundary conditions 3. At each time step, the candidate model set is used to update wind-speed predictions at random, unmeasured location on the 4th story near the east balcony of the building.. Figure 6 presents a comparison of the ranges of wind-speed predictions at the unmeasured location, obtained using an entropybased and a subset-size-based configuration of 6 sensors (Figure 5), provided by the forward-max algorithm. Although a short duration of 15 min is displayed, a measurement period of 2 hours is considered during evaluation. On average, the size of the candidate model set is reduced by 99%, and the average prediction range is 1.7 m/s. From the results, 67 % of the simulated measurements are within the prediction range, which indicates that the accuracy is not significantly affected by the reduction in the number of candidate models. It is observed, that using either selection criterion, a good identification is achieved that captures short-term variations of boundary conditions.
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Figure 6.Comparison of wind-speed prediction ranges at an unmeasured location using an entropy-based and a subset-size-based configuration of seven sensors provided by the forward-max algorithm; a short duration of 15min is displayed from a 2-hr measurement period. 4
DISCUSSION
In this work, three sensor placement algorithms and two selection criteria, inspired by research in the field of infrastructure diagnosis, were compared according to their ability to predict the behavior of time-dependent systems, such as wind speed around buildings [16, 17, 26]. Yet, none of the previous studies compared the performance of the three algorithms and two criteria for improving simulation predictions. Moreover, these studies were concerned with structural diagnosis and have not been applied for full-scale studies of wind around buildings. Important contributions of this work are that modeling errors have been explicitly incorporated in the sensor placement algorithms and ranges of predictions are obtained, for each time instant, instead of a single value at each location. Optimal sensor locations are selected prior to actual measurements, thus CFD simulations are used to obtain possible wind-speed predictions. Steady state analysis and isothermal conditions are assumed during modeling, since it is one of the least costly approaches in terms of time that can be used to study wind flow, thereby allowing multiple CFD simulations. A limitation of this work is that systematic errors and spatial correlations were not included in the framework and modeling errors are assumed to have constant values. In addition, only wind-speed predictions are employed in this study, and predictions of wind direction are not considered. Recent work [25] has demonstrated that values of modeling errors associate with wind direction could be on their upper bound, up to 180 degrees both ways, which is attributed to the steady-state RANS analysis. Error values may also vary from location to location, depending also on input values of boundary conditions. Finally, potential sensor locations are subject to orographic constraints and area coverage is restricted by the size of mounting equipment. Target measurement areas outside the canopy are avoided, since high spatial and temporal climatic variations occur that could lead to erroneous results. Locations on roofs are particularly avoided to minimize the impact of convection on flow when wind speed is low. In addition, limited resources are assumed and only a few points can be measured, which are therefore used to study short-term wind variation near buildings; seasonal variations are not considered. 5
CONCLUSIONS
An optimal sensor placement framework based on a multiple model falsification approach has been successfully used to improve simulation predictions of wind around buildings. The framework can be used to identify optimal sensor configurations prior to measurements that can be used to update wind-speed predictions at unmeasured location near buildings. Following are specific conclusions from the study: 1. Sensor placement based on an incrementally updated forward-max algorithm performs better than the forward and backward algorithms in falsifying model instances of wind-speed. 2. Information entropy is a better sensor placement criterion that the subset-size criterion for falsifying model instances of wind speed; the degree of reduction in model instances depends on the number of sensors in the configuration. 3. Both information entropy and subset-size are good selection criteria for reducing prediction ranges, while providing good identification 4. Sensor locations configured using either an entropy or subset-size selection criterion with an incrementally updated forward-max algorithm, can be used for predicting wind speed at unmeasured locations.
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ACKNOWLEDGMENTS This research is conducted at the Singapore-ETH Centre for Global Environmental Sustainability (SEC), co-funded by the Singapore National Research Foundation (NRF) and ETH Zurich. The authors would like to acknowledge the support of Prof. M. Santamouris, Dr. J-A. Goulet and D.G. Vernay. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]
Murakami, S., Overview of turbulence models applied in CWE–1997. Journal of Wind Engineering and Industrial Aerodynamics, 1998. 74: p. 1-24. Gousseau, P., B. Blocken, T. Stathopoulos, and G.J.F. van Heijst, CFD simulation of near-field pollutant dispersion on a high-resolution grid: a case study by LES and RANS for a building group in downtown Montreal. Atmospheric Environment, 2011. 45(2): p. 428-438. Schatzmann, M. and B. Leitl, Issues with validation of urban flow and dispersion CFD models. Journal of Wind Engineering and Industrial Aerodynamics, 2011. 99(4): p. 169-186. Mochida, A. and I.Y.F. Lun, Prediction of wind environment and thermal comfort at pedestrian level in urban area. Journal of Wind Engineering and Industrial Aerodynamics, 2008. 96(10-11): p. 1498-1527. Goulet, J.A., S. Coutu, and I.F.C. Smith, Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks. Advanced Engineering Informatics, 2013. 27(2): p. 261-269. Raphael, B. and I.F.C. Smith, Finding the right model for bridge diagnosis. Artificial Intelligence in Structural Engineering, 1998. 145: p. 308-319. Raphael, B. and I.F.C. Smith, Computer-Aided Engineering. Vol. 306. 2003: Wiley. van Hooff, T. and B. Blocken, Full-scale measurements of indoor environmental conditions and natural ventilation in a large semi-enclosed stadium: possibilities and limitations for CFD validation. Journal of Wind Engineering and Industrial Aerodynamics, 2012. 104: p. 330-341. Chen, Q., Ventilation performance prediction for buildings: A method overview and recent applications. Building and Environment, 2009. 44(4): p. 848-858. Krause, A., A. Singh, and C. Guestrin, Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. The Journal of Machine Learning Research, 2008. 9: p. 235-284. Osborne, M.A., S.J. Roberts, A. Rogers, S.D. Ramchurn, and N.R. Jennings. Towards real-time information processing of sensor network data using computationally efficient multi-output Gaussian processes. in Proceedings of the 7th international conference on Information processing in sensor networks. 2008. IEEE Computer Society. Das, A. and D. Kempe. Sensor selection for minimizing worst-case prediction error. in Information Processing in Sensor Networks, 2008. IPSN'08. International Conference on. 2008. IEEE. Wu, X., M. Liu, and Y. Wu, In-situ soil moisture sensing: Optimal sensor placement and field estimation. ACM Transactions on Sensor Networks (TOSN), 2012. 8(4): p. 33. Papadopoulou, M., B. Raphael, C. Sekhar, and I.F.C. Smith, Sensor placement for predicting airflow around buildings to enhance natural ventilation. ASHRAE IAQ 2013 Proceedings: Environmental Health in Low Energy Buildings, 2013(EPFL-CONF-195670): p. 240-249. Pavageau, M. and M. Schatzmann, Wind tunnel measurements of concentration fluctuations in an urban street canyon. Atmospheric Environment, 1999. 33(24): p. 3961-3971. Papadimitriou, C., Optimal sensor placement methodology for parametric identification of structural systems. Journal of sound and vibration, 2004. 278(4): p. 923-947. Robert-Nicoud, Y., B. Raphael, and I.F.C. Smith, Configuration of measurement systems using Shannon’s entropy function. Computers & structures, 2005. 83(8): p. 599-612. Kripakaran, P. and I.F.C. Smith, Configuring and enhancing measurement systems for damage identification. Advanced Engineering Informatics, 2009. 23(4): p. 424-432. Robert-Nicoud, Y., B. Raphael, O. Burdet, and I.F.C. Smith, Model identification of bridges using measurement data. Computer‐Aided Civil and Infrastructure Engineering, 2005. 20(2): p. 118-131. Papadopoulou, M., B. Raphael, I.F.C. Smith, and C. Sekhar, Optimal sensor placement for time-dependent systems: application to wind studies around buildings. Journal of Computing in Civil Engineering, (in press). Papadopoulou, M., B. Raphael, I.F.C. Smith, and C. Sekhar, Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error. Entropy, 2014. 16(9): p. 5078-5101. Franke, J., Best Practice Guideline for the CFD Simulation of Flows in the Urban Environment: COST Action 732 Quality Assurance and Improvement of Microscale Meteorological Models2007: Meteorological Inst. Tominaga, Y., A. Mochida, R. Yoshie, H. Kataoka, T. Nozu, M. Yoshikawa, and T. Shirasawa, AIJ guidelines for practical applications of CFD to pedestrian wind environment around buildings. Journal of Wind Engineering and Industrial Aerodynamics, 2008. 96(10): p. 1749-1761. Ansys, ANSYS FLUENT User's Guide, 2011, Ansys, Inc.: Canonsburg, PA. Vernay, D.G., B. Raphael, and I.F.C. Smith, Augmenting simulations of airflow around buildings using field measurements. Advanced Engineering Informatics, 2014. Goulet, J.A. and I.F.C. Smith, Performance-driven measurement system design for structural identification. Journal of Computing in Civil Engineering, 2012. 27(4): p. 427-436.
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