A model-based data-interpretation framework for improving wind predictions around buildings

Although Computational Fluid Dynamics (CFD) simulations are often used to assess wind conditions around buildings, the accuracy of such simulations is often unknown. This paper proposes a data-interpretation framework that uses multiple simulations in combination with measurement data to improve the accuracy of wind predictions. Multiple simulations are generated through varying sets of parameter values. Sets of parameter values are falsified and thus not used for predictions if differences between measurement data and simulation predictions, for any measurement location, are larger than an estimate of uncertainty bounds. The bounds are defined by combining measurement and modeling uncertainties at sensor locations. The framework accounts for time-dependent and spatially-distributed modeling uncertainties that are present in CFD simulations of wind. The framework is applied to the case study of the CREATE Tower located at the National University of Singapore. Values for time-dependent inlet conditions, as well as values for the roughness of surrounding buildings, are identified with measurements carried out around the CREATE Tower. Results show that, on average, ranges of horizontal wind-speed predictions at an unmeasured location have been decreased by 65% when measurement data are used. (C) 2015 Elsevier Ltd. All rights reserved.

Improving simulation predictions of wind around buildings using measurements through system identification techniques

Ian Smith, Didier Vernay

Wind behavior in urban areas is receiving increasing interest from city planners and architects. Computational fluid dynamics (CFD) simulations are often employed to assess wind behavior around buildings. However, the accuracy of CFD simulations is often unknown. Measurements can be used to help understand wind behavior around buildings more accurately. In this paper, a model-based data interpretation framework is presented to integrate information obtained from measurements with simulation results. Multiple model instances are generated from a model class through assigning values to parameters that are not known precisely, including those for inlet wind conditions. The information provided by measurements is used to falsify model instances whose predictions do not match measurements and to estimate the parameter values of the simulation. The information content of measurement data depends on levels of measurement and modeling uncertainties at sensor locations. Modeling uncertainties are those associated with the model class such as effects associated with turbulent fluctuations or thermal processes. The model-based data interpretation framework is applied to the study of the wind behavior around the buildings of the Treelodge@Punggol estate, located in Singapore. The framework incorporates modeling and measurement uncertainties and provides probability-based predictions at unmeasured locations. This paper illustrates the possibility to improve approximations of modeling uncertainties through avoiding falsification of the entire set of model instances. It is concluded that the framework has the potential to infer time-dependent sets of parameter values and to predict time-dependent responses at unmeasured locations.

Elsevier2015

Augmenting simulations of airflow around buildings using field measurements

Ian Smith, Didier Vernay

Computational fluid-dynamics (CFD) simulations have become an important tool for the assessment of airflow in urban areas. However, large discrepancies may appear when simulated predictions are compared with field measurements because of the complexity of airflow behaviour around buildings and difficulties in defining correct sets of parameter values, including those for inlet conditions. Inlet conditions of the CFD model are difficult to estimate and often the values employed do not represent real conditions. In this paper, a model-based data-interpretation framework is proposed in order to integrate knowledge obtained through CFD simulations with those obtained from field measurements carried out in the urban canopy layer (UCL). In this framework, probability-based inlet conditions of the CFD simulation are identified with measurements taken in the UCL. The framework is built on the error-domain model falsification approach that has been developed for the identification of other complex systems. System identification of physics-based models is a challenging task because of the presence of errors in models as well as measurements. This paper presents a methodology to estimate modelling errors. Furthermore, error-domain model falsification has been adapted for the application of airflow modelling around buildings in order to accommodate the time variability of atmospheric conditions. As a case study, the framework is tested and validated for the predictions of airflow around an experimental facility of the Future Cities Laboratory, called “BubbleZERO”. Results show that the framework is capable of narrowing down parameter-value sets from over five hundred to a few having possible inlet conditions for the selected case-study. Thus the case-study illustrates an approach to identifying time-varying inlet conditions and predicting wind characteristics at locations where there are no sensors.

Elsevier2014

Evaluating predictive performance of sensor configurations in wind studies around buildings

Maria Papadopoulou, Ian Smith

A great challenge associated with urban growth is to design for energy efficient and healthy built environments. Exploiting the potential for natural ventilation in buildings might improve pedestrian comfort and lower cooling loads, particularly in warm and tropical climates. As a result, predicting wind behavior around naturally ventilated buildings has become important and one of the most common prediction approaches is computational fluid dynamics (CFD) simulation. While accurate wind prediction is essential, simulation is complex and predictions are often inconsistent with field measurements. Discrepancies are due to the large uncertainties associated with modeling assumptions, as well as the high spatial and temporal climatic variability that influences sensor data. This paper proposes metrics to estimate the expected predictive performance of sensor configurations and assesses their usefulness in improving simulation predictions. The evaluations are based on the premise that measurement data are best used for falsifying model instances whose predictions are inconsistent with the data. The potential of the predictive performance metrics is demonstrated using full-scale high-rise buildings in Singapore. The metrics are applied to assess previously proposed sensor configurations. Results show that the performance metrics successfully evaluate the robustness of sensor configurations with respect to reducing uncertainty of wind predictions at other unmeasured locations. (C) 2016 Elsevier Ltd. All rights reserved.

Elsevier2016

Augmenting simulations of airflow around buildings using field measurements

Ian Smith, Didier Vernay

Elsevier2014