Evaluating modeling uncertainties in the simulation of airflow in cities

Even though computational fluid-dynamics (CFD) has been used to simulate airflow in cities, accurate results are not always obtained because of difficulties related to defining the correct set of values of parameters, including boundary conditions. This paper uses a multi-model system-identification approach for improving the accuracy of airflow modeling. In this approach, multiple model predictions are generated through varying values of parameters that are not known precisely. Measurements are then carried out in order to falsify predictions that are not consistent with the measurements. A rejection criterion is defined through consideration of measurement and inherent modeling uncertainties. This paper proposes a methodology for evaluating modeling uncertainties within a system-identification framework. The methodology has been tested on an experimental facility of the Future Cities Laboratory, called “BubbleZERO”. It is shown that reliable predictions at locations where there are no sensors can be performed through implementation of the model-falsification approach.

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

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

Hierarchical sensor placement using joint entropy and the effect of modeling error

Maria Papadopoulou, Ian Smith

Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy.

MDPI AG2014

Augmenting simulations of airflow around buildings using field measurements

Ian Smith, Didier Vernay

Elsevier2014