Benny Raphael

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.

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.

Warm climates pose challenges to building energy consumption and pedestrian comfort. Knowledge of the wind flow around buildings can help address these issues through improving natural ventilation, energy use, and outdoor thermal comfort. Computational fluid dynamics (CFD) simulations are widely used to predict wind flow around buildings, despite the large discrepancies that often occur between model predictions and actual measurements. Wind speed and direction exhibit a high degree of variability that adds uncertainties in modeling and measurements. Although some studies focus on methods to evaluate and minimize modeling uncertainties, sensor placement has been mostly based on subjective judgment and intuition; no systematic methodology is available to identify optimal sensor locations prior to field measurement. This work proposes a methodology for systematic sensor placement for situations when no measurement data are available and knowledge of the wind environment around buildings is limited. Sequential sensor placement algorithms and criteria are used to identify sensor configurations based on CFD simulation predictions at plausible locations. Optimal sensor configurations are compared for their ability to improve wind speed predictions at another location where no measurements are taken. The methodology is applied to two full-scale building systems of varying size. Results show that the methodology can be applied prior to field measurement to identify optimal configurations of a limited number of sensors that improve wind speed predictions at unmeasured locations.

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.