Abstract
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.
Highlights
With more than half of the global population living in cities and with an estimated annual increase of urban dwellers reaching nearly 60 million [1], much recent research work has focused on urban-related aspects, including studying the wind environment where buildings are, or will be, placed
Computational Fluid Dynamics (CFD) modeling may lead to reasonable predictions, results can be very different from field and laboratory experiments [8]
This paper proposes a hierarchical sensor placement strategy using joint entropy that explicitly incorporates spatial distribution of modeling errors and their values
Summary
With more than half of the global population living in cities and with an estimated annual increase of urban dwellers reaching nearly 60 million [1], much recent research work has focused on urban-related aspects, including studying the wind environment where buildings are, or will be, placed. In small-scale studies, such as those around buildings (distances up to 1–2 km) [7], Computational Fluid. Dynamics (CFD) modeling is commonly used to predict wind behavior. Advantages of CFD modeling are that it allows treatment of a wide range of complicated geometries and it provides detailed information on airflow. CFD modeling may lead to reasonable predictions, results can be very different from field and laboratory experiments [8]. The application of CFD requires experienced users, and predictions are subject to challenges associated with precision, computational storage and execution time [10]. Guidelines are available in the literature on the application of CFD in wind studies around and through urban canyons [11,12]. Recommendations on appropriate boundary conditions have been provided [13,14], while others have proposed methodologies for evaluating environmental models [15]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
