Effects of sensor configuration optimization on airflow estimation in urban environment: A case study with a building group model

Abstract

The complicated flow field in an urban area can be properly estimated using sparse sensor measurements and several estimation methods. Because sensor measurements are the only real-time information provided for estimations, it is necessary to investigate the effects of the sensor placement on the estimation accuracy. This study compared the performance of uniform and optimized sensor configurations in estimating the streamwise velocity field for a block-arrayed building group model. The optimized configuration was produced by a QR decomposition method, which is a data-driven optimization method that uses the input POD11POD-LSE: proper orthogonal decomposition and linear stochastic estimation; POD: proper orthogonal decomposition; LSE: linear stochastic estimation; SCO: sensor configuration optimization; CFD: computational fluid dynamics; LES: large-eddy simulation; ANN: artificial neural network; RMSE: root mean squared error. (Proper Orthogonal Decomposition) modes and deploys sensors to the positions where dominant flow structures can be efficiently measured. The robustness of two estimation methods, inverse POD and POD-LSE (Linear Statistic Estimation), against different sensor configurations was analyzed. The influence of different settings, like the number of POD modes and the number of sensors, on the optimization and estimation were also explored. According to the results, the optimal configuration can reduce the root mean square error (RMSE) by about 86% in inverse POD and 10% in POD-LSE when compared to the uniform one. Applying LSE can relieve the deficiency caused by poorly placed sensors, but since it can only supplement linear information, the configuration optimization is still critical especially to nonlinear estimations.