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Predicting Roof Pressures on a Low-Rise Structure From Freestream Turbulence Using Artificial Neural Networks
This paper presents a generalized approach for predicting (i.e., interpolating) the magnitude and distribution of roof pressures near separated flow regions on a low-rise structure based on freestream turbulent flow conditions. A feed-forward multilayer artificial neural network (ANN) using a backpropagation (BP) training algorithm is employed to predict the mean, root-mean-square (RMS), and peak pressure coefficients on three geometrically scaled (1:50, 1:30, and 1:20) low-rise building models for a family of upwind approach flow conditions. A comprehensive dataset of recently published boundary layer wind tunnel (BLWT) pressure measurements was utilized for training, validation, and evaluation of the ANN model. On average, predicted ANN peak pressure coefficients for a group of pressure taps located near the roof corner were within 5.1, 6.9, and 7.7% of BLWT observations for the 1:50, 1:30, and 1:20 models, respectively. Further, very good agreement was found between predicted ANN mean and RMS pressure coefficients and BLWT data.
Predicting Roof Pressures on a Low-Rise Structure From Freestream Turbulence Using Artificial Neural Networks
This paper presents a generalized approach for predicting (i.e., interpolating) the magnitude and distribution of roof pressures near separated flow regions on a low-rise structure based on freestream turbulent flow conditions. A feed-forward multilayer artificial neural network (ANN) using a backpropagation (BP) training algorithm is employed to predict the mean, root-mean-square (RMS), and peak pressure coefficients on three geometrically scaled (1:50, 1:30, and 1:20) low-rise building models for a family of upwind approach flow conditions. A comprehensive dataset of recently published boundary layer wind tunnel (BLWT) pressure measurements was utilized for training, validation, and evaluation of the ANN model. On average, predicted ANN peak pressure coefficients for a group of pressure taps located near the roof corner were within 5.1, 6.9, and 7.7% of BLWT observations for the 1:50, 1:30, and 1:20 models, respectively. Further, very good agreement was found between predicted ANN mean and RMS pressure coefficients and BLWT data.
Predicting Roof Pressures on a Low-Rise Structure From Freestream Turbulence Using Artificial Neural Networks
Pedro L. Fernández-Cabán (author) / Forrest J. Masters (author) / Brian M. Phillips (author)
2018
Article (Journal)
Electronic Resource
Unknown
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