Predicting Roof Pressures on a Low-Rise Structure From Freestream Turbulence Using Artificial Neural Networks
Predicting Roof Pressures on a Low-Rise Structure From Freestream Turbulence Using Artificial Neural Networks
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DOI：
10.3389/fbuil.2018.00068
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发表时间：
2018-11
影响因子：
3
通讯作者：
Pedro L. Fernández-Cabán;F. Masters;B. Phillips
中科院分区：
文献类型：
--
作者：
Pedro L. Fernández-Cabán;F. Masters;B. Phillips
本文提出了一种基于自由流紊流条件的低层结构分离流区附近顶板压力大小和分布预测（即插值）的广义方法。采用反向传播（BP）训练算法的前馈多层人工神经网络（ANN），对三种几何比例（1:50、1:30和1:20）低层建筑模型的逆风进场流条件下的均值、均方根（RMS）和峰值压力系数进行预测。利用最近发表的边界层风洞（BLWT）压力测量的综合数据集对人工神经网络模型进行训练、验证和评估。平均而言，在1:50、1:30和1:20模型下，位于屋角附近的一组压力水龙头的人工神经网络峰值压力系数预测值分别在BLWT观测值的5.1、6.9和7.7%之内。此外，预测的ANN平均和均方根压力系数与BLWT数据非常吻合。
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.