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首页> 外文期刊>Journal of structural engineering >Multiobjective Aerodynamic Optimization of Tall Building Openings for Wind-Induced Load Reduction
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Multiobjective Aerodynamic Optimization of Tall Building Openings for Wind-Induced Load Reduction

机译:高层建筑开口的多目​​标空气动力学优化,以减轻风荷载

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摘要

The dependency of wind forces and responses on the outer shape of tall buildings provides a unique opportunity to reduce wind effects through the aerodynamic optimization of the outer shape. The current study presents a multiobjective aerodynamic optimization procedure (AOP) by introducing horizontal openings to reduce wind load. The adopted method joins an optimization algorithm, large eddy simulation (LES), and an artificial neural network (ANN) surrogate model. The base building geometry is the Commonwealth Advisory Aeronautical Research Council (CAARC) building. The present optimization process is carried out by introducing three openings along the height of the building. However, the adopted method is useful for any shape modifications. In all cases, opening volumes are maintained at 10% of the total building volume to enable a simple comparative performance assessment. The objective of the optimization process is to identify the optimal aspect ratio and distances between the openings that produce the best aerodynamic performance. Three optimization problems are presented. The first two follow single-objective optimization to reduce the peak base moment coefficients about the x- and y-directions, respectively. Reductions of 47% and 42% are achieved for the moments about the x- and y-directions, respectively. The third optimization is a multiobjective optimization problem, which aims to simultaneously reduce the two base moments.
机译:风力对高层建筑外形的依赖性,为通过外形的空气动力学优化减少风的影响提供了独特的机会。当前的研究通过引入水平开口以减少风荷载,提出了一种多目标空气动力学优化程序(AOP)。采用的方法结合了优化算法,大涡模拟(LES)和人工神经网络(ANN)替代模型。基本建筑物的几何形状是英联邦航空咨询委员会(CAARC)建筑物。通过沿建筑物的高度引入三个开口来执行当前的优化过程。但是,采用的方法对于任何形状修改都是有用的。在所有情况下,开盘量均保持在总建筑量的10%,以便进行简单的比较绩效评估。优化过程的目标是确定产生最佳空气动力学性能的最佳纵横比和开口之间的距离。提出了三个优化问题。前两个遵循单目标优化,分别减小关于x方向和y方向的峰值基本矩系数。分别在x和y方向上的力矩分别减少了47%和42%。第三次优化是一个多目标优化问题,旨在同时减少两个基本矩。

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