For tall buildings, values of wind force coefficients can be obtained from wind tunnel tests or Computational Fluid Dynamics (CFD). This paper is concentrated to analyze a set of CFD data and propose parametric equations for determining force coefficients in the alongwind and crosswind direction (C_fx and C_fy) of tall buildings with horizontal limbs. Initially, a parametric study is performed with CFD analysis considering RANS k − ε turbulence models keeping a constant plan area 22,500 mm². The length and velocity scales are taken as 1:300 and 1:5, respectively. The required design parameters are obtained and used for fitting parametric equations. The CFD data are further utilized for training artificial neural networks of C_fx and C_fy. The results of CFD, ANN, and parametric equations are compared. The parametric equations are validated by employing a wind tunnel study. Finally, three optimization studies are carried out using a genetic algorithm (GA), of which the first two aim to present the maximum and minimum force coefficients considering single objectives. The third optimization is a multi‐objective optimization problem, carried out to simultaneously minimize and maximize the two orthogonal force coefficients. Pareto‐optimal design results are presented.

中文翻译：

---

使用替代模型优化水平肢体高层建筑的顺风和逆风力系数

对于高层建筑，可以从风洞测试或计算流体力学（CFD）中获得风力系数值。本文集中分析一组CFD数据，并提出参数方程式，以确定具有水平肢体的高层建筑在顺风和逆风方向（C _fx和C _fy）的力系数。最初，使用CFD分析进行参数研究，其中考虑了保持恒定平面面积22,500 mm ^2的RANS k - ε湍流模型。长度和速度比例分别取为1：300和1：5。获得所需的设计参数，并将其用于拟合参数方程式。CFD数据进一步用于训练C _fx和C _fy的人工神经网络。比较了CFD，ANN和参数方程式的结果。通过使用风洞研究验证了参数方程。最后，使用遗传算法（GA）进行了三项优化研究，其中前两项旨在考虑单个目标来呈现最大和最小力系数。第三次优化是一个多目标优化问题，旨在同时最小化和最大化两个正交力系数。提出了帕累托最优设计结果。