基于IronPython语言的牛顿迭代算法及其工程应用  

Newton's Iterative Algorithm Based on Ironpython Language and Its Engineering Application

作　　者：张小洁 ZHANG Xiaojie(Western Innovation Research Institute,Xianyang 712000,Shaanxi,China;Shaanxi Polytechnic Institute,Xianyang 712000,Shaanxi,China)

机构地区：[1]西部创新研究院,陕西咸阳712000 [2]陕西工业职业技术学院,陕西咸阳712000

出　　处：《电气传动自动化》2024年第1期19-22,14,共5页Electric Drive Automation

基　　金：陕西工业职业技术学院科研项目(2022YKZX-022)。

摘　　要：本文将牛顿迭代算法应用于仿真优化设计中,提出了一种基于IronPython寻根的闭环算法,并在Ansys Workbench平台以某容器内气流混合过程为例,验证了该算法的可行性和实施效果。该方法能够找到输出特性结果性能参数所对应的输入参数设计点,实现了CAE仿真平台上的“数学求根”,即由既定的输出性能结果参数寻找对应的输入参数设计点。该方法能够快速地实现寻优,提高了工程优化设计的效率,缩短了优化工作的周期,开创了新的设计思路,对现代科技研发具有一定的参考价值。This paper applies Newton's iterative algorithm to simulation optimization design,proposes a closedloop algorithm based on IronPython root finding,and verifies the feasibility and implementation effect of the algorithm using the airflow mixing process in a certain container as an example on the Ansys Workbench platform.This method can find the input parameter design points corresponding to the performance parameters of the output characteristic results,achieving "mathematical root finding" on the CAE simulation platform,that is,finding the corresponding input parameter design points from the established output performance result parameters.This method can quickly achieve optimization,improve the efficiency of engineering optimization design,shorten the cycle of optimization work,create new design ideas,and have certain reference value for modern scientific and technological research and development.

关 键 词：牛顿迭代 设计优化 逆向寻根 闭环算法 

分 类 号：TH122[机械工程—机械设计及理论] TP13[自动化与计算机技术—控制理论与控制工程]