关于TclTK和Python编程在路噪仿真后处理的自动化应用

Automatic Application of Tcl TK and Python Programming in Post-processing of Road Noise Simulation

在整个基于轮心载荷的整车路噪分析中,在载荷、建模、分析、诊断、优化处理上需要耗费大量时间,且这个过程中极易出现错误影响汽车NVH性能的开发。为提高整车路噪分析诊断和结果后处理的效率、准确性,文章利用HyperGraph二次开发功能,结合Tcl/TK和Python语言开发了在整车路噪分析诊断和结果后处理自动化中的应用,明显提高了工作效率、降低人为错误和提升了分析的准确性。当前成果已应用于多个平台车型中的路噪仿真开发中,为每个车型的阶段路噪分析工作综合节省500多个工时。

In the whole wheel core load of the whole vehicle road noise analysis,it takes a lot of time in such as pre and post processing,and is prone to errors that affect the development of vehicle NVH performance.In order to improve the efficiency and accuracy of road noise analysis,diagnosis and result processing,an application for automatic road noise analysis,diagnosis and result post-processing of the whole vehicle was developed by using Hypergraph secondary development function,combined with TCL/TK and Python languages,which obviously improved the work efficiency,reduced human error and improved the accuracy of analysis.The results of this paper have been applied to the development of road noise in multiple platform models,saving a lot of time for the stage of road noise analysis of each model.