结构模型缩聚的改进IRS方法1)
AN IMPROVED IRS METHOD FOR STRUCTURE MODEL CONDENSATION1)
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摘要: 模型缩聚法在结构的静力和动力特性分析中有着广泛的应用。应用模型缩聚法,可以有效降低结构的有限元计算规模,节省计算时间和成本,并能获得和实验测量自由度相匹配的有限元模型。本文在改进模型缩聚方法(improved reduced system, IRS)的基础上,提出一种考虑二阶惯性量的改进IRS方法,有效改进了IRS方法的计算精度,和模型缩聚迭代法(iterated IRS, IIRS)相比,此方法计算量更小且计算精度更高。以桁架结构和框架结构为例对所提二阶IRS方法进行了验证,并将计算结果与精确值、Guyan缩聚解、IRS缩聚解和IIRS缩聚解进行了比较,结果表明了所提方法计算精度最好,具有良好的工程应用前景。Abstract: The model condensation method is widely used in the static and dynamic analysis of structures. With the model reduction, the calculation scale of structural static and dynamic equations can be effectively reduced, with the calculation time and cost saved, and to obtain a reduced finite element model matching the experimental measurement freedoms. A new IRS (improved reduced system) method is proposed in this paper by considering the second-order inertia, to effectively improve the calculation accuracy. Compared with the IIRS (iterated IRS), the proposed second-order IRS method can be used to build a more accurate reduced model with smaller calculation amount. Taking a truss structure and a frame structure as examples, the proposed second-order IRS method is verified, and the calculation results are compared with the exact solution, the Guyan condensation solution, the IRS solution and the IIRS solution. It is shown that the proposed method enjoys the best calculation accuracy, with a good engineering application prospect.