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Improved Craig-Bampton stochastic method for spacecraft vibroacoustic analysis

机译:改进的克拉格-Bampton随机分析方法抗纤维声学分析

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摘要

This paper addresses the susceptibility of finite element models to uncertainty in frequency ranges with relatively high modal density, particularly in the context of vibroacoustic analysis. The principal idea is based on a stochastic finite element method (FEM) technique called Craig-Bampton stochastic method (CBSM). It is a parametric Monte Carlo simulation (MCS) approach that can be performed at a fraction of the otherwise potentially impractical computational cost, due to the use of reduced rather than full system matrices. An enhanced formulation of the CBSM, significantly improving its efficiency by exploiting the block structure of the condensed model's stiffness and mass matrices is derived. The improved method is adapted for use with distributed loads, such as diffuse sound field excitation. Its practical implementation is illustrated through a simple theoretical example followed by a high-complexity spacecraft structure case. In both cases solutions are compared to those for a classic MCS of the non-condensed models. Through an extensive parametric survey, recommendations are given on the ideal perturbation levels and underlying statistical distributions for the improved CBSM's random variables. The proposed technique shows a very strong agreement with the benchmark MCS results. Computational time reductions of over 1 and 3 orders of magnitude against the original CBSM and the MCS, respectively, are demonstrated.
机译:本文解决了有限元模型对具有相对高模态密度的频率范围的不确定性的敏感性,特别是在偶联分析的背景下。主要思想基于随机有限元方法(FEM)技术称为Craig-Bampton随机方法(CBSM)。它是一种参数蒙特卡罗模拟(MCS)方法,其可以以否则可能的不切实际的计算成本的一小部分执行,因为使用减少而不是完整的系统矩阵。通过利用冷凝模型的刚度和大规模矩阵的块结构,增强了CBSM的增强的CBSM,显着提高其效率。改进的方法适用于分布式负载,例如漫射声场激励。其实际实现通过简单的理论示例说明,然后是高度复杂性的航天器结构案例。在这两种情况下,将解决方案与非浓缩模型的经典MCS进行比较。通过广泛的参数调查,提出了理想的扰动水平和改进的CBSM随机变量的底层统计分布。该技术与基准MCS结果表明非常强烈的一致。分别对原始CBSM和MCS进行了超过1和3个级别的计算时间减少。

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