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Linear/Nonlinear Reduced-Order Substructuring for Uncertainty Quantification and Predictive Accuracy Assessment

机译：用于不确定度量和预测准确度评估的线性/非线性缩小阶子结构

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摘要

Modal testing is routinely performed on space craft and launch vehicles to "verify" (calibrate and validate) analytical models. Large multi-component structures such as the Space Transportation System (STS) or "Space Shuttle," and the newly proposed NASA Space Launch System (SLS), are impractical to test in their assembled configurations. Alternatively, substructure testing of these multi-component structures has been performed and used to calibrate/validate analytical models of the substructures, which are then assembled to analyze system response to applied loads. This paper describes a methodology applicable to uncertainty quantification (UQ) of reduced (modal) models of linear (or linearized) finite element models of substructures, as well as reduced (stochastic neural net (SNN)) models of nonlinear substructures, such as joints. The UQ at reduced substructure levels is propagated to higher levels of assembly by efficient means, enabling predictive accuracy assessment at the coupled system level. UQ is based entirely on comparisons on analysis and test data at the substructure level so that recourse to the specification and quantification of element-level random variables is not necessary. Examples are presented to illustrate the methodology.
机译：模态测试是在飞船和运载火箭定期进行“验证”（校准和验证）的分析模型。大的多组分结构，如空间运输系统（STS）或“航天飞机”和新提出的NASA太空发射系统（SLS），在它们的组装配置不切实际的测试。可替代地，这些多组分结构的子结构的测试已被执行并用于校准/验证子结构，然后将其组装到分析系统响应于所施加的负载的分析模型。本文描述了适用于不确定性定量（UQ）的方法的线性的降低（模态）的模型（或线性）子结构的有限元模型，以及降低的（随机神经网络（SNN））的非线性子结构模型，如关节。在降低的子水平UQ被传播到更高水平的组件通过有效的手段，使预测精度评价在耦合系统水平。 UQ完全基于在下部结构上的水平的分析和测试数据比较，以便求助于元素级的随机变量的说明书和量化是没有必要的。例子以说明的方法。

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