Abstract One of the key enablers of valued early decision-making in composite material designs is the ability to account for different aspects/scale of uncertainties within models and design processes. In order to enable well-judged decisions and to improve the trust of industrial decision-makers, measures of uncertainty, risk, and cost involved in materials are crucial. In this work, we provide i) efficient uncertainty analysis (UA) and ii) sensitivity analysis (SA) in composite structures to quantify the influence of input parameters on the output of interest accounting for the stochastic nature in multi-scale modeling with a large number of uncertain parameters. UA also provides a statistical distribution of the output of interest. The influences of different input parameters on the system responses can be estimated by conducting the global sensitivity analysis on the multi-scale models. To this end, a data-driven model approximating the relationship between the inputs and outputs is constructed by using an adaptive Sparse Polynomial Chaos Expansion (SPCE) approach. The sensitivities of the input factors on the system performances are computed analytically from the constructed data-driven model without any additional computational cost. To demonstrate the sensitivity and uncertainty management, two different test cases (composite leafspring and aircraft fuselage airframe) are considered for the stochastic structural analysis in multi-scale composite modeling. In both cases, the combined effects of multi-scale uncertainties are evaluated on the structural performances. Input parameters include the material microstructure (micro-scale uncertainties), composite layers stacking sequence (mesoscale uncertainties), and structural loading (uncertainty in macro-scale). The responses are provided in terms of their variations and probability distributions.

中文翻译：

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复合应用早期设计中的高效不确定性量化和管理

摘要 复合材料设计中有价值的早期决策的关键推动因素之一是能够考虑模型和设计过程中不确定性的不同方面/规模。为了能够做出明智的决策并提高工业决策者的信任度，材料中涉及的不确定性、风险和成本的措施至关重要。在这项工作中，我们提供了 i) 复合结构中有效的不确定性分析 (UA) 和 ii) 敏感性分析 (SA) 以量化输入参数对兴趣输出的影响，考虑到具有大数据的多尺度建模中的随机性不确定参数的数量。UA 还提供感兴趣的输出的统计分布。通过对多尺度模型进行全局敏感性分析，可以估计不同输入参数对系统响应的影响。为此，通过使用自适应稀疏多项式混沌扩展 (SPCE) 方法构建了近似输入和输出之间关系的数据驱动模型。输入因素对系统性能的敏感性是从构建的数据驱动模型中分析计算出来的，没有任何额外的计算成本。为了演示敏感性和不确定性管理，考虑了两个不同的测试案例（复合板簧和飞机机身机身）用于多尺度复合建模中的随机结构分析。在这两种情况下，都评估了多尺度不确定性对结构性能的综合影响。输入参数包括材料微观结构（微观尺度不确定性）、复合层堆叠顺序（中尺度不确定性）和结构载荷（宏观尺度不确定性）。响应是根据它们的变化和概率分布提供的。