Adaptive Reliability Analysis for Multi-fidelity Models using a Collective Learning Strategy
Adaptive Reliability Analysis for Multi-fidelity Models using a Collective Learning Strategy
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DOI：
10.1016/j.strusafe.2021.102141
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Chi Zhang;Chaolin Song;A. Shafieezadeh
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文献类型：
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作者：
Chi Zhang;Chaolin Song;A. Shafieezadeh
在许多科学和工程领域，都有不同保真度的模型可用。准确捕捉系统行为的物理实验或详细模拟被认为是具有低模型不确定性的高保真模型；然而，它们的运行成本很高。另一方面，简化的物理实验或数值模型被视为低保真模型，评估起来更便宜。虽然低保真模型由于精度较低而不适合直接用于可靠性分析，但它们可以提供有关高保真模型趋势的信息，从而提供了以低成本探索设计空间的机会。提出了一种新的可靠性分析方法&自适应多保真高斯过程(AMGPRA)。与目前最先进的mfEGRA方法不同，该方法不是在两个独立的阶段选择训练点和信息源，而是使用新的集合学习函数(CLF)同时找到最优训练点和信息源。CLF能够评估来自信息源的候选训练点的全球影响，并且它适应满足特定概况的任何学习功能。在这种情况下，CLF为量化新训练点的影响提供了一个新的方向，并且可以很容易地扩展新的学习函数，以适应不同的可靠性问题。通过三个数学算例和一个关于输电铁塔风可靠性的工程问题，验证了该方法的有效性。结果表明，与现有的单保真度和多保真度方法相比，该方法具有相似或更高的精度，而计算量更小。AMGPRA的一个关键应用是使用复杂且昂贵的基于物理的计算模型进行高保真的脆弱性建模。
In many fields of science and engineering, models with different fidelities are available. Physical experiments or detailed simulations that accurately capture the behavior of the system are regarded as high-fidelity models with low model uncertainty; however, they are expensive to run. On the other hand, simplified physical experiments or numerical models are seen as low-fidelity models that are cheaper to evaluate. Although low-fidelity models are often not suitable for direct use in reliability analysis due to their low accuracy, they can offer information about the trend of the high-fidelity model thus providing the opportunity to explore the design space at a low cost. This study presents a new approach called adaptive multi-fidelity Gaussian process for reliability analysis (AMGPRA). Contrary to selecting training points and information sources in two separate stages as done in state-of-the-art mfEGRA method, the proposed approach finds the optimal training point and information source simultaneously using the novel collective learning function (CLF). CLF is able to assess the global impact of a candidate training point from an information source and it accommodates any learning function that satisfies a certain profile. In this context, CLF provides a new direction for quantifying the impact of new training points and can be easily extended with new learning functions to adapt to different reliability problems. The performance of the proposed method is demonstrated by three mathematical examples and one engineering problem concerning the wind reliability of transmission towers. It is shown that the proposed method achieves similar or higher accuracy with reduced computational costs compared to state-of-the-art single and multi-fidelity methods. A key application of AMGPRA is high-fidelity fragility modeling using complex and costly physics-based computational models.