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首页> 外文期刊>Journal of Computing and Information Science in Engineering >Physics-Informed Neural Networks for Missing Physics Estimation in Cumulative Damage Models: A Case Study in Corrosion Fatigue
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Physics-Informed Neural Networks for Missing Physics Estimation in Cumulative Damage Models: A Case Study in Corrosion Fatigue

机译：累积损伤模型中缺少物理估计的物理知识的神经网络：腐蚀疲劳的案例研究

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摘要

We present a physics-informed neural network modeling approach for missing physics estimation in cumulative damage models. This hybrid approach is designed to merge physics-informed and data-driven layers within deep neural networks. The result is a cumulative damage model in which physics-informed layers are used to model relatively well understood phenomena and data-driven layers account for hard-to-model physics. A numerical experiment is used to present the main features of the proposed framework. The test problem consists of predicting corrosion-fatigue of an Al 2024-T3 alloy used on panels of aircraft wings. Besides cyclic loading, panels are also subjected to saline corrosion. In this case, physics-informed layers implement the well-known Walker model for crack propagation, while data-driven layers are trained to compensate the bias in damage accumulation due to the corrosion effects. The physics-informed neural network is trained using full observation of inputs (far-field loads, stress ratio, and a corrosivity index defined per airport) and very limited observation of outputs (crack length at inspection for only a small portion of the fleet). Results show that the physics-informed neural network is able to learn how to compensate the missing physics of corrosion in the original fatigue model. Predictions from the hybrid model can be used in fleet management, for example, to prioritize inspection across the fleet or forecast ahead of time the number of planes with damage above a threshold.
机译：我们提出了一种物理知识的神经网络建模方法，可用于累积损伤模型中的物理估计。这种混合方法旨在合并在深神经网络中的物理信息和数据驱动的层。结果是累积损坏模型，其中物理信息层用于模拟相对良好的理解现象和数据驱动层，以用于难以模拟物理学。数值实验用于呈现所提出的框架的主要特征。测试问题包括预测飞机翼面板的A1024-T3合金的腐蚀疲劳。除了循环加载外，面板也经受盐水腐蚀。在这种情况下，物理信息的层实现了用于裂缝传播的众所周知的助行器模型，而数据驱动层被训练以补偿由于腐蚀效应引起的损坏累积中的偏置。物理知识的神经网络使用完全观察输入（远场载荷，应力比和每个机场定义的腐蚀性指数）以及对输出的观察（仅限舰队的一小部分）的输出观察（裂缝长度）进行了非常有限的观察。结果表明，物理知识的神经网络能够学习如何补偿原始疲劳模型中腐蚀的缺失物理。混合模型的预测可用于车队管理，例如，提前在船队或预测上的预测优先考虑，飞机数量高于阈值。

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