Neuro-DynaStress：预测结构部件的动态应力分布
Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components
摘要：
结构构件通常暴露于动态载荷，例如地震、风和爆炸。结构工程师应该能够在灾后或极端灾害事件期间进行实时分析，需要立即纠正，以避免致命的故障。因此，实时预测高度破坏性事件期间的动态应力分布至关重要。目前可用的高保真方法，如有限元模型（FEM），遭受其固有的高复杂性和计算上是禁止的。因此，为了在保持准确性的同时降低计算成本，提出了一种深度学习模型Neuro-DynaStress，以使用偏微分方程（PDE）求解器基于有限元模拟来预测整个应力分布序列。该模型的设计和训练使用的几何形状，边界条件和序列的负载作为输入，并预测序列的高分辨率的应力等值线。所提出的框架的性能相比，使用PDE求解器的有限元模拟。
英文摘要：
Structural components are typically exposed to dynamic loading, such as earthquakes, wind, and explosions. Structural engineers should be able to conduct real-time analysis in the aftermath or during extreme disaster events requiring immediate corrections to avoid fatal failures. As a result, it is crucial to predict dynamic stress distributions during highly disruptive events in real-time. Currently available high-fidelity methods, such as Finite Element Models (FEMs), suffer from their inherent high complexity and are computationally prohibitive. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, Neuro-DynaStress, is proposed to predict the entire sequence of stress distribution based on finite element simulations using a partial differential equation (PDE) solver. The model was designed and trained to use the geometry, boundary conditions and sequence of loads as input and predict the sequences of high-resolution stress contours. The performance of the proposed framework is compared to finite element simulations using a PDE solver.
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