求解流固耦合动力学方程的积分法

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Xin Zhang, Jie Liu, Pu Xue, Shuowen Yan, Yahao Xu, M. S. Zahran
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引用次数: 0

摘要

本文提出了一种求解流固相互作用方程的新方法。该方法结合了Newmark精确积分法(NPIM)和双神经网络(DNN)方法的优点。在DNN积分法的基础上,利用NPIM对指数矩阵和加载向量进行修正。这包括将Newmark-β方法的基本假设纳入动力方程,并从动力平衡方程中消除加速度项。将方程简化为一阶线性方程组。随后,应用PIM逐步将系统集成到NPIM中。采用DNN方法通过对神经网络拟合被积函数与原函数求解非齐次项,利用牛顿-莱布尼茨公式求解积分项。数值算例表明,与深度神经网络方法相比,该方法显著提高了计算效率,并提供了足够的精度。在分析爆炸荷载条件下的大型结构时,这一点尤为明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Integral Method for Solving Dynamic Equations with Fluid–Solid Coupling

An Integral Method for Solving Dynamic Equations with Fluid–Solid Coupling

In this work, a new methodology is presented to mainly solve the fluid–solid interaction (FSI) equation. This methodology combines the advantages of the Newmark precise integral method (NPIM) and the dual neural network (DNN) method. The NPIM is employed to modify the exponential matrix and loading vector based on the DNN integral method. This involves incorporating the basic assumption of the Newmark-β method into the dynamic equation and eliminating the acceleration term from the dynamic equilibrium equation. As a result, the equation is reduced to a first-order linear equation system. Subsequently, the PIM is applied to integrate the system step by step within the NPIM. The DNN method is adopted to solve the inhomogeneous term through fitting the integrand and the original function with a pair of neural networks, and the integral term is solved using the Newton–Leibniz formula. Numerical examples demonstrate that the proposed methodology significantly improves computing efficiency and provides sufficient precision compared to the DNN method. This is particularly evident when analyzing large-scale structures under blast loading conditions.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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