Soni Tejas, Sharma Ashwani, Dutta Rajdeep, Dutta Annwesha, Jayavelu Senthilnath, Sarkar Saikat
Department of Civil Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India.
Department of Machine Intellection, Institute for Infocomm Research Technology and Research Agency for Science, Singapore, Singapore.
R Soc Open Sci. 2022 Apr 6;9(4):220097. doi: 10.1098/rsos.220097. eCollection 2022 Apr.
While fluid-structure interaction (FSI) problems are ubiquitous in various applications from cell biology to aerodynamics, they involve huge computational overhead. In this paper, we adopt a machine learning (ML)-based strategy to bypass the detailed FSI analysis that requires cumbersome simulations in solving the Navier-Stokes equations. To mimic the effect of fluid on an immersed beam, we have introduced dissipation into the beam model with time-varying forces acting on it. The forces in a discretized set-up have been decoupled via an appropriate linear algebraic operation, which generates the ground truth force/moment data for the ML analysis. The adopted ML technique, symbolic regression, generates computationally tractable functional forms to represent the force/moment with respect to space and time. These estimates are fed into the dissipative beam model to generate the immersed beam's deflections over time, which are in conformity with the detailed FSI solutions. Numerical results demonstrate that the ML-estimated continuous force and moment functions are able to accurately predict the beam deflections under different discretizations.
虽然流固耦合（FSI）问题在从细胞生物学到空气动力学的各种应用中普遍存在，但它们涉及巨大的计算开销。在本文中，我们采用基于机器学习（ML）的策略来绕过详细的FSI分析，这种分析在求解纳维-斯托克斯方程时需要繁琐的模拟。为了模拟流体对浸没梁的影响，我们通过作用在梁上的时变力将耗散引入梁模型。在离散设置中，力已通过适当的线性代数运算解耦，这为ML分析生成了真实的力/矩数据。所采用的ML技术，即符号回归，生成计算上易于处理的函数形式来表示力/矩相对于空间和时间的关系。这些估计值被输入到耗散梁模型中，以生成浸没梁随时间的挠度，这些挠度与详细的FSI解一致。数值结果表明，ML估计的连续力和矩函数能够准确预测不同离散化情况下梁的挠度。
