Proper Orthogonal Decomposition and Sparse Identification of Nonlinear Dynamics for the Flow Around Three Tandem Equilateral Triangle Cylinders
Keywords:
POD, SINdyAbstract
Fluid flows are applicable to numerous applications, including turbulence modeling, flow control, and fluid system optimization. Traditional approaches are time-consuming and expensive, and they typically require high-fidelity simulations. Proper Orthogonal Decomposition (POD) and Sparse Identification of Nonlinear Dynamics (SINDy) are combined to efficiently uncover the governing nonlinear dynamics of fluid flows. POD reduces the dimensionality through extracting orthogonal modes from high-dimensional fluid flow data, providing a simplified model that encapsulates key flow physics, and SINDy employs procedures that take advantage of sparsity to compute minimal nonlinear differential equations, uncovering temporal patterns of POD mode amplitudes in which physical processes allowing the flow to maintain the original design are disclosed while making the model interpretable and computationally tractable. The POD-SINDy method applied to the flow over three tandem equilateral triangle cylinders, this method accurately reproduces flow dynamics, allowing new insights to explore fluid dynamics as primitive processes further, yielding an interpretable, computationally cheap model for the advancement of fluid dynamics research and applications.
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