Convolution Neural Network for Fluid Flow Simulations in Cascade with Oscillating Blades
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This paper addresses challenges in fluid-structure interaction (FSI), crucial in aeronautics and turbomachinery. FSI simulations are computationally intensive, especially for fluid flow. We propose using a convolutional neural network (CNN) to predict fluid flow, reducing computational demands compared to traditional methods. The contribution explores unsteady fluid flow in a cascade with moving blades. The developed method assesses blade stability and identifies conditions leading to flutter onset.
