Presentation
Divergence Prediction System for CFD Simulations
DescriptionComputational fluid dynamics (CFD) simulations are essential tools for analyzing complex flow phenomena in engineering and scientific research. These simulations are typically formulated based on the Navier-Stokes equations, which govern the motion of incompressible fluids, and the pressure field is obtained by solving the Poisson equation using iterative solvers. However, iterative convergence is not always guaranteed. In certain cases, the residuals diverge, leading to numerical instability and eventual simulation failure. When divergence occurs after tens of thousands of time steps, it results in substantial waste of computational resources and delays research progress. To address this problem, this study proposes an AI-based divergence prediction system. By utilizing learned data from prior simulations, the proposed method enables prediction of divergence within about one hundred time steps. This early detection allows simulations to be interrupted before significant resources are consumed, thereby improving efficiency and supporting timely progress in computational research.
Event Type
Research and ACM SRC Posters
TimeThursday, 20 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
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