A residual- and realizability-filtered CFD-driven GEP framework reduces computational cost by 42.3 percent and non-realizable models from 58.4 percent to 1.7 percent for turbulence closure discovery in wake flows.
Marconcini, A Data-Driven Approach for Generalizing the LaminarKineticEnergyModelforSeparationandBypassTransition inLow-andHigh-PressureTurbines, ASMEJTurbomach146(2024) 091005
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Realizability-Constrained Machine Learning for Turbulence Closures in Wake Flows
A residual- and realizability-filtered CFD-driven GEP framework reduces computational cost by 42.3 percent and non-realizable models from 58.4 percent to 1.7 percent for turbulence closure discovery in wake flows.