Researchers have developed a CFD-driven sparse symbolic regression framework to create more accurate and interpretable corrections for Reynolds-averaged Navier-Stokes (RANS) transition models. This new approach identifies closed-form corrections for activation functions within the intermittency equation, using a combination of polynomial and exponential terms based on local flow features. The framework, which leverages gradient-free optimization and surrogate models, identified a more parsimonious set of correction terms compared to traditional tuning methods, while maintaining or improving accuracy on both training and unseen data. AI
IMPACT This research could lead to more accurate fluid dynamics simulations, impacting fields that rely on CFD for design and analysis.
RANK_REASON The item describes a new research paper detailing a novel method for improving CFD models. [lever_c_demoted from research: ic=1 ai=0.7]
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