A new evolution equation for C_epsilon2 in k-epsilon modeling that incorporates Reynolds-number dependence and finite cascade time effects, shown to match simulation data for forced and decaying isotropic turbulence across a range of Reynolds numbers.
Hermann Lienhart and Stefan Becker
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DARSM embeds a neural network inside an implicit algebraic Reynolds stress model derived from transport equations, trains it end-to-end via adjoint PDE optimization, and reports 2-4x average velocity error reduction plus generalization from attached to separated flows on duct and hill benchmarks.
A data-driven framework learns a unified, frame-invariant turbulence model from sparse observations across regimes via multi-objective ensemble learning and similarity-based case selection.
citing papers explorer
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A variable-coefficient model for decay of isotropic turbulence capturing effects of finite cascade time and Reynolds number
A new evolution equation for C_epsilon2 in k-epsilon modeling that incorporates Reynolds-number dependence and finite cascade time effects, shown to match simulation data for forced and decaying isotropic turbulence across a range of Reynolds numbers.
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Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
DARSM embeds a neural network inside an implicit algebraic Reynolds stress model derived from transport equations, trains it end-to-end via adjoint PDE optimization, and reports 2-4x average velocity error reduction plus generalization from attached to separated flows on duct and hill benchmarks.