A theory based on inertial-pressure balance predicts the flatness factor of vertical velocity intermittency from second-order statistics, with data collapse across Reynolds numbers and roughness in the inertial sublayer.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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physics.flu-dyn 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
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On the large-scale vertical velocity intermittency of turbulent wall flows
A theory based on inertial-pressure balance predicts the flatness factor of vertical velocity intermittency from second-order statistics, with data collapse across Reynolds numbers and roughness in the inertial sublayer.
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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.