Flow over periodic hills – Numerical and experimental study in a wide range of Reynolds numbers

By Michael Breuer, Nikolaus Peller, C. Rapp, Michael T. Manhart. 2009

351 citations recorded by the source corpus.

Subfields: Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/4236e3affbe4434cfada118e776bd98b8780abcc

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In Computational Mechanics

In Environmental Engineering

Citations in the corpus

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework (2017, methodology): “The training ﬂow is the ﬂow over periodic hill at Re = 5600 [50], and the test ﬂow is also at Re = 5600 but has a steeper hill proﬁle as described in Sec.”
Development of DDES and IDDES Formulations for the k-ω Shear Stress Transport Model (2012, methodology): “LES [15], the length of the computational domain was equal to 9H and its size in the spanwise direction was 4.”
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (2015, methodology): “The periodic hill flow is widely used in the CFD community to evaluate the performance of turbulence models due to the availability of experimental and numerical benchmark data [42].”
Structure of the airflow above surface waves (2016, background): “Flow separation past solid periodic hills has been found to generate an average intensification of the turbulent kinetic energy away from the surface downstream of the hill (see, e.g., Breuer et al. 2009).”
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data (2016, background): “[50] Periodic hills, Re = 5600 (PH5600) DNS by Breuer et al.”
High‐order discontinuous Galerkin spectral element methods for transitional and turbulent flow simulations (2014, background): “[61], who also provided experimental Particle Image Velocimetry measurements for the case.”
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (2018, neutral): “1 281 × 234× 200 LESOCC (Breuer et al. 2009) LES, finite volume method, body-fitting grid, second order discretization”