Anisotropic turbulence modeling for natural channel flow: A numerical approach with finite element method

In this study, we propose a numerical model aimed at improving the understanding and prediction of flow velocities in natural channels. This model specifically focuses on the challenges presented by anisotropic turbulence and bottom roughness. By incorporating the mixing length concept into an algeb...

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Veröffentlicht in:Flow measurement and instrumentation 2024-09, Vol.98, p.102649, Article 102649
Hauptverfasser: Silva, R.N., Camargo, F.R.T., Mendes, R.C.F., Bertolina, R.M., Nunes, M.M., Oliveira, T.F., Brasil, A.C.P.
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Sprache:eng
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Zusammenfassung:In this study, we propose a numerical model aimed at improving the understanding and prediction of flow velocities in natural channels. This model specifically focuses on the challenges presented by anisotropic turbulence and bottom roughness. By incorporating the mixing length concept into an algebraic turbulence model and employing the finite element method with Streamline-Upwind/Petrov–Galerkin (SUPG) stabilization, our model seeks to refine the simulation of axial velocity distribution and secondary motions. Validation was achieved through Acoustic Doppler Current Profiler (ADCP) measurements in three sections of the Canal do Rodeador, showing our model’s predictions of discharge and average velocity to have a deviation of approximately 9.34% from experimental data. The results underline the significance of secondary currents and turbulence anisotropy in shaping channel flow behaviors, offering new insights into the interactions between flow characteristics and channel bed features. This model stands out as a robust tool for hydraulic structure design and hydrokinetic potential evaluation, providing a non-intrusive, cost-effective alternative to traditional methods. •Numerical modeling for turbulent flows in natural channels.•Enhanced prediction of flow dynamics with stabilized FEM/SUPG method.•Significant improvement in flow rate prediction accuracy versus ADCP data.
ISSN:0955-5986
1873-6998
DOI:10.1016/j.flowmeasinst.2024.102649