Advanced Turbulence Models for Large-Scale Atmospheric Boundary Layer Flows

Technical Report · Thu Sep 28 04:00:00 EDT 2023

DOI:https://doi.org/10.2172/2229259· OSTI ID:2229259

Tombouldies, Ananias ^[1]; Min, Misun ^[2]; Fischer, Paul ^[3]; Churchfield, Matt ^[4]; Sprague, Michael ^[4]

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1. Aristotle Univ. of Thessaloniki (Greece)
2. Argonne National Laboratory (ANL), Argonne, IL (United States)
3. Argonne National Laboratory (ANL), Argonne, IL (United States); Univ. of Illinois at Urbana-Champaign, IL (United States)
4. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

We present high-fidelity large-eddy-simulation (LES) modeling approaches for the turbulent atmospheric boundary layer (ABL) flows. Wind energy is a prime example of an application driven by ABL. Generation of electrical energy from farms of wind turbines at night in the stable ABL is a particularly interesting situation. In this report, we consider the well-known GEWEX (Global Energy and Water Cycle Experiment) Atmospheric Boundary Layer Study (GABLS) stably stratified benchmark LES case. We use a high-order spectral element code Nek5000/RS, which is supported under the DOE’s Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms. In our earlier ANL report, we demonstrated our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. In this report, we extend the range of our SGS modeling approaches in the context of the mean-field eddy viscosity (MFEV), to include the solution of an SGS turbulent kinetic energy equation (TKE). We demonstrate the model fidelity of Nek5000/RS in comparison to that of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, with which we studied scaling performance for both codes in comparison on DOE’s leadership computing platforms.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
• DOE Contract Number: AC02-06CH11357; AC36-08GO28308
• OSTI ID: 2229259
• Report Number(s): ANL--23/60; 185513
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING