NSU3D Results for the Second AIAA High-Lift Prediction Workshop

Mavriplis, Dimitri J.; Long, Michael E.; Lake, Troy; Langlois, Marc

doi:10.2514/1.c033042

Cited by 23 publications

(11 citation statements)

References 22 publications

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“…NSU3D has been demonstrated on multiple aerodynamics problems and has been a regular participant in the AIAA High Lift Prediction Workshop (Mavriplis et al, 2015) and the AIAA Drag Prediction Workshop (Park et al, 2014) series. The solver has been demonstrated to have good strong scalability on the National Center for Atmospheric Research (NCAR) supercomputer NWSC-1 Yellowstone.…”

Section: Near-body Flow Solvermentioning

confidence: 99%

Wind farm simulations using an overset hp-adaptive approach with blade-resolved turbine models

Kirby

Brazell

Yang

et al. 2019

The International Journal of High Performance Computing Applica

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Blade-resolved numerical simulations of wind energy applications using full blade and tower models are presented. The computational methodology combines solution technologies in a multi-mesh, multi-solver paradigm through a dynamic overset framework. The coupling of a finite volume solver and a high-order, hp-adaptive finite element solver is utilized. Additional technologies including in-situ visualization and atmospheric microscale modeling are incorporated into the analysis environment. Validation of the computational framework is performed on the National Renewable Energy Laboratory (NREL) 5MW baseline wind turbine, the unsteady aerodynamics experimental NREL Phase VI turbine, and the Siemens SWT-2.3-93 wind turbine. The power and thrust results of all single turbine simulations agree well with low-fidelity model simulation results and field experiments when available. Scalability of the computational framework is demonstrated using 6, 12, 24, 48, and 96 wind turbine setups including the 48 turbine wind plant known as Lillgrund. The largest case consisting of 96 wind turbines and a total of 385 overset grids are run on 44,928 cores at a weak scaling efficiency of 86%. Demonstration of the coupling of atmospheric microscale and Computational Fluid Dynamics (CFD) solvers is presented using the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting Model (WRF) solver and the NREL Simulator fOr Wind Farm Applications (SOWFA) solver.

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Section: Near-body Flow Solvermentioning

confidence: 99%

Wind farm simulations using an overset hp-adaptive approach with blade-resolved turbine models

Kirby

Brazell

Yang

et al. 2019

The International Journal of High Performance Computing Applica

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“…The same mesh was used for Case 2a at the lower Reynolds number. Additional details about the gridding guidelines and specific grids are not provided here, but documentation can be found in [2,3] or on the HiLiftPW-2 website. ** A comparison of grid sizes is shown in Table 2 for the workshop-supplied grids.…”

Section: Resultsmentioning

confidence: 99%

“…An overview and summary of the workshop is given by Rumsey and Slotnick [2]. Many workshop participants have summarized individual submissions and provided additional analysis and computations [3][4][5][6][7][8][9][10][11].…”

mentioning

confidence: 99%

Grid-Adapted FUN3D Computations for the Second High-Lift Prediction Workshop

Lee-Rausch

Rumsey

Park

2015

Journal of Aircraft

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Contributions of the unstructured Reynolds-averaged Navier-Stokes code FUN3D to the 2nd AIAA Computational Fluid Dynamics High-Lift Prediction Workshop are described, and detailed comparisons are made with experimental data. Using workshop-supplied grids, FUN3D results for the simplified high-lift configuration are compared with results from the structured code CFL3D. Using the same turbulence model, both codes compare reasonably well in terms of total forces and moment, and the maximum lift is similarly over-predicted for both codes compared to experiment. By including more representative geometry features such as slat and flap support brackets and slat pressure tube bundles, FUN3D captures the general effects of the Reynolds number variation, but underpredicts maximum lift on workshop-supplied grids in comparison with the experimental data due to excessive separation. However, when output-based, off-body grid adaptation in FUN3D is employed, results improve considerably. In particular, when the geometry includes both brackets and the pressure tube bundles, grid adaptation results in a more accurate prediction of lift near stall in comparison with the wind-tunnel data. Furthermore, a rotation-corrected turbulence model shows improved pressure predictions on the outboard span when using adapted grids.

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“…Grid System D grids were built by the University of Wyoming and Cessna with VGRID v4.0 and have a combination of prisms in the near-wall region and tetrahedra in the rest of domain. 34 A summary of the grid sizes is given in Table 2. The target wall spacing was y + ∼ 2/3 for the high Reynolds number condition; the same mesh was used for Grid Systems C and D at both the low and high Reynolds number conditions.…”

Section: Vb Computational Meshesmentioning

confidence: 99%

Application of a Full Reynolds Stress Model to High Lift Flows

Lee-Rausch

Rumsey

Eisfeld

2016

46th AIAA Fluid Dynamics Conference

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A recently developed second-moment Reynolds stress model was applied to two challenging high-lift flows: (1) transonic flow over the ONERA M6 wing, and (2) subsonic flow over the DLR-F11 wing-body configuration from the second AIAA High Lift Prediction Workshop. In this study, the Reynolds stress model results were contrasted with those obtained from one-and two-equation turbulence models, and were found to be competitive in terms of the prediction of shock location and separation. For an ONERA M6 case, results from multiple codes, grids, and models were compared, with the Reynolds stress model tending to yield a slightly smaller shock-induced separation bubble near the wing tip than the simpler models, but all models were fairly close to the limited experimental surface pressure data. For a series of high-lift DLR-F11 cases, the range of results was more limited, but there was indication that the Reynolds stress model yielded less-separated results than the one-equation model near maximum lift. These less-separated results were similar to results from the one-equation model with a quadratic constitutive relation. Additional computations need to be performed before a more definitive assessment of the Reynolds stress model can be made. Nomenclature a speed of sound, m/s A wing reference area, m 2 AR wing aspect ratio b wing reference span, m c ref wing reference chord, mcomponent of diffusion tensor, m 2 /s 3 d distance to the nearest wall, m F 1 Menter's blending function F T fully turbulent g alternate scale-determining variable, 1/ω, s 1/2 k specific kinetic turbulence energy, m 2 /s 2 M ∞ freestream Mach number O ij normalized rotation tensor used in quadratic constitutive relation correction P two-equation model production term, τ ij (∂û i /∂x j ), kg/(ms 3 ) P ij Cartesian component of Reynolds-stress production tensor, m 2 /s 3 * Research Engineer, Computational AeroSciences Branch, E.Lee-Rausch@nasa.gov. Associate Fellow AIAA.

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NSU3D Results for the Second AIAA High-Lift Prediction Workshop

Cited by 23 publications

References 22 publications

Wind farm simulations using an overset hp-adaptive approach with blade-resolved turbine models

Wind farm simulations using an overset hp-adaptive approach with blade-resolved turbine models

Grid-Adapted FUN3D Computations for the Second High-Lift Prediction Workshop

Application of a Full Reynolds Stress Model to High Lift Flows

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