CFL3D, FUN3D, and NSU3D Contributions to the Fifth Drag Prediction Workshop

Park, Michael A.; Laflin, Kelly R.; Chaffin, Mark S.; Powell, Nicholas; Levy, David W.

doi:10.2514/1.c032613

Cited by 33 publications

(17 citation statements)

References 62 publications

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“…As noted in the preceding section, all computed results show very similar trends and no indication of separated flow. This is in contrast to the FUN3D trends with the SA turbulence model in DPW-V for full viscous terms [6]. The SA results from [6] showed an abrupt reduction in C L and C D for α > 3.00 deg, which was delayed and reduced by the use of a limiter or eliminated with a thin-layer viscous term approximation.…”

Section: B Constant Angle-of-attack Grid Convergence Studymentioning

confidence: 44%

“…The SA results from [6] showed an abrupt reduction in C L and C D for α > 3.00 deg, which was delayed and reduced by the use of a limiter or eliminated with a thin-layer viscous term approximation. This early stall was correlated to a massive separation bubble generated at the root of the wing initiated at a forward shock location [6].…”

Section: B Constant Angle-of-attack Grid Convergence Studymentioning

confidence: 99%

“…FUN3D has been used in previous workshop studies, including DPW-IV [5] and DPW-V [6]. The prior FUN3D workshop studies focused on the use of the standard SpalartAllmaras (SA) turbulence model.…”

mentioning

confidence: 99%

“…However, results from the DPW-V buffet study produced a larger variation than the design case primarily due to the large differences in the predicted side-of-body separation. Park et al [6] summarize the DPW-IV methods and size of the simulated side-of-body separations. They also studied the impact of modeling differences and grid effects on separation extent in the context of DPW-V. A large wing-root separation bubble was not observed in the wind-tunnel tests.…”

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confidence: 99%

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Sixth Drag Prediction Workshop Results Using FUN3D with k-kL-MEAH2015 Turbulence Model

Abdol-Hamid

Carlson

Rumsey

et al. 2018

Journal of Aircraft

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The Common Research Model wing/body configuration is investigated with the k-kL-MEAH2015 turbulence model implemented in FUN3D. This includes results presented at the Sixth Drag Prediction Workshop and additional results generated after the workshop with a nonlinear quadratic constitutive relation variant of the same turbulence model. The workshop-provided grids are used, and a uniform grid refinement study is performed at the design condition. A large variation between results with and without a reconstruction limiter is exhibited on "medium" grid sizes, indicating that the medium grid size is too coarse for drawing conclusions in comparison with experiment. This variation is reduced with grid refinement. At a fixed angle of attack near design conditions, the quadratic constitutive relation variant yielded decreased lift and drag compared with the linear eddy-viscosity model by an amount that was approximately constant with grid refinement. The k-kL-MEAH2015 turbulence model produced wing-root junction flow behavior consistent with wind-tunnel observations.

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Section: B Constant Angle-of-attack Grid Convergence Studymentioning

confidence: 44%

Section: B Constant Angle-of-attack Grid Convergence Studymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Sixth Drag Prediction Workshop Results Using FUN3D with k-kL-MEAH2015 Turbulence Model

Abdol-Hamid

Carlson

Rumsey

et al. 2018

Journal of Aircraft

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“…Studies show that adequate grid resolution is required to capture the full range of turbulence structures in models ranging from simple eddy-viscosity formulations to full LES [Large Eddy Simulation] and DNS [Direct Numerical Simulation] simulations. 8 Park et al 16 summarized the impact of grid resolution on the side-of-body separation bubble seen in simulations of the Common Research Model. Without adequate grid refinement, the side-of-body modeling issue is masked by discretization error, i.e., coarse wing body junction grids tended to suppress the prediction of an enlarged separation bubble.…”

Section: Introductionmentioning

confidence: 99%

Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Towards CFD 2030

Park

Krakos

Michal

et al. 2016

46th AIAA Fluid Dynamics Conference

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Unstructured grid adaptation is a powerful tool to control Computational Fluid Dynamics (CFD) discretization error. It has enabled key increases in the accuracy, automation, and capacity of some fluid simulation applications. Slotnick et al. provide a number of case studies in the CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences to illustrate the current state of CFD capability and capacity. The study authors forecast the potential impact of emerging High Performance Computing (HPC) environments forecast in the year 2030 and identify that mesh generation and adaptivity will continue to be significant bottlenecks in the CFD workflow. These bottlenecks may persist because very little government investment has been targeted in these areas. To motivate investment, the impacts of improved grid adaptation technologies are identified. The CFD Vision 2030 Study roadmap and anticipated capabilities in complementary disciplines are quoted to provide context for the progress made in grid adaptation in the past fifteen years, current status, and a forecast for the next fifteen years with recommended investments. These investments are specific to mesh adaptation and impact other aspects of the CFD process. Finally, a strategy is identified to di↵use grid adaptation technology into production CFD work flows.

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A high‐order element based adaptive mesh refinement strategy for three‐dimensional unstructured grid

Gou

Yuan

Chen

2017

Numerical Methods in Fluids

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Summary Adaptive mesh refinement (AMR) shows attractive properties in automatically refining the flow region of interest, and with AMR, better prediction can be obtained with much less labor work and cost compared to manually remeshing or the global mesh refinement. Cartesian AMR is well established; however, AMR on hybrid unstructured mesh, which is heavily used in the high‐Reynolds number flow simulation, is less matured and existing methods may result in degraded mesh quality, which mostly happens in the boundary layer or near the sharp geometric features. User intervention or additional constraints, such as freezing all boundary layer elements or refining the whole boundary layer, are required to assist the refinement process. In this work, a novel AMR strategy is developed to handle existing difficulties. In the new method, high‐order unstructured elements are first generated based on the baseline mesh; then the refinement is conducted in the parametric space; at last, the mesh suitable for the solver is output. Generating refined elements in the parametric space with high‐order elements is the key of this method and this helps to guarantee both the accuracy and robustness. With the current method, 3‐dimensional hybrid unstructured mesh of huge size and complex geometry can be automatically refined, without user intervention nor additional constraints. With test cases including the 2‐dimensional airfoil and 3‐dimensional full aircraft, the current AMR method proves to be accurate, simple, and robust.

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CFL3D, FUN3D, and NSU3D Contributions to the Fifth Drag Prediction Workshop

Cited by 33 publications

References 62 publications

Sixth Drag Prediction Workshop Results Using FUN3D with k-kL-MEAH2015 Turbulence Model

Sixth Drag Prediction Workshop Results Using FUN3D with k-kL-MEAH2015 Turbulence Model

Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Towards CFD 2030

A high‐order element based adaptive mesh refinement strategy for three‐dimensional unstructured grid

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