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C L Rumsey

Publications and source records attributed to C L Rumsey.

12 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

NASA Symposium on Turbulence Modeling: Roadblocks, and the Potential for Machine Learning

A three-day symposium sponsored by NASA was held in July 2022 in Suffolk, Virginia on the subject of Turbulence Modeling: Roadblocks, and the Potential for Machine Learning. This meeting brought together over 80 experts from academia, government, and industry to discuss critical issues for Reynolds-averaged Navier-Stokes turbulence and transition models, as well as to evaluate the results from a collaborative testing challenge based on data-driven methods and machine learning technology. This report puts this symposium in context with an earlier similar meeting and summarizes many of the questions, discussions, and conclusions that arose from it. Next steps are suggested.

machine learning↗
NASA NTRS2020

Reynolds-Averaged Navier-Stokes Computations of the NASA Juncture Flow Model Using FUN3D and OVERFLOW

Two Reynolds-averaged Navier-Stokes codes, FUN3D and OVERFLOW, are used to assess the capability of Spalart-Allmaras-based turbulence models to predict the flow over the NASA Juncture Flow model. Both free-air and in-tunnel simulations are performed. While the tunnel walls have some influence, it is found to be relatively minor in the juncture region of interest. Results from the two codes are found to be consistent with each other in attached flow regions, but results in the area of separation still show grid and code sensitivity, even on grids as large as 400 million unknowns. Nonetheless, it is possible to draw conclusions regarding the model capabilities. Without a quadratic constitutive relation, the linear model predicts a separation size that is too large. The inclusion of a nonlinear quadratic constitutive relation improves results significantly, but separation still occurs too far upstream. Prediction of turbulent normal stress differences play a key role in this flow. Although the nonlinear model makes better predictions in this regard, they could still be improved, particularly in the streamwise normal component near the wall.

C L Rumsey↗
NASA NTRS2010

TPSAS-NF1676L-10454-DND

We plan to perform the following sets of computations on unadapted (fixed) grids: 1) Structured RANS set 1 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), 2) Structured RANS set 2 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 3) Structured RANS set 3 (time permitting) (Code: CFL3D, Grid: Str-OnetoOne-B-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 4) Unstructured RANS set 1 (Code: FUN3D, Grid: Unst-Mixed-FromTet-Nodecentered-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), and 5) Unstructured RANS set 2 (time permitting) (Code: FUN3D, Grid: Unst-Hex-FromOnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras),. Optional case 3 is not being computed. CFL3D is a structured upwind-biased cell-centered RANS code,1 and FUN3D is an unstructured upwind-biased node-centered RANS code

C L Rumsey↗
NASA NTRSDate not supplied

CFD Comparisons with Updated NASA Juncture Flow Data

The purpose of the NASA Juncture Flow experiment is to acquire high-quality flowfield details deep in the corner of a wing-body junction specifically for the purpose of CFD validation. A second phase of testing was recently completed, which includes both laser doppler velocimetry and particle image velocimetry measurements. This paper describes the recent experiment and its results. It also makes detailed comparisons between the experimental data and a new version of a widely-used CFD turbulence model for Reynolds-averaged Navier-Stokes, which was recently developed to improve separated corner flow predictions. The CFD results generally produce very good qualitative agreement with the experiment, although they are less accurate inside of the separation region, as expected.

Experimental Data↗
NASA NTRSDate not supplied

In Search of Data-Driven Improvements to RANS Models Applied to Separated Flows

The goal of this work is to improve the capability of Reynolds-averaged Navier-Stokes turbulence models for separated flows using data-driven enhancements. The resulting model should be “universal” in the sense that it can be used by anyone and applied to as many flows as possible without concern for unusual or detrimental behavior. At worst, the data-driven corrections should not degrade the accuracy of the baseline model (in this case the Spalart-Allmaras one-equation model), while preserving the Galilean invariance and similar theoretical qualities of the original model. In the literature, most current data-driven improvements to turbulence models are only applicable to very similar types of cases as those used to train the model for a specific class of flows. In this work, the impact of using a wide array of cases in the machine-learning training is described. Unwanted behaviors from trained neural networks are examined, and possible mitigation strategies are proposed. However, to date, consistent and broadly applicable data-driven improvements for separated flows have not been achieved.

turbulence modeling↗
NASA NTRSDate not supplied

Assessment of Numerical and Modeling Errors of RANS based Transition Models for Low-Reynolds Numbers 2-D Flows

In this paper we report the outcome of selected workshops organized as part of the NATO Applied Vehicle Technology (AVT)-313 activity Incompressible Laminar-to-Turbulent Flow Transition Study that focused on assessing the numerical and modeling accuracy of the γ−Reθ and γ transition models coupled to the k−ω Shear-Stress Transport (SST) two-equation eddy-viscosity model. Three different test cases involving nominally 2D flow configurations were selected: flow over a flat plate with two different levels of turbulence intensity at the inlet; flow around the Eppler 387 foil at a Reynolds number of 3×10^5 and angles of attack of 1 deg. and 7 deg. flow around the NACA 0015 foil at a Reynolds number of 1.8×10^5 and angles of attack of 5 deg. and10 deg. The flat plate flow conditions correspond to natural and by-pass transition, whereas the other two test cases include laminar separation bubbles that lead to separation-induced transition. For each test case, the selected quantities of interest include both integral and local flow quantities. Geometrically similar grids with a wide range of grid refinement ratios were generated for each of the test cases to allow the estimation of numerical uncertainties for all quantities of interest selected for this study. Several RANS flow solvers were used, employing common grids with the same boundary conditions and mathematical models. Therefore, it is possible to analyze the consistency of the results, i.e., to check if the intervals defined by the different numerical solutions with their respective uncertainties overlap with each other. Modeling errors can also be addressed for the selected flow quantities that have experimental data available. However, the experimental information available in these cases is not sufficient to guarantee that experiments and simulations are performed with the same settings. Nonetheless, the available experimental data is sufficient to guarantee that modeling errors are significantly reduced with the use of the transition models when compared to simulations performed using only the k−ω SST model.

CFD Modeling↗