journal article·2023-10-13Open access

Testing a Generalized Two-Equation Turbulence Model for Computational Aerodynamics of a Mid-Range Aircraft

Viola Rossano, Giuliano De Stefano

Applied Sciences· Volume 13· Issue 20

Abstract

The generalized k-ω formulation provides a relatively new flexible eddy-viscosity Reynolds-averaged Navier–Stokes modeling approach to turbulent flow simulation, where free coefficients allow for fine-tuning and optimal adjusting of the turbulence closure procedure. The present study addressed the calibration of this versatile model for the aerodynamic design of an innovative mid-range commercial airplane by carrying out a series of simulations for varying model coefficients. Comparing the different solutions with each other, as well as with reference experimental and higher-fidelity numerical data, the performance of the generalized procedure in predicting the aerodynamic loading on the aircraft model was systematically examined. While drawing particular attention to the high-lift regime, the set of model parameters giving the best results was practically determined.
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Research context

OpenAlex topics: Computational Fluid Dynamics and Aerodynamics · Fluid Dynamics and Turbulent Flows · Turbomachinery Performance and Optimization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Future Aircraft Technologies, Leonardo SpA, Corso Castelfidardo 22, 10138 Torino, Italy

Engineering Department, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy

References (33)

Show source-provided references
1. Long-term scenarios for aviation: Demand and emissions of CO2 and NOx10.1016/S0301-4215(98)00021-4 ↗
2. Small air transport aircraft entry requirements evoked by FlightPath 205010.1108/AEAT-02-2015-0065 ↗
3. Slotnick, J., Khodadoust, A., Alonso, J., Darmofal, D., Gropp, W., Lurie, E., and Mavriplis, D. (2014). CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences, NASA Langley Research Center. Technical Report NASA/CR-2014-218178.
4. Menter, F.R., Hüppe, A., Matyushenko, A., and Kolmogorov, D. (2021). An overview of hybrid RANS-LES models developed for industrial CFD. Appl. Sci., 11.10.3390/app11062459 ↗
5. On the role and challenges of CFD in the aerospace industry10.1017/aer.2015.10 ↗
6. A one-equation turbulence model for aerodynamic flows
7. Two-equation eddy-viscosity turbulence models for engineering applications10.2514/3.12149 ↗
8. On the unsteady Reynolds-averaged Navier-Stokes capability of simulating turbulent boundary layers under unsteady adverse pressure gradients10.1063/5.0049509 ↗
9. Large eddy simulation of aircraft at affordable cost: A milestone in computational fluid dynamics10.1017/flo.2021.17 ↗
10. On the possibility (or lack thereof) of agreement between experiment and computation of flows over wings at moderate Reynolds number10.1098/rsfs.2016.0076 ↗
11. Menter, F.R., Matyushenko, A., and Lechner, R. (2018). Notes on Numerical Fluid Mechanics and Multidisciplinary Design, Springer.
12. De Stefano, G., Natale, N., Reina, G.P., and Piccolo, A. (2020). Computational evaluation of aerodynamic loading on retractable landing-gears. Aerospace, 7.10.3390/aerospace7060068 ↗
13. Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning10.1063/5.0149547 ↗
14. A unified method of data assimilation and turbulence modeling for separated flows at high Reynolds numbers10.1063/5.0136420 ↗
15. Amstad, P., So, K.K., and Fischer, M. (2022, January 6–10). Machine-learning assisted optimization of generalized k-omega (GEKO) turbulence model parameters for turbocharger radial compressor. Proceedings of the ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition, Rotterdam, The Netherlands.10.1115/GT2022-78306 ↗
16. Formulation of the k-ω turbulence model revisited10.2514/1.36541 ↗
17. Ge, X., Vasilyev, O.V., De Stefano, G., and Hussaini, M. (2018, January 8–12). Wavelet-based adaptive unsteady Reynolds-averaged Navier-Stokes computations of wall-bounded internal and external compressible turbulent flows. Proceedings of the 56th AIAA Aerospace Sciences Meeting, Kissimmee, FL, USA. AIAA Paper 2018-0545.10.2514/6.2018-0545 ↗
18. Strokach, E., Zhukov, V., Borovik, I., Sternin, A., and Haidn, O.J. (2021). Simulation of a GOx-GCH4 rocket combustor and the effect of the GEKO turbulence model coefficients. Aerospace, 8.10.3390/aerospace8110341 ↗
19. Szudarek, M., Piechna, A., Prusiński, P., and Rudniak, L. (2022). CFD study of high-speed train in crosswinds for large yaw angles with RANS-based turbulence models including GEKO tuning approach. Energies, 15.10.3390/en15186549 ↗
20. Predicting aerodynamic performance of Savonius wind turbine: An application of generalized k-ω turbulence model10.1016/j.oceaneng.2023.115690 ↗
21. Menter, F.R., Lechner, R., and Matyushenko, A. (2019). Best Practice: Generalized k-ω (GEKO) Two-Equation Turbulence Modeling in Ansys CFD, Ansys. Technical Report Ansys.
22. Detached-eddy simulation of a wide-body commercial aircraft in high-lift configuration10.2514/1.C033164 ↗
23. Subgrid-scale stress modelling based on the square of the velocity gradient tensor10.1023/A:1009995426001 ↗
24. Two-equation turbulent viscosity model for simulation of transitional flows: An efficient artificial neural network strategy10.1063/5.0104243 ↗
25. Natale, N., Salomone, T., De Stefano, G., and Piccolo, A. (2020). Computational evaluation of control surfaces aerodynamics for a mid-range commercial aircraft. Aerospace, 7.10.3390/aerospace7100139 ↗
26. Aerodynamic and aeroacoustic investigation of an innovative regional turboprop scaled model: Numerical simulations and experiments10.1007/s13272-020-00437-y ↗
27. AmgX: A Library for GPU accelerated algebraic multigrid and preconditioned iterative methods10.1137/140980260 ↗
28. High resolution NVD differencing scheme for arbitrarily unstructured meshes10.1002/(SICI)1097-0363(19990930)31:2<431::AID-FLD884>3.0.CO;2-T ↗
29. Woeber, C.D., Gantt, E.J., and Wyman, N.J. (2017, January 9–13). Mesh generation for the NASA high lift common research model (HL-CRM). Proceedings of the 55th AIAA Aerospace Sciences Meeting, Grapewine, TX, USA.10.2514/6.2017-0363 ↗
30. Yamamoto, K., Tanaka, K., and Murayama, M. (2012, January 25–28). Effect of a nonlinear constitutive relation for turbulence modeling on predicting flow separation at wing-body juncture of transonic commercial aircraft. Proceedings of the 30th AIAA Applied Aerodynamics Conference, New Orleans, LA, USA.10.2514/6.2012-2895 ↗
31. Assessment of neural network augmented Reynolds averaged Navier Stokes turbulence model in extrapolation modes10.1063/5.0146456 ↗
32. Wavelet-based adaptive unsteady Reynolds-averaged Navier–Stokes simulations of wall-bounded compressible turbulent flows10.2514/1.J058428 ↗
33. Wavelet-based adaptive large-eddy simulation of supersonic channel flow10.1017/jfm.2020.536 ↗