Sensitivity and calibration of turbulence model in the presence of epistemic uncertainties

• Published in: CEAS aeronautical journal 2020, Vol.11 (1), p.33-47
• Main Authors: Da Ronch, Andrea, Panzeri, Marco, Drofelnik, Jernej, d’Ippolito, Roberto
• Format: Article
• Language: English
• Subjects: Aerodynamics; Aerospace Technology and Astronautics; Airfoils; Calibration; Coefficients; Computational fluid dynamics; Design of experiments; Dimensional analysis; Engineering; Machine learning; Model accuracy; Original Paper; Solvers; Spalart-Allmaras turbulence model; Transonic flow; Turbulence models
• ISSN: 1869-5582; 1869-5590
• DOI: 10.1007/s13272-019-00389-y

The solution of Reynolds-averaged Navier–Stokes equations employs an appropriate set of equations for the turbulence modelling. The closure coefficients of the turbulence model were calibrated using empiricism and arguments of dimensional analysis. These coefficients are considered universal, but there is no guarantee this property applies to test cases other than those used in the calibration process. This work aims at revisiting the calibration of the closure coefficients of the original Spalart–Allmaras turbulence model using machine learning, adaptive design of experiments and accessing a high-performance computing facility. The automated calibration procedure is carried out once for a transonic, wall-bounded flow around the RAE 2822 aerofoil. It was found that: (a) an optimal set of closure coefficients exists that minimises numerical deviations from experimental data; (b) the improved prediction accuracy of the calibrated turbulence model is consistent across different flow solvers; and (c) the calibrated turbulence model outperforms slightly the standard model in analysing complex flow features around additional test cases (ONERA M6 wing, axisymmetric transonic bump, forced sinusoidal motion of NACA 0012 aerofoil). A by-product of this study is a fully calibrated turbulence model that leverages on current state-of-the-art computational techniques, overcoming inherent limitations of the manual fine-tuning process.