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Sensitivity and calibration of turbulence model in presence of epistemic uncertainties

Sensitivity and calibration of turbulence model in presence of epistemic uncertainties
Sensitivity and calibration of turbulence model in presence of epistemic uncertainties
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.
uncertainty quantification, Calibration, Turbulence model closure coefficients, machine learning, Sobol indices, Adaptive design of experiments
1-15
Da Ronch, Andrea
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Panzeri, Marco
e253f5de-c3e8-4777-a790-b82bdee6daba
Drofelnik, Jernej
e785f695-61ef-4afc-bf0a-9dc7966f5516
d'Ippolito, Roberto
e7ec19e2-50be-4b5d-82f6-a919fcd83081
Da Ronch, Andrea
a2f36b97-b881-44e9-8a78-dd76fdf82f1a
Panzeri, Marco
e253f5de-c3e8-4777-a790-b82bdee6daba
Drofelnik, Jernej
e785f695-61ef-4afc-bf0a-9dc7966f5516
d'Ippolito, Roberto
e7ec19e2-50be-4b5d-82f6-a919fcd83081

Da Ronch, Andrea, Panzeri, Marco, Drofelnik, Jernej and d'Ippolito, Roberto (2019) Sensitivity and calibration of turbulence model in presence of epistemic uncertainties. CEAS Aeronautical Journal, 1-15. (doi:10.1007/s13272-019-00389-y).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 12 March 2019
e-pub ahead of print date: 10 April 2019
Keywords: uncertainty quantification, Calibration, Turbulence model closure coefficients, machine learning, Sobol indices, Adaptive design of experiments

Identifiers

Local EPrints ID: 429429
URI: https://eprints.soton.ac.uk/id/eprint/429429
PURE UUID: b7dcd930-acab-4342-a763-b87a460d8d6e

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Date deposited: 27 Mar 2019 17:30
Last modified: 19 Jul 2019 16:37

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