On the calibration of the intermittency transition turbulence model for wind turbine airfoil by machine learning algorithm

Abd Bari, Muhammad Anas, Da Ronch, Andrea, Panzeri, Marco and Drofelnik, Jernej (2018) On the calibration of the intermittency transition turbulence model for wind turbine airfoil by machine learning algorithm. In Proceedings of the 31st Congress of the International Council of the Aeronautical Sciences. International Council of the Aeronautical Sciences. 10 pp .

Abstract

Fully turbulent wind turbine computational fluid dynamics simulations have been shown to overpredict the aerodynamic performances. To ensure a correct prediction, modelling of flow transition from laminar to turbulent over the blade is becoming necessary. While several transitional turbulence models exist, the one equation intermittency γ model coupled with the kω SST turbulence model offers a simple framework against wide range of generic industrial application. However, the model is yet to be calibrated for certain cases especially for external aerodynamic flows at low turbulent intensity. In this paper, the epistemic uncertainty of several model constants related to the transitional triggering function is investigated using machine learning. The procedure is demonstrated for the S809 airfoil. It was found that:(a) some coefficients have a large impact on the results at high angles of attack, causing fluctuation of the results and (b) the calibration of the turbulence model is influenced by several factors, for instance, the solver limiters.

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Contributors

Author: Muhammad Anas Abd Bari

Author: Andrea Da Ronch

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