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AACE-AFM-378: Determining Closure Coefficients of Turbulence Models Using Machine Learning

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  • Full or part time
    Dr A Da Ronch
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The solution of the Reynolds-averaged Navier-Stokes (RANS) equations employs an appropriate set of equations for the turbulence modelling. The closure coefficients of the turbulence model have been calibrated using empiricism and arguments of dimensional analysis. Those coefficients are considered ‘universal’, but there is no guarantee this is correct for test cases other than those used in the calibration process. This work aims at revisiting the ‘universality’ of closure coefficients using machine learning, adaptive design of experiments (ADOE) and a high-performance computing facility.

The technical objectives of this research activity are to: a) assess the impact of the closure coefficients of the turbulence model (e.g. Spalart-Allmaras) on the prediction of the aerodynamic characteristics; b) calibrate these coefficients by minimizing the deviations of the numerical results from a set of experimental data; and c) verify the independence of the optimized coefficients from spatial discretization errors and specific solver implementations.

Our approach entails the exploration of the design space using an efficient and accurate ADOE method. The ADOE algorithm consists of an iterative, machine-learning based technique capable of: a) identifying the regions of the design space that are characterized by stronger nonlinearities; b) adaptively selecting the location of the design points in order to maximize the information content that can be extracted by the simulations performed at these locations; and c) identifying the best response surface model (RSM) on the basis of the obtained results. The identified RSM is then employed to: a) calibrate these coefficients on the basis of the available experimental data; and b) perform a sensitivity analysis and identify the closure coefficients that have the largest impact on the model outputs.

The successful candidate will possess an excellent background in mechanical/aerospace engineering, with a strong experience in programming and knowledge in aerodynamics and flight mechanics. Only EU/UK students will be considered.

The University of Southampton is in the top one per cent of world universities and one of the UK’s top 15 research-intensive universities. The University has an international reputation for research, teaching and enterprise activities. The successful candidate will join one of the UK’s most dynamic centres for engineering and environmental research and education, rated number one in the UK for research power in REF 2014.

If you wish to discuss any details of the project informally, please contact Dr Andrea Da Ronch (email: [Email Address Removed]).

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

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