Muhammad Ridho Alhafiz
Contact information
Thesis title
Development of Surrogate Modelling-Assisted CFD-Driven Training for Accelerating Turbulence Closure Discovery with GEP
Research overview
In turbulence closure model development, Gene-Expression Programming (GEP) with CFD-driven training has emerged as a promising technique for acquiring better turbulence models. However, its reliance on CFD simulations makes the computational cost of this method prohibitively high. Thus, developing a technique that can reduce the required CFD simulations to accelerate the training process would advance this method. To achieve this objective, this research aims to implement surrogate modelling-assisted CFD-driven training for turbulence model development with GEP, intending to extend this method for broader application in more complex scenarios where the CFD simulations are too expensive.
Supervisors
Qualifications
B.Sc. Aerospace Engineering, Bandung Institute of Technology (ITB), Indonesia (2022)
M.Sc. Aerospace Engineering, Bandung Institute of Technology (ITB), Indonesia (2023)