Session: 37-01 Machine Learning and Optimization

Paper Number: 82531

82531 - Development of Machine-Learnt Turbulence Closures for Wake Mixing Predictions in Low-Pressure Turbines 

In this work, a DNS – Machine Learning (ML) framework is developed for low-pressure turbine (LPT) profiles to inform turbulence closures in Reynolds-Averaged Navier–Stokes (RANS) calculations. This is done by training the coefficients of Explicit Algebraic Reynolds Stress Models (EARSM) with shallow artificial neural networks (ANN) as a function of input flow features. DNS data are generated with the incompressible Navier–Stokes solver in Nektar++ and validated against experiments. All calculations include moving bars upstream of the profile to capture the effect of incoming wakes. The resulting formulations are then implemented in the Rolls-Royce solver HYDRA and tested a posteriori. The aim is to improve mixing predictions in LPT wakes, compared to the baseline model, Wilcox’s k-omega SST, in terms of velocity profiles, turbulent kinetic energy (TKE) production and mixing losses. LPT calculations are run at Reynolds numbers spanning from ~80k to ~300k, to cover the range of aircraft engine applications. Models for the low and high Reynolds datasets are trained separately and a method is developed to merge the two together. The resulting model is tested on an intermediate Reynolds case. This process is followed for two computational domains: one starting downstream of the profile trailing edge and one including the last portion of the profile. Finally, the developed closures are tested on the entire profile, to confirm the validity of the improvements when the additional effect of transition is included in the simulation. This work explains the methodology used to develop ML-driven closures and shows how it is possible to combine models trained on different datasets.

Presenting Author: Yuri Frey Marioni Imperial College London

Presenting Author Biography: I am at my third year as a PhD student at Imperial College London, working on how to combine high fidelity CFD and Machine Learning to improve RANS turbulence modelling. I am also a Rolls-Royce employee. My passion for aerodynamics started at University of Pisa, where I obtained my Bachelor’s and Master’s degree. After a short internship at Von Karman Institute for fluid-dynamics, I started my journey at Rolls-Royce, first as a graduate trainee and next as an aerodynamicist.

Authors:

Yuri Frey Marioni Imperial College London
Paolo Adami Rolls-Royce Deutschland
Raul Vazquez-Diaz Rolls-Royce plc
Francesco Montomoli Imperial College London
Andrea Cassinelli Imperial College London
Spencer Sherwin Imperial College London