Machine Learning methods have changed the approach to statistical analysis in many fields. In this project we propose to apply and fit the latest technologies in adversarial machine learning to the solution of turbomachinery flows, and in particular to three challenges that are still opened in this field: generation of syntethic turbulence for LES inflow conditions, turbulence modelling in rotating passages, particle cloud tracking.
To this aim an open-source platform will be created, with tools for Exploratory Data Analysis, to reduce and select the significant features in each problem.
Then, adversarial machine learning and variational auto encoders will be exploited to generate a synthetic turbulence for LES inflow conditions. In this way we expect to be able to train an artificial neural network to correctly reproduce the inherent statistics of turbulence. Another work package will be dedicated to correct RANS modelling of rotating passages using a machine-learnt model able to account for the effects of rotation and correct a standard model using LES or DNS data.
The third problem to study will be the use AI to predict the statistical behaviour of a cloud of particles dispersed inside a turbomachinery flow.
All the work will be carried out using our in-house CFD codes that will be interfaced with a Python platform of machine learning techniques through the C++ APIs for Python bridging.
