Machine Learnt Synthetic Turbulence for LES Inflow Conditions

Abstract

LES computations have limited applications in turbomachinery predictions because of the formidable amount of resources they require. Due to the exponential increase of requirements with Reynolds number, LES is usually limited to elements with moderate flow velocities and to investigate flows characterized by multiple length and time scales that overlaps. It is the case of combustion, aeroacoustics, unstable range of operations such as stalled conditions. In all these cases LES can provide unique insights on thermo-fluid-dynamics. The main drawback is that LES is not only very expensive, but also extremely sensitive to inflow conditions. A number of studies pointed out that slight differences in LES inflow conditions can result in a very different flow development and therefore different prediction of performance, noise and so on. It is so sensitive that comparison of computations with different inflow conditions and same arrangement for other parameters result in completely diverging results. There are two major solutions to this problem. First, is to use a cyclic inflow channel to generate a fully turbulent profile to feed to the main simulation. Second, the use of a synthetic turbulence model to generate analytically an unsteady turbulent profile. The drawback of the first approach is the fact that a fully developed inflow can be un-realistic for most turbomachinery applications. On the other hand, the second approach was proved not to be able to correctly reproduce the statistics of turbulence and therefore the synthetic inflows do not provide a real solution to the problem. In this paper we discuss a novel method to generate LES inflow conditions, based on adversarial machine learning. In particular we trained a generative adversarial network (GAN) to reproduce the inflow conditions of a channel flow. In this way, the generator of the GAN is trained to correctly reproduce an unsteady turbulent profile, while the discriminator is used during the training phase as an adversarial agent for the generator. During the validation of the method both the discriminator and the generator will be used to validate the proposed methodology.