Ricerc@Sapienza

Machine Learning (ML) algorithms have become popular in many fields, including applications related to turbomachinery and heat transfer. The key properties of ML are the capability to partially tackle the problem of slowing down of Moore’s law and to dig-out correlations within large datasets like those available on turbomachinery. Data come from experiments and simulations with different degree of accuracy, according to the test-rig or the CFD approach.

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.