The current generation of axial turbomachines is the culmination of decades of experience,
and detailed understanding of the underlying flow physics has been a key factor for …

This review paper surveys some of the progress made to date in the use of machine learning
(ML) for turbulence and heat transfer modeling. We start by identifying the challenges that …

This work aims at making the prediction process of neural network-based turbulence models
more transparent. Due to its black-box ingredients, the model's predictions cannot be …

Fast prediction tools for turbine cooling performance have been demanded by industry for
decades to support the iterative design process and the comprehensive response analysis …

Large-eddy simulations (LES) can provide accurate predictions of wind loads on buildings,
but their high computational cost, and the need to explore all wind directions with a 10° …

A cylindrical and inclined jet in crossflow is studied under two distinct velocity ratios,, using
highly resolved large eddy simulations. First, an investigation of turbulent scalar mixing …

Film cooling has shown great potential in protecting hot section of high-pressure turbine
from melting down. A counter-rotating vortex pair (CVP) is produced downstream of the …

Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately
predict separation induced transition for low pressure turbines. Therefore, in this paper, a …

Over the past two decades high-fidelity simulations have become feasible for most main gas
path turbomachinery components. This paper introduces the key challenges of simulating …

This study examines the predictive capability of deep learning method for adiabatic film
cooling effectiveness distribution with variable operating conditions and geometric layouts. A …