Computational Fluid Dynamics is one of the most relied upon tools in the design and
analysis of components in turbomachines. From the propulsion fan at the inlet, through the …

Membrane-based separation processes has been recently of significant global interest
compared to other conventional separation approaches due to possessing undeniable …

A probabilistic machine learning model is introduced to augment the k− ω SST turbulence
model in order to improve the modelling of separated flows and the generalisability of learnt …

This study introduces a new physics-informed neural networks (PINN)-based prediction
method to determine the temperature pattern of fluid and fins when flow passes over plate …

A LES-informed data-driven approach for improved predictions of the turbulent heat flux
vector has been sought for film and effusion cooling flow applications. Random forest and …

Film and effusion cooling flows contain complex flow that classical Reynolds-averaged
Navier–Stokes (RANS) models struggle to capture. A tensor-basis neural network is …

This study proposes and validates a novel machine-learned (ML) augmented linear
Reynolds averaged Navier Stokes (RANS) model, and the applicability of model assessed …

Indoor environmental quality is crucial for human health and comfort, necessitating precise
and efficient computational methods to optimise indoor climate parameters. Recent …

Computational fluid dynamic (CFD) models and workflows are often developed in an ad hoc
manner, leading to a limited understanding of interaction effects and model behavior under …

The continuous development of machine learning algorithms has stimulated the
technological revolution on turbulence modeling for Reynolds-averaged Navier–Stokes …