The significant growth of artificial intelligence (AI) methods in machine learning (ML) and
deep learning (DL) has opened opportunities for fluid dynamics and its applications in …

A combined convolutional autoencoder–recurrent neural network machine learning model is
presented to directly analyze and forecast the dynamics and low-order statistics of the local …

Symbolic regression techniques are constantly gaining ground in materials informatics as
the machine learning counterpart capable of providing analytical equations exclusively …

Transition modeling represents one of the key challenges in computational fluid dynamics.
While numerical efforts were traditionally devoted to either improving Reynolds-averaged …

In present study, heat transfer augmentation and pressure loss in a double pipe concentric
type heat exchanger with a coiled-wire turbulator inserted in it, are discussed in terms of …

Turbulent mixed convection flow and heat transfer properties in a driven cavity with two
circular cylinders arranged one above another are analyzed numerically with the …

We adopt the stretched spiral vortex sub-grid model for large-eddy simulation (LES) of
turbulent convection at extreme Rayleigh numbers. We simulate Rayleigh–Bénard …

Developing sustainable water transportation technology is essential for solving water
shortage problems. In this study, we proposed a sustainable high-pressure light-driven …

This article presents the application of machine learning (ML) algorithms in modeling the
heat transfer correlations (eg, Nusselt number and friction factor) for a heat exchanger with …

We perform two-dimensional direct numerical simulations to study the effect of porous media
on global transport properties and flow structures in Rayleigh–Bénard (RB) convection at …