The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.

Computational Fluid Dynamics (CFD) has numerous applications in the field of energy research, in modelling the basic physics of combustion, multiphase flow and heat transfer; and in the simulation of mechanical devices such as turbines, wind wave and tidal devices, and other devices for energy generation. With the constant increase in available computing power, the fidelity and accuracy of CFD simulations have constantly improved, and the technique is now an integral part of research and development. In the past few years, the development of multiscale methods has emerged as a topic of intensive research. The variable scales may be associated with scales of turbulence, or other physical processes which operate across a range of different scales, and often lead to spatial and temporal scales crossing the boundaries of continuum and molecular mechanics. In this paper, we present a short review of multiscale CFD frameworks with potential applications to energy problems.

This paper concerns the behaviour of a copper–argon nanofluid confined in a nanochannel. Using molecular dynamics simulations, it is shown that in narrower channels, the thermal conductivity increases by approximately 20 % compared to macroscopic cases. The results suggest that the structured liquid layers surrounding the solid particles occupy a greater percentage of the system in narrower channels, thus enhancing the thermal conductivity of the nanofluid.

The aim of this research is to identify possible mechanisms that govern heat transport at a solid–liquid interface using molecular dynamics. The study reveals that, unlike its bulk analogue, a liquid in a nanochannel sustains long-lived collective vibrations, phonons, which propagate over longer timescales and distances. The larger phonon mean free path in nanochannels is attributed to the greater structural order of the liquid atoms and to the larger liquid relaxation time—the time in which the liquid structure remains unchanged and solid-like. For channels of height less than \(10\sigma\), long-range phonons are the dominant means of heat transfer in the directions parallel to the channel walls. The present findings are in agreement with experiments, which have observed significantly increased liquid relaxation times for the same range of channel heights. Finally, it is argued that confinement introduces additional transverse modes of vibration that also contribute to the thermal conductivity enhancement.

Despite the continuous and remarkable development of experimental techniques for the investigation of microstructures and the growth of nuclei during the solidification of metals, there are still unknown territories around this topic. The solidification in nanoscale can be effectively investigated by means of molecular dynamics (MD) simulations which can provide a deep insight into the mechanisms of the formation of nuclei and the induced crystal structures. In this study, MD simulations were performed to investigate the solidification of pure Aluminium and the effects of the cooling rate on the final properties of the solidified material. A large number of Aluminium atoms were used in order to investigate the grain growth over time and the formation of stacking faults during solidification. The number of face-centred cubic (FCC), hexagonal close-packed (HCP) and body-centred cubic (BCC) was recorded during the evolution of the process to illustrate the nanoscale mechanisms initiating solidification. The current investigation also focuses on the exothermic nature of the solidification process which has been effectively captured by means of MD simulations using 3 dimensional representations of the kinetic energy across the simulation domain.

This paper investigates the effect of surface roughness on fluid viscosity using molecular dynamics simulations. The three-dimensional model consists of liquid argon flowing between two solid walls whose surface roughness was modeled using fractal theory. In tandem with previously published experimental work, our results show that, while the viscosity in smooth channels remains constant across the channel width, in the presence of surface roughness it increases close to the walls. The increase of the boundary viscosity is further accentuated by an increase in the depth of surface roughness. We attribute this behavior to the increased momentum transfer at the boundary, a result of the irregular distribution of fluid particles near rough surfaces. Furthermore, although the viscosity in smooth channels has previously been shown to be independent of the strength of the solid-liquid interaction, here we show that in the presence of surface roughness, the boundary viscosity increases with the solid's wettability. The paper concludes with an analytical description of the viscosity as a function of the distance from the channel walls, the walls’ surface roughness, and the solid's wetting properties. The relation can potentially be used to adjust the fluid dynamics equations for a more accurate description of microfluidic systems.