AI for tribology: Present and future
With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI)
can assist researchers in swiftly extracting valuable patterns, trends, and associations from …
can assist researchers in swiftly extracting valuable patterns, trends, and associations from …
Physics-Informed Machine Learning—An Emerging Trend in Tribology
Physics-informed machine learning (PIML) has gained significant attention in various
scientific fields and is now emerging in the area of tribology. By integrating physics-based …
scientific fields and is now emerging in the area of tribology. By integrating physics-based …
Laboratory investigation of GO-SA-MWCNTs ternary hybrid nanoparticles efficacy on dynamic viscosity and wear properties of oil (5W30) and modeling based on …
M Sepehrnia, S Davoodabadi Farahani… - Scientific Reports, 2023 - nature.com
In the present study, the properties of ternary hybrid nanofluid (THNF) of oil (5W30)-
Graphene Oxide (GO)-Silica Aerogel (SA)-multi-walled carbon nanotubes (MWCNTs) in …
Graphene Oxide (GO)-Silica Aerogel (SA)-multi-walled carbon nanotubes (MWCNTs) in …
Data driven discovery of MOFs for hydrogen gas adsorption
Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-
effective storage materials is a challenge to its widespread use. Metal–organic frameworks …
effective storage materials is a challenge to its widespread use. Metal–organic frameworks …
Tribological properties assessment of metallic glasses through a genetic algorithm-optimized machine learning model
In this work, a machine learning (ML) model, optimized by genetic algorithm, was
established to predict and characterize the tribological behavior of CuZr metallic glasses …
established to predict and characterize the tribological behavior of CuZr metallic glasses …
Current and future trends in tribological research
The paper provides a commentary on the theme of “Current and Future Trends in
Tribological Research: Fundamentals and Applications”, which is a special feature issue …
Tribological Research: Fundamentals and Applications”, which is a special feature issue …
Machine learning for film thickness prediction in elastohydrodynamic lubricated elliptical contacts
This study extends the use of Machine Learning (ML) approaches for lubricant film thickness
predictions to the general case of elliptical elastohydrodynamic (EHD) contacts, by …
predictions to the general case of elliptical elastohydrodynamic (EHD) contacts, by …
A solution for finite journal bearings by using physics-informed neural networks with both soft and hard constrains
Y Xi, J Deng, Y Li - Industrial Lubrication and Tribology, 2023 - emerald.com
Purpose The purpose of this study is to solve the Reynolds equation for finite journal
bearings by using the physics-informed neural networks (PINNs) method. As a meshless …
bearings by using the physics-informed neural networks (PINNs) method. As a meshless …
[HTML][HTML] Data-driven design of brake pad composites for high-speed trains
L Wu, P Zhang, B Xu, J Liu, H Yin, L Zhang… - Journal of Materials …, 2023 - Elsevier
Brake pads play a vital role in controlling the operation of high-speed trains with over 300
km/h. Currently the copper-based composites produced by powder metallurgy techniques …
km/h. Currently the copper-based composites produced by powder metallurgy techniques …
Evaluation of Sampling Algorithms Used for Bayesian Uncertainty Quantification of Molecular Dynamics Force Fields
New Bayesian parameter estimation methods have the capability to enable more physically
realistic and reliable molecular dynamics (MD) simulations by providing accurate estimates …
realistic and reliable molecular dynamics (MD) simulations by providing accurate estimates …