Open this publication in new window or tab >>Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via Orabona 4, Bari 70125, Italy; CNR Institute for Photonics and Nanotechnologies U.O.S. Bari, Physics Department M. Merlin, Via Amendola 173, I-70126, Bari, Italy.
Computational Materials Science, Sandia National Laboratories, Albuquerque, NM 87123, United States.
Department of Mechanical Engineering, Imperial College London, South Kensington Campus, Exhibition Road, London SW7 2AZ, United Kingdom.
AC2T research GmbH, Viktor-Kaplan-Straße 2/C, 2700 Wiener Neustadt, Austria; Institute for Engineering Design and Product Development, TU Wien, Lehárgasse 6 – Objekt 7, 1060 Vienna, Austria.
Department of Mechanical Engineering, Imperial College London, South Kensington Campus, Exhibition Road, London SW7 2AZ, United Kingdom.
Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via Orabona 4, Bari 70125, Italy.
Institute of Civil Engineering, Institute of Materials Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Fraunhofer IWM, MikroTribologie Centrum μTC, Wöhlerstraße 11, 79108 Freiburg, Germany.
Department of Industrial Engineering, University of Padova, Padua, Italy.
IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 55100 Lucca, Italy.
Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via Orabona 4, Bari 70125, Italy.
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; Center for Biomolecular Nanotechnologies, Istituto Italiano di Tecnologia, 73010 Arnesano, Italy.
Mines Paris - PSL University, Centre des matériaux, CNRS UMR 7633, 78000 Versailles, France.
Dept. of Materials Science and Engineering, Saarland University, 66123 Saarbrücken, Germany.
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2026 (English)In: Tribology International, ISSN 0301-679X, E-ISSN 1879-2464, Vol. 218, article id 111326Article, review/survey (Refereed) Published
Abstract [en]
Recent advances in modeling have enhanced our ability to make quantitative predictions for tribological phenomena, thereby unraveling relevant mechanisms. Algorithmic innovations, including those based on multiscale methods and machine learning, have been especially impactful, for example in overcoming long-standing bottlenecks that hinder simulations of systems with strong coupling across disparate scales. However, traditional modeling approaches, such as boundary-element techniques, have also progressed and continue to yield new insights. This article reviews developments from the past decade, examining how both new and established methods have deepened our understanding of experimental results and have furthered theoretical approaches in key tribological areas, including contact mechanics, lubrication, metal friction, and tribo-chemistry. Selected applications, such as tunable interfaces and energy harvesting, illustrate the broad influence of recent developments on fields beyond tribology itself.
Place, publisher, year, edition, pages
Elsevier Ltd, 2026
Keywords
Modeling, Tribology
National Category
Other Mechanical Engineering
Research subject
Machine Elements
Identifiers
urn:nbn:se:ltu:diva-116309 (URN)10.1016/j.triboint.2025.111326 (DOI)001677276800002 ()2-s2.0-105028154290 (Scopus ID)
Note
Full text: CC BY license;
For funding information, see: https://doi.org/10.1016/j.triboint.2025.111326
2026-02-032026-02-032026-03-11