Fundamentals in Building Tribological Digital Twins of Machine Elements, 2nd Edition
A special issue of Lubricants (ISSN 2075-4442).
Deadline for manuscript submissions: 28 February 2025 | Viewed by 190
Special Issue Editor
Interests: tribology of key machine elements; tribochemistry
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine elements, such as bearings, seals, and gears, are critical in many essential applications, such as high-speed trains and aeroengines. Tribological characteristics, such as frictional energy loss, wear loss, and contact stiffness, play essential roles in the design, manufacturing, and life prediction of most machine elements. A better understanding of these tribological characteristics can help researchers and engineers to develop and maintain machine elements with higher standards, such as working under extreme conditions, aiming for longer life, and reducing frictional energy loss. Such breakthroughs in machine elements are crucial in pursuing sustainable development in the field of mechanical equipment.
Many modeling and experimental efforts have been made to reveal the mechanism of tribological phenomena in machine elements. Tribologists have proposed many multiphysics models, empirical models, or hybrids of both models to evaluate and predict lubrication, friction, and wear across length scales and time scales. Physics-based and data-driven approaches are used widely. However, there is still no widely accepted tool that can reflect the tribological performance of machine elements in the full life cycle.
A digital twin is usually defined as a virtual duplicate of a complex system built from models and data fusion, emphasizing the real-time reflection of the physical system with high synchronization and fidelity. The real-time reflection characteristic is a crucial factor needed to improve the current studies of the tribological performance of machine elements. Therefore, building tribological digital twins of machine elements could be a way to push loads of tribological knowledge and techniques toward real-world applications.
Building tribological digital twins requires improvements in the fundamental understanding of all the multiscale/multiphysics processes occurring in tribological systems. Multiscale modeling techniques are required, including atomistic simulation, such as molecular dynamics (MD) and density functional theory (DFT), and continuum methods, such as the finite element method (FEM). Multiphysics modeling, such as the coupling of fluid, solid, and thermal fields, and irreversible time-related phenomena, such as plastic deformation and wear, are also crucial factors. Besides the physics-based techniques, data-driven approaches, such as machine learning, are potential tools for predicting tribological performance.
The real-time digital twin must have real-time measured data from its physical system, which is used to update the digital twin to ensure high synchronization and fidelity. Building tribological digital twins also requires experimental techniques to continuously collect tribological data. These data must be chosen well to assimilate with the simulated data. Data assimilation and updating of digital twin methods are essential tasks in building tribological digital twins.
This Special Issue addresses all studies on fundamentals in building tribological digital twins of machine elements. Contributions are welcome from all scientists working in tribology and related areas.
Dr. Yuechang Wang
Guest Editor
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Keywords
- machine elements
- bearings
- seals
- gears
- hydrodynamic lubrication
- EHL
- mixed lubrication
- roughness
- digital twin
- real-time measurement
- data assimilation
- life prediction
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