With an original education as theoretical physicist, H. Allmaier has worked for more than 10 years in applied tribology developing new simulation and testing methodologies to predict friction and wear. In recent years he expanded his research activities to the field of data science and machine learning to develop robust methods for predictive maintenance and predict failure.
Part of the book: Tribology in Engineering
A current trend in the transport sector seeks to increase the vehicle efficiency and to cut fuel consumption which leads to new technologies and advancements in modern and future combustion engines. Some of these technical progresses lead to highly stressed engine parts and new challenges arise, particularly for journal bearings. The increasing thermal and mechanical load caused by downsized and turbocharged engines, friction reduction by employing low-viscosity lubricants and other emission reduction measures—for utilizing stop-start systems—put additional stress on the crankshaft journal bearings. This contribution focuses on highly stressed journal bearings which operate in the boundary, mixed and hydrodynamic lubrication regime. Therefore, measurements on a journal bearing test-rig are performed which allow an extensive verification of the numerical investigation. For the numerical analysis of friction and wear, a mixed elasto-hydrodynamic simulation approach is developed, which considers the elastic deformation of the contacting components, the complex rheological behaviour of the lubricant and metal-metal contact if the lubricant is unable to separate the contacting surfaces. Both the rheological data and the surface roughness parameters are obtained from measurements. The current challenges are studied in four application-oriented examples and the influencing parameters on a reliable friction and wear prediction are explored.
Part of the book: Advances in Tribology