Jonathan Leung

About Me

He/him. Ph.D. in Materials Science and Engineering and MSc. in Mechanical Engineering from the Georgia Institute of Technology. Currently employed as an Assistant Professor at KTH in the Rail Vehicles unit.

Research

My research involves understanding and predicting fatigue and damage behaviour of engineering materials under extreme conditions and harsh environments. By developing custom computational tools that capture multi-physics mechanisms for damage formation and integrating them into system-level fatigue prediction tools, I increase the ability to predict the influence of various loading, material, and operational factors at multiple length scales.

My background includes non-linear systems analysis, fretting fatigue, viscoelastic deformation, damage development, and the related finite element and multibody simulation modelling. My particular expertise lies in investigating the role of the material microstructure, complex loading patterns, and transient behaviour on the physical deformation and degradation processes. My research has widespread applications, including improving the accuracy of rolling contact fatigue crack initiation prediction and elucidating microstructural damage mechanisms that cause subsurface microstructural transformations in high-strength steels. The tools are used in different sectors, such as rail transportation, green energy systems, electronic packaging, additive manufacturing, and machinery components. My research is funded by several of these industries and multiple government funding agencies (TIMKEN Bearings, Caterpillar, Magna Industries).

My current work focuses on predicting crack initiation in rails under rolling contact loading. By integrating multibody simulations with finite element methods, I simulate the effect of dynamic forces at the wheel-rail interface on crack initiation at the surface and subsurface regions. The cracks are then predicted using multiaxial damage parameters. The coupling of these two computational methods creates a streamlined “pipeline” that efficiently simulates long-term vehicle and track conditions while also predicting the location and severity of cracks from the dynamic load history. The simultaneous investigation of surface and subsurface crack formation improves current rolling contact fatigue predictions and complements predictive maintenance operations, reducing costs and improving system sustainability.