A PhD opening is available in my group, focusing on the development and testing of machine learning potentials for multi-component systems, with focus on predicting dislocation plasticity and grain boundary mechanics in green steels. Motivated candidates with a strong mechanics background are warmly encouraged to apply.
More info about the post and how to apply at:
https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=003…
The Microstructure and Mechanics Group at the Max Planck Institute for Sustainable Materials (formerly known as Max Planck Institut für Eisenforschung) welcomes applications for a PhD position on
“High-Throughput Atomistic Simulations of Microstructure-Induced Failure”
The funding through DAAD is particularly targeting candidates from Eastern Europe, Africa, Central and South America, the Near and Middle East, as well as Asia.
Suppose that we model the entire universe (i.e. the entirety of the known physical universe) as a huge isolated system, using molecular dynamics (MD for short).
The question is: How would you show that the entropy of such a system does in fact always increase? that it neither decreases nor stays the same?
The 15th International Conference of Computational Methods (ICCM2024) this year will be held ONLINE! We are welcoming your submissions to MS-036 Mechanics of soft materials. Deadline for submissions is 30th March. Details available HERE!
The DoD HPC Modernization Program has high-performance computing internship opportunities at the Air Force Research Laboratory. These internships give undergraduate and graduate students the opportunity to perform scientific, computational research alongside AFRL researchers in support of the US Air Force’s mission.
Ernest Rabinowicz’s words, spoken two decades ago in his groundbreaking textbook on the friction and wear of materials [1], continue to resonate today: ’Although wear is an important topic, it has never received the attention it deserves.’ Rabinowicz’s work laid the foundation for contemporary tribology research [2]. Wear, characterized as the removal and deformation of material on a surface due to the mechanical action of another surface, carries significant consequences for the economy, sustainability, and poses health hazards through the emission of small particles. According to some estimates [1, 3], the economic impact is substantial, accounting for approximately 5% of the Gross National Product (GNP).
Despite its paramount importance, scientists and engineers often shy away from wear analysis due to the intricate nature of the underlying processes. Wear is often perceived as a ”dirty” topic, and with good reason. It manifests in various forms, each with its own intricacies, arising from complex chemical and physical processes. These processes unfold at different stages, creating a time-dependent phenomenon influenced by key parameters such as sliding velocity, ambient or local temperature, mechanical loads, and chemical reactions in the presence of foreign atoms or humidity.
The review paper by Vakis et al. [5] provides a broad perspective on the complexity of tribology problems. This complexity has led to numerous isolated studies focusing on specific wear mechanisms or processes. The proliferation of empirical wear models in engineering has resulted in an abundance of model variables and fit coefficients [6], attempting to capture the intricacies of experimental data.
Tribology faces a fundamental challenge due to the multitude of interconnected scales. Surfaces exhibit roughness with asperities occurring at various wavelengths. Only a small fraction of these asperities come into contact, and an even smaller fraction produces wear debris. The reasons behind why, how, and when this occurs are not fully understood. The debris gradually alter the surface profile and interacts with one another, either being evacuated from the contact interface or gripping it, leading to severe wear. Due to this challenge of scales, contributions of numerical studies in wear research over the past decades sum up to less than 1% (see Fig. 1). Yet, exciting opportunities exist for modeling, which we attempt to discuss here.
While analyzing a single asperity contact may not unveil the entire story, it arguably represents the most fundamental level to comprehend wear processes. This blog entry seeks to encapsulate the authors’ perspective on this rapidly evolving topic. Acknowledging its inherent bias, the aim is to spark controversies and discussions that contribute to a vibrant blogosphere on the mechanics of the process.
The subsequent section delves into the authors’ endeavors in modeling adhesive wear at the asperity level. Section 3 navigates the transition to abrasive wear, while Section 4 explores opportunities for upscaling asperity-level mechanisms to the meso-scale, with the aspiration of constructing predictive models. Lastly, although the primary focus of this blog entry is on modeling efforts, it would be remiss not to mention a few recent advances on the experimental front.
3 PhD positions funded through the Horizon Europe Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks as part of the “Bridging Models at Different Scales To Design New Generation Fuel Cells for Electrified Mobility (BLESSED)” project.
In our latest article, “Uncovering stress fields and defects distributions in graphene using deep neural networks”: https://doi.org/10.1007/s10704-023-00704-z , we showed that conditional generative adversarial networks (cGANs) could transform complex deformation fields into stress fields by eliminating the need to evaluate elasticity distributions and develop complex nonlinear constitutive relations.
We at MIT, welcome you to join us this summer, in person, for a hands-on design-to-product Predictive Multiscale Materials Design course from June 13-17, 2022. All course registrants will receive an MIT certificate upon completion.
https://professional.mit.edu/course-catalog/predictive-multiscale-mater…
