Fatigue

The workshop takes place on-line only on June 14 from 2 PM CET. It is intended to serve as a networking event, where interested researchers present their focused domain. The subsequent brainstorming will help us to evaluate whether there is a will for a broader cooperation among the participants, and if to transform this workshop into something more regular.

If you do not feel strong enough to present, you are still invited to join us - simply let me know to my e-mail address to get the access to the workshop. No fee requested.

Dear all

Please fin attached a PhD proposal for the beginning of the 2023 academic year, proposed by the Institut P' (Poitiers, France) in collaboration with the LSPM lab (University Sorbonne Paris Nord), aiming at modelling fatigue crack propagations in metallic materials under gazeous hydrogen environnement.

The unified mechanics theory (UMT) is ab-initio unification of the second law of thermodynamics and Newton's universal laws of motion, in which Boltzmann's second law of entropy formulation governs dissipation & degradation. Hence, the unified mechanics theory does not require any empirical dissipation & degradation potential function or an empirical void evolution function. Material degradation is quantified on the Thermodynamic state index (TSI) axis based on the specific entropy production, which starts at zero and asymptotically approaches one at failure.

The unified mechanics theory (UMT) was used to develop a model to predict the fatigue life of pre-corroded steel samples with BCC structure. Details of the experimental validation are also provided.

https://doi.org/10.1016/j.matdes.2022.111383

I would like to draw your attention to our recently proposed predictive method based on a semi-empirical model (LEFM) and Neural Network, exploiting the Physics-informed Machine Learning concept. We show how the accuracy of state-of-the-art fatigue predictive models, based on defects present in materials, can be significantly boosted by accounting for additional morphological features via Physics-Informed Machine Learning.

Structural Integrity and MEchanical Design (SIMED) group is seeking a PhD candidate to conduct research in computational solid mechanics, mainly applied to structural problems. The successful applicant will work at the Polytechnic Engineering and Architecture Department (DPIA) of the University of Udine, under Dr Enrico Salvati’s supervision.

Structural Integrity and MEchanical Design (SIMED) group is seeking a 1-year full-time Postdoctoral Researcher in Machine Learning applied to fracture mechanics. The successful applicant will work at the Polytechnic Engineering and Architecture Department (DPIA) of the University of Udine, under Dr Enrico Salvati’s supervision.

The project focuses, but not limited to, on feasibility study and application of Machine Learning methods to applied and numerical fatigue fracture mechanics problems.

Mutian Hua, Shuwang Wu, Ximin He

Bioinspired Soft Materials Group

University of California, Los Angeles

Introduction

I am looking for a PhD candidate who will be sponsered by my Institute to work in the area of fracture mechanics. 

ABAQUS experience would be an advantage and concepts in fracture mechanics and finite elements will be desirable.

If you are interested to work in this area at Indian Institute of Technology Ropar in Metallurgical and Materials Engg. department, please dont hesitate to contact me at abhishek.tiwari [at] iitrpr.ac.in