I would like to bring your attention to four ASME awards and we welcome your nominations. The deadline is fairly soon on February 1!  

Society awards given by ASME Materials Division:

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.

Dr. Jan Fuhg has just joined UT Austin as an assistant professor. He has a postdoc position in computational solid mechanics and physics-informed machine learning. Please see attached file for details and contact Dr. Fuhg (jan.fuhg [at] utexas.edu) if you are interested.

If you design laminated structures, such as sandwich panels, you might be interested in knowing that the through-the-thickness normal stress, properly disregarded in homogeneous structures, may play a fundamental role in triggering delamination.

Dear Colleagues, 

within the 26th International Congress of Theoretical and Applied Mechanics (ICTAM2024) to be held in Daegu, South Korea, 25-30 Aug 2024, Henrik M. Jensen (Aarhus University, Denmark) and myself are organising the Thematic Session 'SM12 - Plasticity, viscoplasticity and creep'. 

We would like to invite you to contribute to this Thematic Session. 

The Extended Abstract Submission is open until January 15, 2024.

Best regards,

The Computations for Advanced Materials and Manufacturing Laboratory (CAMML) in the College of Engineering and Physical Science at the University of Wyoming has an immediate opening for a Postdoctoral Researcher, in the area of multiscale reduced order modeling and design of heterogeneous materials under volumetric and interfacial damage. The project will build on our existing work in Refs. [1-3], and further advance it for modeling of lithium-ion battery system.

Ruobing Bai of the Northeastern University will be the next Editor of the iMechanica Journal Club. The Journal Club was initiated in January 2007, soon after the launch of iMechanica. It has quickly grown into a flagship feature of iMechanica, epitomizing the vibrant topics and dynamic researchers at the frontier of mechanics.

Dynamic fracture is a never-ending story. In 1951, EH Yoffe obtained an analytical solution for a crack of constant length travelling at constant speed along a plane. She used a Galilean transformation to get a solution for arbitrary speeds. The situation seems strange with a crack tip where the material breaks and a lagging tip where the material heals. However, there are applications. One that I encountered was several mode II cracks that travel in the contact plane between a brake pad and a brake disc. The moving cracks were blamed for the causing squeaking noise when braking.

Hanxun Jin (a,b), Horacio D. Espinosa (b)
a Division of Engineering and Applied Science, California Institute of Technology
b Department of Mechanical Engineering, Northwestern University

In recent years, Machine Learning (ML) has become increasingly prominent in Solid Mechanics. Its diverse applications include extracting unknown material parameters, developing surrogate models for constitutive modeling, advancing multiscale modeling, and designing architected materials. In this Journal Club, we will focus our discussion on the recent advances and challenges of ML when experimental data is involved. With broad community interest, as reflected by the increasing number of publications in this field, we have recently published a review article in Applied Mechanics Reviews titled “Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review”. Moreover, a recent insightful paper from Prof. Sam Daly’s group also discussed some perspectives in this field. In this Journal Club, we would like to introduce and share insights into this exciting field.

The research is concerned with modeling of electroactive soft materials.

Preference will be given to candidates with background in nonlinear finite element methods and constitutive modeling.