The following is an excerpt.
SFI CASA´s new PhD candidate Håvard Næss aims to make numerical simulations more accurate concerning plasticity and fracture. Without increasing the computational and calibration costs.
Name: Håvard Næss
From: Mære/Steinkjer, Norway
Background: Master’s degree in Mechanical engineering from NTNU. I wrote my master’s thesis on the structural response of plated offshore structures due to violent wave impacts. The MSc thesis was a part of the KPN SLADE project. It was mainly a numerical study. However, the simulations were validated using available experimental results to investigate important modelling parameters in dynamic fluid-structure interaction problems.
Could you give a short description of the PhD- project?
To study how machine learning can contribute to solving the upscaling problem in material mechanics.
What is the goal?
The goal is to make numerical simulations more accurate concerning plasticity and fracture without increasing the computational and calibration costs.
Who needs this knowledge?
The industry can save money in design by applying this technology. Besides, more accurate simulations lead to less material consumption and thus more sustainable structures. Furthermore, the research community will be interested in the potential of machine learning in upscaling problems.
Why did you choose a doctorate in SFI CASA?
A PhD position offers the opportunity to focus on a narrow field for a longer period. In fact, and I have considered it for several years. Through the last year of my master’s degree, I got to know the research group, and I found the field very interesting. As a result of that, I decided to join the group as a PhD candidate.