Additionalinformation

Additional Information

In the department CAE & Vehicle Safety in Trollhättan, you will be part of the team that performs strength, durability and NVH analysis to develop the performance and attributes of vehicles and systems of the future. Our customers are both local and international.

We aim to find two students that can do this project together. Welcome with your application!

#AFRY Making Future

Contacts:

Ilia Dokhani, Senior CAE Engineer, [email protected]

Robert Hansson, Lead CAE Engineer, [email protected]

At AFRY, we engineer change in everything we do. Change happens when brave ideas come together. When we collaborate, innovate technology, and embrace challenging points of view. That’s how we're making future. We are actively looking for qualified candidates to join our inclusive and diverse teams across the globe. Join us in accelerating the transition towards a sustainable future.

Companydescription

Company Description

AFRY is a European leader in engineering, design, and advisory services, with a global reach. We accelerate the transition towards a more sustainable society. We are more than 19,000 devoted experts in infrastructure, industry, energy and digitalization. We accelerate the transition towards a sustainable society for generations to come.AFRY is among engineers rated as one of the most popular employers in Sweden.

Jobdescription

Job Description

Background

Artificial Intelligence is rapidly being introduced into engineering simulation workflows. One promising application is AI-based surrogate models that can predict simulation results significantly faster than traditional finite element analyses. This thesis aims to evaluate the applicability and potential efficiency gains of surrogate models in structural CAE analysis.

Objective

The study should investigate:

• Prediction accuracy compared to traditional FE simulations
• Required amount of training data
• Potential reduction in simulation turnaround time
• Applicability for implicit and explicit analyses

The final objective is to establish guidelines for when AI-based surrogate models should be used and when traditional FE simulations remain the preferred approach.

Methods

The study will use a finite element structure model as a case study. Surrogate models will be developed using data from both implicit and explicit analyses. The predictive accuracy, computational efficiency and required engineering effort will be compared with conventional FE simulations.

Deliverables

A report containing:

 Evaluation of AI-based surrogate models for structural CAE

 Assessment of accuracy and potential time savings

 Evaluation of applicability for implicit and explicit analyses

 Guidelines and recommendations for industrial use

Qualifications

Master Student at Applied Mechanics or equivalent program. Thesis project will be performed in Trollhättan.