Thesis work: 30hp - Test data correlation to CFD prediction

Introduction
Scania’s purpose is to drive the shift towards a sustainable transport system, creating a world of mobility - better for business, society, and the environment. Exploring and accelerating the development of tomorrow's transport system is one of the driving forces. Imagine that you could impact the next generation Scania vehicles by working in a dynamic & diverse team environment! Yes, this opportunity gives you significant exposure to the vehicle development process.

You will have the opportunity to develop your engineering skills, learn if the Scania work culture suits your future career goals and to show your abilities and talents in a 20 weeks thesis project.

Background
Simulation and testing are core parts of the development process for the next generation of vehicles and have been so for many years. To accurately predict or verify results is key. Over the years simulation has played a larger role in the development process and automated workflows are important to provide faster design iterations.

Though simulations can provide many answers, they cannot completely replace tests on fully integrated systems and tests will therefore continue to be an important part of the design process.

Comparison between test and simulation is today a manual and time-consuming taks. Improvements to our existing processes are required and most likely fundamental for utilization of emerging technologies within AI and machine learning.

Objective and job description
Main focus of the work will be towards understanding the test data structure and how to consume it in an automated fashion by the CFD simulations (and vice-versa). The process also needs to consider AI and machine learning, and how the data can be consumed by such models. 

The second part will include utilizing the test data and CFD simulations based on the test input and seeing how the prediction is as of today and what can be improved in our current simulation process. 

The thesis project will require work in both Windows and Linux environments, Python code, Git utilization for version control and running STAR-CCM+ on clusters. The solution must be modular and be able to integrate with existing processes, scalable and prepared for AI integration.
Work will be conducted on both existing cooling performance data, but also new data generated during the thesis work. It could be possible to participate in cooling performance tests at Scania’s climatic wind tunnels.

Education
You are within your final year, pursuing a master's degree in Mechanical/ Aerospace Engineering, or equivalent, with a specialization in Computational Fluid Dynamics.

Experience from working on computer cluster environments with Linux interface, unsteady state simulations, and exposure to scripting simulation workflow are desirable. Knowledge in Git is beneficial as well as having a genuine interest in scripting and automation. 

Number of students
1 - 2

Start date
January (exact dates can be discussed later) and estimated time required is 20 weeks.

Contact persons and supervisors
Supervisor, Henrik Rosendahl (TGRMST1), henrik.rosendahl@scania.com   
Manager, Joakim Lindholm (TGRMST1), joakim.lindholm@scania.com 

Application
Your application must include a CV, personal letter, and transcript of grades.

A background check might be conducted for this position. We conduct interviews continuously and may close the recruitment earlier than the date specified.