Job
- Level
- Experienced
- Job Field
- Software, Data
- Employment Type
- Full Time
- Contract Type
- Internship / school internship
- Salary
- from 3.500 β¬ Gross/Project
- Location
- Graz
- Working Model
- Onsite
Job Summary
In this master thesis, you will analyze simulations and test data regarding temperature and thermal mechanical fatigue. You will develop an AI model to predict valve seat bridge lifespan, focusing on critical influencing parameters.
Job Technologies
Your role in the team
- Review and analyze existing simulation and test data from AVL iCA FE and powertrain databases.
- Validate AI-based temperature field predictions compared to virtual simulations.
- Validate AI-based TMF predictions against thermal shock tests and virtual simulations.
- Identify key parameters (e.g., temperature, geometry, coolant flow) influencing temperature and TMF life prediction.
- Develop and train an AI model to predict temperature and TMF of valve seat bridges.
- Confirm simulation accuracy by identifying and validating key parameters influencing thermal fatigue life.
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Our expectations of you
Qualifications
- Bachelor of Science in domains similar to Mechanical Engineering, Physics or a related field.
- Interest in Internal Combustion Engine (ICE) technology and conducting mechanical analysis using numerical modeling and simulation techniques.
- Familiarity with structural mechanics principles and 3D Finite Element Analysis (FEA) simulation methods is appreciated.
- Strong interest in AI methodologies and programming, particularly with Python, for developing and automating various aspects of the project.
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