Master thesis: Physic-Driven Machine Learning

Master Thesis - Physic-Driven Machine Learning for Solid Mechanics in High Temperature Gas Turbine Application
Background
Siemens Energy Finspång develops and manufactures gas turbines for power generation and operation of compressors and pumps. Siemens Energy is also a total contractor for distribution and transmission projects in Sweden, and provides total solutions in electrification, automation and digitalization for the process and basic industries.
Gas turbines are designed to operate during long periods of time and in tough environments. This puts high demand on the turbine components regarding their life cycle. One ambition is to predict the behavior of different components using machine learning. One method is to use Multi Objective Optimization. The number of design points required results in more or less good results. To minimize the simulation time in optimization loops a need for more efficient simulation techniques is needed. By replacing finite element modelling with surrogate models trained with machine learning algorithms will speed up the MDO work.
The task for this master thesis is to implement findings from literature to a solid mechanic approach. Including geometries loaded by rotation, gas pressure and high temperatures will further stretch the knowledge in this area.
Objectives
• Perform a literature review regarding Physic Driven Machine Learning (report)
• Implement from literature an elastic-plastic thermomechanical set up with simplified geometry (preferably with possibility to compare results with analytical solution)
• Further develop Machine Learning features by adding more loads (pressure, rotational, temperature and contacts)
• Write a Master Thesis report
LET'S TALK ABOUT YOU
Your profile
• AI/Machine learning with some experience in solid/fluid mechanics
• Skilled in coding in Python
• Interested in design automatization and optimization
• Open minded and curious
LET’S TALK ABOUT US
"Let’s make tomorrow different today" is our genuine commitment at Siemens Energy to all customers and employees on the way to a sustainable future.
Siemens Energy is one of the world’s leading energy technology companies. The company works with its customers and partners on energy systems for the future, thus supporting the transition to a more sustainable world. With its portfolio of products, solutions and services, Siemens Energy covers almost the entire energy value chain – from power generation and transmission to storage. The portfolio includes conventional and renewable energy technology, such as gas and steam turbines, hybrid power plants operated with hydrogen, and power generators and transformers. More than 50 percent of the portfolio has already been decarbonized.
A majority stake in the listed company Siemens Gamesa Renewable Energy (SGRE) makes Siemens Energy a global market leader for renewable energies. An estimated one-sixth of the electricity generated worldwide is based on technologies from Siemens Energy. Siemens Energy employs 91,000 people worldwide in more than 90 countries and generated revenue of around €29 billion in fiscal year 2019. In Sweden Siemens Energy has 2600 employees in 10 locations.
Read more about Siemens Energy here:
www.siemens-energy.com
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Job & Career: https://www.siemens-energy.com/global/en/company/jobs.html
Application
Don’t hesitate – apply via https://jobs.siemens-energy.com/jobs id nr 224283 not later than 2021-06-27.
For questions about the role, please contact the recruiting manager Dr Olle Skrinjar on tel. 0737455581
For questions about the recruitment process please contact the responsible recruiter Nelly Johansson on tel. 0122-81505.
After applying for the Master thesis position the selection of suitable candidates will be based on a multiple choise test and a programming assignment that will be sent out to all candidates.
Location: Finspång
Trade Union Representatives:
Veronica Andersson, Unionen, 0122-840 21
Simon Von Eckardstein, Sveriges Ingenjörer, 0122-842 24
Jan Lundgren, Ledarna, 0122-812 33
Jonny Persson, IF Metall, 0122-817 69