Swedish web site
Examensarbeten och uppsatser / Final Theses
Framläggningar på IDA / Presentations at IDA
Se även framläggningar annonserade hos ISY och ITN i Norrköping / See also presentations announced at ISY and ITN in Norrköping (in Swedish)
If nothing is stated about the presentation language then the presentation is in Swedish.
På grund av rådande distansläge kommer framläggningar våren 2020 ske
på distans. Se mer information på sidan om digitala framläggningar
(även länk till vänster). Vid krav på lösenord för att komma in till
exjobbspresentationen, vänligen kontakta examinator för lösenord (skriv in
personens namn i sökfältet uppe till höger och välj "Sök
IDA-anställda" i menyn).
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Due to current distance mode thesis presentations during spring of 2020 will take place online. See more information on the page for online presentations (also link in the menu). If password is required to access the online presentation, please contact the examiner (type in the examiner's name in the search bar in the top right, and choose "Sök IDA-anställda" in the menu).
/
Due to current distance mode thesis presentations during spring of 2020 will take place online. See more information on the page for online presentations (also link in the menu). If password is required to access the online presentation, please contact the examiner (type in the examiner's name in the search bar in the top right, and choose "Sök IDA-anställda" in the menu).
- 2021-03-03 kl 10:15 i https://liu-se.zoom.us/j/66329911225
Design Optimization in Gas Turbines using Machine Learning
Författare: Mathias Berggren, Daniel Sonesson
Handledare: George Osipov
Examinator: Petru Eles
Nivå: Avancerad (30hp)
• In this thesis the authors investigate how machine learning can be utilized to speed up the design optimization process of gas turbines. To achieve this it is studied if the Finite Element Analysis (FEA) steps of the design process can be replaced with machine learning algorithms. The study is done using a component with given constraints that is given by Siemens Energy AB. With this component, two approaches of using machine learning is tested. One which utilizes design parameters, i.e. raw floating point number describing the component, such as the height and width. And one approach which takes a mesh describing the component as input. It is concluded that using design parameters is a viable way to achieve this, and results from using different amount of data samples is presented and evaluated.
Page responsible: Ola Leifler
Last updated: 2020-06-11
