Design Optimization in Gas Turbines using Machine Learning : A study performed for Siemens Energy AB

• Main Authors: Mathias, Berggren, Daniel, Sonesson
• Format: Others
• Language: English
• Published: Linköpings universitet, Programvara och system 2021
• Subjects: Computer Science; Design Optimization; DO; MDO; Surrogate Models; Surrogates; Response Surface Methods; Metamodels; Metamodeling; Machine Learning; ML; FEA; Finite Element Analysis; FEM; Finite Element Method; Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-173920

In this thesis, the authors investigate how machine learning can be utilized for speeding up the design optimization process of gas turbines. The Finite Element Analysis (FEA) steps of the design process are examined if they can be replaced with machine learning algorithms. The study is done using a component with given constraints that are provided by Siemens Energy AB. With this component, two approaches to using machine learning are tested. One utilizes design parameters, i.e. raw floating-point numbers, such as the height and width. The other technique uses a high dimensional mesh as input. It is concluded that using design parameters with surrogate models is a viable way of performing design optimization while mesh input is currently not. Results from using different amount of data samples are presented and evaluated.