Topology Optimization of gas turbine guide vanes
Title: Topology Optimization of gas turbine guide vanes
DNr: LiU-compute-2022-30
Project Type: LiU Compute
Principal Investigator: Jonas Lundgren <>
Affiliation: Linköpings universitet
Duration: 2022-10-03 – 2024-03-01
Classification: 20301


In a previous project (LiU-compute-2021-40), the supercomputing resources at NSC granted the opportunity to develop a model for optimal design of the cooling channels inside components subjected to an external hot gas flow. The computations are made using Topology Optimization, a design method often used together with the Finite Element method for structural/flow/thermal analysis. During the previous project, it was confirmed that the PETSc suite was suitable as a basis for the computations, and the results obtained is included in a paper currently under review [1]. The computational examples included in that paper was performed on a somewhat simplified and symmetric model. We want to extend the analysis to a more industrial-like geometry. For this geometry, an even higher resolution is necessary out of mainly two reasons: 1) No symmetry conditions can be utilized 2) Some parts of the new geometry is very narrow, requiring more finite elements The higher resolution needed creates larger linear systems to be solved, to obtain the state solutions (e.g. the flow field). This is the bottleneck of these types of design problems. We seek to continue the computations with larger and more time consuming examples, to investigate the validity of our model for more general and complex geometries, and hence, we want to extend the opportunity to utilize the resources provided by NSC. In a best case scenario, the designs obtained from these simulations are "good enough" to be realized by additive manufacturing/3D-printing, by the end of this project. References: [1] Lundgren, J., Lundgren, J.-E., Thore, C.-J., Flow-heat topology optimization of internally cooled high temperature applications using a voxelization approach for domain initialization, Engineering Optimization, Taylor and Francis. Manuscript submitted for publication, sept 2022.