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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.24322 |
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| _version_ | 1866914510317551616 |
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| author | Krüger, Patrick Gottschalk, Hanno Krebs, Werner Werdelmann, Bastian |
| author_facet | Krüger, Patrick Gottschalk, Hanno Krebs, Werner Werdelmann, Bastian |
| contents | The need to burn 100% H2 in high efficient gas turbines featuring low NOx combustion in premix mode require the complete redesign of the combustion system to ensure stable operation without any flashback. Since all engine frames featuring a power range from 4 MW up to 600 MW are affected, a huge design effort is expected. To reduce this effort, especially to transfer knowledge between the different engine classes, generative design methods using latest AI technology will provide promising potential. In this work, this challenge is approached utilizing the current advances in generative artificial intelligence. We train an Invertible Neural Network (INN) on an expandable database of geometrically parameterized combustor designs with simulated performance labels. Utilizing the INN in its inverse direction, multiple design proposals are generated which fulfill specified performance labels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_24322 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks Krüger, Patrick Gottschalk, Hanno Krebs, Werner Werdelmann, Bastian Artificial Intelligence 68T07 The need to burn 100% H2 in high efficient gas turbines featuring low NOx combustion in premix mode require the complete redesign of the combustion system to ensure stable operation without any flashback. Since all engine frames featuring a power range from 4 MW up to 600 MW are affected, a huge design effort is expected. To reduce this effort, especially to transfer knowledge between the different engine classes, generative design methods using latest AI technology will provide promising potential. In this work, this challenge is approached utilizing the current advances in generative artificial intelligence. We train an Invertible Neural Network (INN) on an expandable database of geometrically parameterized combustor designs with simulated performance labels. Utilizing the INN in its inverse direction, multiple design proposals are generated which fulfill specified performance labels. |
| title | Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks |
| topic | Artificial Intelligence 68T07 |
| url | https://arxiv.org/abs/2604.24322 |