Data-driven multifidelity surrogate models for rocket engines injector design
Surrogate models of turbulent diffusive flames could play a strategic role in the design of liquid rocket engine combustion chambers. The present article introduces a method to obtain data-driven surrogate models for coaxial injectors, by leveraging an inductive transfer learning strategy over a U-N...
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| Автори: | , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Cambridge University Press
2025-01-01
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| Серія: | Data-Centric Engineering |
| Предмети: | |
| Онлайн доступ: | https://www.cambridge.org/core/product/identifier/S263267362400056X/type/journal_article |
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