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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2025-01-01
|
| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S263267362400056X/type/journal_article |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850190408688074752 |
|---|---|
| author | Jose Felix Zapata Usandivaras Michael Bauerheim Bénédicte Cuenot Annafederica Urbano |
| author_facet | Jose Felix Zapata Usandivaras Michael Bauerheim Bénédicte Cuenot Annafederica Urbano |
| author_sort | Jose Felix Zapata Usandivaras |
| collection | DOAJ |
| description | 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-Net with available multifidelity Large Eddy Simulations (LES) data. The resulting models preserve reasonable accuracy while reducing the offline computational cost of data-generation. First, a database of about 100 low-fidelity LES simulations of shear-coaxial injectors, operating with gaseous oxygen and gaseous methane as propellants, has been created. The design of experiments explores three variables: the chamber radius, the recess-length of the oxidizer post, and the mixture ratio. Subsequently, U-Nets were trained upon this dataset to provide reasonable approximations of the temporal-averaged two-dimensional flow field. Despite the fact that neural networks are efficient non-linear data emulators, in purely data-driven approaches their quality is directly impacted by the precision of the data they are trained upon. Thus, a high-fidelity (HF) dataset has been created, made of about 10 simulations, to a much greater cost per sample. The amalgamation of low and HF data during the the transfer-learning process enables the improvement of the surrogate model’s fidelity without excessive additional cost. |
| format | Article |
| id | doaj-art-55afbc5828b94d6eaec60c1007d41a12 |
| institution | OA Journals |
| issn | 2632-6736 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj-art-55afbc5828b94d6eaec60c1007d41a122025-08-20T02:15:17ZengCambridge University PressData-Centric Engineering2632-67362025-01-01610.1017/dce.2024.56Data-driven multifidelity surrogate models for rocket engines injector designJose Felix Zapata Usandivaras0https://orcid.org/0000-0003-4111-8577Michael Bauerheim1https://orcid.org/0000-0001-9550-9077Bénédicte Cuenot2https://orcid.org/0000-0002-7061-3668Annafederica Urbano3https://orcid.org/0000-0002-7576-8685Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, Toulouse, FranceCentre Europén de Recherche et de Formation Avancé en Calcul Scientifique (CERFACS), Toulouse, FranceCentre Europén de Recherche et de Formation Avancé en Calcul Scientifique (CERFACS), Toulouse, FranceFédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, Toulouse, FranceSurrogate 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-Net with available multifidelity Large Eddy Simulations (LES) data. The resulting models preserve reasonable accuracy while reducing the offline computational cost of data-generation. First, a database of about 100 low-fidelity LES simulations of shear-coaxial injectors, operating with gaseous oxygen and gaseous methane as propellants, has been created. The design of experiments explores three variables: the chamber radius, the recess-length of the oxidizer post, and the mixture ratio. Subsequently, U-Nets were trained upon this dataset to provide reasonable approximations of the temporal-averaged two-dimensional flow field. Despite the fact that neural networks are efficient non-linear data emulators, in purely data-driven approaches their quality is directly impacted by the precision of the data they are trained upon. Thus, a high-fidelity (HF) dataset has been created, made of about 10 simulations, to a much greater cost per sample. The amalgamation of low and HF data during the the transfer-learning process enables the improvement of the surrogate model’s fidelity without excessive additional cost.https://www.cambridge.org/core/product/identifier/S263267362400056X/type/journal_articleRocket engineshear-coaxial injectorsurrogate modelingtransfer learning |
| spellingShingle | Jose Felix Zapata Usandivaras Michael Bauerheim Bénédicte Cuenot Annafederica Urbano Data-driven multifidelity surrogate models for rocket engines injector design Data-Centric Engineering Rocket engine shear-coaxial injector surrogate modeling transfer learning |
| title | Data-driven multifidelity surrogate models for rocket engines injector design |
| title_full | Data-driven multifidelity surrogate models for rocket engines injector design |
| title_fullStr | Data-driven multifidelity surrogate models for rocket engines injector design |
| title_full_unstemmed | Data-driven multifidelity surrogate models for rocket engines injector design |
| title_short | Data-driven multifidelity surrogate models for rocket engines injector design |
| title_sort | data driven multifidelity surrogate models for rocket engines injector design |
| topic | Rocket engine shear-coaxial injector surrogate modeling transfer learning |
| url | https://www.cambridge.org/core/product/identifier/S263267362400056X/type/journal_article |
| work_keys_str_mv | AT josefelixzapatausandivaras datadrivenmultifidelitysurrogatemodelsforrocketenginesinjectordesign AT michaelbauerheim datadrivenmultifidelitysurrogatemodelsforrocketenginesinjectordesign AT benedictecuenot datadrivenmultifidelitysurrogatemodelsforrocketenginesinjectordesign AT annafedericaurbano datadrivenmultifidelitysurrogatemodelsforrocketenginesinjectordesign |