Deep convolutional autoencoders for the time–space reconstruction of liquid rocket engine flames

The integration of high-fidelity numerical simulations into the rocket engine design-cycle promises to cut costs in an ever more competitive launch market. Although still being intractable in the optimization of most of combustion devices, their potential can be harnessed via appropriate reduced ord...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings of the Combustion Institute Ročník 40; číslo 1-4; s. 105382
Hlavní autoři: Zapata Usandivaras, José F., Bauerheim, Michael, Cuenot, Bénédicte, Urbano, Annafederica
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 2024
Elsevier
Témata:
Autoencoders
Deep learning
Fluid Dynamics
Physics
Reduced order modeling
Rocket engine
Deep learning
Autoencoders
Reduced order modeling
Rocket engine
ISSN:1540-7489, 1540-7489
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The integration of high-fidelity numerical simulations into the rocket engine design-cycle promises to cut costs in an ever more competitive launch market. Although still being intractable in the optimization of most of combustion devices, their potential can be harnessed via appropriate reduced order models (ROMs). In recent years, significant progress has been made in the ROM literature through the adoption of non-linear methods, coming from the realm of deep learning. These methods promise to deliver where conventional linear techniques, such as Proper Orthogonal Decomposition (POD), fall short due to their inherent qualities. In the current work, we carry out a first attempt to develop purely data-driven emulators for a shear-coaxial liquid rocket engine injector. In particular, a Convolutional Neural Network autoencoder (CNN-AE) with a Multi-Layer Perceptron (MLP) latent dynamics encoder architecture is considered. The main objective is to assess the capacity of autoencoders based models to reconstruct, in time and space, the instantaneous temperature field of a non-canonical, complex, reactive, flow configuration. The data used is made of Large Eddy Simulations (LES) of three distinct, real-life inspired, injector configurations. The autoencoder based models show superior reconstruction capabilities compared to POD with similar latent-space dimension. Moreover, the time–space reconstructed fields preserve the spectral content of the snapshots, with only a small loss in gain at high frequencies. These results serve as proof-of-concept for further developments on surrogate models of liquid rocket engine injectors.
ISSN:1540-7489
1540-7489
DOI:10.1016/j.proci.2024.105382