Auswahl der wissenschaftlichen Literatur zum Thema „Injecteurs coaxiaux“
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Dissertationen zum Thema "Injecteurs coaxiaux"
Beduneau, Jean-Luc. „Caractérisation expérimentale des flammes non-prémélangées H₂/O₂ : application aux cas des injecteurs coaxiaux de moteurs fusées“. Rouen, INSA, 2001. http://www.theses.fr/2001ISAM0005.
Der volle Inhalt der QuelleBOUKERMOUCHE, AHMED. „Mise au point et developpementde mesures de la granulometrie et de la concentration de la phase liquide dans un jet diphasique engendre par des injecteurs coaxiaux“. Université Louis Pasteur (Strasbourg) (1971-2008), 1989. http://www.theses.fr/1989STR13080.
Der volle Inhalt der QuelleZapata, Usandivaras Jose. „Surrogate models based on large eddy simulations and deep learning for coaxial rocket engine injector design“. Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0024.
Der volle Inhalt der QuelleThe design of rocket propulsion systems is under growing pressure of reducing development costs. The use of CFD codes for the simulation of rocket engine combustion processes can provide an economical alternative to costly experiments which have traditionally been at the core of liquid rocket engines (LREs) development. Nonetheless, a holistic approach for preliminary design analysis and optimization is not yet practical, as the exploration of the entire engine design space via high-fidelity numerical simulations is intractable. Appropriate surrogate models may circumvent this dilemma through fast restitution times, without significant accuracy loss. The liquid rocket engine injector is a key subsystem within the LRE, whose design directly impacts flame development, combustion efficiency, and thermal loads. The multiscale nature of turbulent, non-premixed combustion, makes the modeling of injection, particularly complex. In this work, we proceed to evaluate data driven strategies for obtaining surrogate models of LRE shear coaxial injectors. A specific emphasis is taken on supervised, deep learning (DL) techniques for regression tasks. The base injector configuration is inspired on an existing experimental rocket combustor from TUM, operating with a GOx/GCH 4 mixture. We begin by conducting a proof-of-concept (PoC), by offline sampling a database of ∼3600 Reynolds Averaged Navier Stokes (RANS), 2D axisymmetric simulations of single element coaxial injectors spanning a 9 dimensional parameter space comprising geometry and combustion regime. Subsequent models of scalar quantities of interest (QoIs),1D wall heat flux profile, and 2D average temperature field are trained and validated. The models use Fully Connected Neural Networks and an adapted U-Net for the 2D case. The results perform well against other established surrogate modeling methods over the test dataset. The RANS approach has evident shortcomings when dealing with turbulent combustion applications. Instead, Large Eddy Simulations (LES), are in principle, better suited to model turbulent combustion, while furnishing information about dynamical flow features. We proceed to replicate the (PoC) efforts, albeit on a database of ∼100 LES of shear coaxial injectors spanning a 3D design space, at a much larger cost per sample than RANS. A dedicated LES data generation pipeline is put in place. Due to the cost, the LES are low-fidelity (LF) in view of the modeling simplifications, i.e. coarse meshes, global chemistry, etc. CNNs and U-Nets are used to obtain surrogate models of scalar QoIs and 2D stationary fields with satisfactory performance over the LF prediction task. To improve the overall fidelity of the surrogate, a multi-fidelity (MF) approach is considered by leveraging inductive transfer learning over our U-Nets. The decoding layers are retrained and validated over a smaller pool of ∼10 of high-fidelity (HF) samples, i.e. finer resolution. The MF surrogate performs well in the HF prediction task over the test samples, with the desired flame topology, at a lower computational cost of the offline sampling stage. The dynamic data of LES, motivates the development of reduced order models (ROMs) for the spatio-temporal prediction of the injector flame. We develop emulators of a LRE injector flame by means of convolutional autoencoders (CNN-AE) and multi-layer perceptron (MLP) for propagating in time the latent vectors. The reconstructed spectral content of the signal outperforms that of a standard POD with equal latent space dimension, demonstrating the superior compression capability of the CNN-AE. However, manifold regularity concerns are raised when propagating the emulator beyond the training horizon. Finally, this work evidences the challenges and opportunities of the use of DL for the prediction of stationary and dynamical features of LES data for a complex reactive flow configuration of a LRE coaxial injector
Care, Isabelle. „Etude d'un injecteur coaxial assisté“. Rouen, 1990. http://www.theses.fr/1990ROUES041.
Der volle Inhalt der QuelleCessou, Armelle. „Stabilisation de la combustion diphasique turbulente au-dessus d'un injecteur coaxial méthanol/air“. Rouen, 1994. http://www.theses.fr/1994ROUES039.
Der volle Inhalt der QuelleZamuner, Bernard. „Etude expérimentale et numérique du brouillard en combustion issu d'un injecteur coaxial liquide-gaz“. Châtenay-Malabry, Ecole centrale de Paris, 1995. http://www.theses.fr/1995ECAP0433.
Der volle Inhalt der QuelleLe, Visage David. „Pulvérisation d'un jet issu d'un injecteur coaxial assisté : géométrie de l'injecteur, modélisation et approche cryogénique“. Poitiers, 1996. http://www.theses.fr/1996POIT2258.
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