Facing the need to increase the accuracy of rocket engines design tools, the present seminar describes an innovative methodology under development for the design and optimization of rocket engine combustion chambers using numerical simulations and deep learning.
The New Space has brought about a paradigm shift in the space world. New private players entered in the market. They do not necessarily bring technological innovations, but innovations in production and development methodologies. The big challenge in this context is therefore to be able to reduce the price of access to space, which means, among others, reducing the cost of developing the propulsion system. Indeed, Liquid Rocket Engines (LRE), which are the best candidates for future space applications, are complex systems, characterized by several subsystems interacting. They are usually designed in the preliminary phase making use of system tools that rely on semi-empirical correlations for the components. The high uncertainty associated with these models propagate on the whole system design. In particular, when dealing with combustion chambers and injectors, low order models fail to be predictable: these components are characterized by the high nonlinear phenomena characterizing turbulent diffusion flames at high pressure. In the end, LRE development strongly relies on experimental tests going up to full scale tests, that are very expensive. On the other hand, numerical simulations of rocket combustion chambers have become popular in the last years. Computational fluid dynamics simulations have demonstrated the ability to be predictable in terms of heat fluxes, flame dynamics and overall performance estimations. Depending on the required level of details and phenomena to be captured, both Reynolds Averaged Naviers-Stockes (RANS) equations and Large Eddy Simulations (LES) can be used to simulate rocket engines combustion chambers in real configurations. However, the restitution times are too long to be useful in the framework of a whole propulsive system design (hours for RANS, thousands of hours for LES).
The objective of the work presented in this seminar is to introduce a methodology to jointly use CFD data and artificial intelligence (in particular deep learning) in order to extract surrogate models for injector and combustion chambers of rocket engines. These surrogate models are meant to be used for the design and optimization of single components and in the framework of a multi-disciplinary analysis of the whole propulsive system.
First, the experimental test case of a single coaxial injector that is taken as a reference point in order to define the correct numerical setup, is presented. Then, a design of experiments is generated by varying nine parameters: seven geometrical (diameters, contraction area ratios) and two operative conditions (mass flow rate and O/F). RANS simulations are carried out to generate the dataset with the commercial code Ansys Fluent, generating around 3600 simulations. The data are then used to train surrogate models of different fidelity. These consists in global or averaged quantities (0D), the wall heat flux profile (1D) and the temperature field (2D). Attention is given on the selection of the proper machine learning technique. For low dimensional outputs, results show that deep neural networks outperform other standard machine learning tools, namely Radial Basis Function and Kriging. Regarding high-dimensional outputs, convolutional neural networks with gradient-based loss functions are found effective to capture the large and smooth temperature variations, as well as the thin and sharp temperature gradients at the flame front. Eventually the models are used in the framework of an optimization problem. Results highlight the benefits of new design and optimization tools based on deep learning, capable of real-time predictions of complex flow fields. The seminar ends with an overview of the challenges that will be faced in order to extend the methodology to LES data.
2 marzo 2022
La Dott.ssa Annafederica Urbano, Associate Professor presso ISAE Supaero - DCAS (Toulouse), terrà mercoledì 2 marzo alle ore 12:00 nell'aula videoconferenze del DIMA un seminario dal titolo:
"Surrogate models for coaxial injectors based on CFD and Deep Learning"
Si invitano gli interessati a partecipare.
Short Bio
Annafederica Urbano is an associate professor in Space and Launcher Systems at ISAE SUPAERO. She carries out her research activities in the Space Advanced Concepts team at the department of Design and Control of Aerospace vehicles (DCAS) since 2019. She is an expert in space propulsion and space transportation systems. She obtained her Ph.D. in 2012 from La Sapienza University of Rome with a thesis on the regenerative cooling system of methane liquid rocket engines. Her research areas of expertise are related with the numerical simulation of reactive and two-phase flows in liquid rocket engines: supercritical combustion, thermoacoustic instabilities, nucleate boiling in micro-gravity, two phase compressible flows, transcritical fluids and heat transfer deterioration. Part of her activity is also devoted to the space system analysis (rocket engines and launchers) : multi-disciplinary analysis and optimization, surrogate models from CFD and artificial intelligence.
She is author or co-author of more than 40 journal and conference papers (17 ranked-A papers).