Subject PostDoc

The demand for space launch capabilities in Europe is increasing, with a focus on reducing costs, improving performance, offering flexible solutions, and managing the carbon footprint. Reusing rocket stages addresses these challenges, necessitating the development of reusable engines, such as the PROMETHEUS engine, which requires effective regulation during operation. Research by CNES and CRAN has explored engine regulation within a reuse context, accounting for material wear/degradation over successive flights to extend in a safety manner the useful life of components. The reuse of an engine throughout its nominal lifecycle necessitates integrating unknown variable dynamics into the control loop, which leads to intelligent reconfiguration of the control law by forecasting the health state evolution and estimating the defective component's lifespan. This postdoctoral research project aligns with these efforts to consider both Health Monitoring Systems based on the recent approach on Large Language Models learning with optimal control for Reusable Engines. Large Language Models (LLMs) like GPT-3 and LLAMA-3, based on "Transformers," have shown remarkable ability in capturing sequential and semantic relationships in complex datasets. Building on LLMs, we aim to develop a prognostic module/approach. Component degradation results in data output shifts or changes relative to input data. LLMs, trained to recognize and predict degradation dynamics in sequential data, can effectively model these changes as recently illustrated in [1]. This research aims to develop a prognostic module for LPRE (Liquid Propellant Rocket Engines), capable of estimating current health status using incoming data and predicting Remaining Useful Life (RUL) through advanced learning techniques using LLMs. Additionally, a second major objective is to design a control reconfiguration loop that incorporates prognostic-based information to ensure optimal system operation under degradation. This approach aims to extend the system's RUL while maintaining stability. Our prior collaboration with CNES will be leveraged for this aspect, utilizing existing controller knowledge which performs well under nominal conditions. This post-doctoral project will target dissemination of the work in high quality scientific journals (Q1) where the candidate will be expected to publish.