research associate (m,f,x) for 3 years beginning as soon as possible with 38,83 hours per week (TV-L E 13)

The Chair of Thermal Turbomachines and Aeroengines in the Faculty of Mechanical Engineering at the Ruhr University Bochum (LSTTF) is among the leading research groups in Germany in the field of experimental and numerical investigation of propulsion and energy transformation systems. Cutting-edge numerical and experimental research is carried out, covering a broad spectrum of topics from classic turbomachinery aerothermodynamics, aeroelastics, two-phase flows and non-ideal compressible fluid dynamics with a strong focus on the design and optimisation of modern fluid energy machines to be employed in innovative cycles for power generation, energy storage and process engineering and using essentially non-conventional working fluids (such as steam, supercritical as well as transcritical CO2 flow, hydrogen, ammonia, organic fluids and mixtures).

The EU-funded project INDIGO (https://news.rub.de/presseinformationen/wissenschaft/2023-01-20-eu-projekt-flughaefen-fuer-das-flugzeug-der-zukunft) aims at identifying the margins of improvement in airport local air quality and noise resulting from the introduction of a new non-conventional mid-range aircraft featuring distributed propulsion based on hybrid electric/sustainable and conventional fuel powertrain and large aspect-ratio wings capable to fly quietly and in zero-to-low-emission mode (i.e. electric and SAF) at low altitudes near airports and resort to conventional aviation fuel only when required, e.g. at higher altitudes or to recharge batteries during cruise. Within the project the ideal candidates will work with industry and research partners to develop a low-dimensional, high-fidelity aero-thermo-acoustic model of a new hybrid-electric engine suitable for deployment with ultra-high-aspect ratio aircrafts. Both high-fidelity computational approaches and model reduction methods will be adopted and further developed to integrate multiphysics-models in common computation tool. Particularly, an efficient and accurate reduced model of the combustion system, capable of handling a variety of fuels (from SAF to hydrogen) will have to be developed based on high-fidelity calculations, carried out using the open-source code OpenFOAM and other in-house tools. The methods of machine learning, particularly Physics-Informed networks will be also employed to identify defining parameters for the model reduction. We look for highly motivated, enthusiastic and highly qualified research assistants, who will carry out the proposed research engaging in numerical investigations with the possibility of obtaining a doctorate.