Data models for large aircraft aerodynamics using next-generation computational fluid dynamics

Lead Research Organisation: University of Liverpool
Department Name: Electrical Engineering and Electronics


High-fidelity aerodynamic data, enabled through computational fluid dynamics simulations and large-scale experimental campaigns, is critical in satisfying stringent constraints on aircraft performance while meeting the most ambitious targets on sustainability in the aviation sector. The project focus is on the numerical aspects of aerodynamic data generation using, and co-developing, a flow code that exploits the latest algorithms devised for highly parallel computing architectures.
Tools and methods to generate high-fidelity aerodynamic models capable of encapsulating a number of critical dynamic phenomena will be developed. These phenomena affect the aircraft's performance and its environmental footprint in the high-speed transonic flow regime. This challenge then motivates the need for state-of-the-art computational fluid dynamics (CFD) tools and built-in fast algorithms operating in the frequency domain, while using high-performance computing systems. Despite best-in-class CFD technology and computing facilities, quick turnaround times in the design cycles for aircraft wing aerodynamics necessitate the use of supervised or unsupervised Machine Learning algorithms that can represent the uncertainty associated with interpolation and extrapolation across real-world data in vast parameter spaces
The main focuses of the project include the further development of a state-of-the-art CFD code and simulation of aircraft wing aerodynamics on high-performance computing systems necessitated by the need to generate the required data for modelling the intricate transonic aerodynamic phenomena. Also, the critical assessment of the latest CFD technology on suitable use cases in collaboration with the industrial partner's domain experts to foster the acceptance and integration into end-user processes, while challenging the current industrial practice. Finally, the exploration of Machine Learning algorithms to derive practical tools for wing design problems that can incorporate data from disparate sources.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023445/1 31/03/2019 29/09/2027
2889801 Studentship EP/S023445/1 30/09/2023 29/09/2027 Daniel Nash