PhD Studentship in Aeronautics: Machine Learning Modelling for Nonlinear Aeroelastics (AE0069)

Title: Machine Learning Modelling for Nonlinear Aeroelastics

Start date: 1^st October 2025 (earliest), 1^st October 2026 (latest).

Supervisor: Dr Urban Fasel

Introduction: Airbus has an ambitious agenda for developing new wing technologies to dramatically increase fuel efficiency and reduce CO2 of their next generation aircraft. Increasing wing span and reducing structural weight are two of the most attractive means to realise this goal. However, this can only be achieved through the development of advanced load alleviation technologies such as wing pop-up spoilers. New predictive tools are required that can model the nonlinear aeroelastic effects to the required accuracy and in a time efficient manner so they can inform design decisions. The aim of this project is to develop scientific machine learning methods for nonlinear aeroelastic reduced order modelling to analyse loads and loads alleviation strategies of next generation aircraft wings.

Objectives: The objectives of this Airbus and EPSRC-funded PhD project are to develop scientific machine learning methods that can automatically identify nonlinear aeroelastic reduced-order models and discover dominant physical processes directly from data:

• Identify suitable machine learning methods such as the sparse identification of nonlinear dynamics (SINDy) and apply and extend the methods to discover nonlinear aeroelastic models from simulation, wind tunnel, and flight test data provided by Airbus.
• Evaluate the aeroelastic models to analyse loads and load alleviation technologies such as wing pop-up spoilers.
• Investigate dynamic stall phenomena and their effect on load reduction of wing pop-up spoilers using the developed machine learning models.
• Explore the potential to integrate the machine learning models into the current industry loads and aeroelastic modeling tool chain of Airbus.

Learning outcomes: You will develop expertise in nonlinear aeroelasticity, numerical modelling, and scientific machine learning.

Duration: 3.5 years.

Funding: Full coverage of tuition fees and an annual tax-free stipend of £21,237 for Home, EU and International students. Information on fee status can be found on our Fees and Funding webpages.  

Eligibility: You must possess (or expect to gain) a First class honours MEng/MSci or higher degree or equivalent in Aeronautics, Mechanical Engineering, Computing, Physics or related areas. In particular, we invite applications from candidates with a strong mathematical background and an interest in modelling and machine learning. A background in aeroelasticity is desirable.

How to apply:  Please submit your online application via our Apply webpages. 

When submitting your application, you will need to use the following details:

• Search course/Programme:“Aeronautics Research (PhD)”
• Research Topic:Please use reference number AE0069
• Research Supervisor: Dr Urban Fasel
• Research Group:Aero

For queries regarding the application process, please contact Lisa Kelly: l.kelly@imperial.ac.uk

Application deadline: 31^st March 2025.

For further information: For questions about the project, please email Dr Urban Fasel, u.fasel@imperial.ac.uk. You can also learn more about Imperial at www.imperial.ac.uk/study/pg.

 Equality, Diversity and Inclusion: Imperial is committed to equality and valuing diversity. We are an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employer and are working in partnership with GIRES to promote respect for trans people.