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Using machine learning for early-stage aircraft wing design under uncertainty


Project Description

Description of Project: Early-stage aircraft structural design must explore a large design space to ensure optimal aerodynamic and load bearing performance. This is especially important as we enter an age of novel aerostructures with enhanced capabilities such as morphing wings. At this stage (as opposed to the subsequent detailed design stage), designers must also cope with uncertainties that exist due to lack of design maturity, knowledge about aerodynamic loads and model predictions. The PhD project aims to use machine learning to develop a data-driven robust aircraft wing design toolbox in collaboration with Airbus. Machine learning is seeing a rapid uptake in manufacture and design due to its ability to predict optimal design/operational conditions given countless possibilities and covariates. Machine learning will be a key enabler for the project to properly trade optimum performance against the risks of achieving the performance and meeting the constraints. A Bayesian machine learning approach will be used within a data-driven framework for robust design and optimization of aircraft structures under a set of stringent design constraints imposed by considerations of weight penalty, flexibility in flight conditions, flight envelopes and risk minimization

Research Environment: The student will benefit from an excellent research training environment provided by the Applied and Computational Mechanics group at Cardiff University’s School of Engineering and also by Airbus. Airbus is a world leader in wing design, engineering and manufacturing and is a key centre for the design, testing and integration of fuel systems and landing gear. Airbus will host and train the student at their research facility in Filton (Bristol) for about 15% of the time of the PhD studentship. The student will also receive training in the use of high performance computing facilities which will provided at both Cardiff and Airbus.

Learning and Development Opportunities: Training will be provided by the supervisors on Bayesian machine learning approaches, especially with regard to stochastic surrogate models and inverse problems. During the time spent at Airbus, training will be provided on the use of industry-grade simulation software for testing aircraft configurations. The wide range of professional development courses available in Cardiff will equip the student with a wide range of skills ranging from project management to research methodology and use of advanced software. The student is expected to produce high quality journal articles for scientific publication during the course of his/her PhD and will be expected to participate and present his/her research in international conferences.


Candidates should hold or expect to gain a first-class degree or a good 2.1 (or their equivalent) in Engineering or a related subject, or the equivalent qualifications gained outside the UK. Applicants with a Lower Second Class degree will be considered if they also have a Master’s degree or relevant industrial experience. Desirable skills: · A good background in coding in Matlab / Python / Fortran / C. · Good project management and communication skills · Research experience Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent).

EPSRC DTP studentship (Industrial Partner: Airbus). Start Date: October 2020. 3.5 years full time.

Applicants should submit an application for postgraduate study via the Cardiff University webpages (http://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/engineering ) including:
. An upload of your CV
· A personal statement/covering letter
· Two references (applicants are recommended to have a third academic referee, if the two academic referees are within the same department/school)
· Current academic transcripts Applicants should select Doctor of Philosophy (Engineering), with a start date of July or October 2020. In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select "I will be applying for a scholarship / grant" and specify that you are applying for advertised funding, reference AK-DTP-2020

Funding Notes

Tuition fees at the home/EU rate (£4,407 in 2020/21) and an annual stipend equivalent to current Research Council rates (£15,285 stipend for academic year 2020/21)

This studentship is open to Home or EU students who have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. Other nationalities are eligible provided if they have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least three years prior to the start of the studentship (not as a student).

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