Company
Aerospace
MasterThesis:Physics-InformedMachineLearningwithApplicationsinHydrogenFuelPropulsionSystems
Neural analysis suggests this role is
optimal for entry candidates.
“Master Thesis: Physics-Informed Machine Learning with Applications in Hydrogen Fuel Propulsion Systems. Skills: Physics-Informed Machine Learning. Assess sub-systems for PIML modelling. Identify components for PIML modelling”
What You'll Achieve.
Structured assessment of sub-systems; Justified selection of target application; PIML-based method/workflow; Predict behavior/degradation; Account for interpretability; Account for uncertainty quantification; Conference publication; Journal publication
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
MSc student in Aerospace Engineering, MSc student in Physics, MSc student in Computer Science, Programming in Python, Machine learning packages in Python, Completed a Machine Learning course, Completed an AI course, Solid physics/engineering background
Nice to Have
PyTorch experience
What You'll Do.
Assess sub-systems for PIML modelling
Identify components for PIML modelling
Conduct literature study on PIML
Analyze PIML for PHM context
Design PIML framework
Implement PIML framework
Validate framework on datasets
Benchmark against state-of-the-art
Full Job Description
What are you going to do? Background The Airbus A380 ZEROe demonstrator aircraft used for testing hydrogen-powered propulsion (source: [https://simpleflying.com/rolls-royce-patent-hydrogen-electric-engine-systems-explained/](https://simpleflying.com/rolls-royce-patent-hydrogen-electric-engine-systems-explained/)) Hydrogen fuel systems are pivotal to the future of aviation, offering a pathway to net-zero carbon emissions. However, their adoption introduces substantial maintenance challenges, as operating with hydrogen involves harsher conditions, stricter safety requirements, and more intricate system behaviour than conventional fuels. Prognostics and Health Management (PHM) can address these challenges by enabling a shift towards condition-based, predictive, and prescriptive maintenance strategies, reducing unplanned downtime, extending component life, and lowering operational costs while improving safety. Realising these benefits, however, depends on the quality of the underlying predictive models, and current approaches face important limitations: purely data-driven models struggle to generalise across operating regimes and require large volumes of representative data, while traditional physics-based models are computationally costly and constrained by simplifying assumptions. Physics-Informed Machine Learning (PIML) offers a promising alternative by embedding physical laws into data-driven frameworks, enabling more accurate, generalisable, and efficient prediction of component behaviour and degradation. This project therefore aims to explore the application of PIML to hydrogen fuel propulsion systems The assignment will include the following tasks: * Preliminary assessment and identification of which sub-systems and components of hydrogen fuel propulsion systems are most suitable for PIML-based modelling, given the current state of the technology and data availability. For this, we have identified several possible components and corresponding datasets (see below)
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