Master Thesis: Physics-Informed Machine Learning in Hydrogen Fuel Propulsion

Position: Master Thesis: Physics-Informed Machine Learning with Applications in Hydrogen Fuel Propulsion [...]
Functieomschrijving
The Airbus A380 ZEROe demonstrator aircraft is used for testing hydrogen-powered propulsion (source: ). 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.

Assignment 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).

A literature study on PIML and maintenance of hydrogen fuel propulsion systems, including an analysis on how the PIML approach can be used within a PHM context to support the monitoring and health management of hydrogen propulsion systems.

Designing and implementing a PIML framework to model key phenomena in the selected sub-systems or components.

Validating the framework on real-world or high-fidelity simulation datasets and benchmarking it against state-of-the-art methods.

The Final Outcomes Of This Assignment Will Be

A structured assessment, as part of a literature study, identifying the hydrogen fuel propulsion sub-systems or components most suitable for PIML-based modelling, together with a justified selection of the target application.

A PIML-based method and/or workflow capable of predicting the behaviour and/or degradation of the selected hydrogen fuel propulsion system, sub-system, or component, accounting for interpretability and uncertainty quantification.

A technical Master Thesis report, following the guidelines of your faculty, describing the approach, results, and conclusions of the work.

Optionally a conference and/or journal publication.

Available Datasets and Models

Proton Exchange Membrane fuel cell (IEEE PHM Data Challenge 2014)

Hamidi S, Haghighi S, Askari K. Dataset of Standard Tests of Nafion 112 Membrane and Membrane Electrode Assembly (MEA) Activation Tests of Proton Exchange Membrane (PEM) Fuel Cell. Chem Rxiv. 2020; doi:
10.26434/chemrxiv.

Zuo, Jian, Hong Lv, Daming Zhou, Qiong Xue, Liming Jin, Wei Zhou, Daijun Yang, and Cunman Zhang. Long-term dynamic durability test datasets for single proton exchange membrane fuel cell. Data in Brief 35 (2021): 106775.

Relevant Physics-based Models

McKay, Denise

A., Jason

B. Siegel, William Ott, and Anna G. Stefanopoulou. Parameterization and prediction of temporal fuel cell voltage behavior during flooding and drying conditions. Journal of Power Sources 178, no. 1 (2008): 207-222.

Duration
Standard duration of a thesis at your faculty.

What do we expect from you?

You are an MSc student in Aerospace Engineering, Physics, Computer Science or a similar master.

You have experience with programming in Python and packages for machine learning within Python, for instance PyTorch.

You have completed a Machine Learning or AI course (e.g., DSAIT
4005, DSAIT
4115, AE2224-II).

You have a solid physics/engineering background that allows you to understand the physics of failure models.

Your new working environment
Royal NLR has been the ambitious research organisation with the will to keep innovating for over 100 years. With that drive, we make the world of transportation safer, more sustainable, more efficient and more effective. We are on the threshold of breakthrough innovations. Plans and ideas start to move when these are fed with the right energy. Over 800 driven professionals work on research and innovation.

From aircraft engineers to psychologists and from mathematicians to application experts.

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