PhD Computational Modelling and AI-Based Digital Twins for Hydrogen and Energy Storage in Petroleum and Energy Infrastructure

Details

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

The global transition towards net-zero energy systems increasingly relies on hydrogen technologies, energy storage, and the repurposing of existing petroleum infrastructure. Pipelines, pressure vessels, and subsurface systems originally designed for oil and gas operations are now being considered for hydrogen transport and storage, presenting critical challenges related to material degradation, structural integrity, and long-term reliability.

This PhD project will develop advanced computational and AI-based modelling frameworks to support the safe and sustainable deployment of hydrogen and energy storage systems within petroleum and energy infrastructure. The research will be carried out within the University of Aberdeen’s strong environment in energy, mechanics, and computational modelling, combining finite-element simulations, multiscale materials modelling, and physics-informed machine learning. The research is also relevant to carbon capture and storage (CCUS) systems, where existing petroleum infrastructure is repurposed for low-carbon energy applications

The project will focus on one clearly defined research pathway, depending on the candidate’s background and interests:

Pathway 1 – Hydrogen and Energy Storage Infrastructure

Computational modelling of materials and structures used in hydrogen transport and storage, including pipelines, pressure vessels, and related components. Emphasis will be placed on deformation, damage, degradation, and lifetime prediction under hydrogen and cyclic loading conditions.

Pathway 2 – Materials for Energy Storage Systems

Multiscale modelling of materials relevant to hydrogen and emerging energy storage technologies (Lithium ion batteries and solid state batteries), linking microstructure, mechanical response, and degradation mechanisms to improve durability and performance.

Pathway 3 – AI-Driven Digital Twins for Energy Infrastructure

Development of physics-informed machine-learning models and digital twins that integrate simulation and experimental data to enable predictive assessment of structural integrity, reliability, and performance of energy systems.

The research is also directly relevant to petroleum engineers transitioning into hydrogen, CCUS, and energy-storage roles, particularly in the assessment and reuse of existing oil and gas infrastructure. The project aligns strongly with international energy-transition priorities and offers excellent preparation for careers in academia, national laboratories, energy companies, and advanced engineering industries.

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Mechanical, Petroleum, Materials, Civil, Chemical, Aerospace, or related Engineering disciplines. Experience or interest in energy systems, infrastructure, computational modelling, finite-element analysis, or data-driven methods is desirable.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for PhD in Engineering to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project title on the application form. If you do not include these details, it may not be considered for the studentship.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

Informal enquiries can be made by contacting Dr A Siddiq at amir.siddiq@abdn.ac.uk. If you require any additional assistance in submitting your application or have any queries about the application process, please don’t hesitate to contact us at researchadmissions@abdn.ac.uk