[School of Engineering PhD Scholarships] Physics-informed and explainable reduced-order modelling for integrated subsurface hydrogen and thermal energy storage

About the project

About the Project The proposed PhD will develop a physics-informed reduced-order modelling (ROM) framework for integrated subsurface hydrogen and thermal-energy storage, using biogas-to-hydrogen conversion as an application case. The concept offers the potential for downhole biogas reforming, hydrogen separation and direct subsurface sequestration of the resulting CO2, while exploiting high-pressure and high-temperature wellbore conditions and heat recovery to improve process efficiency. A major challenge is that high-fidelity multiphase and multiphysics flow simulators are computationally demanding, particularly when assessing heterogeneous geological systems and repeated cycles of injection, storage and extraction. The primary objective of this PhD is therefore to develop a computationally efficient, explainable and physics-informed reduced-order model that can provide rapid prediction of pressure, fluid-flow and thermal behaviour while retaining the essential physics of the underlying system. The research will pursue four main objectives: 1. Develop high-fidelity multiphase, compositional and thermal models representing biogas-derived H2/CO2 systems and subsurface heat storage. 2. Develop a capacitance–resistance model (CRM)-based reduced-order model 3. Integrate machine learning with the reduced-order model to capture unresolved nonlinear behaviour while maintaining physical constraints and interpretability. 4. Apply the resulting model to optimise hydrogen production, thermal charging and discharging, and overall subsurface energy-system operation. High-fidelity simulations will investigate the effects of heterogeneous geological formations, well configurations, injection rates, pressure, temperature, fluid composition and cyclic operating strategies. Particular emphasis will be placed on developing a modified “gas-compressibility” capacitance–resistance model capable of representing the distinctive storage and flow behaviour of gas-rich subsurface systems. Machine-learning methods will subsequently be used either to learn effective model parameters or to represent the residual between reduced-order and high-fidelity solutions. Explainable-AI approaches, including sensitivity analysis, SHAP and symbolic regression, will be used to identify the geological and operational factors controlling hydrogen recovery, thermal breakthrough and energy efficiency. The successful candidate will be supported by an interdisciplinary supervisory and research team with strong experience in subsurface energy engineering, numerical modelling, hydrogen, geothermal energy and data-driven methods, as well as an established track record of PhD training. The student will gain advanced training in multiphase and compositional flow modelling, heat and mass transfer, reduced-order modelling, machine learning, explainable AI, uncertainty analysis and optimisation. The project will also provide experience in translating high-fidelity multiphysics models into computationally efficient engineering tools and validating them against industrially relevant scenarios. Industrial collaborators will provide application-focused guidance and help translate the developed modelling framework towards practical subsurface energy-storage applications. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Chemical or Mechanical engineering (or international equivalent) with desirable skills in numerical methods OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Chemical or Mechanical engineering (or international equivalent) with desirable skills in numerical methods. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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