Turning Up the Heat: Modelling and Scale-Up of Thermochemical Energy Storage

The overarching aim of this project is to develop modelling tools that combine physics-based and data-driven methods to support the design and scale-up of thermochemical energy storage (TCES) systems. The specific goals are:

• Develop a physics-based model for heat and mass transfer in simple TCES configurations, using partial differential equations (PDEs).
• Build a Physics-Informed Neural Network (PINN) that integrates the physics-based model with experimental data to improve predictive accuracy.
• Incorporate uncertainty quantification into the PINN to account for variability in material properties and operating conditions.
• Validate the PINN using new experimental data to ensure reliability and generalisability.
• Use the trained PINN to explore scale-up scenarios, predicting system performance under different design and operating conditions to support future deployment.

• Open-source software: the main outcome of the project will be an open-source package to efficiently simulate the TCES systems. This package will be robust and well-documented, so it can be used by other researchers, industry and policymakers.
• Scientific publications: the outcomes of the research will be disseminated in scientific publications in top journals.
• New collaborations: TCES modelling is at an early stage, creating strong opportunities for partnerships with academia and industry. The student will play a key role in building these links, positioning themselves as an emerging leader in a rapidly growing field.

Physics-based & data-driven modelling: They will learn to build and combine physical models (PDEs) with machine learning tools (PINNs), and analyse experimental data to validate and improve models, extracting insight to guide system design and upscale. They will use tools like PyBaMM and TensorFlow, widely used in industry.

Software development: They will gain hands-on experience writing modular, well-documented code, adopting reproducibility and open-source practices.

Teaching & outreach: They will help prepare teaching materials and contribute to training activities related to the software. They may also support teaching in Maths and Engineering, and engage in outreach to communicate science to the public.

Communication: They will learn to present modelling results clearly to academic, industrial, and non-specialist audiences.

Interdisciplinary collaborations: They will work across maths and engineering, gaining experience in collaborative, interdisciplinary research.

These skills position you for careers in AI research, computational materials science, national laboratories, tech industry or academic research. The HetSys training provides a foundation for these skills through dedicated courses and cohort activities.

We require at least a II(i) honours degree at BSc or an integrated masters degree (e.g. MPhys, MChem, MSci, MEng etc.) in a physical sciences, mathematics or engineering discipline. We do not accept applications from existing PhD holders.

If you are an overseas candidate please check here that you hold the equivalent grades before applying.

For postgraduate study in HetSys, the term “overseas” or “international” student refers to anyone who does not qualify for UK home fee status. This includes applicants from the European Union (EU), European Economic Area (EEA), and Switzerland, unless they hold settled or pre-settled status under the UK’s EU Settlement Scheme.