Multi-Physics Dynamic Modelling and Physics-Informed AI for Particle-Based Long-Duration Energy Storage Systems

About the project

About the Project This PhD project focuses on the development of next-generation high-fidelity modelling, computational fluid dynamics (CFD), and physics-informed artificial intelligence frameworks for particle-based long-duration energy storage (LDES) systems operating under highly variable renewable energy conditions. As modern power systems increasingly rely on intermittent wind and solar generation, there is an urgent need for intelligent, flexible, and thermally efficient energy storage technologies capable of supporting grid stability and deep decarbonisation. The research will investigate the transient thermo-fluid behaviour, heat transfer, particle transport, and dynamic system response of high-temperature particle-based thermal energy storage systems subjected to rapidly changing charging and discharging conditions. The project aims to develop advanced predictive and control methodologies that enable intelligent real-time operation under uncertain renewable generation, electricity demand, and market conditions. A major component of the project will involve the development of high-fidelity multi-physics CFD models to study complex particle dynamics and turbulent heat transfer within key system components. The student will employ state-of-the-art numerical techniques to analyse transient heat transfer, turbulence, particle-fluid interactions, thermal stratification, and system-level thermodynamic behaviour across multiple spatial and temporal scales. In parallel, the project will integrate emerging Physics-Informed Neural Networks (PINNs), reduced-order modelling, and AI-enabled digital twin technologies to accelerate simulations, improve predictive capability, and enable real-time system optimisation and control. These hybrid physics-AI approaches will combine first-principles thermo-fluid models with machine learning techniques to create computationally efficient yet highly accurate models suitable for online monitoring, fault detection, optimisation, and adaptive control. The successful candidate will gain expertise in particle dynamics, turbulent heat transfer, AI for energy storage systems, and advanced computational modelling, positioning them at the forefront of emerging digital energy technologies. The project offers opportunities to work with large-scale experimental facilities, industrial datasets, and cutting-edge computational platforms. The student will join an internationally leading research team at University of Manchester and become part of a major international consortium involving leading UK industrial partners and more than 20 academic and research institutions across the UK, Europe, and the USA. This provides a unique opportunity to work within a highly collaborative multidisciplinary environment spanning academia, industry, advanced energy technologies, and AI-driven engineering research. Eligibility Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Funding This 3.5-year PhD project is fully funded and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026. We recommend that you apply early as the advert may be removed before the deadline. Before you apply We strongly recommend that you contact the supervisor for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project. How to apply Apply online through our website: https://uom.link/pgr-apply-2425 When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees. Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. After you have applied you will be asked to upload the following supporting documents: Final Transcript and certificates of all awarded university level qualifications Interim Transcript of any university level qualifications in progress CV Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it). Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference) English Language certificate (if applicable) If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk . Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status. We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

Research areas