PhD Studentship: Dynamic Analysis of Particle-Based Long-Duration Energy Storage Systems

Application deadline: All year round

Research theme: Mechanical Engineering; Heat Transfer; Thermal Engineering; Energy Storage; Particle Dynamics

How to apply: uom.link/pgr-apply-2425

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.

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.

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.

To apply please contact the main supervisor; Prof Yasser Mahmoudi Larimi - yasser.mahmoudilarimi@manchester.ac.uk. 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.

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