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

The University of Manchester; Manchester; On-site; GBP 20,000 - 24,000; Full time

Benefits offered by this job

Tax-free stipend

Fees fully covered

Job summary

The University of Manchester invites applications for a 3.5-year PhD Studentship in Mechanical Engineering, focused on high-fidelity modelling, CFD, and physics-informed AI for particle-based long-duration energy storage under variable renewables.

The successful candidate will develop multi-physics CFD models and PINNs, study particle dynamics and turbulent heat transfer, and enable real-time monitoring and adaptive control.

Qualifications

• A strong background in mechanical/energy engineering or related discipline is expected.
• Experience with CFD/ multi-physics modelling is advantageous.

Responsibilities

• Develop multi-physics CFD models to study particle dynamics and turbulent heat transfer.
• Integrate PINNs and AI-enabled digital twins for real-time monitoring and adaptive control.

Skills

Particle dynamics

CFD

Physics-Informed Neural Networks

AI for energy storage

Digital twin

Computational modelling

Education

2.1 honours degree or master's in relevant science/engineering

Job description

Application deadline: All year round
Research theme: Mechanical Engineering; Heat Transfer; Thermal Engineering; Energy Storage; Particle Dynamics

This 3.5-year PhD studentship is open to both Home (UK) and international applicants. The successful candidate will receive a tax-free annual stipend at the UKRI rate (£21,805 for 2026/27, subject to annual uplift), with tuition fees fully covered. The stipend is expected to increase each year. The start date is October 2027.

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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.

£21,805 tax-free annual stipend and tuition fees fully covered