PhD Studentship in Dynamic Analysis of Particle-Based Long-Duration Energy Storage Systems
Develop next-generation AI-driven computational models for advanced particle-based energy storage under variable renewable energy conditions.
Best for:Best for engineers or scientists with a strong background in computational fluid dynamics (CFD), particle dynamics, and AI-driven modeling, seeking to contribute to cutting-edge energy storage technologies and digital energy systems.
Overview
This PhD studentship at The University of Manchester focuses on advancing high-fidelity computational fluid dynamics (CFD) and physics-informed artificial intelligence (AI) frameworks for particle-based long-duration energy storage (LDES) systems. The project aims to address critical challenges in grid stability and decarbonization by improving the transient thermo-fluid behavior, heat transfer, and dynamic response of these systems under rapidly changing renewable energy conditions. The successful candidate will contribute to developing hybrid physics-AI models, integrating CFD, digital twins, and machine learning to optimize real-time system performance. The research is part of a major international consortium involving leading academic and industrial partners, offering a multidisciplinary environment at the intersection of energy, AI, and computational engineering.
What you'll do
- Develop high-fidelity multi-physics CFD models to analyze transient thermo-fluid behavior and particle dynamics in energy storage systems.
- Integrate physics-informed neural networks (PINNs) and reduced-order modeling techniques to enhance simulation accuracy and efficiency.
- Design and implement AI-driven digital twin technologies for real-time monitoring, fault detection, and system optimization.
- Conduct experimental validation using large-scale facilities and industrial datasets.
- Collaborate with international academic and industrial partners to advance energy storage technologies.
- Publish research findings in top-tier academic journals and present at conferences.
Requirements
Degree required: At least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering discipline (e.g., Mechanical Engineering, Thermal Engineering, Computational Fluid Dynamics, or related fields).
Degree offered: PhD
Language: English; proficiency sufficient for academic research and collaboration (no specific test mentioned).
Instruction: English
- Strong background in computational fluid dynamics (CFD) and particle dynamics modeling.
- Experience with numerical techniques for transient heat transfer and turbulence analysis.
- Familiarity with programming languages such as Python, C++, or MATLAB.
- Knowledge of machine learning and AI techniques, particularly physics-informed neural networks (PINNs).
- Ability to work with large-scale computational platforms and experimental facilities.
- Open to both UK (Home) and international applicants.
- No specific age or residency restrictions mentioned.
Funding & terms
How to apply
Deadline: 2027-01-23
Applicants should contact the supervisor directly via email. Include details of your current level of study, academic background, relevant experience, and a paragraph explaining your motivation for the PhD project. Apply through the university’s PhD application portal using the provided link.
- CV
- Academic transcripts (BSc and MSc)
- Motivation letter or paragraph explaining interest in the project
- Details of academic background and relevant experience
Contact
Yasser Mahmoudi Larimi· Supervisor
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