Simulation and Analysis of Ultra-High Efficiency Free-Piston Engine Power Generators
About the Project
Achieving thermal efficiencies beyond 50% represents a transformative opportunity for low-carbon power generation. Dual-piston Free-Piston Engine Generators (FPEGs), which directly couple a linear combustion engine to an electrical generator, offer a fundamentally new pathway to highly efficient, compact, and fuel-flexible power systems. These devices have strong potential in hybrid vehicles, distributed generation, and off-grid energy systems where efficiency, robustness, and fuel flexibility are critical.
This PhD project aims to deliver a step change in FPEG performance by combining first-of-a-kind experimental data with advanced computational modelling and artificial intelligence-driven control. Building on unique datasets generated by the University in collaboration with H2CHP Ltd, the research will address key technical barriers that currently limit efficiency, stability, and controllability in free-piston systems.
The project will develop high-fidelity numerical models of dual-piston FPEGs using advanced moving-mesh CFD coupled with detailed chemical kinetics. These models will be used to analyse and optimise gas exchange, combustion dynamics, and energy conversion processes across a range of net-zero fuels, including hydrogen, ammonia, and synthetic e-fuels. Particular emphasis will be placed on low-temperature combustion strategies (such as HCCI and RCCI) which offer the potential for ultra-high efficiency with inherently low emissions when properly controlled.
A defining innovation of this research is the integration of AI-based control frameworks into the engine design process. Reinforcement learning and machine-learning algorithms will be trained on large simulation datasets to develop adaptive control strategies capable of real-time optimisation of piston motion, combustion phasing, and electrical load management. This approach moves beyond traditional rule-based control, enabling the engine to respond intelligently to changing operating conditions, fuels, and power demands.
The successful candidate will gain expertise at the intersection of combustion science, computational fluid dynamics, machine learning, and advanced energy systems, while working on a technology with clear commercial and societal relevance. The project offers a rare opportunity to contribute to the development of next-generation net-zero power generators, combining fundamental research with strong industrial engagement and a clear pathway to real-world impact.
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