Towards ultra-high efficiency free piston engine generator

This PhD project aims to achieve a breakthrough in thermal efficiency (>50%) for dual-piston Free-Piston Engine Generators (FPEGs). The research will pioneer a novel, integrated methodology combining high-fidelity computational modelling with artificial intelligence to overcome key barriers in performance.

The investigation will focus on optimising core gas exchange and combustion processes, critical for efficiency. A sophisticated numerical framework will be developed, coupling moving-mesh CFD with detailed chemical kinetics to evaluate advanced scavenging designs and low-temperature combustion modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia.

A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning, will be trained on simulation data to create dynamic, adaptive control systems that optimise operation in real-time across multiple variables.

This research will deliver a validated roadmap to 60% efficiency, novel engine designs, and an open-source AI optimisation tool. The outcomes will provide significant academic and practical contributions to developing ultra-clean, sustainable power generation technologies.

A resilient candidate with strong background in mechanical engineering, prefer to have experience in IC engines, combustion and CFD. 

Open to candidates who pay UK/home rate fees. See UKCISA for further information.

You will need to meet the minimum entry requirements for our PhD programme.