Contact for Application Enquires: Dr Zinedine Khatir
In the alteration to a decarbonized electric power system, variable renewable energy (VRE) resources such as wind and solar cells play a key role due to their availability, scalability, and affordability. However, the degree to which VRE resources can be effectively installed to decarbonize the electric power system hinges on the future availability and cost and reliability of energy storage technologies (batteries). Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) are promising technologies to aid decrease the quantity of fuel expended for transportation. In both HEVs and PHEVs, the battery pack is a vital element to allowing their fuel savings potential. The battery is also one of the most costly components in the vehicle. One of the most important factors impacting both the performance and life of a battery is temperature. In particular, operating a battery at elevated temperatures reduces its life. The effects of heat and thermal management of structures is more and more critical as performance limits are tested further by the need to have lighter, smaller and more efficient designs. Convection, conduction and radiation loads are obvious, but the need to include effect of power losses thermal energy from friction of particles and external sources such as fluid flow within battery means that analyst need to have more tools at their disposal to simulate thermal models accurately. This PhD project will entail the use of Computational Fluid Dynamics (CFD) combine with design optimization techniques to investigate thermal management of batteries. The thermal performance of the bus bar as well as microchannel cooling strategies will be analyzed for optimum cooling outcome. The research will focus on reducing cost of battery by reducing thermal losses and improve through active cooling techniques. Experimental work will be undertaken should time and resources permit.
The successful applicant will have, or be expecting, a good Degree and/or Masters (or equivalent) in engineering or physical sciences, with a strong interest in multidisciplinary computational engineering and science, energy and industrial applications and have a strong thermal and fluid dynamics background and knowledge of the use and development of CFD tools, specifically OpenFOAM would be an advantage. Experience in CFD-based Design Optimisation techniques and their applications (i.e. Robust and Bayesian Optimisation, Meta-modelling, Surrogate Modelling, Machine/Deep Learning), as well as design of thermal storage and thermal airflow systems are particularly welcome.
How to apply:
Applicants who apply for this project will be considered on a competitive basis in March 2021 against candidates shortlisted for this project. Early submission is advised, and a complete application must be received before the advert’s closing date.
The successful candidate will be notified by 26 March 2021.
To apply, please complete the project proposal form ensuring that you quote the project reference, and then complete the online application where you will be required to upload your proposal in place of a personal statement as a pdf document.
You will also be required to upload two references, at least one being an academic reference, and your qualification/s of entry (Bachelor/Masters certificate/s and transcript/s).