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Job Record #16881
TitlePhD in Machine Learning and Flow in Porous Media
CategoryPhD Studentship
EmployerThe University of Manchester
LocationUnited Kingdom, Manchester
InternationalYes, international applications are welcome
Closure DateMonday, March 29, 2021
The use of porous materials (i.e. solid medium permeated by a network of pores), in particular, for 
passive control of flow and thermal purposes has received considerable academic and industrial 
attention over the past five decades. Examples include transpiration cooling, packed bed energy 
storage and battery and LED cooling, as well as noise control for aerospace and wind turbine 
applications. In such applications, fundamental understanding of interaction between the fluid flow 
and the porous medium at the pore-scale is deterministic for an efficient thermal and flow/noise 
control using porous materials.  


In this 3-year PhD programme we will investigate the flow and thermal control using porous 
materials for application to aerospace and renewable energy systems. The study performs 
fundamental analysis of fluid flow and heat transfer in porous materials with the aim of achieving an 
in-depth understanding of the flow features at the pore-scale deploying machine learning technique. 
The project will be conducted in collaboration with the University of Bristol for experimental 
measurements on the velocity field. The PhD student has the opportunity to visit University of Bristol 
for data collection. The PhD student will work closely with the industrial partners (BL Refrigeration 
and Air Conditioning Ltd. and Glen Dimplex Heating & Ventilation Ltd). The student will join an active 
research team including three academics and four postdoctoral and PhD students working on 
different aspects of porous materials for thermal management application. 


Applicants must have an undergraduate or Master’s degree with a background in Mechanical, 
Aerospace Engineering, Physics, Applied Mathematics, or a related discipline. Applicants with an 
interest in machine learning and turbulent flow analysis will be considered. Experience in using CFD 
software such as Fluent and OpenFoam is an advantage.  


Enquirers are welcome to contact Dr Yasser Mahmoudi with queries or expressions of interest at

Contact Information:
Please mention the CFD Jobs Database, record #16881 when responding to this ad.
NameDr Yasser Mahmoudi Larimi
Email ApplicationNo
Record Data:
Last Modified16:51:01, Sunday, December 06, 2020

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