www.acad.jobs : academic jobs worldwide – and the best jobs in industry
                
     
Position: PhD Position - Simulation and modelling of hydrogen turbulent flames with High Performance Computing and Machine Learning
Institution: University of Edinburgh
Location: Edinburgh, Scotland, United Kingdom
Duties: Perform large-scale simulations (Direct Numerical Simulations) of turbulent hydrogen flames using numerical grids with tens of billions of points on supercomputers with up to two hundred thousand processors; Analyse results to develop simplified models for the simulation of hydrogen flames to be used in Large Eddy Simulation (LES) and Reynolds Average Navier-Stokes (RANS) approaches; Use data from large-scale simulations to design and train Machine Learning models; Implement traditional and machine learning models in the open source CFD suite OpenFoam and assessment of model performance
Requirements: Minimum entry qualification - an Honours degree at 2: 1 or above (or International equivalent) in a relevant science or engineering discipline. The candidate should have a master’s degree in either Physics, Mathematics, or Engineering. Competencies and/or experience in computational fluid mechanics, machine learning, computational science, and simulation software (e.g., OpenFOAM) will be positively considered, but are not essential
   
Text: Skip to main content HomeAboutStudyResearchAlumniBusinessContact Us School of Engineering > Schools & Departments Search form Search SCHOOL OF ENGINEERING You are hereHome » Study » Postgraduate Research » Study for a PhD » PhD Opportunites » Simulation and modelling of hydrogen turbulent flames with High Performance Computing and Machine Learning Simulation and modelling of hydrogen turbulent flames with High Performance Computing and Machine Learning Hydrogen will play an important role in the energy transition. Differently from hydrocarbon fuels, the chemical energy stored in hydrogen can be released without the emission of CO2. Major companies are currently implementing hydrogen-based solutions ranging from domestic burners to half-gigawatt gas-turbines for electricity production. In addition, the possibility to use hydrogen in aeronautical engines is gaining popularity. While hydrogen is the simplest molecule, hydrogen combustion is characterized by complex non-linear phenomena, in particular in the turbulent regime. Understanding their dynamics and learning how to describe them with simplified models is key for the design of the next-generation devices and to take full advantage of the benefits that hydrogen can offer. In this PhD project, we will combine high performance computing (HPC) on some of the largest supercomputers in the world with sophisticated data analysis approaches based on statistical inference and machine learning. The overarching goal is the development of reliable, predictive, and inexpensive models for the simulation of hydrogen combustion in turbulent flames, with particular focus on configurations of industrial relevance. The tasks and objectives of this PhD project are: Perform large-scale simulations (Direct Numerical Simulations) of turbulent hydrogen flames using numerical grids with tens of billions of points on supercomputers with up to two hundred thousand processors. Analyse results to develop simplified models for the simulation of hydrogen flames to be used in Large Eddy Simulation (LES) and Reynolds Average Navier-Stokes (RANS) approaches. Use data from large-scale simulations to design and train Machine Learning models. Implement traditional and machine learning models in the open source CFD suite OpenFoam and assessment of model performance. The PhD candidate will: Develop a wide range of skills in computational engineering, fluids mechanics and combustion, statistics, machine learning. Have access to state-of-the-art computing facilities, including the newly installed Archer2, the flagship UK supercomputer. Work in an international team with collaborators at RWTH Aachen University, Germany, Université libre de Bruxelles, Belgium, Sapienza University of Rome, Italy, Princeton University, USA. Informal enquiries may be addressed to Dr. Antonio Attili antonio.attili@ed.ac.uk Further Information: This position will remain open until filled, so may close early if a suitable candidate is found. The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity Closing Date: Thursday, August 18, 2022 Direct Numerical Simulation of a highly turbulent premixed flameClick here to Apply Now Principal Supervisor: Dr Antonio Attili Eligibility: Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline. The candidate should have a master’s degree in either Physics, Mathematics, or Engineering. Competencies and/or experience in computational fluid mechanics, machine learning, computational science, and simulation software (e.g., OpenFOAM) will be positively considered, but are not essential. Further information on English language requirements for EU/Overseas applicants. Funding: Tuition fees and stipend are available for Home students (UK or EU with settled/pre-settled status). International/EU without settled/pre-settled status students can apply, but the funding only covers the Home fee rate. Further information and other funding options. Informal Enquiries: antonio.attili@ed.ac.uk UndergraduatePostgraduate TaughtPostgraduate ResearchStudy for a PhDPhD OpportunitesPhD ScholarshipsCDTsOur ResearchMSc by Research, MPhil and Visiting ResearchersPGR Additional InformationInternational StudentsWhy Edinburgh?Fees and FundingVisit UsApplySupport ServicesCareersStudent CommunityGo Abroad THE UNIVERSITY OF EDINBURGH Terms & Conditions Privacy Notice Privacy & Cookies Website Accessibility Freedom of information publication scheme Website Issue Reporting MyEd login Students Staff/PGR Alumni Social Media Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh.
Please click here, if the job didn't load correctly.







Please wait. You are being redirected to the job in 3 seconds.