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Job Record #19168
TitlePhD within the Centre of Computational Engineering Sciences
CategoryPhD Studentship
EmployerCranfield University
LocationUnited Kingdom, Bedfordshire, Bedford
InternationalNo, only national applications will be considered
Closure DateWednesday, June 19, 2024

Multi-dimensional Optimal Order Detection techniques for Arbitrary Lagrangian Eulerian (schemes) for compressible flows PhD

A fully funded PhD studentship with a bursary of £25,000 p.a. (tax-free) plus UK Home student fees for four years in the Centre of Computational Engineering Sciences at Cranfield University.

This is a great opportunity to work closely with AWE, for advancing state-of- the-art mathematical models and simulation capabilities. We are looking for an enthusiastic and motivated researcher with a strong interest in computational fluid dynamics (CFD), to join a diverse and inclusive research group. The Advanced Numerical Methods (ANM) Group specialises in cutting-edge research and consulting services focused on pioneering methods for CFD.

This exciting research seeks to evolve the forefront of computational fluid dynamics (CFD) methods for compressible flows in the emerging Exascale High- Performance Computing (HPC) landscape. The aim is to achieve high-order of accuracy but also to obtain physically meaningful results that can advance the landscape of engineering simulations.

The choice of the best numerical method, governing equations, and framework is far from obvious and unique. Employing a MOOD-style algorithm that continuously scrutinises every facet of the numerical solution, ensuring adherence to both physical and numerical admissible criteria. This algorithm unlocks several possibilities and enables high-accuracy and computational efficiency across single- and multi-physics simulations.

However for Arbitrary Lagrangian Eulerian (ALE) frameworks, conservation errors, dissipation errors in regions of low-mesh quality, and carbuncle are just some of the issues that persist. The primary goal of this project is to advance the MOOD techniques for ALE frameworks by establishing new metrics that can accurately capture conservation errors during mesh deformation, bypassing/minimising the carbuncle phenomenon, establishing new physical and numerical admissible detection criteria for ALE, evaluate any potential benefits of high-order methods in the ALE context, and advance an available open source CFD software with the newly formulated algorithms/methods/frameworks.

Cranfield University’s Computational Engineering Sciences Centre and Advanced Numerical Methods Group will host this project, benefiting from a proven track record in pioneering and implementing cutting-edge methodologies across the entire spectrum of computational fluid dynamics and close collaborations with other research groups around the globe. AWE will serve as the industry sponsor for this project, providing valuable support by computational physics specialists.

The results of this project will contribute to an improved understanding of the possibilities and limitations of high-order MOOD methods for ALE schemes of compressible flows. The developed tools and methods will be implemented in massively parallel open-source CFD software and demonstrate best practices for method/equations/formulation selection for emerging architectures of Exascale High Performance Computing (HPC) facilities.

The student involved in this project will acquire valuable skills through several experiences including attending modules from our MSc in CFD, attending workshops for Exascale HPC in national facilities, visiting several European research groups that the centre is in close collaboration for enhancing the depth and breadth of their research methods and growing their network, and close collaboration with our partner, AWE. The student will be supported to present in international conferences, and workshops and further enrich their skillset.

Entry requirements

Applicants should have an equivalent of a first or second-class UK honours degree in mathematics, physics, engineering or other closely background. The candidate should have advanced programming skills in any of the following programming languages (Fortran,C++, C, Python). Successful applicant will carry out research activities in the aforementioned area and disseminate research outputs through scientific publications, software development, seminars and conference presentations.


Sponsored by EPSRC, AWE and Cranfield University, this DTP studentship will provide a bursary of up to £25,000 (tax free) plus fees* for four years.

To be eligible for this funding, applicants must be UK national.

Diversity and Inclusion at Cranfield

At Cranfield, we value our diverse staff and student community and maintain a culture where everyone can work and study together harmoniously with dignity and respect. This is reflected in our University values of ambition, impact, respect and community. We welcome students and staff from all backgrounds from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing.

We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and sponsors of International Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

If you are eligible to apply for this research studentship please complete the online application form.

For further information please contact
Dr Panagiotis Tsoutsanis

Contact Information:
Please mention the CFD Jobs Database, record #19168 when responding to this ad.
NameDr Panagiotis Tsoutsanis
Email ApplicationNo
Phone+44 1234 754635
Record Data:
Last Modified18:13:32, Monday, May 13, 2024

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