PhD Studentship in Aeronautics: Machine Learning-based Control of Reacting Flow Extreme Events

Start date

Between 1st August 2027 and 1st July 2028

Application deadline

23:59, 4th January 2027 (UK time)

Introduction

The development of sustainable propulsion systems, based on hydrogen or sustainable aviation fuels (SAFs), is increasing the risk of extreme events occurring in these. These are rare events where the flow in the combustor suddenly takes extreme states far, potentially with grave consequences. Examples are flashback in hydrogen or thermoacoustic instabilities triggered by the varying SAFs properties. The safe and reliable operation of these future sustainable propulsion systems thus requires the ability to anticipate their occurrence through measurable precursors and the deployment of real‑time mitigation based on those precursors.

Traditional methods

Traditional methods have generally been unable to tackle this due to the turbulent nature of the underlying flows and the complex multiscale/multiphysics nonlinear interactions at the origin of such extreme events. Recent advances in differentiable solver and machine learning offer a pathway to enable such their mitigation and control.

Project Description

In this project, you will develop a framework for the control of reacting flow extreme events that combines a differentiable reacting flow solver and a machine learning-based controller. The differentiable solver will provide short-term state dependency used by machine learning-based controller to obtain an optimal mitigation control preventing the reacting flow extreme event. This will be applied across various reacting flow applications, leveraging novel reacting flow actuators such spray water injection or electrically-driven fuel droplet control.

Research Group

You will join a research group on the European project CONTEXT, at the intersection of scientific machine learning and fluid dynamics, under the supervision of Dr. Anh Khoa Doan. You will also have the opportunity to interact with experts in turbulence and machine learning across the department, such as Prof. Luca Magri. Dr. George Rigas or Dr. Andrea Novoa.

Learning opportunities

You will develop expertise in reacting flow physics, machine learning for fluids and control, and high-fidelity simulations. You will have the opportunity of using cutting-edge facilities such as Imperial College HPC facilities.

Professional Development

You will have access to engaging professional development workshops in areas such as research communication, computing and data science, and professional progression through our Early Career Researcher Institute, and will have the opportunity to participate to international conferences.

Supervisors

Dr. Nguyen Anh Khoa Doan

Duration

3.5 years.

Funding

Full tuition fees and an annual tax‑free stipend of £23,805 for Home, EU and International students. Find out more about fee status

Eligibility

Candidates will be expected to achieve/have achieved a First class honours MEng/MSci or higher degree (or international equivalent) in: Aeronautical/Mechanical Engineering, Applied Mathematics, Computer/Data Science, or Applied or Computational Physics

Experience

We are looking for students with experience in: Fluid Mechanics, Turbulence, CFD, Machine Learning, Reacting Flows

Contact

For project information, email: Dr. Nguyen Anh Khoa Doan

Admissions queries

For admissions questions, please read our Frequently Asked Questions. If your question isn’t answered, email the PhD Administrator (Admissions)

Equality, Diversity and Inclusion

Imperial is committed to equality and valuing diversity. We are an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employer and are working in partnership with GIRES to promote respect for trans people.

Salary

£23,805 - please see advert

#J-18808-Ljbffr