PhD Studentship in Aeronautics: Real-time Data Assimilation for Multi-physics Flows

Imperial College London; Greater London; In loco; GBP 21.000 - 26.000; Tempo pieno

Vantaggi offerti da questo lavoro

Full tuition fees

Tax-free stipend £23,805 per year

3.5-year funding

Descrizione del lavoro

Imperial College London’s Department of Aeronautics invites applications for a fully-funded PhD studentship to develop bias-aware data assimilation methods that learn from data while respecting physics. The project will output a model that updates as measurements are assimilated, with low-order dyn systems and scaling to higher-dimensional multi-physics problems.

The studentship lasts 3.5 years with full tuition fees and a tax-free stipend.

Competenze

• First class honours MEng/MSci or higher in Aeronautics, Mechanical Engineering, Mathematics, Statistics or Physics.
• Background in computational physics, data assimilation, or dynamical systems is advantageous.
• Applicants from all backgrounds are welcome.

Mansioni

• Develop bias-aware data assimilation methods embedded in physics-based models.
• Learn from measurements while preserving governing physics.
• Scale methods from low-order to higher-dimensional multi-physics problems.

Conoscenze

Data assimilation

Multi-physics

Computational mathematics

Formazione

First class honours MEng/MSci or higher

Descrizione del lavoro

Start date:

Between 1st August 2027 and 1st July 2028

Application deadline:

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

Introduction:

Every model is wrong, and in engineering the errors matter. From aircraft wings to wind turbines and combustors, engineering systems are governed by fluid flows that interact with other physical processes, such as structural vibrations, acoustics and chemical reactions. On the one hand, physics-based models of these multi-physics systems are extrapolative – they provide predictions on phenomena that have not been observed. However, their parameters are uncertain, and their equations neglect part of the physics. On the other hand, measurements provide information on the true state of the system, but they are sparse and noisy. When operating conditions change rapidly, model errors grow, and forecasts lose accuracy precisely when they are needed most. Real-time data assimilation, which combines models with measurements, offers a way forward. However, most existing methods assume that the model is unbiased and they cannot cope with rapid changes in operating conditions, such as a gust hitting an aircraft wing or a sudden change in the load of a combustor. A fully-funded PhD studentship is available at the Department of Aeronautics, Imperial College London, to develop bias-aware data assimilation methods that estimate and correct model error as the dynamics evolve. The output of the project will be a model that learns from data any time that measurements are assimilated, without violating the physics. We will prototype the methods on low-order dynamical systems, and we will scale them up to higher-dimensional multi-physics problems.

Supervisors:

Dr. Andrea Nóvoa

Duration:

3.5 years.

Funding:

Full tuition fees and an annual tax-free stipend of £23,805 for Home, EU and International students.

Eligibility:

Candidates will be expected to achieve/have achieved a First class honours MEng/MSci or higher degree (or international equivalent) in: mathematical or engineering disciplines such as Aeronautics, Mechanical Engineering, Mathematics, Statistics or Physics. A background in computational physics or mathematics, data assimilation, or dynamical systems for fluid mechanics is an advantage. We look for curious and motivated candidates who enjoy working at the interface between physics, mathematics and computation. We welcome applications from people of all backgrounds.

Contact:

For more specific project information, email: Dr. Andrea Nóvoa

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.

£23,805 - please see advert