PhD Studentship in Aeronautics: Developing a Digital Twin for Composite Pressure Tanks (AE0080)

Start Date: Between 1 August 2026 and 1 July 2027

Introduction: Hydrogen fuel offers a clean and efficient alternative to conventional fossil fuels and is widely regarded as a cornerstone of the transition toward a net-zero energy future. Despite its enormous potential as a clean aviation fuel, hydrogen has not yet become a practical option for powering aircraft due to several major technological and safety challenges. The foremost issue lies in storage, hydrogen’s low volumetric energy density requires it to be stored either as a compressed gas at extremely high pressures or as a cryogenic liquid at very low temperatures (−253 °C). Both approaches demand lightweight yet robust composite tanks that can safely withstand cyclic loading, impact, and fatigue over long service lives. In addition, hydrogen embrittlement, leakage, and long-term material degradation raise critical reliability concerns for flight certification.

Structural Health Monitoring (SHM) and Digital Twinning (DT) technologies can address the above challenges and transform feasibility into reality. In this project we will develop SHM technologies to provide continuous, real-time insight into the condition of hydrogen storage systems. We will develop methodologies (such as acoustic emission method) detecting early signs of damage, leaks, or degradation before they become critical. We will also leverage the latest developments in machine learning combining physical models, sensor data, computational methods, and damage and fracture mechanics concepts to create a virtual replica of the composite tank, enabling predictive maintenance, lifetime assessment, and forecasting its future states. Together, these technologies can significantly enhance safety, reliability, and design optimization to make hydrogen-powered aviation both viable and certifiable for future flight.

You will develop a strong expertise in computational mechanics, structural health monitoring and machine learning with a particular focus on fundamental aspects that can have far-reaching impacts in diverse fields.

Supervisors: Prof. Zahra Sharif Khodaei, Professor of Aerospace structural durability and health monitoring and Prof. M.H. Ferri Aliabadi, Professor of Aerostructures

Duration: 3.5 years.

Funding: Full coverage of tuition fees and an annual tax-free stipend of £22,780 for Home, EU and International students.

Eligibility: Due to the competitive nature of these studentships, candidates will be expected to achieve/have achieved a First class honours MEng/MSci or higher degree (or international equivalent) in: Engineering (Aeronautics or Mechanical Engineering).

A strong background in structural mechanics and computational methods is essential, and familiarity with topics such as machine learning and structural health monitoring is desired. The candidate must be motivated to undertake fundamental research that is highly mathematical in nature.

How to apply:

• Stage 1: Submit your 2-page curriculum vitae (CV), transcripts and 300-word statement explaining your motivation for applying to this PhD Studentship to: Supervisor Review Form. Our supervisors will perform a comprehensive review to long-list candidates.

Deadline: 8 January 2026

• Stage 2: Supervisors will email further instructions and an application link to long-listed candidates, inviting them to make a formal application to the PhD Studentship.

Contact: For questions about the project: Prof. Sharif Khodaei

For queries regarding the application process, email Lisa Kelly, PhD Administrator

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.

Advert information