About the project
This project aims to develop advanced digital twin technology for space object re-entry monitoring, addressing multiphysics coupling and hybrid uncertainties. The project focuses on uncertainty quantification, robust model updating, and real-time data integration to improve re-entry prediction accuracy.
This project addresses the growing challenge of uncontrolled space debris re-entry, posing risks to residential areas and Earth’s sustainability. The research aims to revolutionize space object re-entry monitoring by developing a robust digital twin system that combines advanced uncertainty quantification, real-time data integration, and efficient multiphysics simulation.
Key objectives include:
- creating hybrid aleatory (random) and epistemic (unknown-but-fixed) uncertainty models to accurately predict re-entry outcomes
- developing highly efficient data-driven surrogate models to balance computational efficiency and precision
- building a digital twin capable of real-time data assimilation and bidirectional interaction for re-entry trajectory monitoring
- providing robust predictions of space debris re-entry trajectories and impact regions, accounting for multi-source and mixed uncertainties
You will gain expertise in cutting-edge techniques like stochastic model updating, Bayesian inference, and digital twin technology. You will work on a unique combination of forward uncertainty propagation and inverse model updating methods, contributing to the next generation of aerospace monitoring systems.
The project is based in our Department of Aeronautics and Astronautics, a leading institution with a strong track record in aerospace research. You will collaborate with experts from academia and industry, gaining access to advanced facilities and a multidisciplinary research environment.
Your work will have a profound impact on enhancing the safety and sustainability of space exploration, setting the foundation for better risk assessment and mitigation of space debris.
You will contribute to an innovative project with real-world applications and global significance.
