Contact Dr Junjie Zhao
Areas of expertise
- Aeronautical Systems
- Autonomous Systems
- Aviation Management & Operations
- Computing, Simulation & Modelling
- Instrumentation, Sensors and Measurement Science
- Systems Engineering
- Vehicle Health Management
Background
Junjie is a Research Fellow in Intelligent Systems Engineering for Digital Aviation with the Centre for Autonomous and Cyber-Physical Systems in the School of Aerospace, Transport and Manufacturing. Before taking the academic role, he received his PhD in Aerospace from the same school's Centre for Propulsion and Thermal Power Engineering in 2022. His doctoral research focused on Machine Learning (ML) and Information Fusion based aircraft engine health monitoring in the context of Digital Twin. He joined Cranfield in 2016, obtaining his MSc in Aerospace Propulsion, followed by a double degree MSc in Aerospace Propulsion from Northwestern Polytechnical University, China, in 2018. His main research interests include Air Traffic Management(ATM)/Unmanned Traffic Management (UTM) Integration, Urban Air Mobility, Digital Twin, Synthetic Environment, Information Fusion, Explainable Artificial Intelligence (AI), and System of Systems (SoS).
Research opportunities
Aligned with the current research on future Unmanned Aerial Systems (UAS) operations, ATM/UTM integration and UAM applications within the ATM Laboratory, we have research opportunities on Digital Twin building, emerging test methodology, Explainable ML, software development and other data-driven solutions.
Current activities
He is involved in the Laboratory’s research and development environment building, research projects, and teaching activities.
Research projects: Led the project proposal (with an 80% of sale allocation) and participated in the delivery of the project SPAAM (Stackable Programme on Advanced Air Mobility), July 2023-December 2023, funded by UKRI under the Future Flight Closing the Skills Gap Competition, ID: 10069102. Currently focusing on project HADO (High-intensity Autonomous Drone Operations), 2022-2024, funded by UKRI under the Future Flight Challenge Phase 3 programme, ID: 10024815. Previously involved in the project AMEC (Air Mobility Ecosystem Consortium), 2022-2024, funded by UKRI under the Future Flight Challenge Phase 3 programme, ID: 1002320; project Blueprint, 2022-2024, funded by UKRI under the Future Flight Challenge Phase 3 programme, ID: 10025964.
Teaching activities: Designs and supervises the group design project (GDP) for the Advanced Air Mobility Systems MSc Course; Delivers lectures in the Introduction to Advanced Air Mobility module of the Advanced Air Mobility Systems MSc Course; Delivers lectures in the Aerospace Software Engineering and ADA module of the AVD MSc Course. Supports course development based on the SPAAM project.
ATM/UTM Lab Research and Development Environment: Led the data model development and currently leads Extension-1 development on the Digital Twin for future Advanced Air Mobility operations.
Student supervision: Currently the second supervisor for one PhD and one MSc student.
Current areas of interest: His current areas of interest include Digital Twin building in the synthetic environment to enable virtual/mix-reality tests for UAS operations and UAM applications; Multimodal transport mode to accommodate emerging UAS/UAM operations; AI solutions for future UAS operations in the context of Digital Twin; Novel test methodology development for the emerging air transport systems.
Clients
Operational Solutions Limited (OSL)
Thales
NATS
ANRA
Publications
Articles In Journals
- Chen Y-Z, Zhang W-G, Tsoutsanis E, Zhao J, Tam ICK, .... (2025). An advanced performance-based method for soft and abrupt fault diagnosis of industrial gas turbines. Energy, 321
- Kuang B, Nnabuife SG, Whidborne JF, Sun S, Zhao J, .... (2024). Self-supervised learning-based two-phase flow regime identification using ultrasonic sensors in an S-shape riser. Expert Systems with Applications, 236(February)
- Zhao J, Kallaka R, Conrad C, Gong T, Xu Y, .... (2024). Integrating digital twin technologies into the group design project for the Advanced Air Mobility Systems MSc course. IFAC-PapersOnLine, 58(16)
- Zhao J, Gong T, Nnamani C, Conrad C, Fremond R, .... (2024). Developing a stackable programme based on the advanced air mobility systems MSc course. IFAC-PapersOnLine, 58(16)
- Zhao J, Li Y & Sampath S. (2022). Convolutional neural network denoising autoencoders for intelligent aircraft engine gas path health signal noise filtering. Journal of Engineering for Gas Turbines and Power, 145(6)
- Zhao J, Li Y-G & Sampath S. (2022). A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics. Applied Energy, 332(February)
- Chen Y-Z, Tsoutsanis E, Xiang H-C, Li Y-G & Zhao J-J. (2022). A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions. Applied Energy, 317(July)