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
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 Center for Propulsion and Thermal Power Engineering in 2022. His PhD research was on Machine Learning and Information Fusion based aircraft engine health monitoring in the context of Digital Twin. He came to Cranfield in 2016 and completed his MSc in Aerospace Propulsion. He also received a second 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, and System of Systems (SoS).
Aligning the current research on future Unmanned Aerial Systems (UAS) operations, ATM/UTM integration and UAM applications within ATM Laboratory, we have research opportunities on Explainable Machine Learning, Digital Twin building, emerging test methodology, software development and other data-driven solutions.
Junjie is a Research Fellow within the ATM Laboratory in the Centre for Autonomous and Cyber-Physical Systems. He is involved in the activities of the Laboratory’s research and development environment, research projects, and teaching activities.
Junjie participates in the Introduction to Advanced Air Mobility module of the Advanced Air Mobility Systems MSc Course.
Junjie joins the ongoing research projects within the ATM Laboratory. These projects include project HADO (High intensity Autonomous Drone Operations), 2022-2024, funded by UKRI under the Future Flight Challenge Phase 3 programme, ID: 10024815; 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.
His areas of interest include Digital Twin building in the synthetic environment to enable virtual/mix-reality tests for UAS operations and UAM applications; Artificial Intelligence (AI) solutions for future UAS operations in the context of Digital Twin; Novel test methodology development for the emerging air transport systems.
Operational Solutions Limited (OSL)
Articles In Journals
- Zhao J, Li Y-G & Sampath S (2023) A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics, Applied Energy, 332 (February) Article No. 120520.
- 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) Article No. 119148.
- Zhao J, Li Y-G & 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, Available online 31 October 2022 Article No. GTP-22-1234.
- Zhao J, Conrad C, Delezenne Q, Xu Y & Tsourdos A (2023) A digital twin mixed-reality system for testing future advanced air mobility concepts: a prototype. In: 2023 Integrated Communication, Navigation and Surveillance Conference (ICNS), Herndon, VA, 18-20 April 2023.
- Zhao J & Li YG (2020) Abrupt fault detection and isolation for gas turbine components based on a 1D convolutional neural network using time series data. In: AIAA Propulsion and Energy 2020 Forum, Online, 24-28 August 2020.