Browsing by Author "Sun, Zhen"

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    Augmented visualization cues on primary flight display facilitating pilot's monitoring performance
    (Elsevier, 2019-11-14) Li, Wen-Chin; Horn, Andreas; Sun, Zhen; Zhang, Jingyi; Braithwaite, Graham
    There have been many aviation accidents and incidents related to mode confusion on the flight deck. The aim of this research is to evaluate human-computer interactions on a newly designed augmented visualization Primary Flight Display (PFD) compared with the traditional design of PFD. Based on statistical analysis of 20 participants interaction with the system, there are significant differences on pilots’ pupil dilation, fixation duration, fixation counts and mental demand between the traditional PFD design and augmented PFD. The results demonstrated that augmented visualisation PFD, which uses a green border around the “raw data” of airspeed, altitude or heading indications to highlight activated mode changes, can significantly enhance pilots’ situation awareness and decrease perceived workload. Pilots can identify the status of flight modes more easily, rapidly and accurately compared to the traditional PFD, thus shortening the response time on cognitive information processing. This could also be the reason why fixation durations on augmented PFDs were significantly shorter than traditional PFDs. The augmented visualization in the flight deck improves pilots’ situation awareness as indicated by increased fixation counts related to attention distribution. Simply highlighting the parameters on the PFD with a green border in association with relevant flight mode changes will greatly reduce pilots’ perceived workload and increase situation awareness. Flight deck design must focus on methods to provide pilots with enhanced situation awareness, thus decreasing cognitive processing requirements by providing intuitive understanding in time limited situations.
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    Digital twin architecture for a sustainable control system in aircraft engines
    (Springer , 2024-08-08) Farsi, Maryam; Namoano, Bernadin; Latsou, Christina; Subhadu, Vaishnav Venkata; Deng, Haoxuan; Sun, Zhen; Zheng, Bohao; D’Amico, Davide; Erkoyuncu, John Ahmet; Karakoc, T. Hikmet; Colpan, Can Ozgur; Dalkiran, Alper
    Over the past decades, climate change has remained one of the major global challenges in the world. In the aviation and aerospace industry, the environmental sustainable development strategies towards carbon-neutral mainly focus on efficiency and demand measures, sustainable fuels, renewable energies, and removal and carbon offsetting. The carbon dioxide equivalent (CO2e) emissions footprint of an aircraft is primarily determined by energy and fuel efficiency. The advanced engine control systems of an aircraft can optimise the engine performance to achieve energy efficiency, fuel optimal consumption, and emission reduction. This paper proposed a digital twin architecture of a sustainable aircraft control system that allows the system to collect, analyse, and optimise sustainability-related data and to provide insight to operators, engineers, maintainers, and designers. The required information, knowledge and insight databases across flight environment, engine specification, and gas emissions are identified. The research argued that the proposed architecture could enhance engine energy efficiency, fuel consumption, and CO2e footprint reduction and enable (near) real-time data monitoring, proactive anomaly detection, forecasting, and intelligent decision-making within an automated sustainability control system. This research suggests ontology-based digital twin as an effective approach to further develop a cognitive twin that facilitates automated decision-making within the aircraft control system.