Browsing by Author "Deng, Haoxuan"

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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.
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    A framework for constructing a common knowledge base for human-machine system to perform maintenance tasks
    (Cranfield University, 2022-11-08) Deng, Haoxuan; Khan, Samir; Erkoyuncu, John Ahmet
    A reliable and comprehensive maintenance is important to promise the system running in a normal state, but it is skill-intensive and heavily dependent on human labor. With the development of predictive maintenance in industry, an optimized solution can be posed for maintaining assets with less downtime and cost. However, most of current research on this topic is limited on a top-level algorithm design for prediction, but few consider how to perform the maintenance tasks according to the prediction results at a particular occasion and condition. Besides, the complexity of system is exploded, and it may take people much effort to cover every detail to achieve a credible maintenance result. Thus, machine is introduced to collaborate with human by undertaking some work and suggesting actions to take in order to reduce human physical and mental workload. This paper aims to present a framework to integrate human knowledge and machine learning into a common knowledge base to enable human and machine can contribute to shift the final maintenance decision from planning to performing. The proposed framework is based on a knowledge graph generated by ontology and machine learning, which can be conveniently retrieved by human via questions answering system or visualization platform and efficiently computed by machine via graph representation learning. Consequently, domain knowledge can be formally represented, systematically managed and easily reused by human-machine teaming to attack domain-specific problems. In a long term, the evolving knowledge based, with an accumulation on samples and information, can guide the team to draw a reasonable and delicate strategy for overhaul and recondition, moreover, ensure the next generation of maintenance: prescriptive maintenance.
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    Quantifying the interrelationship between friction, wear, and noise: a comparative study on aluminum, brass, and steel
    (Elsevier, 2025-03) Tian, Yang; Khan, Muhammad; Deng, Haoxuan; Omar, Intisar
    Friction-induced wear and noise affect the performance and lifespan of industrial components, yet models often address them separately. This study proposes a model linking wear volume, coefficient of friction (COF), and noise. Ball-on-disc tribometer tests on 6082 aluminum, UNS C38500 brass, and 304 stainless steel were conducted under various loads and speeds. Key findings reveal thermal expansion affects wear in aluminum but minimally impacts brass and steel. The aluminum-based equation also predicts noise for brass and steel, with errors under 10 % within 5–15 N loads and 0.21–0.63 m/s speeds, suggesting broader applicability. This model provides a simplified approach to linking friction, wear, and noise, offering potential improvements in wear monitoring and noise control for mechanical systems.