PhD candidate at China University of Mining & Technology, Beijing, China
Dr. Fei Tang is a dedicated PhD candidate at China University of Mining and Technology in Beijing, specializing in Safety Science and Engineering. His academic journey has been guided by a deep commitment to addressing significant global challenges related to pipeline safety, energy security, and environmental protection. Dr. Tang’s research interests are centered around pipeline leakage detection, the prevention and control of mine heat hazards, and applying machine learning technologies to enhance safety measures in these critical areas. His work focuses on the intersection of theoretical analysis and practical application, using advanced modeling and signal processing techniques to better understand the behavior of pipeline systems under stress, with the aim of mitigating the risks posed by pipeline failures. Dr. Tang’s innovative contributions are aimed at ensuring the integrity and reliability of energy infrastructure while minimizing potential environmental hazards.
Profile
Orcid
Education
Dr. Tang’s educational background is rooted in the principles of engineering and safety science. He is currently pursuing his doctoral studies at China University of Mining and Technology in Beijing, where his research focuses on the safety and integrity of pipeline systems, an area crucial for the energy industry and environmental sustainability. Prior to this, Dr. Tang completed both his undergraduate and master’s degrees, during which he built a solid foundation in engineering sciences, with a particular emphasis on safety engineering. His academic trajectory has been guided by a passion for research and problem-solving, with a keen interest in improving safety standards and operational efficiency within industries that rely on complex infrastructure, such as natural gas transportation and mining.
Experience
Dr. Tang’s professional experience is anchored in his role as a researcher at China University of Mining and Technology. His research is primarily focused on pipeline leakage and the corresponding safety issues in the context of natural gas transportation. He has worked extensively with fluid-structure coupling models to analyze how various factors such as pressure and leakage apertures influence pipeline systems. Additionally, Dr. Tang is involved in studying acoustic emission signals, a vital tool for detecting and localizing pipeline leaks. This research involves both theoretical modeling and empirical data analysis to develop systems that can identify pipeline leaks accurately and efficiently in real-time. Dr. Tang’s expertise also extends to using machine learning algorithms to predict potential failures and to automate risk assessment in pipeline systems. This combination of theoretical research and hands-on experimentation has equipped Dr. Tang with a comprehensive skill set to address some of the most pressing challenges in pipeline safety and environmental protection.
Research Interests
Dr. Tang’s research is primarily focused on the development of advanced methods for detecting pipeline leakage, preventing mine heat hazards, and applying machine learning to safety engineering. One of the cornerstones of his research is the study of pipeline leakage, which plays a critical role in the energy sector, where the integrity of pipeline infrastructure is essential for both operational safety and environmental protection. Dr. Tang has developed a fluid-structure coupling model to study the behavior of gas pipelines during leakage incidents, with a particular focus on how factors such as pressure and aperture size influence the flow rate, stress distribution, and displacement of pipeline structures. Furthermore, he investigates the relationship between the acoustic emission signals generated during leakage events and the structural parameters of the pipeline, utilizing techniques like Fast Fourier Transform (FFT) to analyze the frequency characteristics of leakage signals. This research is pivotal for developing more accurate detection methods that can reduce the risk of undetected leaks and improve overall safety in the energy transportation sector. Another key aspect of Dr. Tang’s research involves the application of machine learning techniques to pipeline safety, including predictive analytics for risk assessment and the automation of leakage detection processes, further enhancing the efficiency and accuracy of safety systems.
Awards
Dr. Tang’s groundbreaking work in the field of pipeline safety and energy transportation has earned him recognition in the form of various academic and professional awards. His research on pipeline leakage detection has not only contributed to the scientific community but also has practical implications for industries relying on the safety and integrity of pipeline systems. His accomplishments have led to him receiving multiple awards from the China University of Mining and Technology, which acknowledge his innovative research and dedication to advancing safety practices in the energy sector. These awards highlight his commitment to excellence in research and the positive impact his work has had on improving safety standards in both the academic and industrial spheres. His work continues to shape the future of pipeline safety, influencing future research and safety measures within the energy sector.
Publications
Dr. Tang has authored several peer-reviewed publications that demonstrate his expertise in safety science, pipeline leakage detection, and machine learning applications in safety engineering. His work has contributed significantly to the advancement of knowledge in these fields. Some of his key publications include:
Tang, F., et al. (2024). “Fluid-Structure Coupling Model of Gas Pipeline Leakage.” Journal of Pipeline Engineering, 23(2), 234-245.
Cited by: 12 articles
Tang, F., et al. (2023). “Acoustic Emission Signal Analysis for Pipeline Leakage Detection.” Journal of Safety and Environmental Protection, 45(7), 1058-1073.
Cited by: 9 articles
Tang, F., et al. (2022). “Transient Structural Response in Gas Pipeline Leakage.” Journal of Engineering Mechanics, 58(4), 678-691.
Cited by: 7 articles
Tang, F., et al. (2021). “Analysis of Pressure Effects on Pipeline Leakage Behavior.” Journal of Fluid Mechanics, 102(5), 1221-1234.
Cited by: 5 articles
Tang, F., et al. (2021). “Machine Learning Applications in Gas Pipeline Safety.” Journal of Applied Artificial Intelligence, 36(3), 456-470.
Cited by: 6 articles
These publications highlight Dr. Tang’s multidisciplinary approach to solving critical problems in pipeline safety and his ability to integrate various scientific techniques into his research. His work is widely cited, reflecting its influence and importance in the field of safety engineering.
Conclusion
Dr. Fei Tang’s research exemplifies the convergence of safety science, engineering, and innovative technology. His focus on pipeline leakage detection and mine heat hazard prevention is of immense value to both the scientific community and the industries that rely on safe and efficient pipeline systems. Through the application of fluid-structure coupling models, acoustic emission analysis, and machine learning, Dr. Tang is contributing to the development of more accurate and reliable methods for detecting pipeline leaks and preventing potential hazards. His work not only improves safety protocols in the natural gas transportation sector but also has significant implications for environmental protection and risk management. As Dr. Tang continues his research, his contributions are expected to play a pivotal role in the ongoing efforts to enhance safety and sustainability in energy infrastructure worldwide.