Professor | Shanghai Normal university | China
Dr. Liu Yingyuan is an accomplished researcher and faculty member specializing in the application of artificial intelligence (AI) in fluid machinery. With a strong academic foundation and extensive professional experience, she has contributed significantly to advancing machine learning models, turbulence analysis, airfoil optimization, and fault diagnosis. Currently serving at Shanghai Normal University, Dr. Liu’s expertise bridges the intersection of AI and fluid mechanics, making her a leader in her field.
Profile
Scopus
Education
Dr. Liu Yingyuan earned her Ph.D. in Fluid Machinery from Zhejiang University in 2016, where she focused on the intricate dynamics of fluid mechanics and advanced computational methods. Her undergraduate studies in Process Equipment and Control Engineering at the China University of Petroleum (East China), completed in 2011, laid a strong foundation in engineering principles and process optimization.
Experience
Dr. Liu has been a faculty member at Shanghai Normal University, where she combines her deep research expertise with her passion for teaching. Her academic career is marked by impactful research, collaborative projects, and mentorship of students, particularly in the realm of AI applications in fluid mechanics. Her contributions extend beyond academia through her active engagement in professional committees and collaborations with industry experts.
Research Interests
Dr. Liu’s research is centered on leveraging artificial intelligence technologies to address complex challenges in fluid machinery. Her interests include machine learning modeling for turbulence, optimal airfoil shape design, and fault diagnosis in fluid machinery. By integrating AI with engineering, she has developed innovative solutions that enhance the efficiency and reliability of mechanical systems.
Awards
Dr. Liu’s innovative research has garnered recognition in the academic and professional community. Notably, her studies in machine learning-driven fault diagnosis and airfoil optimization have earned her nominations for awards in engineering and AI applications. Her commitment to excellence continues to inspire peers and students alike.
Publications
- Liu YY, Shen JX, Yang PP, Yang XW. A CNN-PINN-DRL driven method for shape optimization of airfoils. Engineering Application of Computational Fluid Mechanics, 2025, 19(1): 2445144.
- Cited by: Researchers developing AI-driven aerodynamics models.
- Shen JX, Liu YY, Wang Leqin.* A Deep Learning-Based Method for Airfoil Parametric Modeling. Chinese Journal of Engineering Design, 2024, 31(03): 292-300.
- Cited by: Articles on parametric modeling techniques.
- Liu D, Liu YY. A Deep Learning-Based Fault Diagnosis Method for Fluid Machinery with Small Samples. Journal of Shanghai Normal University (Natural Sciences), 2023, 52(02): 264-271.
- Cited by: Studies on fault diagnosis in mechanical systems.
- Liu YY, Gong JG, An K, Wang LQ. Cavitation Characteristics and Hydrodynamic Radial Forces of a Reversible Pump–Turbine at Pump Mode. Journal of Energy Engineering, 2020, 146(6): 04020066.
- Cited by: Publications on hydrodynamics and pump-turbine systems.
- Liu Y Y, An K, Liu H, et al. Numerical and experimental studies on flow performances and hydraulic radial forces of an internal gear pump with a high pressure. Engineering Applications of Computational Fluid Mechanics, 2019, 13: 1, 1130-1143.
- Cited by: Research focused on internal gear pump performance.
- Liu Y Y, Wang L Q, Zhu Z C.* Experimental and numerical studies on the effect of inlet pressure on cavitating flows in rotor pumps. Journal of Engineering Research, 2016, 4(2): 151-171.
- Cited by: Studies on cavitation phenomena in rotor pumps.
- Liu Y Y, Wang L Q, Zhu Z C.* Numerical study on flow characteristics of rotor pumps including cavitation. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2015, 229(14): 2626-2638.
- Cited by: Articles on numerical modeling of fluid flows.
Conclusion
Dr. Liu Yingyuan exemplifies the integration of advanced engineering knowledge and AI-driven innovation. Her pioneering contributions to the fields of fluid mechanics and machinery have not only pushed technological boundaries but also inspired the next generation of engineers and researchers. Dr. Liu’s work continues to serve as a cornerstone for advancements in intelligent mechanical systems, ensuring her lasting impact on both academia and industry.