Dr. Qinqin Mu | Aviation – Engineer at Aircraft Strength Research Institute of China
Qinqin Mu is a highly accomplished aerospace engineer, currently serving as a Senior Engineer at the Aircraft Strength Research Institute under the Aviation Industry Corporation of China (AVIC), Beijing. With over 15 years of professional experience in structural dynamics, aero-engine design, and mechanical systems reliability, she has made significant strides in advancing safety-critical technologies within the aviation sector. Known for her scientific rigor and collaborative approach, Mu has played pivotal roles in both defense-related and commercial engineering projects, consistently applying advanced vibration modeling, structural testing, and damage tolerance assessments to improve aero-engine performance.
Academic Profile:
Education
Qinqin Mu holds a Ph.D. in Mechanical Engineering from a leading technical university in China, where she specialized in vibration behavior and rotor–stator interaction under dynamic loads. Her doctoral research formed the basis of her long-term contributions to aero-engine safety and reliability. She has also undergone several professional training programs in advanced simulation tools and experimental mechanics, positioning her as a technical leader in dynamic system evaluations and fatigue damage forecasting.
Experience
Since joining AVIC in 2009, Mu has gained extensive hands-on experience in structural analysis, high-speed rotating systems, and failure diagnostics. She is widely recognized for her work on rubbing dynamics in turbine engines and has contributed to the design of new-generation aero-engines with improved operational reliability. Her contributions span across conceptual design, computational validation, and real-world system integration. Beyond her research contributions, she also mentors young engineers, leads technical committees, and plays an advisory role in cross-functional research groups within AVIC and national consortiums.
Research Interests
Qinqin Mu’s core research interests include aero-engine rotor–stator dynamics, friction-induced vibration, thermal-mechanical fatigue, and flexible structural interactions between coated blades and casings. Her work uniquely blends finite element modeling with experimental verification, offering deep insight into transient dynamic behavior under critical operating conditions. Her research not only contributes to better turbine reliability but also supports fuel efficiency and sustainability by reducing unexpected in-service failures. She is particularly interested in exploring interdisciplinary links between materials science and structural dynamics in future research.
Awards
Qinqin Mu has received internal recognition at AVIC for engineering excellence and collaborative innovation. Her recent research contributions have led to nominations for national aerospace innovation awards and industry-specific recognition for technical leadership. She was shortlisted for a collaborative research award within her institution and is currently nominated for the Best Research Article Award in recognition of her outstanding scientific contributions to rotor–stator interaction modeling.
Publications 📚
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🌀 “Design and Testing of a Device to Investigate Dynamic Performance of Aero-Engine Rotor–Stator Rubbing Dynamics” – Eng, 2025 (DOI: 10.3390/eng6070162), cited by 8 articles, introduces a novel device to test rubbing effects in aero-engines.
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⚙️ “Study on Rubbing-Induced Vibration Characteristics Considering the Flexibility of Coated Casings and Blades” – Machines, 2024 (DOI: 10.3390/machines12070481), cited by 11 articles, explores the nonlinear interaction between flexible blades and coating layers.
Conclusion
Qinqin Mu stands out as a dedicated and innovative researcher whose work directly enhances the performance and reliability of aero-engines. Her integrated approach, combining theoretical modeling, experimental validation, and practical application, represents a valuable contribution to the field of mechanical and aerospace engineering. With her strong publication record, leadership in collaborative projects, and commitment to high-impact research, she is a fitting nominee for the Best Research Article Award. Her future research is expected to focus on smart diagnostics and AI-driven failure prediction systems for rotating machinery, further extending her impact in both academic and industrial settings.