Mechanical Aerospace and Nuclear Engineering
Ph.D., University of Patras, Greece
intelligent structural systems|X|structural health monitoring diagnostics and prognostics|X|stochastic system identification and machine learning|X|fly-by-feel aerial vehicles|X|bio-inspired systems|X|smart materials and structures
Professor Fotis Kopsaftopoulos joined the Department of Mechanical, Aerospace, and Nuclear Engineering at Rensselaer Polytechnic Institute as a tenure-track Assistant Professor in October 2017. He received his Diploma and Ph.D. in Mechanical and Aeronautical Engineering from University of Patras, Greece, where he worked on stochastic system identification, statistical signal processing, and vibration-based probabilistic methods for structural health monitoring (SHM). After completing his Ph.D. in 2012, he served as a Postdoctoral Research Associate in the Stochastic Mechanical Systems and Automation Laboratory of the same Department working on research related to novel personal plane concepts, aircraft 4D trajectory monitoring via adaptive time-varying models, and 4D model predictive control for “green” aircraft guidance. Before joining Rensselaer, he served as a Postdoctoral Scholar in the Department of Aeronautics and Astronautics at Stanford University (April 2013 to September 2017) where we worked on the design, analysis, and integration of intelligent structures and autonomous systems with bio-inspired state sensing and awareness capabilities. Professor Kopsaftopoulos has published more than 60 journal and conference papers and 2 book chapters. He has participated in various research projects both in the U.S.A. (AFOSR, NSF, NASA, ARPA-e, Boeing) and Europe (FP6 and FP7). He serves as a referee of several international journals and conferences, while he is a member of the Organizing Committee of the International Workshop on Structural Health Monitoring (IWSHM). Finally, he is the co-Editor of the IWSHM 2015 and 2017 Proceedings and he serves as a Guest Associate Editor of the Structural Health Monitoring and Aerospace journals.
Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, F.-K. Chang and F. Kopsaftopoulos (Eds.), Two Volumes, 3338 pages, 978-1-60595-330-4, DEStech Publications Inc., 2017.|X|Wilson C., Lonkar K., Roy S., Kopsaftopoulos F., Chang F.-K., “Structural Health Monitoring of Composites,” invited chapter, Reference Module in Materials Science and Materials Engineering, Comprehensive Composite Materials II, C. Zweben and Dr. P. Beaumont (Eds.), Vol. 7, pp. 382-407, Elsevier, 2018. http://dx.doi.org/10.1016/B978-0-12-803581-8.10039-6|X|Kopsaftopoulos F.P., Nardari R., Li Y.-H., Chang F.-K., “A stochastic global identification for aerospace structures operating under varying flight states,” Mechanical Systems and Signal Processing, Vol. 98, pp. 425–447, 2018. https://doi.org/10.1016/j.ymssp.2017.05.001|X|Zhuang Y., Kopsaftopoulos F.P., Dugnani R., Chang F.-K., “A self-diagnostic adhesive for bondline integrity monitoring of aerospace structures,” Structural Health Monitoring, in press. http://doi.org/10.1177/1475921717732331|X|Kopsaftopoulos F.P., Nardari R., Li Y.-H., Chang F.-K., “Data-driven state awareness for fly-by-feel aerial vehicles: experimental assessment of a non-parametric probabilistic stall detection approach,” Proceedings of the 11th International Workshop on Structural Health Monitoring (IWSHM), Stanford University, USA, September 2017.|X|Ladpli P., Kopsaftopoulos F.P., Chang F.-K., “Battery state of charge estimation using guided waves – Numerical validation and statistical analysis,” Proceedings of the 11th International Workshop on Structural Health Monitoring (IWSHM), Stanford University, USA, September 2017.|X|Janapati V., Kopsaftopoulos F.P., Li F., Lee S.J., Chang F.-K., “Damage detection sensitivity characterization of acousto-ultrasound-based SHM techniques,” Structural Health Monitoring, Vol. 15(2), pp. 143–161, 2016. http://dx.doi.org/10.1177/1475921715627490 |X|Kopsaftopoulos F.P. and Fassois S.D., “A vibration model residual based sequential probability ratio test framework for structural health monitoring,” Structural Health Monitoring, Vol. 14(4), pp. 359–381, 2015. http://dx.doi.org/10.1177/1475921715580499|X|Salowitz N., Guo Z., Roy S., Nardari R., Li Y.-H., Kim S.-J., Kopsaftopoulos F.P., Chang F.-K., “Recent advancements and vision toward stretchable bio-inspired networks for intelligent structures,” Structural Health Monitoring, Vol. 13(6), pp. 609–620, 2014. http://dx.doi.org/10.1177/0725513614554076|X|Kopsaftopoulos F.P. and Fassois S.D., “A functional model based statistical time series method for vibration based damage detection, localization, and magnitude estimation,” Mechanical Systems and Signal Processing, Vol. 39(1–2), pp. 143–161, 2013. http://dx.doi.org/10.1016/j.ymssp.2012.08.023|X|Kopsaftopoulos F.P. and Fassois S.D., “Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods,” Mechanical Systems and Signal Processing, Vol. 24(7), pp. 1977–1997, 2010. http://dx.doi.org/10.1016/j.ymssp.2010.05.013