Intelligent Structures Laboratory
Research Field
Dr. Yuan’s long-term goal is to create new and unique innovations in the area of smart structures and to bring these advances into the classroom.
At the graduate level, Dr. Yuan teaches Structural Health Monitoring (MAE 589). This class exposes students to state-of-the-art sensors and signal processing methods for in in-situ, continual monitoring of the health of structural systems.
At the undergraduate level, Dr. Yuan teaches the Bio-flight option of Aerospace Senior Design (MAE 478 and 479). In this year-long course, students study the different flight principles of birds. They select the feature(s) that can best be translated into human flight, and then build working prototype aircraft that utilize the selected feature(s). The class is also unique in that it brings in the latest research in bio-flight, in particular, understanding of bi-flight resulting from recent studies, and new methods of actuation and sensing tailored to aircraft morphing.
In his research, Dr. Yuan looks for graduate students with a multi-disciplinary background. He trains his students to methodically solve challenging technical problems and his students are drawn to Dr. Yuan’s research, in large part, because his research involves making advancements to multi-disciplinary problems that are both challenging and of high societal impact.
Outside of work, Dr. Yuan enjoys travel and chatting with students and friends.
The laboratory to be built in Taiwan is currently in progress.
The laboratory’s current research focuses on the development of digital twin framework for unmanned systems including drones, surface, and unerwater vehicles.
Memberships, Honors, and Awards
- Fellow of The Royal Aeronautical Society (RAeS), 2024.
- Fellow of The International Society for Optics and Photonics (SPIE), 2021.
- Fellow of The American Society of Mechanical Engineers (ASME), 2015.
- 2026 John J. Montgomery Aerospace Medal, ASME, 2026.
- 2025 SHM Life Achievement Award, The 14th International Workshop on Structural Health Monitoring, Stanford University, California, September 2025.
- 2023 SHM Hans-Juergen Schmidt Award, The 14th International Workshop on Structural Health Monitoring, Stanford University, California, September 2023.
- 2023 R. J. Reynolds Tobacco Company Award, College of Engineering, North Carolina State University, July 2023.
- 2023 Lifetime Achievement Award in Nondestructive Evaluation (NDE), International Society for Optics and Photonics (SPIE), March 2023.
- 2022 Yushan Fellow, (玉山學者), Ministry of Education, Taiwan, 2022.
- 2019 Alumni Association Distinguished Graduate Professorship Award, North Carolina State University, 2019.
- 2018 NASA Mentoring Award, NASA Langley Research Center, August 2018.
- 2016 Research Excellence Award, MAE, North Carolina State University, 2016.
- 2013 Outstanding Alumni Award of Engineering Science Department, National Cheng-Kung University, Taiwan, 2013.
- 2013 SHM Person of the Year Award, The 9th International Workshop on Structural Health Monitoring, Stanford University, California, 2013.
- 2013 Research Outstanding Award, MAE, North Carolina State University, 2013.
- 2011 Research Excellence Award, MAE, North Carolina State University, 2011.
- 1992 US Air Force Research Initiation Award, 1992.
With Students (Selected in the last seven years)
- Doctoral Student of the Year Award, MAE, NCSU, Mr. Bryce Abbott, 2023.
- The Best Paper Award from National Institute of Aerospace. Paper Title: Fatigue Damage Prognosis of Adhesively Bonded Joints via ANNs-based Surrogate Model,” by Dr. K. R. Lyathakula and F. G. Yuan, Intl Journal of Fatigue, 2021.
- The Best Paper Award from National Institute of Aerospace. Paper Title: Automated in-process cure monitoring of composite laminates using a guided wave-based system with high temperature piezoelectric transducers,” by Dr. T. Hudson and F, G. Yuan, Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, Vol. 1, No. 2, 2018: 021008
- The Best Paper Award. Paper title: IWSHM 2017: Damage-scattered Wave Extraction in an Integral Stiffened Isotropic Plate: a Baseline-subtraction-free Approach by Drs. J. He, P. E. Leser, W. P. Leser, and F. G. Yuan, Structural Health Monitoring Journal, 2018.
- The Best PHM Application Paper Award to Dr. Patrick Leser, for his paper entitled “Probabilistic Prognosis of Non-planar Fatigue-crack Growth,” The Annual Conference of The PHM Society, Denver, Colorado, October 2016.
- Ph.D. Dissertation Award, NASA IFAR-ICAR Award for Dr. William Leser, November 2015.
- Best Paper Award with Dr. Mohammad Harb, The 10th International Conference on Structural Health Monitoring, Stanford University, California, September 2015.
Ph.D. ：Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign, 1986
M.S. ：Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign, 1981
B.S. ：Engineering Science. National Cheng-Kung University, Taiwan, 1977
Job Description
The intern will explore both supervised and unsupervised deep learning approaches for flight data and sensor monitoring (FDSM). A comprehensive NASA multivariate time-series flight dataset will be used for model development and comparative evaluation. In addition, motor anomaly detection will be investigated using conventional machine learning techniques. The project includes performance comparison, analysis of strengths and limitations of deep learning models, and evaluation of their applicability in real-world UAV operations.
Preferred Intern Educational Level
- Senior undergraduate or graduate student (Master’s level preferred)
- Major in Electrical Engineering, Computer Science, Aerospace Engineering, Data Science, Artificial Intelligence, or related fields
Skill sets or Qualities
- Strong knowledge of machine learning and deep learning fundamentals
- Experience with time-series data analysis
- Proficiency in Python and deep learning frameworks (e.g., TensorFlow or PyTorch)
- Familiarity with supervised and unsupervised learning techniques
- Understanding of anomaly detection methods
- Basic knowledge of UAV systems or aerospace systems (preferred)
- Analytical thinking and strong problem-solving skills
- Ability to conduct comparative model evaluation and performance analysis
- Good research documentation and technical writing skills
Job Description
The intern will conduct simulation-based studies to evaluate the feasibility of using piezoelectric sensor arrays for damage localization in composite structures. Using ABAQUS for finite element analysis (FEA), the intern will model guided wave propagation in composite plates and analyze reflected wave signals captured by embedded sensors. A physics-based damage detection framework will be developed, incorporating time-of-flight analysis and Gaussian Mixture Models (GMM) for statistical damage localization.
Preferred Intern Educational Level
- Senior undergraduate or graduate student (Master’s level preferred)
- Major in Aerospace Engineering, Mechanical Engineering, Civil Engineering, Materials Science, or related engineering fields
Skill sets or Qualities
- Basic knowledge of structural mechanics and composite materials
- Familiarity with Structural Health Monitoring (SHM) concepts
- Experience with finite element analysis (FEA), preferably ABAQUS
- Understanding of wave propagation and signal processing fundamentals
- Knowledge of statistical modeling methods (e.g., Gaussian Mixture Models)
- Programming skills (e.g., MATLAB or Python) for data analysis
- Strong analytical and problem-solving skills
- Ability to interpret simulation results and technical data
- Interest in aerospace structural safety and research-oriented work