Research Interests

• Multi-physics coupled simulation of offshore wind turbines   

• Reliability design and optimization of offshore structures   

• Time series prediction algorithms in load and power prediction   

• Digital twin technology and high-fidelity modeling technology   

• Structural health monitoring technology and fatigue life prediction

Education and Work Experience

• 2018.09 – 2022.05, PhD, Mechanical Engineering, INSA Rouen, France

• 2016.08 – 2018.07, Master of Engineering, Mechanical Engineering, INSA Lyon, France

• 2013.09 – 2017.07, Bachelor of Engineering, Detection, Guidance, and Control Technology, Northwestern Polytechnical University, China

• 2025.10 – Present, Associate Professor, School of Marine Science and Engineering, South China University of Technology, China

• 2022.10 –2025.05, Postdoctoral Fellow, Marine and Offshore Technology, University of Stavanger, Norway

Research Achievement

Dr. Ren's research focuses on offshore wind turbine simulation, extreme value/fatigue analysis methods for marine structures, active learning algorithm development, and digital twin modeling. By 2025, he had published over 20 papers in international journals and conferences, including 9 papers as first/corresponding author in Q1 JCR journals. Additionally, he has led a Norwegian wind energy project and served as a key researcher on five projects funded by Norway, Denmark, and the European Union, with cumulative project funding exceeding 15 million RMB. His research achievements have garnered high recognition and citations from the international academic community.

Some selected projects:

• 2022 – 2025  Research Council of Norway, Dynavac Separator: A sustainable and responsible system to handle offshore drilling waste, key researcher.

• 2024 – 2025 European EEA project，D3OM-WIND: Data-driven design, optimisation and condition monitoring of next-generation wind turbine generators，key researcher.

• 2023 – 2024  Research Council of Norway, Optimizing manufacturing effciency with AISToolbox, key researcher.

Some selected journal papers:

Journal Articles

• Ren, C. *, & Xing, Y. (2025). Active learning with a multi-point enrichment strategy for Multi-Location Fatigue Assessment of offshore wind turbines. Engineering Structures, 336, 120344.

• Ren, C.*, & Xing, Y. (2024). An efficient active learning Kriging approach for expected fatigue damage assessment applied to wind turbine structures. Ocean Engineering, 305, 118034.

• Ren, C.*, & Xing, Y. (2023). AK-MDAmax: Maximum fatigue damage assessment of wind turbine towers considering multi-location with an active learning approach. Renewable Energy, 215, 118977.

• Ren, C.*, Aoues, Y., Lemosse, D., & De Cursi, E. S. (2023). Reliability assessment of an offshore wind turbine jacket under one ultimate limit state considering stress concentration with active learning approaches. Ocean Engineering, 281, 114657.

• Ren, C.*, Aoues, Y., Lemosse, D., & De Cursi, E. S. (2022). Ensemble of surrogates combining Kriging and Artificial Neural Networks for reliability analysis with local goodness measurement. Structural Safety, 96, 102186.

• Ren, C.*, Aoues, Y., Lemosse, D., & De Cursi, E. S. (2021). Comparative study of load simulation approaches used for the dynamic analysis on an offshore wind turbine jacket with different modeling techniques. Engineering Structures, 249, 113308.