Overview
Profile
I am a PhD student in the Renewable Energy department working under the supervision of Dr Ian Ashton and Professor Lars Johnanning. My PhD thesis refers to developing met-ocean modelling alongside algorithms for predicting the navigation and operation of autonomous offshore marine systems.
My research will explore methods to integrate measured data into wider spatial data from met-ocean models and satellite earth observation for the management of autonomous systems offshore, and the met-ocean model will also be studied in exploring the autonomous systems to be more intelligent.
Research interests
- Offshore Renewable Energy
- Met-ocean Data Modelling
- Machine Learning used in Ship and Offshore Structures
- Response and Performance of Ship and Offshore Structures
Qualifications
2013 MSc in Offshore Floating Systems (University of Strathclyde)
2011 BSc in Naval Architecture and Ocean Engineering (Jiangsu University of Science and Technology, China)
Career
2016–2019 Structural Engineer & Smart Ship Project Engineer, SDARI, CSSC
2014–2016 Structural Engineer & Naval Architect, ZPMC, CCCC
Read more at https://www.linkedin.com/in/%E4%BD%B3%E6%AC%A3-%E9%99%88-52990b91/
Publications
Copyright Notice: Any articles made available for download are for personal use only. Any other use requires prior permission of the author and the copyright holder.
| 2024 | 2023 | 2022 | 2021 | 2016 |
2024
- Chen J. (2024) A Spatiotemporal Machine Learning Framework for Nearshore Wave Modelling.
2023
- Ashton I, Chen J, Steele E, Pillai A. (2023) Surrogate wave modelling to improve operational wave data for offshore wind farms, DOI:10.5194/egusphere-egu23-15848. [PDF]
- Pillai A, Ashton I, Chen J, Steele E. (2023) Comparison of NWP Models Used in Training Surrogate Wave Models, DOI:10.5194/egusphere-egu23-12355. [PDF]
2022
- Chen J, Ashton IGC, Pillai AC. (2022) Wave Record Gap-Filling Using a Low-Rank Tensor Completion Model, Volume 8: Ocean Renewable Energy, DOI:10.1115/omae2022-79897.
- Chen J, Ashton I, Pillai A. (2022) Wave Record Gap Filling Methodology and Impact on Short-Term Forecasts, ASME 41st International Conference on Ocean, Offshore & Arctic Engineering (OMAE2022), Hamburg, Germany, 5th Jun - 10th Mar 2022.
2021
- Chen J, Pillai A, Johanning L, Ashton I. (2021) Using Machine Learning to Derive Spatial Wave Data: A Case Study for a Marine Energy Site, Environmental Modelling and Software, DOI:10.1016/j.envsoft.2021.105066.
2016
- Yang Y, Chen J-X, Huang S. (2016) Mooring line damping due to low-frequency superimposed with wave-frequency random line top end motion, OCEAN ENGINEERING, volume 112, pages 243-252, DOI:10.1016/j.oceaneng.2015.12.026. [PDF]
