Dr. Jincheng Zhang

Dr. Jincheng Zhang

Assistant Professor,
School of Engineering,
The University of Warwick

Publications

You can also find my articles on my Google Scholar profile.

Journal Papers

  1. J. Zhang and X. Zhao, Digital twin of wind farms via physics-informed deep learning, Energy Conversion and Management 293, 117507, 2023.
  2. J. Zhang, X. Zhao, D. Greaves, and S. Jin, Modeling of a hinged-raft wave energy converter via deep operator learning and wave tank experiments, Applied Energy 341, 121072, 2023.
  3. R. Li, J. Zhang, X. Zhao, D. Wang, M. Hann, and D. Greaves, Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments, Applied Energy 348, 121529, 2023.
  4. R. Li, J. Zhang, and X. Zhao, Multi-fidelity modeling of wind farm wakes based on a novel super-fidelity network, Energy Conversion and Management 270, 116185, 2022.
  5. R. Li, J. Zhang, and X. Zhao, Dynamic wind farm wake modeling based on a bilateral convolutional neural network and high-fidelity LES data, Energy 258, 124845, 2022.
  6. J. Zhang, X. Zhao, S. Jin, and D. Greaves, Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning, Applied Energy 324, 119711, 2022.
  7. J. Zhang and X. Zhao, Wind farm wake modeling based on deep convolutional conditional generative adversarial network, Energy 238, 121747, 2021.
  8. J. Zhang and X. Zhao, Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning, Applied Energy 300, 117390, 2021.
  9. H. Dong, J. Zhang, and X. Zhao, Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations, Applied Energy 292, 116928, 2021.
  10. J. Zhang and X. Zhao, Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements, Applied Energy 288, 116641, 2021.
  11. J. Zhang and X. Zhao, Machine-learning-based surrogate modeling of aerodynamic flow around distributed structures, AIAA Journal 59 (3), 868-879, 2021.
  12. J. Zhang, X. Zhao and X. Wei, Reinforcement learning-based structural control of floating wind turbines, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, DOI: 10.1109/TSMC.2020.3032622.
  13. J. Zhang and X. Zhao, A novel dynamic wind farm wake model based on deep learning, Applied Energy 277, 115552, 2020.
  14. J. Zhang and X. Zhao, Quantification of parameter uncertainty in wind farm wake modeling, Energy 196, 117065, 2020.
  15. J. Zhang and S. Fu, An efficient approach for quantifying parameter uncertainty in the SST turbulence model, Computers & Fluids 181, 173-187, 2019.
  16. J. Zhang and S. Fu, An efficient Bayesian uncertainty quantification approach with application to k-ω-γ transition modeling, Computers & Fluids 161, 211-224, 2018.

Conference Papers

  1. J. Zhang and X. Zhao, Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks, TORQUE2024, Journal of Physics: Conference Series. IOP Publishing, 2767(9): 092017, 2024.
  2. R. Li, J. Zhang, and X. Zhao, Deep learning-based wind farm power prediction using Transformer network, Proceedings of the 2022 European Control Conference (ECC), London, July 2022.
  3. J. Zhang, X. Zhao, and X. Wei, Data-driven structural control of monopile wind turbine towers based on machine learning, Proceedings of the 21st IFAC World Congress, Berlin, July 2020.

Preprints

  1. R. Li, J. Zhang, and X. Zhao, Long-distance and high-impact wind farm wake effects revealed by SAR: a global-scale study, arXiv preprint arXiv:2311.18124, 2023.
  2. R. Li, J. Zhang, and X. Zhao, Wake effects of offshore wind farm clusters revealed by SAR and WRF, arXiv preprint arXiv:2312.13942, 2023.