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Contact Information

Contact

Postal Address

City, University of London
Northampton Square
London
EC1V 0HB
United Kingdom

About

Overview

Dr. Jinghua Wang received his BEng in Harbour, Waterway and Coastal Engineering and Msc in Harbour, Coastal and Nearshore Engineering at Ocean University of China in 2006 and 2011, respectively. He received his PhD degree in Hydraulics at City University of London in 2015, and subsequently joined the FSI research group as a research fellow at City.

Dr. Wang's research focuses on the numerical modelling of ocean waves, and rogue wave dynamics in particular. He has developed a numerical model called the Enhanced Spectral Boundary Integral Method (ESBI), featuring high computational efficiency and accuracy, to simulate ocean waves on a large spatiotemporal scale, which outperforms other conventional numerical methods. He also suggested a criterion for selecting suitable theoretical models to simulate ocean waves for engineering practices and applied sciences. Recently, He proposed a robust fully nonlinear numerical model to efficiently simulate wave and varying current interactions.

Qualifications

  1. PhD, City, University of London, United Kingdom, 2015
  2. MSc, Ocean University of China, China, 2011
  3. BEng, Ocean University of China, China, 2010

Employment

  1. Research Fellow, City, University of London, Jan 2016 – present

Publications

Conference papers and proceedings (7)

  1. Li, Q., Yan, S., Wang, J., Ma, Q.W., Xie, Z. and Sriram, V. (2018). Numerical simulation of focusing wave interaction with FPSO-like structure using FNPT-NS Solver.
  2. Wang, J., Ma, Q.W. and Yan, S. (2018). Examination on errors of two simplified models for simulating weakly spreading seas.
  3. Wang, J., Ma, Q.W. and Yan, S. (2017). On differences of rogue waves modeled by three approaches in numerical wave tank.
  4. Yan, S., Ma, Q.W., Wang, J. and Zhou, J. (2016). Self-adaptive wave absorbing technique for nonlinear shallow water waves.
  5. Wang, J., Ma, Q.W. and Yan, S. (2016). Numerical investigation on spectrum evolution of narrow-banded random waves in shallow water based on KdV and fully nonlinear model.
  6. Wang, J.H. and Ma, Q.W. (2015). Numerical Investigation on Limitation of Boussinesq Equation for Generating Focusing Waves.
  7. Yan, S., Zhou, J.T., Ma, Q.W., Wang, J., Zheng, Y. and Wazni, B. (2013). Fully nonlinear simulation of tsunami wave impacts on onshore structures.

Journal articles (8)

  1. Wang, J., Ma, Q. and Yan, S. (2018). A fully nonlinear numerical method for modeling wave–current interactions. Journal of Computational Physics, 369, pp. 173–190. doi:10.1016/j.jcp.2018.04.057.
  2. Wang, J., Ma, Q.W., Yan, S. and Qin, H. (2018). Numerical study on the quantitative error of the Korteweg–de Vries equation for modelling random waves on large scale in shallow water. European Journal of Mechanics, B/Fluids, 71, pp. 92–102. doi:10.1016/j.euromechflu.2018.04.004.
  3. Wang, J., Ma, Q.W., Yan, S. and Chabchoub, A. (2018). Breather Rogue Waves in Random Seas. Physical Review Applied, 9(1). doi:10.1103/PhysRevApplied.9.014016.
  4. Wang, J., Yan, S. and Ma, Q. (2018). Deterministic numerical modelling of three-dimensional rogue waves on large scale with presence of wind. Procedia IUTAM, 26, pp. 214–226. doi:10.1016/j.piutam.2018.03.021.
  5. Wang, J., Ma, Q. and Yan, S. (2017). On quantitative errors of two simplified unsteady models for simulating unidirectional nonlinear random waves on large scale in deep sea. Physics of Fluids, 29(6). doi:10.1063/1.4989417.
  6. Wang, J., Ma, Q.W. and Yan, S. (2016). A hybrid model for simulating rogue waves in random seas on a large temporal and spatial scale. Journal of Computational Physics, 313, pp. 279–309. doi:10.1016/j.jcp.2016.02.044.
  7. Wang, J., Yan, S. and Ma, Q.W. (2015). An improved technique to generate rogue waves in random sea. CMES - Computer Modeling in Engineering and Sciences, 106(4), pp. 263–289.
  8. Wang, J. and Ma, Q.W. (2015). Numerical techniques on improving computational efficiency of spectral boundary integral method. International Journal for Numerical Methods in Engineering, 102(10), pp. 1638–1669. doi:10.1002/nme.4857.