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Personal profile

Personal profile

Dr Li has mainly been doing research in Computational methods for fluid flow. He proposed methods for strealine tracking and adaptive mesh refinement methods based on qualitative theory of differential equations since 2000. The proposed methods have been verified accuracy, reliability and use of less computational costs. His other resaerch interests include neural network and mathematical modelling.

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  • 1 Similar Profiles
Conservation Engineering & Materials Science
Driven Cavity Flow Mathematics
Lid-driven Cavity Mathematics
Flow of fluids Engineering & Materials Science
Computational fluid dynamics Engineering & Materials Science
Vortex flow Engineering & Materials Science
Adaptive Mesh Refinement Mathematics
Mesh Refinement Mathematics

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Research Output 2005 2019

  • 99 Citations
  • 7 h-Index
  • 22 Article
  • 11 Conference paper

Accuracy verification of a 2D adaptive mesh refinement method using backward facing step flows of low Reynolds numbers

Li, J. & Li, M., 2019, Proceedings of the International Conference on Computational Methods: The 10th International Conference on Computational Methods. Liu, G. R., Cui, F. & Xu, G. (eds.). USA, Vol. 6.

Research output: Book chapter/Published conference paperConference paper

CFD analysis of 2D unsteady flow past a square cylinder at low Reynolds numbers

Li, Z. & Wood, R., Jan 2018, AMCSE 2017 - International Conference on Applied Mathematics, Computational Science and Systems Engineering. Bardis, N. (ed.). EDP Sciences, Vol. 16. 3 p. 02003

Research output: Book chapter/Published conference paperConference paper

Unsteady flow
Computational fluid dynamics
Reynolds number
Finite volume method
Differential equations
2 Citations (Scopus)
Adaptive Mesh Refinement
Incompressible flow
Steady flow
Steady Flow
Incompressible Flow
Open Access
Square Cylinder
Mesh Refinement
unsteady flow
Low Reynolds number
low Reynolds number
1 Citation (Scopus)
Driven Cavity Flow
cavity flow
Lid-driven Cavity
Adaptive Mesh Refinement
Computational complexity