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Iham Zidane

CFD Specialist

Renewable energy consultant with an experience portfolio focused on wind power and hybridization. Proven track in modeling wind turbines working under severe conditions using Computational Fluid Dynamics (CFD). Demonstrable experience in R&D project management through participation in several EU-funded programs such as ERASMUS+ and HORIZON. Assistant professor in AAST and exploring entrepreneurial opportunities in the renewable energy space.

Publications

  • An investigation into the design of a device to treat haemorrhagic stroke
  • Performance of a wind turbine blade in sandstorms using a CFD-BEM based neural network
  • On the role of surface roughness in the aerodynamic performance and energy conversion of horizontal wind turbine blades: a review
  • COMPUTATIONAL FLUID DYNAMICS STUDY OF DUSTY AIR FLOW OVER NACA 63415 AIRFOIL FOR WIND TURBINE APPLICATIONS
  • Modeling and simulation of a hybrid power generation system of wind turbine, micro-turbine and solar heater cells
  • Trajectory control of a laparoscopic 3-PUU parallel manipulator based on neural network in SIMSCAPE SIMULINK environment
  • On Non-Kolmogorov turbulence in blood flow and its possible role in mechanobiological stimulation
  • On non-Kolmogorov turbulence in blood flow and its possible role in mechanobiological stimulation
  • Robotics in laparoscopic surgery - A review
  • Effect of upstream deflector utilization on H-Darrieus wind turbine performance: An optimization study
  • Finite element analysis of a stemmed hip prosthesis to reduce stress shielding in the proximal femur
  • The impact of the corrugated absorber shape on the performance of a hemispherical solar still for water desalination: an experimental study
  • Solving Kinematics of a Parallel Manipulator Using Artificial Neural Networks
  • Design and Development of Path Planning and Obstacle Avoidance System for Driverless Pod Using Artificial Neural Network

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Co-workers & collaborators

Khalid Saqr

Fluid Dynamicist

Khalid Saqr

Iham Zidane's public data