Dr. Vladimir Parezanovic
Dr. vladimir parezanovic Assistant Professor Aerospace Engineering

Contact Information
vladimir.parezanovic@ku.ac.ae +971 (0)2 312 3967


Dr. Vladimir Parezanovic joined Khalifa University in August 2017 as an Assistant Professor of the Aerospace Engineering Department. He has more than fifteen years of experience in experimental research on fundamental topics in fluid mechanics and aerodynamics on several ANR (Agence Nationale de la Recherche) and CNES (Centre Nationale d’Etudes Spatiales) research projects. He obtained his MSc and PhD degrees in 2008 and 2011 (respectively) from Ecole Polytechnique – ParisTech (Palaiseau, France), in the domain of fluid mechanics. He also holds a BSc degree (Dipl. Ing.) from the University of Belgrade (Serbia) in Aeronautical Engineering. He specializes in turbulent wakes, fluid instabilities, flow control, machine learning, and wind tunnel experiments.

Dr. Parezanovic has designed and built experiments studying two-dimensional mixing layers, cylinder flows, flow separation phenomena, boundary layer/shockwave interaction, and tri-dimensional wakes of axisymmetric bluff bodies. Often, his focus was on flow control, using diverse types of actuators and control schemes: from steady perturbations, to closed-loop control using Dielectric-Barrier-Discharge (plasma jet) actuators. His secondary field of expertise is Machine Learning, focusing on applications in flow control and prediction. In addition to the above, his research interests also include bio-inspired flow sensing and control, compressed sensing techniques, and non-deterministic models for flow control. His teaching interests and experience is mainly in classical mechanics, fluid mechanics, aerodynamics, and experimental measurements.

  • PhD Fluid Mechanics, Ecole Polytechnique
  • MSc Fluid Mechanics and Energetics, Ecole Polytechnique
  • BSc (Dipl. Ing.) Aeronautical Engineering, University of Belgrade

  • Advanced Aerodynamics (ECCE654)
  • Aerodynamics I (AERO335)
  • Aerodynamics II (AERO336)
  • Engineering Dynamics (AERO201)
  • Experimental Methods in Aerodynamics (CHEG213)
  • Viscous Flows (AERO431)

Research Interests
  • Aircraft, drone, and bluff body wakes
  • Experimental Aerodynamics
  • Flow control methods and actuator design
  • Machine Learning
  • Hydrodynamic instabilities
  • Wind tunnel testing

Research Projects

[RIG-2023-024] Drone Aerodynamics and Wake Interactions: Towards Urban Air Mobility

This project addresses the interaction of Uncrewed Aircraft Systems (UAS) with wakes of other similar craft and wakes of large-scale urban objects such as buildings. Contrary to fixed-wing aircraft, there is very little information and no internationally accepted standards concerning the wake characteristics of Vertical Take-Off and Landing (VTOL) craft for cruise, and ascent/descent, and how such wake will affect the dynamics of neighbouring UAS. This is a key challenge to be tackled to ensure safe separation in the foreseen UAS Traffic Management (UTM) system which will have to deal with small VTOL craft operating in a highly constricted urban airspace which is subject to significant environmental flow unsteadiness. This project proposes to explore the aerodynamic phenomena associated with small VTOL flight in urban areas and dense traffic and develop a Separation Assurance and Collision Avoidance (SA/CA) analytical framework (models and associated simulation tools) suitable for VTOL UAS platforms in representative operational conditions. In the future, such an analytical framework could be extended to larger, passenger-carrying VTOL platforms (e.g., air taxis) adopting similar design approaches.

[AARE20-138] Predictive modelling of a turbulent airwake behind a ship’s superstructure using Machine Learning

Modern warships and commercial maritime vessels often employ helicopters for a variety of missions and tasks, such as: reconnaissance, communication, Anti-Submarine Warfare (ASW), light supply transport, emergency medical or VIP transport. Some modern frigates or destroyers even depend solely on helicopters as their ASW component. It is clear in such cases that helicopter operations are crucially important to continue even in severe weather conditions.
One of the major factors contributing to the complexity of helicopter seaborne operations are the unsteady aerodynamic phenomena, arising from the airflow separation from the bluff rear of the ship (hangar deck). These phenomena are responsible for intense turbulent fluctuations and intermittent changes in wind direction near the helipad, leading to an immense increase of the pilot workload at the most crucial moments of the landing procedure or even a complete shutdown of flight deck operations. A key issue here is having knowledge of the instantaneous state of the ship’s airwake at any instance, which is impossible to obtain in practical conditions at sea.
The aim of the current proposal is to develop predictive models for the turbulent wake of the ship’s superstructure using experimental measurements and high-fidelity numerical modelling. Data driven Machine Learning (ML) techniques will be employed to create a model which can (in practical conditions) be used to derive the full airwake information from a limited set of sensors, typically used on real ships.

[RIG-2023-048] Biomimetic-joint-thrust system for underwater propulsion

Jet propulsion is an energy-efficient mechanism employed in traditional aerospace and marine engines, consisting of a fast-moving jet of fluid to generate a propulsive thrust. This mechanism is also at the base of the swimming strategies of several marine species. By pointing the jet outlet indifferent directions and by changing the amount of water drawn, cephalopods (squid or octopi) can modify the direction and speed of their jet propulsion. Inspired by this, we present a soft jet propulsor that can control the outlet position and orientation. The design combines the actuation required for the volume squeezing at the base of the jet propulsion mechanism, with a second actuator to define the orientation of the propulsor. This thruster has the potential to unveil an effective solution for a broad range of tasks currently unsolved, combining the high-speed manoeuvrability of traditional rigid jet propellers with the main advantages of soft robotics.

Research Staff and Graduate Students:

Tauha Irfan Khan PhD student
Neelam Majeed PhD student


(KU job id: 230000DV) Post-Doctoral Fellow on [AARE20-138] Predictive modelling of a turbulent airwake behind a ship’s superstructure using Machine Learning (up to 1Y)



Post-Doctoral Fellow on [RIG-2023-024] Drone Aerodynamics and Wake Interactions: Towards Urban Air Mobility (up to 3Y)