Contact Dr Antonios Antoniadis

Areas of expertise

Background


Dr Antoniadis holds a BEng from the University of Sussex in Automotive Engineering and a MSc in Mechanical Engineering from University College London. He was awarded a PhD by Cranfield University title "High-Order Methods on Mixed-Element Unstructured Meshes for Aeronautical Applications" in 2013. He worked for the Directorate of Enterprise and Industry, European Commission in automotive legislation.

Current activities

Dr Antoniadis is a Lecturer in Computational Engineering Science and the Director of Computational Fluid Dynamics (CFD) Masters program at Cranfield University. He leads a Virtual/Augmented Reality (VR/AR) laboratory within the Aircraft Integration Research center. He also leads a research group in computational engineering science on the development of:
  • High-order numerical methods on unstructured grids.
  • Data-driven, scale-resolving and hybrid models for turbulence.
  • Software platforms for human-computer interaction for CAE applications in VR/AR.
  • Numerical schemes for rotating wings and turbo-machinery.
  • Multi-fidelity methods for fluid dynamics.
  • High-Performance Computing parallelisation adaptive techniques on graph type data structures.
  • Techniques for multi-physics modeling.
  • Artificial Intelligence and Machine Learning techniques.
  • Multi-objective optimisation models.
  • Stochastic models.
with Applications:
  • Helicopter computational aerodynamics and fluid structure interaction.
  • Aerodynamic performance prediction of extended formation flights.
  • Morphing technologies for unmanned aerial vehicles.
  • Analysis of extremely short take-off and landing all surface aircraft.
  • Wind turbine and wind farm modelling.
  • CFD of civil aircraft, drag prediction and high-lift devices.
  • Virtual Reality for CFD and FEA.
  • Fluid-structure interaction of wheel-rim-tyre systems.
  • Multi-phase flow of ethylene production.
  • Supersonic shock wave boundary layer interaction.
  • Scale resolving turbulence modelling around bluff bodies.
  • Airflow modelling of external aerodynamics.
  • Modelling of chemical reactive flows and combustion in IC engines and microcombustors.
  • Direct-Monte Carlo techniques for rarefied flow dynamics.
  • Machine vision for driver departure warning systems on SBC.
  • Deep learning techniques for turbulence modelling, shape parameterisation, meshing and optimisation.

Clients

  • BAE-Systems
  • AIRBUS
  • RedBull Racing
  • Qinetiq
  • European Commission
  • ItsFresh
  • MBDA
  • Ricardo
  • Jaguar Land Rover

Publications

Articles In Journals

Conference Papers