Machine-learning (ML) is a form of artificial intelligence where a model is developed by analyzing and interpreting large amounts of data. The ML algorithm learns and recognizes patterns and proposes a specific model to represent a complex system. The objective of this research is to develop data-driven models for challenging engineering applications. More specifically, with the CARNOT project we will develop ML-based digital twins for thermo-fluid applications with a focus on plasma-assisted combustion (PAC). PAC is a promising technique towards clean combustion as it helps reducing the fuel consumption and pollutant emissions. Digital twins will help us understand the intrinsic mechanisms that lead to favorable ignition and combustion and alleviate the cost of predictive multi-dimensional numerical simulations.
Short titleCARNOT
StatusNot started
Effective start/end date1/12/2030/11/24

    Research areas

  • Machine-learning, computational fluid dynamics, combustion, clean energy

    Flemish discipline codes

  • Artificial intelligence not elsewhere classified
  • Scientific computing not elsewhere classified
  • Other engineering and technology not elsewhere classified
  • Thermodynamics not elsewhere classified
  • Energy generation, conversion and storage engineering not elsewehere classified
  • Aerospace engineering not elsewehere classified

ID: 54443835