Scientific Objectives

  1. Develop methods that can predict and control thermoacoustics from TRL 2 to TRL 9.
  2. Apply machine learning algorithms to improve models to predict thermoacoustics in aircraft engines and derive combustor hardware design implications from the predictions.
  3. Devise and adapt machine learning algorithms to thermoacoustic experiments at the laboratory scale and to industrial scale for aircraft engines.
  4. Advance acoustic and combustion models to capture the interaction of acoustics with liquid fuel sprays with high accuracy.
  5. Generate a sophisticated experimental data base for thermoacoustics of liquid fuel combustion for validation of the methods developed in MAGISTER.