6 work packages: Machine Learning, Combustion models, Acoustic models, Validation data, Aircraft engines and Training.
ESR 1 Cambridge trained Bayesian ensembles of Neural Networks on a laboratory rig and a set of full-scale engines from an industrial partner. His neural network model was able to input the noise from the system and output a.o. the distance from instability.
ESR 2 Armines has designed estimation algorithms for systems with thermoacoustic instabilities, based on physical models of this phenomenon. The algorithms successfully estimated a.o. acoustic impedances of an electrically heated Rijke tube.
ESR 3 GE: The dynamic response to inlet velocity fluctuations of flames can be modeled through neural networks which have been trained on selected CFD runs. The limit cycle amplitudes of a combustion system were predicted by coupling the trained neural network model with an acoustic solver. The trained neural network model can predict the burner system time evolution.
ESR 4 ANSYS: Pure control theory and machine learning techniques were combined. An observer algorithm was designed using artificial neural network methods for low error estimation nonlinear systems states.
ESR 5 TUM: CFD simulations were combined with System Identification (SI) to estimate flame dynamics. The uncertainties in flame models obtained were quantified through a Gaussian Process ML algorithm.
ESR 6 CERFACS: Implemented in AVBP a multicomponent evaporating spray model with detailed chemistry. Simulated were spray flames of two multicomponent liquid fuels in the large scale LOTAR configuration at ONERA to determine the transfer function.
ESR 7 UT: Unsupervised machine learning algorithms of Proper Orthogonal Decomposition are used to deconstruct the complex flow fields into their basic modes to identify acoustic characteristics.
ESR 8 Cambridge: obtained experimental data from a thermoacoustic rig and assimilated this into a ThermoAcoustic model. The physics-based model was tuned to match experimental data to be accurate with known error bounds.
ESR 9 TUM: improved models for acoustically absorbing combustor liners consisting of a perforated wall backed by a cavity. Impedance measurements of perforation patterns were performed, and an analytical method was developed with ML.
ESR 10 UT: In high-order unsteady CFD solvers with LES, non-reflective boundary conditions (NRBCs) are crucial for preserving the numerical accuracy of the solution.NRBCs were investigated for a high-order discontinuous Galerkin solver.
ESR 11 KIT: At KIT a facility was built to generate an oscillating airflow for the MAGISTER designed burner. The spray response was found to fluctuate with the same frequency as the air velocity at constant liquid flow rate.
ESR 12 UT: Self- excited high amplitude oscillations were studied, and post processed by chaotic analysis to obtain the geometric order of the generated phase portraits . A test rig wwas built ith the MAGISTER burner for liquid fuel combustion at high pressure.
ESR 13 GEDE: Machine learning algorithms were used for the interpolation and extrapolation of Flame Describing Functions aiming to allow a minimal amount of data points to be collected while still providing enough information to model the FDF accurately. A method of optimally selecting data points at test time using Gaussian Processes was demonstrated. Next it was demonstrated that the prediction of combustion instabilities is improved by supervised machine learning without any need for manual calibration and the quality of the warning is improved.
ESR 14 Safran tech: Investigated were Lattice Boltzmann methods to the problem of primary atomization using the colour gradient method to model immiscible two-phase flows. This method has been generalized to an arbitrary Equation of State, relieving the limitation between density and sound speed ratios.
ESR 15 SHE: performed CFD simulation of hydrogen combustion at high pressure. CFD simulations were done of the Safran spinning combustion concept.
