Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron

TL;DR

This study demonstrates that neural networks can effectively learn the nonlinear flame response from a single CFD simulation, enabling accurate prediction of flame dynamics and limit cycle oscillations in combustion systems.

Contribution

The paper introduces a neural network approach, specifically a multi-layer perceptron, to model nonlinear flame responses using minimal CFD data, improving prediction of complex oscillations.

Findings

Neural network accurately predicts flame describing function (FDF).

Model captures higher harmonics in flame response.

Neural network coupled with acoustic solver predicts limit cycle oscillations effectively.

Abstract

This paper demonstrates the ability of neural networks to reliably learn the nonlinear flame response of a laminar premixed flame, while carrying out only one unsteady CFD simulation. The system is excited with a broadband, low-pass filtered velocity signal that exhibits a uniform distribution of amplitudes within a predetermined range. The obtained time series of flow velocity upstream of the flame and heat release rate fluctuations are used to train the nonlinear model using a multi-layer perceptron. Several models with varying hyperparameters are trained and the dropout strategy is used as regularizer to avoid overfitting. The best performing model is subsequently used to compute the flame describing function (FDF) using mono-frequent excitations. In addition to accurately predicting the FDF, the trained neural network model also captures the presence of higher harmonics in the flame response. As a result, when coupled with an acoustic solver, the obtained neural network model is better suited than a classical FDF model to predict limit cycle oscillations characterized by more than one frequency. The latter is demonstrated in the final part of the present study. We show that the RMS value of the predicted acoustic oscillations together with the associated dominant frequencies are in excellent agreement with CFD reference data.