Bayesian inference of flame impulse responses

TL;DR

This paper introduces a Bayesian framework for identifying flame impulse responses from noisy data, improving robustness and physical interpretability over traditional methods, and demonstrating effectiveness on turbulent combustion simulations.

Contribution

It reformulates flame impulse response identification as a Bayesian inference problem using physically motivated models, enabling principled model selection and incorporation of prior knowledge.

Findings

Bayesian model comparison selects a three-Gaussian impulse response.

The Bayesian approach reduces spurious features compared to traditional methods.

It remains robust with significantly less data, reducing computational costs.

Abstract

The impulse response of a flame to acoustic velocity perturbations is a key quantity for predicting thermoacoustic stability, but its identification from sparse, noisy observations requires solving an ill-posed inverse convolution problem. This is typically achieved with system identification methods, which require hand-tuning of regularization, model order, and sampling parameters, and provide no principled mechanism for incorporating prior physical knowledge. In this paper, we reformulate the identification problem within a Bayesian framework. The impulse response is represented as a physically motivated distributed time delay model, whose parameters correspond to convective delays and dispersive broadening. For a given number of pulses, the model parameters are inferred from the data using Bayesian parameter inference. The number of pulses is then selected using Bayesian model comparison, which balances data fit against model complexity to identify the simplest model capable of explaining the data. The framework is demonstrated on broadband-forced large eddy simulation data from a turbulent swirl-stabilized burner. Bayesian model comparison selects a three-Gaussian impulse response for this flame, consistent with physical interpretations in previous work. Compared with system identification, the Bayesian approach produces impulse responses with fewer spurious features and enables straightforward enforcement of a known low-frequency gain. Finally, we show that the Bayesian approach is robust to significant reductions in recording length, making it appealing for impulse response identification from costly simulations, where there is an incentive to minimize computational cost.