Physics-informed data-driven prediction of Jet A-1 spray characteristics using time-resolved flame chemiluminescence and sparse Mie scattering

TL;DR

This paper presents a physics-informed, data-driven framework that predicts spray droplet counts in jet fuel combustion using flame chemiluminescence data, enabling better understanding of spray dynamics with sparse measurements.

Contribution

The study introduces a novel framework combining physics and data-driven methods to predict spray droplet counts from flame chemiluminescence, applicable to sparse data scenarios.

Findings

The predicted droplet counts match well with measurements.

Flame chemiluminescence frequency correlates with droplet oscillations.

The framework effectively predicts spray characteristics in different combustion conditions.

Abstract

A time-lag and linear regression-based framework is developed and its performance is assessed for predicting temporally resolved spray number of droplets using flame chemiluminescence and a sparse number of droplets data. Separate pressure, interferometric laser imaging for droplet sizing, shadowgraphy, and flame chemiluminescence are performed for the spray characterization. Simultaneous 10 kHz flame chemiluminescence and 0.2 Hz Mie scattering measurements are performed for the purposes of the framework development and the number of droplets prediction. Both methane and/or Jet A-1 are used in the experiments. Three conditions corresponding to perfectly premixed methane and air, Jet A-1 spray, and Jet A-1 spray in premixed methane and air flames are examined. For all test conditions, the fuels and air flow rates are adjusted to produce a fixed power of 10 kW. The results show that the frequency of the spatially averaged flame chemiluminescence as well as the number and the mass of the droplets (for both reacting and non-reacting conditions) oscillations frequencies match; however, these frequencies do not match that of the pressure fluctuations. This suggests that the flame chemiluminescence dynamics is driven by the fuel injection system. For signals with matching frequency content, a data-driven framework is developed for predicting an objective signal (the spray number of droplets) using an input signal (the flame chemiluminescence). The performance of the developed framework is assessed for tested spray conditions and the predicted number of droplets agrees well with those measured. For gas turbine engine combustion research, the developed framework is of importance, as it facilitates understanding the time-resolved spray characteristics for instances that the spray data is available sparsely.