A statistical model to predict ignition probability

TL;DR

This paper introduces a statistical model that predicts ignition probability maps in gas turbines using flow data from a single non-reacting test, reducing the need for extensive experiments.

Contribution

The model uniquely constructs flame kernel trajectory statistics from flow data to accurately predict ignition probability maps, streamlining ignition system design.

Findings

Accurately predicts ignition maps using minimal data

Demonstrates effectiveness across different combustion modes

Reduces experimental and simulation costs

Abstract

Ignition capability is a critical design constraint for aeronautical gas turbines. However the current trend toward overall lean burn is detrimental to the engine ignition and relight and the ignition system must be adapted to ensure a fast and reliable light-round in all circumstances. As ignition is a stochastic phenomenon, the optimization of an ignition system requires to build ignition probability maps, which is difficult and costly with either experiment or numerical simulation as both require many tests. This work proposes a model to predict the ignition probability map, knowing only flow statistics in non-reacting conditions, i.e., with only one test. The originality of the model is to construct statistics of the flame kernel trajectory, which are then combined with local flow indicators to evaluate the ignition probability at the considered sparking location. Application to a swirled burner operated in premixed, non-premixed and spray combustion modes illustrates the model concepts and demonstrates its ability to recover the experimental ignition map with good accuracy.