Analysis of the combustion modes in a rocket-based combined cycle combustor using unsupervised machine learning methodology

Abstract

Combustion mode analysis is essential to a rocket-based combined cycle (RBCC) combustor because it may experience multiple combustion modes during the operation. In this study, a method based on an autoencoder and a K-means algorithm was proposed for combustion mode analysis. Flame chemiluminescence images and schlieren images of three combustion modes observed in an RBCC combustor were used to evaluate this method. Two autoencoders that followed the same encoder–decoder architecture were developed separately to generate the latent space representations of flame chemiluminescence images and schlieren images. In the latent space, the centroids and boundaries of different combustion modes were determined using the K-means algorithm. Each autoencoder was trained using 750 images and tested using another 3000 images. The method achieved an accuracy up to 99% on both flame chemiluminescence images and schlieren images. The images generated by the decoder suggested that the autoencoder captured the important features (e.g., primary reaction zone and shock wave) of the reacting flow field. The autoencoder developed for flame chemiluminescence images also successfully detected the combustion mode transition during an ignition process, which suggested that it had the potential to monitor the combustion mode in a real time manner. However, the autoencoder failed on monitoring combustion mode transition when it came to the schlieren images because the optical access of the training data was not exactly the same. Therefore, it is essential to ensure that the optical accesses of different combustion modes are exactly the same when schlieren images are employed for combustion mode analysis.