An LSTM-based approach to detect transition to lean blowout in swirl-stabilized dump combustion systems

Abstract

Lean combustion is environment friendly with low NOX emissions providing better fuel efficiency in a combustion system. However, approaching towards lean combustion can make engines more susceptible to an undesirable phenomenon called lean blowout (LBO) that can cause flame extinction leading to sudden loss of power. During the design stage, it is quite challenging for the scientists to accurately determine the optimal operating limits to avoid sudden LBO occurrences. Therefore, it is crucial to develop accurate and computationally tractable frameworks for online LBO prediction in low NOX emission engines. To the best of our knowledge, for the first time, we propose a deep learning approach to detect the transition to LBO in combustion systems. In this work, we utilize a laboratory-scale swirl-stabilized combustor to collect acoustic data for different protocols. For each protocol, starting far from LBO, we gradually move towards the LBO regime, capturing a quasi-static time series dataset at different conditions. Using one of the protocols in our dataset as the reference protocol, we find a transition state metric for our trained deep learning model to detect the imminent LBO in other test protocols. We find that our proposed approach is more precise and computationally faster than other baseline models to detect the transition to LBO. Therefore, we endorse this technique for monitoring the operation of lean combustion engines in real time.

Keywords

• Deep learning
• LSTM
• Detection of lean blowout
• Transition to LBO
• Confusion matrix