A Deep Learning Approach to Detect Lean Blowout in Combustion Systems

TL;DR

This paper introduces a novel deep learning framework for real-time detection of lean blowout in combustion systems, improving accuracy and speed over existing methods, and aiding in safer, more efficient engine operation.

Contribution

It is the first to apply deep learning for online lean blowout detection, providing a computationally efficient and accurate method for combustion system monitoring.

Findings

Deep learning model accurately detects LBO transition states.

Proposed method outperforms baseline models in speed and accuracy.

Framework suitable for real-time combustion monitoring.

Abstract

Lean combustion is environment friendly with low NOx emissions and also provides better fuel efficiency in a combustion system. However, approaching towards lean combustion can make engines more susceptible to lean blowout. Lean blowout (LBO) is an undesirable phenomenon that can cause sudden 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 occurrence. Therefore, it is crucial to develop accurate and computationally tractable frameworks for online LBO detection in low NOx emission engines. To the best of our knowledge, for the first time, we propose a deep learning approach to detect lean blowout in combustion systems. In this work, we utilize a laboratory-scale combustor to collect data for different protocols. We start far from LBO for each protocol and gradually move towards the LBO regime, capturing a quasi-static time series dataset at each condition. Using one of the protocols in our dataset as the reference protocol and with conditions annotated by domain experts, we find a transition state metric for our trained deep learning model to detect LBO in the other test protocols. We find that our proposed approach is more accurate and computationally faster than other baseline models to detect the transitions to LBO. Therefore, we recommend this method for real-time performance monitoring in lean combustion engines.