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

作　　者：Tryambak Gangopadhyay Somnath De Qisai Liu Achintya Mukhopadhyay Swarnendu Sen Soumik Sarkar 

机构地区：[1]Amazon Web Services,Amazon,Santa Clara,CA,USA [2]Department of Aerospace Engineering,Indian Institute of Technology Madras,Chennai,India [3]Department of Mechanical Engineering,Iowa State University,Ames,IA,USA [4]Department of Mechanical Engineering,Jadavpur University,Kolkata,India

出　　处：《Energy and AI》2024年第2期32-41,共10页能源与人工智能（英文）

基　　金：supported in part by National Science Foundation, USA grants CNS1954556 and CNS 1932033.

摘　　要：Lean combustion is environment friendly with low NO_(x)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 NO_(x)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.

关 键 词：Deep learning LSTM Detection of lean blowout Transition to LBO Confusion matrix 

分 类 号：TK1[动力工程及工程热物理—热能工程] TP3[自动化与计算机技术—计算机科学与技术]