Dynamic Data-Driven Prediction of Lean Blowout in a Swirl-Stabilized Combustor. (September 2015)

• Record Type: Journal Article
• Authors: Sarkar, Soumalya; Ray, Asok; Mukhopadhyay, Achintya; Sen, Swarnendu
• Is Part Of: International journal of spray and combustion dynamics. Volume 7:Number 3(2015)
• Page Start: 209
• Page End: 241
• Publication Date: 2015-09
• Subjects: Data-driven Dynamics -- Lean Blowout -- Gas Turbine Combustor -- Symbolic Dynamics -- Probabilistic Finite State Automata; Combustion engineering -- Periodicals; Fluid dynamics -- Periodicals; Combustion -- Periodicals; Spraying -- Periodicals; Combustion; Combustion engineering; Fluid dynamics; Spraying; Periodicals; 541.361
• DOI: 10.1260/1756-8277.7.3.209
• Languages: English
• ISSNs: 1756-8285

Abstract

This paper addresses dynamic data-driven prediction of lean blowout (LBO) phenomena in confined combustion processes, which are prevalent in many physical applications (e.g., land-based and aircraft gas-turbine engines). The underlying concept is built upon pattern classification and is validated for LBO prediction with time series of chemiluminescence sensor data from a laboratory-scale swirl-stabilized dump combustor. The proposed method of LBO prediction makes use of the theory of symbolic dynamics, where (finite-length) time series data are partitioned to produce symbol strings that, in turn, generate a special class of probabilistic finite state automata (PFSA). These PFSA, called D -Markov machines, have a deterministic algebraic structure and their states are represented by symbol blocks of length D or less, where D is a positive integer. The D -Markov machines are constructed in two steps: (i) state splitting, i.e., the states are split based on their information contents, and (ii) state merging, i.e., two or more states (of possibly different lengths) are merged together to form a new state without any significant loss of the embedded information. The modeling complexity (e.g., number of states) of a D -Markov machine model is observed to be drastically reduced as the combustor approaches LBO. An anomaly measure, based on Kullback-Leibler divergence, is constructed to predict the proximity of LBO. The problem of LBO prediction is posed in a pattern classification setting and the underlying algorithms have been tested on experimental data at different extents of fuel-air premixing and fuel/air ratio. It is shown that, over a wide range of fuel-air premixing, D -Markov machines with D > 1 perform better as predictors of LBO than those with D = 1.