The Lean Blowout Prediction Techniques in Lean Premixed Gas Turbine: An Overview

• Main Authors: Bahashwan, A.A., Ibrahim, R.B., Omar, M.B., Faqih, M.
• Format: Article
• Published: 2022
• Online Access: http://scholars.utp.edu.my/id/eprint/33838/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142663426&doi=10.3390%2fen15228343&partnerID=40&md5=6aa7592540f05b7e6af7acd64c326e89

The lean blowout is the most critical issue in lean premixed gas turbine combustion. Decades of research into LBO prediction methods have yielded promising results. Predictions can be classified into five categories based on methodology: semi-empirical model, numerical simulation, hybrid, experimental, and data-driven model. First is the semi-empirical model, which is the initial model used for LBO limit prediction at the design stages. An example is Lefebvre�s LBO model that could estimate the LBO limit for eight different gas turbine combustors with a ±30 uncertainty. To further develop the prediction of the LBO limit, a second method based on numerical simulation was proposed, which provided deeper information and improved the accuracy of the LBO limit. The numerical prediction method outperformed the semi-empirical model on a specific gas turbine with ±15 uncertainty, but more testing is required on other combustors. Then, scientists proposed a hybrid method to obtain the best out of the earlier models and managed to improve the prediction to ±10 uncertainty. Later, the laboratory-scale combustors were used to study LBO phenomena further and provide more information using the flame characteristics. Because the actual gas turbine is highly complex, all previous methods suffer from simplistic representation. On the other hand, the data-driven prediction methods showed better accuracy and replica using a real dataset from a gas turbine log file. This method has demonstrated 99 accuracy in predicting LBO using artificial intelligence techniques. It could provide critical information for LBO limits prediction at the design stages. However, more research is required on data-driven methods to achieve robust prediction accuracy on various lean premixed combustors. © 2022 by the authors.