A data-driven approach using machine learning is presented for the identification of the critical flame location for the early detection of an incipient lean blowout(LBO)in a realistic gas turbine engine combustor und...A data-driven approach using machine learning is presented for the identification of the critical flame location for the early detection of an incipient lean blowout(LBO)in a realistic gas turbine engine combustor under engine-relevant conditions.This method is demonstrated by utilizing the temperature(T)and the hydroxyl radical mass fraction(YOH)data from high fidelity large eddy simulations(LES)of Jet-A combustion.The fuel flow rate is progressively reduced in numerical simulations with a fixed airflow rate to mimic experimental studies of LBO in the gas turbine combustor.These simulations are the first of their kind for a fully resolved realistic combustor geometry with adaptive mesh refinement and have accurately captured the dynamics of the LBO process and global lean blowout equivalence ratio.Time-series of T and YOH are extracted in the primary zone of the combustor,from stable flame condition to LBO condition,to train the machine learning model.A Support Vector Machine(SVM)model with radial basis function is successfully developed to identify the critical flame location for early detection of incipient LBO condition in a practical combustor for the first time.The performance of the SVM model is quantified using the F-score,and the critical flame location corresponds to the maximum value of the F-score.The critical flame location is found to be in the flame root region and is effective in the early detection of incipient LBO.The conventional statistical measures are compared with the results of the trained machine learning model to assess the feasibility of the latter for online flame health monitoring.The machine learning model successfully prognosticated the LBO approximately 20 ms before the event,and this study has shown significant promise for the use of the SVM model in engine prognostics and health management.展开更多
基金the financial support from The Graduate School at Purdue University,and NASA Tools and Transformational Technologies(TTT)program with award number NNX15AU91A.We acknowledge the license and technical support from Convergent Science Inc.
文摘A data-driven approach using machine learning is presented for the identification of the critical flame location for the early detection of an incipient lean blowout(LBO)in a realistic gas turbine engine combustor under engine-relevant conditions.This method is demonstrated by utilizing the temperature(T)and the hydroxyl radical mass fraction(YOH)data from high fidelity large eddy simulations(LES)of Jet-A combustion.The fuel flow rate is progressively reduced in numerical simulations with a fixed airflow rate to mimic experimental studies of LBO in the gas turbine combustor.These simulations are the first of their kind for a fully resolved realistic combustor geometry with adaptive mesh refinement and have accurately captured the dynamics of the LBO process and global lean blowout equivalence ratio.Time-series of T and YOH are extracted in the primary zone of the combustor,from stable flame condition to LBO condition,to train the machine learning model.A Support Vector Machine(SVM)model with radial basis function is successfully developed to identify the critical flame location for early detection of incipient LBO condition in a practical combustor for the first time.The performance of the SVM model is quantified using the F-score,and the critical flame location corresponds to the maximum value of the F-score.The critical flame location is found to be in the flame root region and is effective in the early detection of incipient LBO.The conventional statistical measures are compared with the results of the trained machine learning model to assess the feasibility of the latter for online flame health monitoring.The machine learning model successfully prognosticated the LBO approximately 20 ms before the event,and this study has shown significant promise for the use of the SVM model in engine prognostics and health management.
