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• W3156847105 abstract "Combustion instability is one of the most plaguing challenges for general industrial thermal facilities, such as furnaces, gas turbine and hot air heaters. Considerable researches have been reported on developing effective active control means to mitigate combustion instability. The quick and accurate prediction of oscillating combustion characteristics is the premise of developing an active control model. In the present work, neural network models are constructed to predict temperature and key radical evolutions with historical data and system parameters as inputs. Active subspace (AS) method is adopted to identify the important direction of the system-parameter space in neural network, reduce the number of neurons in the input and middle layers, and simplify the network structure. Results show that the system-parameter space of eight dimensions can be reduced to a one-dimensional space using AS method. The neural network structures are greatly simplified, and the numbers of neurons are significantly reduced by 50%, which shortens the training time by 72%, and reduces single-step prediction time in hardware system by 60%. Compared with the neural network with full system parameters, the neural network with reduced parameters by AS method can capture the oscillating combustion characteristics and accurately predict the temperature evolution and mass fraction of radical H and CH." @default.
• W3156847105 created "2021-04-26" @default.
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• W3156847105 date "2021-04-16" @default.
• W3156847105 modified "2023-09-24" @default.
• W3156847105 title "Exploring active subspace for neural network prediction of oscillating combustion" @default.
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• W3156847105 doi "https://doi.org/10.1080/13647830.2021.1915500" @default.
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