Detection of precursors of combustion instability using convolutional recurrent neural networks
Résumé
Many combustors are prone to Thermoacoustic Instabilities (TAI). Being able to avoid TAI is mandatory to efficiently operate a system without sacrificing neither performance nor safety. Based on Deep Learning techniques, and more specifically Convolutional Recurrent Neural Networks (CRNN)1, this study presents a tool able to detect and translate precursors of TAI in a swirled combustor for different fuel injection strategies. The tool is trained to use only time-series recorded by a few sensors in stable conditions to predict the proximity of unstable operating points on a mass flow rate / equivalence ratio operating map, offering a real-time information on the margin of the system versus TAI. This allows to change operating conditions, and detect the directions to avoid in order to remain in the stable domain.
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