Abstract

Diagnostics tools are the underpinnings for the experimental study of combustion phenomena. The inherent dynamic and three-dimensional (3-D) nature of turbulent flames has imposed strict requirements to the measurement techniques, which should provide both temporally and spatially resolved information of the target flames. Time-resolved volumetric tomography is one of such methods that meet the stringent demands of combustion diagnostics. However, this technique usually suffers from both high computational and experimental costs. This work aims to mitigate its limitations by developing a hybrid deep neural network that integrates the classical convolutional neural network with a state-of-the-art video interpolation model. Such a network can produce high frame rate 3-D flame voxels from low frame rate two dimensional (2-D) images, reducing the computational costs and at the same time relaxing the hardware requirement. Our study has shown that the temporal resolution can be enhanced by 15-fold. Thus, kilohertz (kHz)-rate flame tomography can potentially be realized with cost-effective industrial cameras. This also facilitates the study of ultra-rapid combustion phenomena, which cannot be resolved (greater than megahertz required) even with the most expensive commercial high-speed cameras. This technique has also been found to have a strong noise immunity, and acceptable results can still be obtained even when the noise level reaches 30%.

© 2020 Optical Society of America

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