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Combining high-speed video cameras and optimal measurement techniques with digital sensors controlled by a data acquisition system can yield a combination of experimental tools to explore boiling process thermophysics and heat transfer mechanisms. Imaging can provide qualitative and quantitative information that complements data provided by temperature, pressure, and more sensors. This paper summarizes the results of an exploration of machine learning strategies to optimally combine and analyze boiling process images and digital sensor information from experiments. We specifically sought a convolution neural network to analyze the vaporization of deposited water droplets on superheated surfaces that may have varying degrees of nucleate boiling effects. Through experimentation, we found that a hybrid parallel-series convolution/neuron neural network design worked very effectively. The network could extract the regime of droplet vaporization (conduction driven only, conduction plus nucleate boiling, or explosive boiling), the liquid morphology, and could predict the vaporization regime, the wall superheat, and mean heat transfer rate as a function of image input and operating system parameters. Using data collected from the droplet deposition experiment, this network design has been trained to predict the mean heat transfer rate with a root mean square percent error (RMSPE) of only 3.3% and 7.2% on a training and testing dataset respectively. The hybrid network developed in this research appears to be a promising strategy for analyzing experimental data for physical systems that are best investigated experimentally with a combined use of imaging and digital sensor instrumentation. 

Combining high-speed video cameras and optimal measurement techniques with digital sensors controlled by a data acquisition system can yield a combination of experimental tools to explore boiling process thermophysics and heat transfer mechanisms. Imaging can provide qualitative and quantitative information that complements data provided by temperature, pressure, and more sensors. This paper summarizes the results of an exploration of machine learning strategies to optimally combine and analyze boiling process images and digital sensor information from experiments. We specifically sought a convolution neural network to analyze the vaporization of deposited water droplets on superheated surfaces that may have varying degrees of nucleate boiling effects. Through experimentation, we found that a hybrid parallel-series convolution/neuron neural network design worked very effectively. The network could extract the regime of droplet vaporization (conduction driven only, conduction plus nucleate boiling, or explosive boiling), the liquid morphology, and could predict the vaporization regime, the wall superheat, and mean heat transfer rate as a function of image input and operating system parameters. Using data collected from the droplet deposition experiment, this network design has been trained to predict the mean heat transfer rate with a root mean square percent error (RMSPE) of only 3.3% and 7.2% on a training and testing dataset respectively. The hybrid network developed in this research appears to be a promising strategy for analyzing experimental data for physical systems that are best investigated experimentally with a combined use of imaging and digital sensor instrumentation.