Search for: All records

Abstract Combining high-speed video cameras and optical measurement techniques with digital sensors controlled by a data acquisition system can provide an effective means of exploring boiling process thermophysics and heat transfer mechanisms. Imaging can provide qualitative and quantitative information that complements data provided by temperature, pressure, and other 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 (CNN) to analyze the vaporization of deposited water droplets on superheated surfaces that may have varying degrees of nucleate boiling effects. Two specialized CNN models were developed in this study that can simultaneously analyze both image and digital data. One of our CNN model designs (case B) was trained to take an image of the vaporization process and nonthermal digital data as input and predict thermal heat transfer performance. This model predicts performance remarkably well given its nonthermal inputs, matching independent heat flux test data to a root-mean-square percent error (RMSPE) of 10.3%. This model appears to learn how the variations of nucleate boiling, vapor recoil activity, and local dryout over the surface vary with surface temperature and/or heat flux from changes in boiling system images. We also describe a CNN model (case C) that takes digital nonthermal data, digital thermal data, and image information and provides a high-fidelity prediction of vaporization heat transfer performance. This model predicted performance very well—better than our conventional fit to data (case A) and on par with best fits to quality nucleate boiling heat transfer data in the literature. This type of trained model fit independent heat flux test data to an RMSPE of 5.8%. Our results indicate that training this type of model which predicts performance from input image information and digital operating condition thermal data makes the resulting predictive model more accurate and robust. The successful use of the hybrid CNN models described here suggests that there is a strong correlation between two-phase morphology variations and changes in heat transfer performance. The hybrid CNN modeling approach developed in this research appears to be a promising strategy for analyzing experimental data for physical systems that are best investigated experimentally with combined use of imaging and digital sensor instrumentation. Possible use of this type of modeling in other systems is also discussed.