Machine learning enabled condensation heat transfer measurement

Coalescence-Induced Droplet Jumping for Electro-Thermal Sensing

“…Furthermore, it is important to note that several recent works utilize various machine learning approaches to characterize heat transfer rates during dropwise or jumping droplet condensation. , One of the models utilizes deep convolutional neural networks to estimate the droplet shedding frequency for dropwise condensation by performing feature extraction from condensation videos. However, challenges remain for characterizing the jumping droplet condensation mode due to out-of-plane droplet departure . Another recent work utilized a similar deep learning approach to characterize the droplet growth rate on a surface to evaluate the size-dependent heat transfer rate per droplet and droplet size distribution as a function of droplet radius .…”

“…However, challenges remain for characterizing the jumping droplet condensation mode due to out-of-plane droplet departure. 69 Another recent work utilized a similar deep learning approach to characterize the droplet growth rate on a surface to evaluate the sizedependent heat transfer rate per droplet and droplet size distribution as a function of droplet radius. 71 Our work suggests an alternative procedure to evaluate the heat flux by analyzing the trajectories or terminal velocities of a few jump droplets during flight.…”

“…This electro-thermal transport process has the potential to be controlled via external electric fields, and it has been further studied for energy harvesting as well as directed hot spot cooling of electronics. , In this work, we show the potential of jumping droplet condensation as a novel and highly sensitive electro-thermal sensing platform, especially for condensation heat transfer measurements having low supersaturations and heat fluxes (<1.5 W/cm 2 ) and in cases where manufacturing large-condensing-area samples (>1 cm 2 ) is challenging. For low supersaturations between the saturated vapor and condensing surface, noise in the temperature measurements can start to exceed the resolution of the measuring devices, thereby causing difficulties in obtaining the heat flux experimentally …”

Staying Dry and Clean: An Insect’s Guide to Hydrophobicity

“…Other applications for insect-inspired surfaces include protective and self-cleaning paints and coatings for vehicles and buildings, hydrophobic antennas, windows, windshields of vehicles, non-medical microfluidics devices (e.g., no-loss analysis channels), metal surface refinements for applications in energy systems and computing components, and hydrophobic antimicrobial textiles [ 36 , 118 , 119 , 120 ]. Furthermore, (super)hydrophobic materials paired with other attributes, such as structural color or transparency, seen in the Hoplia coerulea beetle, could be used for specialized self-cleaning coatings on solar cells and panels [ 49 ].…”

Advances in micro and nanoengineered surfaces for enhancing boiling and condensation heat transfer: a review

“…Statistical analysis of the segmented droplet images during condensation on hydrophobic and superhydrophobic flat surfaces enabled studying transient measurement of heat flux and the effects of single droplet heat transfer rate and the droplet size distribution on the total heat transfer rate from the surface. In another study, a methodology was proposed to measure condensation heat flux (with uncertainty less than 10%) using condensation videos by detecting and counting falling droplets from condensing tubes, 299 using an object detection network called EfficientDet. 300 By eliminating the need for temperature sensors, the proposed method achieved lower uncertainty compared to the conventional experimental methods.…”