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Nucleate boiling is perhaps one of the most efficient cooling methodologies due to its large heat flux with a relatively low superheat. Nucleate boiling often occurs on surfaces oriented at different angles; therefore, understanding the behavior of bubble growth on various surface orientations is of importance. Despite significant advancement, numerous questions remain regarding the fundamentals of bubble growth mechanisms on oriented surfaces, a major source of enhanced heat dissipation. This work aims to accurately measure three-dimensional (3D), space- and time-resolved, local liquid temperature distributions surrounding a growing bubble on oriented surfaces that quantify the heat transfer from the superheated liquid layer during bubble growth. The dual tracer laser-induced fluorescence thermometry technique combined with high-speed imaging captures transient 2D temperature distributions within a 0.3 ºC accuracy at a 30 μm resolution. The results show that the temperature close to the heated surface and bubble interface exhibits an acute transient behavior at the time of bubble departure, and the growing bubble works as a pump to remove heat from the surface with a temperature difference of up to 10 °C during its growth and departure. The experimental results are compared with data available in the literature to validate the accuracy of the technique. It was found that the heat transfer coefficient close to the bubble interface and heater is approximately 1.3 times higher than the heat transfer coefficient in the bulk liquid. 

Boiling heat transfer associated with bubble growth is perhaps one of the most efficient cooling methodologies due to its large latent heat during phase change. Despite the significant advancements, numerous questions remain regarding the fundamentals of bubble growth mechanisms, which is a major source of enhanced heat dissipation. This work aims to accurately measure three-dimensional (3D), space and time-resolved, local liquid temperature distributions surrounding a growing bubble to quantify the heat transfer in the superheated liquid layer during bubble growth. The dual tracer laser-induced fluorescence thermometry technique combined with high-speed imaging captures transient 2D temperature distributions, that will render 3D temperature distributions by combining multiple 2D layers, within a 0.3 °C accuracy at a 30 μm resolution. Two fluorescent dyes, fluorescein and sulforhodamine B, were used to measure transient temperatures, by account of their temperature-sensitive emissions. The results show that the temperature close to the heated surface and bubble interface exhibits an acute transient behavior at the time of bubble departure. The growing bubble works as a pump to remove heat from the surface with a peak temperature difference of up to 10 °C during its growth and departure. The experimental results were compared with previously reported studies to validate the accuracy of the technique. It was found that the heat transfer coefficient close to the bubble interface and heater is approximately 1.3 times higher than the heat transfer coefficient in the bulk liquid. 

In this paper, a multilayer perceptron (MLP)-type artificial neural network model with a back-propagation training algorithm is utilized to model the bubble growth and bubble dynamics parameters in nucleate boiling with a non-uniform electric field. The influences of the electric field on different parameters that describe bubble’s behaviors including bubble waiting time, bubble departure frequency, bubble growth time, and bubble departure diameter are considered. This study models single bubble dynamic behaviors of R113 created on a heater in an inconsistent electric field by utilizing a MLP neural network optimized by four different swarm-based optimization algorithms, namely: Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC) algorithm, and Particle Swarm Optimization (PSO). For evaluating the model effectiveness, the MSE value (Mean-Square Error) of the artificial neural network model with various optimization algorithms is measured and compared. The results suggest that the optimal networks in the two-hidden layer and three-hidden layer models for the bubble departure diameter improve MSE by 33.85% and 35.27%, respectively, when compared with the best response in the one-hidden layer model. Additionally, for bubble growth time, the networks with two hidden layers and three hidden layers have the 44.51% and 45.85% reduction in error, when compared with the network with one hidden layer, respectively. For the departure frequency, the error reduction in the two-layer and three-layer networks is 46.85% and 62.32%, respectively. For bubble waiting time, the best networks in the two hidden-layer and three hidden-layer models improve MSE by 52.44% and 62.27% compared with the best 1HL model response, respectively. Also, the two algorithms of SSA and GWO are able to compete well (comparable MSE) with the PSO and ABC algorithms.