Critical heat flux diagnosis using conditional generative adversarial networks

The critical heat flux (CHF) represents the maximum heat flux in the nucleate boiling process, marking an abrupt increase in surface temperature. As a crucial factor in high heat-flux systems to ensure safe operation and prevent system damage, CHF diagnosis has been extensively researched, leading to the development of various mechanistic models explaining the triggering mechanisms of CHF [1][2][3][4]. Among these models -- such as the hydrodynamic instability model, macrolayer dryout model, and interfacial lift-off model -- the hot/dry spot model suggests that irreversible dry patch formation leads to increasing temperature, resulting in the postulation that the development of the irreversible dry spot's temperature hinders the wetting of the heated surface by the supplied liquid [5]. The dry patch is first generated at high heat flux, then coalesces and expands again under the remnant bubble to trigger CHF [6]. To validate and improve such models, visual observation methods have been developed [7][8]. Total reflection visualization and (TR) infrared thermometry (IR) are arguably the most important techniques for visualizing the formation of dry patches while measuring the coincidental temperature evolution of the liquid-vapor system [9][10][11]. Through the methods, the behavior of the bubble structure and dry patch under flow boiling has been observed, and the hydrodynamic mechanism of the irreversible dry patch have been analyzed. Also, there have been attempts to determine CHF based on the temperature of the dry patch periphery [6][12]. Besides, following recent advancements in Convolutional Neural Networks (CNNs), which excel in capturing visual information characteristics, neural networks are expected to have the potential to simplify infrared thermal imaging, as the process typically involves tedious experimental setups and extensive data reduction [13].