The critical heat flux (CHF) is an essential safety boundary in boiling heat transfer processes employed in high heat flux thermal-hydraulic systems. Identifying CHF is vital for preventing equipment damage and ensuring overall system safety, yet it is challenging due to the complexity of the phenomena. For an in-depth understanding of the complicated phenomena, various methodologies have been devised, but the acquisition of high-resolution data is limited by the substantial resource consumption required. This study presents a data-driven, image-to-image translation method for reconstructing thermal data of a boiling system at CHF using conditional generative adversarial networks (cGANs). The supervised learning process relies on paired images, which include total reflection visualizations and infrared thermometry measurements obtained from flow boiling experiments. Our proposed approach has the potential to not only provide evidence connecting phase interface dynamics with thermal distribution but also to simplify the laborious and time-consuming experimental setup and data-reduction procedures associated with infrared thermal imaging, thereby providing an effective solution for CHF diagnosis.

翻译：临界热通量是沸腾传热过程中的重要安全边界，广泛应用于高热通量热工水力系统。准确识别临界热通量对于防止设备损坏和保障系统安全至关重要，但由于其现象复杂性，这一任务极具挑战性。为深入理解该复杂现象，研究者已开发多种方法，然而高分辨率数据的获取受限于巨大的资源消耗。本研究提出了一种数据驱动的图像到图像转换方法，利用条件生成对抗网络重构沸腾系统在临界热通量条件下的热工数据。该监督学习过程依赖于配对图像，这些图像包含流动沸腾实验中获得的全反射可视化影像与红外测温记录。我们提出的方法不仅能够揭示相界面动力学与热分布之间的关联证据，还可简化红外热成像技术中繁琐耗时的实验装置搭建和数据处理流程，从而为临界热通量诊断提供有效解决方案。