Abstract
Pool boiling heat transfer plays a critical role as a high heat flux thermal control technology in a variety of scientific and application scenarios. However, automatic identifications of pool boiling stages with non-invasive technology achieving outstanding performance is barely reported. In this paper, we develop a novel artificial intelligence (AI) platform called Boiling-stages Explainable and Automatic Recognition (BEAR) and integrate it into our own FIND Multiphysics software. Inspired by the top-down and bottom-up attentional control mechanism of the human brain, BEAR aims to mimic the cognitive and discriminative processes of human experts while giving full play to the computational advantages of personal computers. This aim is decomposed into three goals and realized by the corresponding modules forming the architecture. BEAR is effective with an identification accuracy of up to 99.1 %. It is cost-efficient as the training time of the best-performing model embedded is 9.06 s with the model’s size of 65kB. It takes only about 0.048 ∼ 0.197 s per image to perform the entire automated identification process on a personal computer with an 8-core processor. Even a small dataset with low spatial and temporal resolution, comprising 33 images of 256 × 256 pixels, can yield results that are comparable to state-of-the-art literature in the field. Moreover, BEAR provides a platform with the scalability to carry a variety of algorithms for different application purposes, which can be beneficial in facilitating innovative modeling and further deployment in future application devices.
Published Version
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