Chen Sheng, Su Zhixuan, Dai Min, Xue Chenyang, Tao Jiping, Hai Zhenyin
School of Aerospace Engineering, Xiamen University, Xiamen 361102, China.
Sensors (Basel). 2024 Nov 22;24(23):7445. doi: 10.3390/s24237445.
In industrial measurement, temperature field measurement typically relies on thermocouples and spectroscopic techniques. These traditional methods often suffer from insufficient precision, resulting in prevalent low-resolution measurements in real thermal scenarios. To address this challenge, we propose a novel general super-resolution approach for temperature field measurement in various thermal scenarios, leveraging the low-resolution (LR) data obtained from sensor array technology. The method incorporates skip connections and multi-path learning, along with physical information loss, to enhance accuracy. To validate the effectiveness of the approach, simulations across three two-dimensional thermal scenarios are conducted: the heating process in silicon chips, the thermodynamic process of hot and cold water mixing, and the convective heat transfer phenomena involved in metal sheet dissipation under airflow. The results show that the learning model can accurately predict the HR temperature. The proposed approach offers a pathway for generating HR solutions, bypassing traditional time-consuming simulation processes while ensuring data accuracy. By utilizing a fixed model and a lightweight physical loss function, we simplify the deployment process, facilitating applications in computational fluid dynamics (CFD) solutions, engineering measurements, and related fields.
在工业测量中，温度场测量通常依赖于热电偶和光谱技术。这些传统方法往往精度不足，导致在实际热场景中普遍存在低分辨率测量。为应对这一挑战，我们提出了一种新颖的通用超分辨率方法，用于在各种热场景中进行温度场测量，利用从传感器阵列技术获得的低分辨率（LR）数据。该方法结合了跳跃连接和多路径学习，以及物理信息损失，以提高精度。为验证该方法的有效性，我们在三种二维热场景中进行了模拟：硅芯片中的加热过程、热水和冷水混合的热力学过程，以及气流下金属板散热中涉及的对流换热现象。结果表明，学习模型能够准确预测高分辨率（HR）温度。所提出的方法为生成高分辨率解决方案提供了一条途径，绕过了传统的耗时模拟过程，同时确保了数据准确性。通过使用固定模型和轻量级物理损失函数，我们简化了部署过程，便于在计算流体动力学（CFD）解决方案、工程测量及相关领域中的应用。
