基于CFD的地表温度传感器的设计与实验研究
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南京信息工程大学

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国家自然科学基金(41875035);江苏省产学研合作项目(BY2022544);江苏省研究生科研与实践创新计划项目(SJCX22_0334);江苏省高校大学生创新创业训练计划项目(202210300011Z)


Design and experimental research of surface temperature sensor based on CFD
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1.Nanjing University of Information Science &2.Technology

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National Natural Science Foundation of China (41875035); Jiangsu Province Industry-University-Research Cooperation Project (BY2022544); Jiangsu Postgraduate Research and Practice Innovation Program Project (SJCX22_0334); Jiangsu Province College Students Innovation and Entrepreneurship Training Program Project (202210300011Z)

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    摘要:

    地面温度变化速度为每10年升高0.1 K,然而受太阳辐射的影响,在实际测量时传统防辐射罩会产生约1 K量级的太阳辐射误差。为提升地表温度测量精度,降低工作能耗,本文设计一种基于压电振片振动加速传感器探头辐射热量扩散的温度传感器。首先利用计算流体动力学(Computational Fluid Dynamics, CFD)方法计算温度传感器在多物理因素下的辐射误差,然后使用神经网络算法对数据进行拟合分析,最后搭建外场实验平台,将传感器放置在真实环境中来验证方案的可行性。实验结果表明,本文提出的地表温度传感器具有较高的温度测量精度,其修正值与基准值的绝对误差和均方根误差分别为0.041 K和0.055 K,也验证了神经网络算法修正效果的优越性。

    Abstract:

    The ground temperature changes at a rate of 0.1 K every 10 years, however, due to the influence of solar radiation, when measured in practice, conventional radiation shields sensor will produce a solar radiation error of about 1 K. In order to improve the accuracy of surface temperature measurement and reduced working energy consumption, this paper designs a temperature sensor based on the diffusion of radiant heat from a piezoelectric ceramic vibration acceleration sensor probe. Firstly, the Computational Fluid Dynamics (CFD) method is used to calculate the radiation error of the temperature sensor under multi physics factors, and then the data is fitted and analyzed using the neural network algorithm, and finally the field experiment platform is built to place the temperature sensor in the real environment to verify the feasibility of the scheme. The experimental results show that the absolute error and root mean square error of the correction value and reference value of the surface temperature sensor are 0.041 K and 0.055 K, respectively, which also verifies the superiority of the correction effect of the neural network algorithm.

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  • 收稿日期:2022-05-01
  • 最后修改日期:2022-08-17
  • 录用日期:2022-08-18
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