临近空间探空仪温度传感器设计

Design of temperature sensor for near space radiosonde

针对开展临近空间科学探测的需求增加,设计了一种基于微型珠状热敏电阻的临近空间探空仪温度传感器。首先用计算流体动力学(CFD)方法对探头进行仿真并计算出太阳辐射误差;然后使用逆向传播(BP)神经网络和遗传算法优化的逆向传播(GA-BP)神经网络训练数据比较预测模型;并搭建模拟临近空间条件的低气压风洞实验平台测试不同参数下的太阳辐射误差,比较测试结果与预测模型输出数据,证实传感器的测量精度。实验表明,本文提出的传感器探头的平均测量误差为0.0073 K,误差均方根为0.0098 K。

In response to the increasing demand for scientific exploration in proximity space,a temperature sensor for near-space sounding instruments has been devised,predicated upon miniature bead-shaped thermistors.This methodology encompasses several pivotal stages:Primarily,the Computational Fluid Dynamics technique was enlisted to simulate and quantify the solar radiation error entailing the sensor probe.Subsequently,a backpropagation network and optimized through a genetic algorithm-based backpropagation neural network were employed to train on the accumulated dataset,thus comparing and facilitating the construction of a predictive model.Furthermore,a low pressure wind tunnel experimental platform was erected to emulate conditions reminiscent of those in the near-space milieu.This permitted the evaluation of solar radiation errors under diverse parameter configurations.The obtained test results were then compared with the data output from the predictive model to validate the precision of the sensor's measurements.The experiments revealed that the average measurement error of the sensor probe was 0.0073 K,with a root mean square error of 0.0098 K.