一种风压转换近地风速检测装备设计与试验

【目的】针对目前相关设备风场自干扰大的问题,为更好地获取无人机(Unmanned arial vehicle,UAV)近地风场参数信息,以风压信号为基础,采用皮托管风速传感器设计一种风压转换近地风速检测装备。【方法】利用轴流风机对叶轮风速传感器和皮托管风速传感器的感应位置进行风速干扰对比试验,利用热线式标准风速仪测定干扰前后的风速变化,利用轴流风机以相同的风速检测该装备所有传感器的测量风速并记录,得到该系统的一致性。将同一风速下标准热线式风速仪的测量风速和该系统的传感器测量风速进行对比,找出皮托管风速传感器准确度较低的风速段,利用Matlab软件对风压信号–风速值进行拟合。【结果】在10.00~15.00 m·s–1风速下皮托管风速传感器对直流风速的削弱不超过1%,而叶轮传感器达到20%以上。系统所采用的30个风压变送器在风速为15.00 m·s~~(–1)时的最大绝对差异为0.96 m·s–1,最大相对差异为6.40%,变异系数为1%。以三次公式进行拟合后,误差平方和(SSE)为0.099 6,拟合优度(R2)0.96。【结论】本系统能够有效检测无人机下旋翼风速数据,且比叶轮式风速传感器网络测量系统在减少干扰方面具有明显优势,可以为研究无人机田间作业提供有效帮助。

Design and experiment of a wind speed detection equipment with wind pressure conversion near ground

Objective The current wind speed detection equipment is influenced by wind speed interference in the wind field. To solve this problem, this paper presented a new kind of wind speed detection equipment using pitot tube speed sensors based on wind pressure signal to obtain better unmanned aerial vehicle(UAV) wind parameters near ground.Method We first used axial fan to test the interference of induction position of pitot tube wind speed sensor as well as vane anemometer, and then used hot wire anemometer to detect the changes of wind speeds before and after interference. In the end, we used axial fan to test and record wind speeds of all pitot sensors at the same wind speed, and obtained the consistency of the system. The measured wind speed of the standard hot wire anemometer was compared with that of the sensor in the system at the same wind speed to find out the range of wind speed in which the accuracy of pitot tube wind speed sensor was low. Matlab software was used to fit the wind pressure signal and wind speed value.Result At the wind speed of 10-15 m·s^-1, the reduction of wind speed interfered by pitot tube did not exceed 1%, while reached more than 20% for the vane anemometer. The maximum absolute discrepancy of all 30 sensors of this equipment was 0.96 m·s^-1 when the wind speed was 15.00 m·s^-1, the maximum relative discrepancy was 6.40%, and the coefficient of variation was about 1%. Fitting with three-time formula, the sum of squares for error was 0.099 6, the goodness of fit (R²) was more than 0.96.Conclusion Comparing to the current methodology, the proposed system can effectively detect the rotor wind speed data of UAV and has the significant advantage of reducing interference. This system can effectively help research the field work of UAV.