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首页> 外文期刊>Journal of atmospheric and oceanic technology >In Situ Calibration of Hot-Film Probes Using a Collocated Sonic Anemometer: Implementation of a Neural Network
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In Situ Calibration of Hot-Film Probes Using a Collocated Sonic Anemometer: Implementation of a Neural Network

机译：使用并置声波风速计的热膜探头的原位校准：神经网络的实现

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Although the integral quantities of atmospheric turbulence are conveniently measured using sonic anemometers, obtaining relevant finescale variables such as the kinetic energy dissipation using conventional hot-film/wire techniques remains a challenge because of two main difficulties. The first difficulty is the mean wind variability, which causes violation of the requirement that mean winds have a specific alignment with the hot-film/wire probe. To circumvent this problem, a combination of collocated sonic and hot-film anemometers, with the former measuring mean winds and aligning the latter in the appropriate wind direction via an automated platform, is successfully designed and implemented. The second difficulty is the necessity of frequent and onerous calibrations akin to hot-film anemometry that lead to logistical difficulties during outdoor (field) measurements. This is addressed by employing sonic measurements to calibrate the hot films in the same combination, with the output (velocity) to input (voltage) transfer function for the hot film derived using a neural network (NN) model. The NN is trained using low-pass-filtered hot-film and sonic data taken in situ. This new hot-film calibration procedure is compared with the standard calibration method based on an external calibrator. It is inferred that the sonic-based NN method offers great potential as an alternative to laborious standard calibration techniques, particularly in the laboratory and in stable atmospheric boundary layer settings. The NN approximation technique is found to be superior to the conventionally used polynomial fitting methods when used in conjunction with unevenly spaced calibration velocity data generated by sonic anemometers.
机译：尽管可以使用声速风速计方便地测量大气湍流的总量，但是由于两个主要困难，因此获得相关的小尺度变量（例如，使用常规热膜/金属丝技术获得的动能耗散）仍然是一个挑战。第一个困难是平均风的可变性，这导致违反了平均风与热膜/金属丝探针有特定对准的要求。为了解决这个问题，成功设计并实现了并置声波和热膜风速仪的组合，前者测量平均风，并通过自动化平台将后者对准适当的风向。第二个困难是必须像热膜风速仪那样频繁且繁琐的校准，这会导致在室外（野外）测量过程中出现后勤困难。通过使用声音测量以相同的组合来校准热膜，通过使用神经网络（NN）模型导出热膜的输出（速度）到输入（电压）传递函数，可以解决此问题。使用低通滤波的热膜和现场采集的声音数据来训练NN。将此新的热膜校准程序与基于外部校准器的标准校准方法进行了比较。可以推断，基于声波的NN方法具有巨大的潜力，可替代费力的标准校准技术，尤其是在实验室和稳定的大气边界层设置中。当与声速计产生的不均匀校准速度数据结合使用时，发现NN逼近技术优于传统的多项式拟合方法。

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    E.Kit; A. Cherkassky; T. Sant; H. J. S. Fernando;

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    School of Mechanical Engineering, Tel-Aviv University, Ramat Aviv, Tel Aviv 69978, Israel;

    Department of Management and Control, Shenkar College, Ramat Gan, Israel;

    Center for Environmental Fluid Dynamics, and Department of Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona;

    Center for Environmental Fluid Dynamics, and Department of Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona;

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