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3D-calibration of three- and four-sensor hot-film probes based on collocated sonic using neural networks

机译:基于神经网络的并置声波对三传感器和四传感器热膜探头的3D校准

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摘要

High resolution measurements of turbulence in the atmospheric boundary layer (ABL) are critical to the understanding of physical processes and parameterization of important quantities, such as the turbulent kinetic energy dissipation. Low spatio-temporal resolution of standard atmospheric instruments, sonic anemometers and LIDARs, limits their suitability for fine-scale measurements of ABL. The use of miniature hot-films is an alternative technique, although such probes require frequent calibration, which is logistically untenable in field setups. Accurate and truthful calibration is crucial for the multi-hot-films applications in atmospheric studies, because the ability to conduct calibration in situ ultimately determines the turbulence measurements quality. Kit et al (2010 J. Atmos. Ocean. Technol. 27 23-41) described a novel methodology for calibration of hot-film probes using a collocated sonic anemometer combined with a neural network (NN) approach. An important step in the algorithm is the generation of a calibration set for NN training by an appropriate low-pass filtering of the high resolution voltages, measured by the hot-film-sensors and low resolution velocities acquired by the sonic. In Kit et al (2010 J. Atmos. Ocean. Technol. 27 23-41), Kit and Grits (2011 J. Atmos. Ocean. Technol. 28 104-10) and Vitkin et al (2014 Meas. Sci. Technol. 25 75801), the authors reported on successful use of this approach for in situ calibration, but also on the method's limitations and restricted range of applicability. In their earlier work, a jet facility and a probe, comprised of two orthogonal x-hot-films, were used for calibration and for full dataset generation. In the current work, a comprehensive laboratory study of 3D-calibration of two multi-hot-film probes (triple-and four-sensor) using a grid flow was conducted. The probes were embedded in a collocated sonic, and their relative pitch and yaw orientation to the mean flow was changed by means of motorized traverses. The study demonstrated that NN-calibration is a powerful tool for calibration of multi-sensor 3D-hot film probes embedded in a collocated sonic, and can be employed in long-lasting field campaigns.
机译:大气边界层(ABL)中湍流的高分辨率测量对于理解物理过程和重要量的参数化(例如湍动能耗散)至关重要。标准大气仪器,声波风速计和LIDAR的时空分辨率低,限制了它们对ABL精细测量的适用性。使用微型热膜是一种替代技术,尽管此类探头需要频繁校准,这在现场设置中在逻辑上是站不住脚的。准确和真实的校准对于大气研究中的多热膜应用至关重要,因为原位进行校准的能力最终决定了湍流测量的质量。 Kit等人(2010 J. Atmos。Ocean。Technol。27 23-41)描述了一种使用并置声波风速计和神经网络(NN)方法校准热膜探针的新颖方法。该算法的重要步骤是通过对高分辨率电压进行适当的低通滤波来生成用于NN训练的校准集,该高分辨率电压由热膜传感器测量,并由声波获取低分辨率速度。在Kit等人(2010 J. Atmos。Ocean。Technol。27 23-41),Kit and Grits(2011 J. Atmos。Ocean。Technol。28 104-10)和Vitkin等(2014 Meas。Sci。Technol。 25 75801),作者报告了这种方法在原位校准中的成功使用,但也报道了该方法的局限性和适用范围的限制。在他们的早期工作中,使用了一个喷射设备和一个由两个正交X射线热胶片组成的探头进行校准和生成完整的数据集。在当前的工作中,进行了一项全面的实验室研究,该研究使用网格流对两个多热膜探头(三重和四传感器)进行3D校准。探头被嵌入到并置的声波中,并且它们的相对螺距和相对于平均流量的偏航方向通过机动导线来改变。这项研究表明,NN校准是校准嵌入在并置声波中的多传感器3D热膜探头的强大工具,可用于持久的野战。

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