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首页> 外文期刊>Computers & geosciences >Multi-instrument turbulence toolbox (MITT): Open-source MATLAB algorithms for the analysis of high-frequency flow velocity time series datasets
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Multi-instrument turbulence toolbox (MITT): Open-source MATLAB algorithms for the analysis of high-frequency flow velocity time series datasets

机译：多仪器湍流工具箱（MITT）：用于分析高频流速时间序列数据集的开源MATLAB算法

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摘要

The measurement of flow velocity at high frequencies (20-200 Hz) has been made easier over the last couple of decades by the development and commercialization of a variety of instruments, many of which are capable of measuring multiple sampling volumes simultaneously. A variety of methods has been proposed to remove errors in velocity time series and classify the quality of data. However, most methods are applied using custom algorithms written to treat custom data formats and remain hidden from a wider audience. The objective of this paper is to introduce and document a new set of open-source algorithms, written in Matlab, that comprise the Multi-Instrument Turbulence Toolbox (MITT). The algorithms are designed to: (ⅰ) organize the data output from multiple instruments into a common format; (ⅱ) present the data in a variety of interactive figures for visualization and assessment; (ⅲ) clean the data by removing data spikes and noise; and (ⅳ) classify data quality. We hope that these algorithms will form the nucleus of an evolving toolbox that will help to accelerate the training of hydraulic researchers and practitioners, ensure a consistent application of methods for turbulence analysis, remove the bias of poor quality data from scientific literature, and ease collaboration through the sharing of data and methods.
机译：在过去的几十年中，由于各种仪器的开发和商业化，使得高频（20-200 Hz）流速的测量变得更加容易，其中许多仪器能够同时测量多个采样量。已经提出了多种方法来消除速度时间序列中的误差并对数据质量进行分类。但是，大多数方法是使用为处理自定义数据格式而编写的自定义算法来应用的，并且对于更广泛的受众而言仍然是隐藏的。本文的目的是介绍并记录一组用Matlab编写的新的开源算法，其中包括多仪器湍流工具箱（MITT）。这些算法旨在：（ⅰ）将多种仪器的数据输出组织成一种通用格式； （ⅱ）以各种互动方式展示数据以进行可视化和评估； （ⅲ）清除数据尖峰和噪音以清理数据； （ⅳ）对数据质量进行分类。我们希望这些算法将形成一个不断发展的工具箱的核心，这将有助于加快对水力研究人员和从业人员的培训，确保对湍流分析方法的一致应用，消除科学文献中质量差的数据的偏见，并简化协作通过共享数据和方法。

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