Burst Random Amplitude Ramp : 2.0V

This is a zip file that contains 10 repetitions for burst random (multiple white noise) excitation at 2.0V LMS output voltage. This test is performed on the starboard wing of the BAE T1A Hawk housed at the Labratory for Verification and Validation (lvv.ac.uk). The wing is excited using a single shaker and data is recorded using 55 uni-accelerometers and the excitation load cell. <br> The significance of the naming conventions is explained in the "ORDA_Readme.pdf" file. This file also contains the code needed to load the file into python for analysis called "hdf5_loader.py". This contains a function called load_hdf5 that takes the path of the hdf5 file and loads it into a single dictionary. The dictionary is separated into "Meta" for the testing parameters and the sensor names to identify the sensor of interest. For the sensor names, the naming convention is described in the readme file along with what types of data are included and the citation reference. The uploaded *.zip file contains all the repetitions for the testing parameters (nominally 10 repetitions). <br> This test was performed under the Alan Turing Institute funded project <em>Digital Twins for High-Value Engineering Systems (DTHIVE)</em> with continuing support from the EPSRC funded project <em>Digital Twins for Improved Dynamic Design (DigiTwin)</em>. For more information, please contact the PI, Professor David Wagg.

本数据集以压缩包形式存储，包含在2.0V LMS输出电压下开展的猝发随机（多重白噪声）激励试验的10组重复样本。本次试验在设于验证与确认实验室（lvv.ac.uk）的BAE T1A鹰式教练机右翼上开展。该机翼采用单台振动台施加激励，通过55个单轴加速度计（uni-accelerometers）与激励力传感器（load cell）采集试验数据。
命名规则的详细说明见"ORDA_Readme.pdf"文档。该文档同时附带用于将HDF5格式文件导入Python以进行分析的代码"hdf5_loader.py"，其中包含名为load_hdf5的函数：该函数接收HDF5文件路径，将数据加载至单一字典中。该字典分为"Meta"模块与传感器标识模块：前者存储试验参数，后者用于识别目标传感器。传感器命名规则、包含的数据类型与参考文献均在该README文档中予以说明。本上传的压缩包包含该试验参数下的全部重复样本（标称共10组）。
本试验由艾伦·图灵研究所资助项目《高价值工程系统数字孪生（Digital Twins for High-Value Engineering Systems, DTHIVE）》支持，并得到英国工程与自然科学研究委员会（EPSRC）资助项目《面向动态设计优化的数字孪生（Digital Twins for Improved Dynamic Design, DigiTwin）》的持续支撑。如需获取更多信息，请联系项目负责人David Wagg教授。