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arXiv 1706.01987physics.chem-phcs.CE
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ChemKED:用于化学动力学实验的人机可读数据标准

ChemKED: a human- and machine-readable data standard for chemical kinetics experiments

Bryan W. Weber, Kyle E. Niemeyer

摘要:

基本的实验测量的数量,如点火延迟时间,层流火焰速度和物种分布(除其他外)服务的重要作用,在理解燃料化学和验证化学动力学模型。然而,尽管文献中这些信息的重要性和丰富性,社区缺乏广泛采用的标准格式。这阻碍了社区的共享和广泛使用。在这里,我们介绍了一种新的化学动力学实验数据格式,ChemKED,以及相关的基于Python的包,用于验证和处理ChemKED格式的文件,称为PyKED。我们还回顾了过去和相关的工作,并激发了对新解决方案的需求。ChemKED目前支持激波管和快速压缩机的自燃延迟时间测量。ChemKED格式的文件包含模拟实验数据点所需的所有信息,包括数据的不确定性。ChemKED基于YAML数据序列化语言,旨在作为人类和机器可读的标准,便于创建和自动使用。ChemKED和PyKED的开发在BSD 3条款许可下在GitHub上公开进行,欢迎社区的贡献。未来的发展计划包括支持层流火焰,射流搅拌反应器和形态测量的实验数据。

英文摘要:

Fundamental experimental measurements of quantities such as ignition delay times, laminar flame speeds, and species profiles (among others) serve important roles in understanding fuel chemistry and validating chemical kinetic models. However, despite both the importance and abundance of such information in the literature, the community lacks a widely adopted standard format for this data. This impedes both sharing and wide use by the community. Here we introduce a new chemical kinetics experimental data format, ChemKED, and the related Python-based package for validating and working with ChemKED-formatted files called PyKED. We also review past and related efforts, and motivate the need for a new solution. ChemKED currently supports the representation of autoignition delay time measurements from shock tubes and rapid compression machines. ChemKED-formatted files contain all of the information needed to simulate experimental data points, including the uncertainty of the data. ChemKED is based on the YAML data serialization language, and is intended as a human- and machine-readable standard for easy creation and automated use. Development of ChemKED and PyKED occurs openly on GitHub under the BSD 3-clause license, and contributions from the community are welcome. Plans for future development include support for experimental data from laminar flame, jet stirred reactor, and speciation measurements.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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