ChemKED: A Human‐ and Machine‐Readable Data Standard for Chemical Kinetics Experiments

Weber, Bryan W.

doi:10.1002/kin.21142

Cited by 21 publications

(16 citation statements)

References 23 publications

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“…We collected experimental data from the literature that describe the autoignition in shock tubes of the four butanol isomers [10][11][12][13] and converted these to the ChemKED standard [19]. We did not consider measurements taken by Bec et al [12] and Zhu et al [13] using the constrained-reaction-volume method.…”

Section: Experimental Data Curationmentioning

confidence: 99%

“…We did not consider measurements taken by Bec et al [12] and Zhu et al [13] using the constrained-reaction-volume method. Weber and Niemeyer [19] describe the ChemKED (Chemical Kinetics Experimental Data) format in detail. In brief, ChemKED is a human-and machine-readible, YAML-based file format for describing fundamental combustion measurements with sufficient information to simulate experimental data points.…”

Section: Experimental Data Curationmentioning

confidence: 99%

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Assessing impacts of discrepancies in model parameters on autoignition model performance: A case study using butanol

Sirumalla

Mayer

Niemeyer

et al. 2018

Combustion and Flame

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Side-by-side comparison of detailed kinetic models using a new tool to aid recognition of species structures reveals significant discrepancies in the published rates of many reactions and thermochemistry of many species. We present a first automated assessment of the impact of these varying parameters on observable quantities of interest-in this case, autoignition delay-using literature experimental data. A recent kinetic model for the isomers of butanol was imported into a common database. Individual reaction rate and thermodynamic parameters of species were varied using values encountered in combustion models from recent literature. The effects of over 1,600 alternative parameters were considered. Separately, experimental data were collected from recent publications and converted into the standard YAML-based ChemKED format. The Cantera-based model validation tool, PyTeCK, was used to automatically simulate autoignition using the generated models and experimental data, to judge the performance of the models. Taken individually, most of the parameter substitutions have little effect on the overall model performance, although a handful have quite large effects, and are investigated more thoroughly. Additionally, models varying multiple parameters simultaneously were evolved using a genetic algorithm to give fastest and slowest autoignition delay times, showing that changes exceeding a factor of 10 in ignition delay time are possible by cherry-picking from only accepted, published parameters. All data and software used in this study are available openly.

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Section: Experimental Data Curationmentioning

confidence: 99%

Section: Experimental Data Curationmentioning

confidence: 99%

Assessing impacts of discrepancies in model parameters on autoignition model performance: A case study using butanol

Sirumalla

Mayer

Niemeyer

et al. 2018

Combustion and Flame

Self Cite

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“…In this paper, we focus on experimental data and model development in the combustion kinetics domain, where a number of initiatives to collect experimental data in a systematic way have already been developed [23,26,54,58], along with initiatives to define better community data reporting standards. 2 Even if this paper focuses on a specific domain, the use cases, the requirements and the solutions analyzed are common to many other scientific domains, as discussed in Section 3.…”

Section: Introductionmentioning

confidence: 99%

Towards a scientific data framework to support scientific model development

Scalia

Pelucchi

Stagni

et al. 2019

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Editor: Silvio Peroni (https://orcid.org/0000-0003-0530-4305) Solicited reviews: HannaĆwiek-Kupczyńska (https://orcid.org/0000-0001-9113-567X); Aliaksandr Birukou (https://orcid.org/0000-0002-4925-9131); Stian Soiland-Reyes (https://orcid.org/0000-0001-9842-9718)Abstract. The sharing of scientific and scholarly data has been increasingly promoted over the last decade, leading to open repositories in many different scientific domains. However, data sharing and open data are not final goals in themselves, the real benefit is in data reuse, which allows leveraging investments in research and enables large-scale data-driven research progress.This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). 2451-8484 © 2019 -IOS Press and the authors. G. Scalia et al. / Towards a scientific data framework to support scientific model developmentFocusing on reuse, this paper discusses the design of an integrated framework to automatically take advantage of large amounts of scientific data extracted from the literature to support research, and in particular scientific model development. Scientific models reproduce and predict complex phenomena and their development is a rather challenging task, within which scientific experiments have a key role in their continuous validation. Starting from the combustion kinetics domain, this paper discusses a set of use cases and a first prototype for such a framework which leads to a set of new requirements and an architecture that can be generalized to other domains. The paper analyzes the needs, the challenges and the research directions for such a framework, in particular those related to data management, automatic scientific model validation, data aggregation and data analysis, to leverage large amounts of published scientific data for new knowledge extraction.

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“…Successful efforts have been made to provide a uniform and standardized data‐storing format, which is easily exchangeable within different research groups. An example of such an effort is the recently developed ChemKED, solving the problem of a standardized data‐storing format for combustion . Another example, developed by Weber et al, for rapid compression machines, shows an automated processing and analysis solution for combustion experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…An example of such an effort is the recently developed ChemKED, solving the problem of a standardized data-storing format for combustion. 15 Another example, developed by Weber et al, 16 for rapid compression machines, shows an automated processing and analysis solution for combustion experimental data. An example for developing catalytic reaction mechanisms is the open software tool, CaRMeN, 17,18 that provides a way to archive and combine experimental and modeling information as well as computer simulations.…”

Section: Introductionmentioning

confidence: 99%

QUANTIS: Data quality assessment tool by clustering analysis

Symoens

Aravindakshan

Vermeire

et al. 2019

Int J of Chemical Kinetics

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Automatically generated kinetic networks are ideally validated against a large set of accurate, reproducible, and easy‐to‐model experimental data. However, although this might seem simple, it proves to be quite challenging. QUANTIS, a publicly available Python package, is specifically developed to evaluate both the precision and accuracy of experimental data and to ensure a uniform, quick processing, and storage strategy that enables automated comparison of developed kinetic models. The precision is investigated with two clustering techniques, PCA and t‐SNE, whereas the accuracy is probed with checks for the conservation laws. First, the developed tool processes, evaluates, and stores experimental yield data automatically. All data belonging to a given experiment, both unprocessed and processed, are stored in the form of an HDF5 container. The demonstration of QUANTIS on three different pyrolysis cases showed that it can help in identifying and overcoming instabilities in experimental datasets, reduce mass and molar balance closure discrepancies, and, by evaluating the visualized correlation matrices, increase understanding in the underlying reaction pathways. Inclusion of all experimental data in the HDF5 file makes it possible to automate simulating the experiment with CHEMKIN. Because of the employed InChI string identifiers for molecules, it is possible to automate the comparison experiment/simulation. QUANTIS and the concepts demonstrated therein is a potentially useful tool for data quality assessment, kinetic model validation, and refinement.

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ChemKED: A Human‐ and Machine‐Readable Data Standard for Chemical Kinetics Experiments

Cited by 21 publications

References 23 publications

Assessing impacts of discrepancies in model parameters on autoignition model performance: A case study using butanol

Assessing impacts of discrepancies in model parameters on autoignition model performance: A case study using butanol

Towards a scientific data framework to support scientific model development

QUANTIS: Data quality assessment tool by clustering analysis

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