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首页> 外文期刊>MATEC Web of Conferences >Development of the Multifactor Computational Models of the Solid Propellants Combustion by Means of Data Science Methods. Propellant Combustion Genome Conception
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Development of the Multifactor Computational Models of the Solid Propellants Combustion by Means of Data Science Methods. Propellant Combustion Genome Conception

机译：通过数据科学方法开发固体推进剂燃烧的多因素计算模型。推进剂燃烧基因组观念

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摘要

The results of usage of data science methods, in particular artificial neural networks, for the creation of new multifactor computational models of the solid propellants (SP) combustion that solve the direct and inverse tasks are presented. The own analytical platform Loginom was used for the models creation. The models of combustion of double based SP with such nano additives as metals, metal oxides, termites were created by means of experimental data published in scientific literature. The goal function of the models were burning rate (direct tasks) as well as propellants composition (inverse tasks). The basis (script) of a creation of Data Warehouse of SP combustion was developed. The Data Warehouse can be supplemented by new experimental data and metadata in automated mode and serve as a basis for creating generalized combustion models of SP and thus the beginning of work in a new direction of combustion science, which the authors propose to call "Propellant Combustion Genome" (by analogy with a very famous Materials Genome Initiative, USA). "Propellant Combustion Genome" opens wide possibilities for accelerate the advanced propellants development Genome" opens wide possibilities for accelerate the advanced propellants development.
机译：提供了数据科学方法，特别是人工神经网络，用于创建解决直接任务的固体推进剂（SP）燃烧的新多因素计算模型的数据科学方法的使用结果。自己的分析平台Loginom用于模型创建。通过在科学文献中发表的实验数据，通过在科学文献中发表的实验数据来创建与金属，金属氧化物，白蚁等纳米添加剂的燃烧模型。模型的目标函数是燃烧速率（直接任务）以及推进剂组成（反对任务）。开发了SP燃烧数据仓库创建的基础（脚本）。数据仓库可以通过自动化模式的新实验数据和元数据补充，并作为创建SP的广义燃烧模型的基础，从而在燃烧科学的新方向上的工作开始，该作者提出称之为“推进剂燃烧基因组“（通过与非常着名的材料基因组倡议，美国类比）。 “推进剂燃烧基因组”开辟了加速的广泛可能性，提高推进剂发育基因组“开辟了广泛的可能性，以加速先进的推进剂发育。

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