Development of the Multifactor Computational Models of the Solid Propellants Combustion by Means of Data Science Methods. Propellant Combustion Genome Conception

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.

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Development of the Multifactorial Computational Models of the Solid Propellants Combustion by Means of Data Science Methods –Phase III

AIAA Propulsion and Energy 2019 Forum ◽

10.2514/6.2019-3957 ◽

2019 ◽

Author(s):

Victor S. Abrukov ◽

Alexander N. Lukin ◽

Nichith C ◽

Charlie Oommen ◽

Mikhail V. Kiselev ◽

...

Keyword(s):

Data Science ◽

Computational Models ◽

Phase Iii ◽

Science Methods ◽

Solid Propellants

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Development of the Multifactorial Computational Models of the Solid Propellants Combustion by Means of Data Science Methods - Phase I

53rd AIAA/SAE/ASEE Joint Propulsion Conference ◽

10.2514/6.2017-5078 ◽

2017 ◽

Author(s):

Victor S. Abrukov ◽

Alexander N. Lukin ◽

Charlie Oommen ◽

VR Sanal Kumar ◽

Nichith Chandrasekaran ◽

...

Keyword(s):

Phase I ◽

Data Science ◽

Computational Models ◽

Science Methods ◽

Solid Propellants

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Recent Advancements in Study of Effects of Nano Micro Additives on Solid Propellants Combustion by Means of the Data Science Methods

Defence Science Journal ◽

10.14429/dsj.69.12948 ◽

2019 ◽

Vol 69 (1) ◽

pp. 20-26 ◽

Cited By ~ 4

Author(s):

Victor S. Abrukov ◽

Alexander N. Lukin ◽

Darya A. Anufrieva ◽

Charlie Oommen ◽

V. R. Sanalkumar ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Kinetic Parameters ◽

Data Science ◽

Computational Models ◽

Science Methods ◽

Solid Propellants ◽

Rocket Propellants ◽

Artificial Neural ◽

Solid Rocket

The efforts of Russian-Indian research team for application of the data science methods, in particular, artificial neural networks for development of the multi-factor computational models for studying effects of additive’s properties on the solid rocket propellants combustion are presented. The possibilities of the artificial neural networks (ANN) application in the generalisation of the connections between the variables of combustion experiments as well as in forecasting of “new experimental results” are demonstrated. The effect of particle size of catalyst, oxidizer surface area and kinetic parameters like activation energy and heat release on the final ballistic property of AP-HTPB based propellant composition has been modelled using ANN methods. The validated ANN models can predict many unexplored regimes, like pressures, particle sizes of oxidiser, for which experimental data are not available. Some of the regularly measured kinetic parameters extracted from non-combustion conditions could be related to properties at combustion conditions. Results predicted are within desirable limits accepted in combustion conditions.

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Development of the Multifactorial Computational Models of the Solid Propellants Combustion by Means of Data Science Methods – Phase II

2018 Joint Propulsion Conference ◽

10.2514/6.2018-4961 ◽

2018 ◽

Author(s):

Victor S Abrukov ◽

Alexander N. Lukin ◽

Charlie Oommen ◽

Nichith Chandrasekaran ◽

Rajaghatta S. Bharath ◽

...

Keyword(s):

Phase Ii ◽

Data Science ◽

Computational Models ◽

Science Methods ◽

Solid Propellants

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Genome Approach and Data Science Methods for Accelerated Discovery of New Solid Propellants with Desired Properties

AIAA Propulsion and Energy 2020 Forum ◽

10.2514/6.2020-3929 ◽

2020 ◽

Cited By ~ 1

Author(s):

Victor S Abrukov ◽

Alexander N. Lukin ◽

Nichith Chandrasekaran ◽

Charlie Oommen ◽

Thianesh U.K ◽

...

Keyword(s):

Data Science ◽

Science Methods ◽

Solid Propellants

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Merging Materials and Data Science: Opportunities, Challenges, and Education in Materials Informatics

MRS Advances ◽

10.1557/adv.2020.171 ◽

2020 ◽

Vol 5 (7) ◽

pp. 329-346 ◽

Cited By ~ 1

Author(s):

Thomas J. Oweida ◽

Akhlak Mahmood ◽

Matthew D. Manning ◽

Sergei Rigin ◽

Yaroslava G. Yingling

Keyword(s):

Data Science ◽

Materials Science ◽

Optimization Techniques ◽

Materials Informatics ◽

Science And Engineering ◽

Educational Challenges ◽

Engineering Problems ◽

Materials Science And Engineering ◽

Materials Genome ◽

Materials Genome Initiative

ABSTRACTSince the launch of the Materials Genome Initiative (MGI) the field of materials informatics (MI) emerged to remove the bottlenecks limiting the pathway towards rapid materials discovery. Although the machine learning (ML) and optimization techniques underlying MI were developed well over a decade ago, programs such as the MGI encouraged researchers to make the technical advancements that make these tools suitable for the unique challenges in materials science and engineering. Overall, MI has seen a remarkable rate in adoption over the past decade. However, for the continued growth of MI, the educational challenges associated with applying data science techniques to analyse materials science and engineering problems must be addressed. In this paper, we will discuss the growing use of materials informatics in academia and industry, highlight the need for educational advances in materials informatics, and discuss the implementation of a materials informatics course into the curriculum to jump-start interested students with the skills required to succeed in materials informatics projects.

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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