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International Journal of Energetic Materials and Chemical Propulsion

Publication de 6  numéros par an

ISSN Imprimer: 2150-766X

ISSN En ligne: 2150-7678

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 0.7 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.1 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00016 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.18 SJR: 0.313 SNIP: 0.6 CiteScore™:: 1.6 H-Index: 16

Indexed in

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR CREATION OF "BLACK BOX" MODELS OF ENERGETIC MATERIALS COMBUSTION

Volume 7, Numéro 5, 2008, pp. 373-382
DOI: 10.1615/IntJEnergeticMaterialsChemProp.v7.i5.20
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RÉSUMÉ

The possibilities of artificial neural networks (ANN) technologies for modeling and characteristics prediction of energetic materials burning are discussed. For the first time, the "black box" computational model of burning characteristics prediction for a propellant is created. It allows one to predict the temperature profiles in propellant combustion waves by means of data about heat of combustion, burning rate, and pressure. Another kind of "black box" computational model is also discussed, which allows one to determine a propellant mixture providing a necessary burning rate for various pressures. Methods for the creation of such models are discussed. The examples of ANN being used for the temperature profile prediction for nitrocellulose and ammonium perchlorate based composite propellants under various experimental conditions are presented.

RÉFÉRENCES
  1. Ablameyko, S., Goras, L., Gori, M., and Piuri, V., Neural Networks for Instrumentation, Measurement and Related Industrial Applications.

  2. BasegroupLabs: http://www.basegroup.ru/english.htm.

  3. Zenin, A.A., Thesis on a Scientific Degree of the Doctor of Sciences.

  4. Zenin, A.A., Physical Processes for Combustion and Explosion.

  5. Lewis, B., Pees, R.N., and Taylor, H.S., Processes of Combustion.

CITÉ PAR
  1. Abrukov Victor, Kochakov Valery, Smirnov Alexander, Abrukov Sergey, Anufrieva Darya, Knowledge-based system is a goal and a tool for basic and applied research, 2015 9th International Conference on Application of Information and Communication Technologies (AICT), 2015. Crossref

  2. Abrukov Victor S, Lukin Alexander N., Oommen Charlie, Chandrasekaran Nichith, Bharath Rajaghatta S., Sanal Kumar VR, Kiselev Mikhail V, Anufrieva Darya A, Development of the Multifactorial Computational Models of the Solid Propellants Combustion by Means of Data Science Methods – Phase II, 2018 Joint Propulsion Conference, 2018. Crossref

  3. Abrukov Victor S., Lukin Alexander N., C Nichith, Oommen Charlie, V. Kiselev Mikhail, A. Anufrieva Darya, Sanal Kumar VR, 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, 2019. Crossref

  4. Mariappan Amrith, Choi Hanlim, Abrukov Victor S, Anufrieva Darya A., Lukin Alexander N., Sankar Vigneshwaran, Sanal Kumar VR, The Application of Energetic Materials Genome Approach for Development of the Solid Propellants Through the Space Debris Recycling at the Space Platform, AIAA Propulsion and Energy 2020 Forum, 2020. Crossref

  5. Abrukov Victor S., Lukin Alexander N., Oommen Charlie, Sanal Kumar VR, Chandrasekaran Nichith, Sankar Vigneshwaran, Murugesh Pavithra, 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, 2017. Crossref

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