Abstract

Review Article

Methodology for Studying Combustion of Solid Rocket Propellants using Artificial Neural Networks

Victor Abrukov*, Weiqiang Pang and Darya Anufrieva

Published: 11 March, 2024 | Volume 8 - Issue 1 | Pages: 001-007

The combustion properties of energetic materials have been extensively studied in the scientific literature. With the rapid advancement of data science and artificial intelligence techniques, predicting the performance of solid rocket propellants (SRPs) has become a key focus for researchers globally. Understanding and forecasting the characteristics of SRPs are crucial for analyzing and modeling combustion mechanisms, leading to the development of cutting-edge energetic materials. This study presents a methodology utilizing artificial neural networks (ANN) to create multifactor computational models (MCM) for predicting the burning rate of solid propellants. These models, based on existing burning rate data, can solve direct and inverse tasks, as well as conduct virtual experiments. The objective functions of the models focus on burning rate (direct tasks) and pressure (inverse tasks). This research lays the foundation for developing generalized combustion models to forecast the effects of various catalysts on a range of SRPs. Furthermore, this work represents a new direction in combustion science, contributing to the creation of a High-Energetic Materials Genome that accelerates the development of advanced propellants.

Read Full Article HTML DOI: 10.29328/journal.aac.1001048 Cite this Article Read Full Article PDF

Keywords:

Solid rocket propellant; Artificial neural networks; Burning rate; Multifactor computational model

References

  1. Yan QL, Zhao FQ, Kuo KK, Zhang XH, Zeman S, DeLuca LT. Catalytic effects of nano additives on decomposition and combustion of RDX-, HMX-, and AP-based energetic compositions. Progress in Energy and Combustion Sci. 2016; 57: 75-136.
  2. Pang W, Li Y, DeLuca LT, Liang D, Qin Z, Liu X, Xu H, Fan X. Effect of Metal Nanopowders on the Performance of Solid Rocket Propellants: A Review. Nanomaterials (Basel). 2021 Oct 17;11(10):2749. doi: 10.3390/nano11102749. PMID: 34685188; PMCID: PMC8537742.
  3. Klinger D, Casey A, Manship T, Son S, Strachan A. Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques. Propellants, Explosives, Pyrotechnics. 2023; 48(4): e202200267.
  4. Kalil T, Wadia C. Materials Genome Initiative for Global Competitiveness, A whitepaper, Executive office of the president National Science and Technology Council, Washington, D.C. 20502, June 24, 2011.
  5. Wang Y, Liu Y, Song S, Yang Z, Qi X, Wang K, Liu Y, Zhang Q, Tian Y. Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach. Nat Commun. 2018 Jun 22;9(1):2444. doi: 10.1038/s41467-018-04897-z. PMID: 29934564; PMCID: PMC6015015.
  6. Materials of a special large international seminar dedicated to the results of the project prepared by a large group of authors: The workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers”, published in May 2019: https://www.nature.com/articles/s41524-019-0173- 4)
  7. Liu Y, Esan OC, Pan Z, An L. Machine learning for advanced energy materials. Energy and AI. 2021; 3: 100049.
  8. Chen A, Zhang X, Zhou Z. Machine learning: Accelerating materials development for energy storage and conversion. InfoMat. 2: 553-576.
  9. Zhou T, Song Z, Sundmacher K. Big Data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering. 2019; 5: 1017-1026.
  10. Kang P, Liu Z, Abou-Rachid H, Guo H. Machine-Learning Assisted Screening of Energetic Materials. J Phys Chem A. 2020 Jul 2;124(26):5341-5351. doi: 10.1021/acs.jpca.0c02647. Epub 2020 Jun 23. PMID: 32511924.
  11. Himanen L, Geurts A, Foster AS, Rinke P. Data-Driven Materials Science: Status, Challenges, and Perspectives. Adv Sci (Weinh). 2019 Sep 1;6(21):1900808. doi: 10.1002/advs.201900808. Erratum in: Adv Sci (Weinh). 2020 Jan 22;7(2):1903667. PMID: 31728276; PMCID: PMC6839624.
  12. Chen A, Zhang X, Zhou Z. Machine learning: Accelerating materials development for energy storage and conversion. InfoMat. 2020; 2: 553-576.
  13. Yuan WL, He L, Tao GH, Shreeve JM. Materials-Genome Approach to Energetic Materials. Acc Mater Res. 2021; 2: 692−696.
  14. Wang LL, Xiong Y, Xie WY, Niu LL, Zhang CY. Review of crystal density prediction methods for energetic materials. Chinese Journal of Energetic Material Hanneng Cailiao. 2020; 28; 1-12.
  15. Zhang ZX, Cao YL, Chen C, Wen LY, Ma YD, Wang BZ, Liu YZ. Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis. 2023.
  16. Chuan L, Chenghui W, Ming S, Yan Z, Yuan Y, Qiaolin G, Guangchuan W, Yanzhi G. Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime. Journal of Chemical Information and Modeling. 2022; 62: 10.
  17. Jonas R, Jonathan M, Julie W, Matthew S, Song L. An Explosophore-Based Approach towards the Prediction of Energetic Material Sensitivity Properties. 2021. 10.33774/chemrxiv-2021-16f6w-v2.
  18. Brian B. Deep learning for energetic material detonation performance. AIP Conference Proceedings. 2020; 2272. 070002.
  19. Yang ZF, Wang JN, Zhang C, Zheng W, Chen N, Zhang J, Pi WF. Effects of nano-materials on combustion properties of DB and CMDB propellants. Chin J Explos Propellants. 2013; 36: 69–72.
  20. Yuan ZF, Zhao FQ, Zhang JQ, Song XD, Gao HX, Zheng W, Wang Y, Pei JF, Wang J. Effect of nano-nickel powder on combustion properties of Al-CMDB and CL-20-CMDB propellants. Chin J. Explos Propellants. 2016; 39: 99–103.
  21. Yuan ZF, Yang YJ, Zhao FQ, Zhang JQ, Song XD, Gao HX, Xu SY. Effects of different content of nanomaterials on the combustion performance of RDX-CMDB propellants. Chin J Explos Propellants. 2019; 42: 566–571.
  22. Abrukov VS, Karlovich EV, Afanasyev VN, Semenov YV, Abrukov SV. Сreation of propellant combustion models by means of data mining tools. International Journal of Energetic Materials and Chemical Propulsion. 2010; 385-396.
  23. Chandrasekaran N, Bharath RS, Oommen C, Abrukov VS, Kiselev MV, Anufrieva DA, Sanal Kumar VR. Development of the Multifactorial Computational Models of the Solid Propellants Combustion by Means of Data Science Methods – Phase II, 2018 Joint Propulsion Conference, AIAA Propulsion and Energy Forum. 2018; 4961.
  24. Abrukov V, Lukin A, Anufrieva D, Oommen C, Sanalkumar V, Chandrasekaran N, Bharath R. Recent Advancements in Study of Effects of Nano Micro Additives on Solid Propellants Combustion by Means of the Data Science Methods. Defence Science Journal. 2019; 69(1): 20-26.
  25. Mariappan A, Choi H, Abrukov VS, Anufrieva DA, Lukin AN, Sankar V, Sanalkumar VR. The Application of Energetic Materials Genome Approach for Development of the Solid Propellants through the Space Debris Recycling at the Space Platform. Conference: AIAA Propulsion and Energy 2020 Forum. AIAA. 2020-3898.
  26. Abrukov VS, Lukin AN, Chandrasekaran N, Sanal Kumar VR, Anufrieva DA. Genome approach and data science methods for accelerated discovery of new solid propellants with desired properties. AIAA Propulsion and Energy 2020 Forum. AIAA. 2020-3929.
  27. Abrukov VS, Lukin AN, Oommen C, Sanal Kumar VR, Chandrasekaran N, Sankar V, Kiselev MV, Anufrieva DA. Development of the Multifactorial Computational Models of the Solid Propellants Combustion by Means of Data Science Methods - Phase III. Technology and Investment, 2019, Proceedings of the 55th AIAA/SAE/ASEE Joint Propulsion Conference 2019, AIAA Propulsion and Energy Forum, Indianapolis, Indiana, 19-22 August 2019, AIAA 2019-3957.
  28. Abrukov VS, Pang W, Anufrieva DA. Neural networks are a methodological basis of materials genome. Trends Comput Sci InfTechnol. 2023; 8(1): 012-015.

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