Prediction of burn rate of ammonium perchlorate–hydroxyl-terminated polybutadiene composite solid propellant using supervised regression machine learning algorithms

Q3 Earth and Planetary Sciences
Dhruv A. Sawant, Vijaykumar S. Jatti, Anup Vibhute, A. Saiyathibrahim, R. Murali Krishnan, Sanjay Bembde, K. Balaji
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引用次数: 0

Abstract

The objective of the paper is to explore the fields of propulsion for rockets and defence systems to meet the increasing demands for cost-effectiveness and faster and friendly manufacturing processes to increase the efficiency of the burn time/rate of solid rocket motors. This particular research includes the use of powerful machine learning algorithms applied on the burn rate dataset to predict the best burn rate. The main focus of this particular research is based on the burning rate study which has been carried out at ambient and different pressures of 2.068 MPa, 4.760 MPa and 6.895 MPa with the use of binder as Hydroxyl-Terminated Polybutadiene, oxidizer as Ammonium Perchlorate and a catalyst as Iron Oxide. Two types of models are designed and created to predict the best burn rate from the experiments conducted namely; Empirical Mathematical Model and Machine Learning Regression. Empirical modelling gave an accuracy of 47% while Machine Learning Regression gave a prediction accuracy of nearly 99%.

利用监督回归机器学习算法预测高氯酸铵-端羟基聚丁二烯复合固体推进剂的燃烧速率
本文的目的是探索火箭和国防系统推进领域,以满足日益增长的成本效益和更快、友好的制造过程的需求,以提高固体火箭发动机的燃烧时间/速率的效率。这项特殊的研究包括使用强大的机器学习算法应用于燃烧速率数据集来预测最佳燃烧速率。以端羟基聚丁二烯为粘结剂,高氯酸铵为氧化剂,氧化铁为催化剂,在2.068 MPa、4.760 MPa和6.895 MPa的环境压力和不同压力下进行了燃烧速率的研究。设计并建立了两种模型,分别为:经验数学模型与机器学习回归。经验建模的准确率为47%,而机器学习回归的预测准确率接近99%。
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来源期刊
Aerospace Systems
Aerospace Systems Social Sciences-Social Sciences (miscellaneous)
CiteScore
1.80
自引率
0.00%
发文量
53
期刊介绍: Aerospace Systems provides an international, peer-reviewed forum which focuses on system-level research and development regarding aeronautics and astronautics. The journal emphasizes the unique role and increasing importance of informatics on aerospace. It fills a gap in current publishing coverage from outer space vehicles to atmospheric vehicles by highlighting interdisciplinary science, technology and engineering. Potential topics include, but are not limited to: Trans-space vehicle systems design and integration Air vehicle systems Space vehicle systems Near-space vehicle systems Aerospace robotics and unmanned system Communication, navigation and surveillance Aerodynamics and aircraft design Dynamics and control Aerospace propulsion Avionics system Opto-electronic system Air traffic management Earth observation Deep space exploration Bionic micro-aircraft/spacecraft Intelligent sensing and Information fusion
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