Dhruv A. Sawant, Vijaykumar S. Jatti, Anup Vibhute, A. Saiyathibrahim, R. Murali Krishnan, Sanjay Bembde, K. Balaji
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Prediction of burn rate of ammonium perchlorate–hydroxyl-terminated polybutadiene composite solid propellant using supervised regression machine learning algorithms
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%.
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