Prediction of burn rate of ammonium perchlorate–hydroxyl-terminated polybutadiene composite solid propellant using supervised regression machine learning algorithms
Aerospace Systems, ISSN: 2523-3955
2024
- 2Citations
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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%.
Bibliographic Details
Springer Science and Business Media LLC
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