The objective of the paper is to explore the fields of propulsion for rockets and defence systems tomeet the increasing demands for cost-effectiveness and faster and friendly manufacturing processes to increase the ef...The objective of the paper is to explore the fields of propulsion for rockets and defence systems tomeet 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%.展开更多
文摘The objective of the paper is to explore the fields of propulsion for rockets and defence systems tomeet 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%.
