Abstract
Purpose
– The paper aims to extend the knowledge base for design and optimization of Star grain which is well known for its simplicity, reliability and efficiency. Star grain configuration is considered to be among the extensively used configurations for the past 60 years. The unexplored areas of treatment of ballistic constraints, non-neutral trace and freedom from use of generalized design equations and sensitivity analysis of optimum design point are treated in detail to bridge the gap. The foremost purpose is to expand the design domain by considering entire convex Star family under both neutral and non-neutral conditions.
Design/methodology/approach
– This research effort optimizes Star grain configuration for use in Solid Rocket Motors with ballistic objective function (effective total impulse) and parametric modelling of the entire convex Star grain family using solid modelling module. Internal ballistics calculations are performed using equilibrium pressure method. Optimization process consists of Latinized hypercube generated initial population and Swarm Intelligence optimizer’s ability to search design space. Candidate solutions are passed to solid modelling module to simulate the burning process. Optimal design points, critical geometrical and important ballistic parameters (throat diameter, burn rate, characteristic velocity and propellant density) are then tested for sensitivities through Monte Carlo simulation.
Findings
– The proposed approach takes the design of Star grain configuration to a new level with introduction of parametric modelling and sensitivity analysis, thus, offering practical optimum design points for use in various mission scenarios. The proposed design and optimization process provides essential data sets which can be useful prior to the production of large number of solid rocket motors. Results also advocate the adequacy of design from engineering perspective and practicality.
Research limitations/implications
– Results showed that few design parameters are sensitive to uncertainties. These uncertainties can be investigated in future by a robust design method.
Practical implications
– Monte Carlo simulation can prove to be vital considering the production of a large number of motor units and enlightens the necessity to obtain statistical data during manufacturing.
Originality/value
– This paper fulfils long-sought requirement on getting free from use of generalized set of equations for commonly used Star grain configurations.