Solid Rocket Motor Design Using Hybrid Optimization

A particle swarm/pattern search hybrid optimizer was used to drive a solid rocket motor modeling code to an optimal solution. The solid motor code models tapered motor geometries using analytical burn back methods by slicing the grain into thin sections along the axial direction. Grains with circular perforated stars, wagon wheels, and dog bones can be considered and multiple tapered sections can be constructed. The hybrid approach to optimization is capable of exploring large areas of the solution space through particle swarming, but is also able to climb “hills” of optimality through gradient based pattern searching. A preliminary method for designing tapered internal geometry as well as tapered outer mold-line geometry is presented. A total of four optimization cases were performed. The first two case studies examines designing motors to match a given regressive-progressive-regressive burn profile. The third case study studies designing a neutrally burning right circular perforated grain (utilizing inner and external geometry tapering). The final case study studies designing a linearly regressive burning profile for right circular perforated (tapered) grains. 1. Introduction The ability to perform the optimization phase of design in a timely manner can be key to the ultimate success of the design. With recent developments in computing efficiency and modeling techniques, a computer can evaluate thousands of candidate designs for a complex system on the preliminary design level that it once took an engineer to evaluate a handful of designs. In the case of the rocket motor, engineers now have the capability to design motors that can match a specified pressure-time curve, motor specs such as weight, and optimally fit motors into missile systems [1, 2]. Numerous tools have been developed for modeling solid rocket motors. Industry tools such as solid performance program (SPP) [3] and simpler tools developed for preliminary design use [4–7] perform the burnback by approximating the grain cross sections as 2D and 3D shapes that have analytical burnback solutions. These tools can provide a wide range of information in a relatively short amount of time, making them suitable candidates for optimization schemes. The motor modeling scheme employed for this study is an extension of the method outlined in reference [5]. The prediction results from this code has been compared to space shuttle booster data with favorable results. In this model, the internal geometry can taper along the grain length as well as the outer mold line. This brings about numerous advantages