Virtual prototyping of solid propellant rockets

cerns in rocket motor design because of the enormous cost of typical payloads and, in the case of the Space Shuttle and other manned vehicles, for the crew’s safety. In the spring of 1999, for example, a series of three consecutive launch failures collectively cost more than US$3.5 billion. The most notorious launch failure, of course, was the tragic loss of the Space Shuttle Challenger and its seven crew members. Thus, there is ample motivation for improving our understanding of solid rocket motors (SRMs) and the materials and processes on which they are based, as well as the methodology for designing and manufacturing them. The use of detailed computational simulation in the virtual prototyping of products and devices has heavily influenced some industries— for example, in automobile and aircraft design— but to date, it hasn’t made significant inroads in rocket motor design. Reasons for this include the market’s relatively small size and the lack of sufficient computational capacity. Traditional design practices in the rocket industry primarily use top-down, often one-dimensional modeling of components and systems based on gross thermomechanical and chemical properties, combined with engineering judgement based on many years of experience, rather than detailed, bottom-up modeling from first principles. Moreover, there has been a tendency to study individual components in isolation, with relatively little emphasis on the often intimate coupling between the various components. For example, SPP1—an industry-standard code for analyzing solid propulsion systems—includes a fairly detailed model of the propellant thermochemistry, but no structural analysis and no detailed model of internal flow. One of our primary goals at the Center for Simulation of Advanced Rockets (CSAR) is to develop a virtual prototyping tool for SRMs based on detailed modeling and simulation of their principal components and the dynamic interactions among them. Given a design specification—geometry, materials, and so on—we hope to be able to predict the entire system’s resulting collective behavior with sufficient fidelity to determine both nominal performance characteristics and potential weaknesses or failures. Such a “response tool” could explore the space of design parameters much more quickly, cheaply, and safely than traditional build-andtest methods. Of course, we must validate such a