Development of a Novel Continuous Processing Technology for Functionally Graded Composite Energetic Materials Using an Inverse Design Procedure

摘要

For a variety of applications, the functional requirement of a material can vary with location within a structure. One way to address this has been to use different materials, joined together so as to take care of the functional requirements at different locations. This unfortunately gives rise to undesirably high stress concentrations at the interface, when the structure is loaded, which might lead to failure. Attempts at controlling these stresses have led to the concept of Functionally Graded Materials (FGMs). FGMs are structures that possess gradual variations in material behavior that enhance material and/or structural performance. For example, at one point the material may be hard and at another point it may be soft. The description of this functional variation is known as the FGM architecture. Typical architectural parameters include layer thickness, t, and composition gradient, p. In designing FGMs, it is desirable to determine the architectural parameters that optimize system performance for a given application by modeling the relationship between the processing of a FGM, the microstructures that develop, and their related properties. FGMs are being applied to a variety of structural and nonstructural applications. Recently, FGM concepts have become of interest to the U.S. Navy to improve large caliber gun propellant performance by replacing a 7-perf grain with a single perf grain that has the same performance, but burns more efficiently because it possesses a functionally graded architecture. In the case of composite energetic materials used as solid rocket propellants, referred to as a grain, the volume fraction of ingredients, such as 30 and 200 micron AP particles (VAP) can be varied along the length of the grain to produce a corresponding difference in burn rate. It can be noted that the burn rate is related not only the volume fraction of AP particles, but the particle size distribution as well.