Microstructure of Composite Propellants Using Simulated Packings and X-Ray Tomography

Abstract

C OMPOSITE solid propellants are heterogeneous media in which particles are embedded in a rubber binder. For industrial applications, such particles are commonly ammonium perchlorate (AP) and aluminum (Al), the sizes of which are typically below 100 m. As a general rule, simple mixture laws are often inappropriate for granular systems because they cannot capture the inherent microstructural features. This conclusion also stands for composite propellants, because various macroscale phenomena are strongly linked with the structure at microscale. They may be encountered in the field of detonation (e.g., hot-spot formation during deflagration– detonation transition), combustion (e.g., aluminum agglomeration and fine AP/binder premixed flames), mechanics (e.g., filler/binder adhesion), etc. This obviously motivates an improved knowledge of propellant microstructure and also the ability to “build” representative numericalmodels of structures as input data for detailedmultiphysics modeling (namely, computational fluid dynamics or finite element method codes). Those numerical microstructures are classically randompackings of spheres, which have been studied formany years because they serve as useful models for a variety of physical systems (granular, porous, anamorphous materials, etc.). Yet, random packings should faithfully reflect the features of the actual propellant spatial structure, not only global properties (as volume fraction, for instance), but also second-order spatial statistics. This can only be checked with some in situ experimental characterization of the structure. This work aims at comparing simulated random packings on a typical industrial AP/Al propellant together with experimental microstructure data to evaluate to which extent such simulated packing describe the actual microstructure. Experimental data are obtained through x-ray tomography of a propellant sample. This approach leads to genuine 3D statistical data in a nonintrusive way and allows a relative automatization of data sampling and processing. II. Random Packings