An aluminum agglomeration model based on realistic pocket distribution via microcomputed tomography

The condensed combustion products (CCPs) resulted from aluminum agglomeration significantly affect the operation and safety of solid rocket motor, so it is essential to create a precise agglomeration model to predict the size of the CCPs of aluminized solid propellant. To accomplish this, high-precision microcomputed tomography (micro-CT) scanning was employed to obtain the quasi-three-dimensional structure of the propellant. The distribution of the ammonium perchlorate (AP) pockets was recognized via artificial intelligence (AI) method. The results revealed that the pocket size was mainly influenced by the AP grade. As the fraction of the coarse AP fraction declined from 43.1 % to 23.1 %, the average pocket diameter reduced from 388.91 to 68.23 μm. Size distribution predictions were subsequently performed for the agglomerations based on the pocket model theory. The equation relationship between agglomeration coefficient and propellant formulation was presented and corrected through mathematical fitting. The proposed agglomeration model was validated using the CCPs collection experiments of six aluminized propellants at 7 MPa. The agglomeration model produced an average particle size prediction error of <9.8 %, and the goodness of fit of the agglomerate distribution was >0.85. Subsequent analysis indicated that the agglomerate size mainly depended on the percentage of coarse AP, the burn rate, and the size distribution of raw aluminum particles. The present model is expected to offer a new way to achieve the accurate prediction of solid propellant agglomeration behaviors.