Study on Burning Surface Regression Algorithm under Erosive Burning Based on CT Images of Solid Rocket Motor Grain

Abstract

The presence of the erosive burning effect during the operation of a solid rocket motor (SRM) is one of the most important factors affecting the proper operation of the motor. To solve the effects of the operating process of the motor under erosive burning, a synthesis algorithm based on the actual CT images is proposed to combine the Level-set (LS) method with the minimum distance function (MDF) method for the simulation of the burning surface regression of the grain under erosive burning. The Hamilton–Jacobi control equation can be solved exactly for the discrete form of LS. To improve the computational efficiency of the LS method, the minimum distance field is initialized and only the distortion grid is adjusted during the reinitialization. The third-order TVD Runge–Kutta method is used to solve the problem of numerical oscillation and improve the calculation accuracy. The experiments simulate the burning process of the NAWC No. 6 partial grain under erosive burning, which can provide the main basis for the performance design of solid propellant. The experimental results show that the method has good applicability to three-dimensional complex grains. It can realize the simulation of grains under erosive burning and its calculation accuracy is high.

Hybrid Rocket Engine Burnback Simulations Using Implicit Geometry Descriptions

“…Funami and Takano [26] analyzed a star-fractal geometry of a hybrid rocket engine with a three-dimensional Cartesian grid LSM. Liu et al [27] also employed a three-dimensional Cartesian LSM and qualitatively compared their results for a solid rocket motor against CT images. In a recent study, Chao-Fan et al [28] used the LSM for solid rocket motor burnback simulations.…”

Regression transients modeling of solid rocket motor burning surfaces with physics-guided neural network

“…Research on the simulation of burning surface regression primarily includes the analytical [4], mesh [5], solid construction [6], minimum distance [7], and level-set (LS) [8,9] methods. Because the burning rate is both spatially and temporally dependent [7], the precise prediction of the SRM performance implies that the regression simulation algorithm is applicable under both uniform and non-uniform burning conditions.…”

“…In contrast, the LS method is well adapted to the calculation of burning surface regression at non-uniform burning rates, which regards the zero-LS of the signed distance function (SDF) as the active interface. Furthermore, it assesses the evolution of the zero-LS by solving the initial value partial differential equation (PDE) to achieve a dynamic tracking of the SRM burning surface, which is theoretically applicable to the burning surface regression of grains with arbitrary shapes [9].…”