Reverse Design of Solid Propellant Grain for a Performance-Matching Goal: Shape Optimization via Evolutionary Neural Network

Abstract

The reverse design of solid propellant grain for a performance-matching goal, one of the most challenging directions of the solid rocket motor designing work, is limited by the traditional semi-empirical parameter-driven optimization methods based on some predefined grain configurations. Grain designers call for a new method that can automatically provide brand-new grain shapes beyond the traditional ones. In this work, a shape optimization method based on the evolutionary neural network is proposed to achieve the reverse design of two-dimensional (2D) grains. Firstly, the modified ellipse-form eikonal equation is solved by using the finite element method to realize the burn-back analysis of 2D grains in any shape on a fixed unstructured mesh. Then, the neural network is introduced to determine the spatial distribution of the propellant to define the grain shape. The hyperparameters of the network are continuously evolved with the aid of the genetic algorithm. Finally, the optimal grain shape that matches the performance goal most is obtained. The method is verified in different scenarios. The result shows that the design can precisely match the given pressure-time curve of star grains and slotted-tube grains. Furthermore, the method can automatically evolve a new dendritic-shaped grain that matches the given dual-thrust pressure-time curve. Since the reverse design uses the concept of shape optimization, it does not require any pre-selection of the grain shape, and the designers shall be free from defining different kinds of geometric parameters for specific grain configurations. Consequently, the method has the potential to apply in the reconstruction of an actual grain and the conceptual design of innovative grain configurations.

Trajectory Optimization with Complex Obstacle Avoidance Constraints via Homotopy Network Sequential Convex Programming

“…The obstacle's "growth" process is a kind of interface regression phenomenon. This phenomenon has been extensively studied in optics [37], combustion [38,39], and image processing [40]. Sethian [41,42] proposed the level-set method and the fast marching method.…”

“…Although these two methods have been widely used, there are still many difficulties in solving hyperbolic partial differential equations. Mokrý [37] and Li [39] proposed a method to solve an ellipse-form eikonal equation, which can be directly embedded in the finite element software. The original eikonal equation is as follows:…”

“…If α is small, it will be difficult for the Newton-Raphson method to converge. To take into account the convergence and error, with the aid of a large number of numerical simulations, a reasonable range of α was concluded [39]:…”