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.

Space vehicles’ real-time trajectory optimization is the key to future automatic guidance. Still, the current sequential convex programming (SCP) method suffers from a low convergence rate and poor real-time performance when dealing with complex obstacle avoidance constraints (OACs). Given the above challenges, this work combines homotopy and neural network techniques with SCP to propose an innovative algorithm. Firstly, a neural network was used to fit the minimum signed distance field at obstacles’ different “growth” states to represent the OACs. Then, the network was embedded with the SCP framework, thus smoothly transforming the OACs from simple to complex. Numerical simulations showed that the proposed algorithm can efficiently deal with trajectory optimization under complex OACs such as a “maze”, and the algorithm has a high convergence rate and flexible extensibility.

Solid propellant grain reverse design aims to discover optimal grain geometries by shape optimization methods to match the desired solid motor performance curves. To maximize the performance matching degree and the propellant loading fraction simultaneously, this study develops a multi-objective evolutionary neural network for the grain reverse design, where the burning surface regression calculation is efficiently employed using the fast-sweeping method. Then, grain shape feature extraction and pattern analysis are achieved through image singular value decomposition and self-organizing mapping, respectively. Finally, the design case of a dual-thrust motor and a Mars ascent vehicle show that the method can well balance the performance-matching degree and propellant loading fraction. Moreover, without any training data set, it can generate dozens of grain shape patterns, highlighting their diversity and providing new ideas for solid rocket motor designers. Our method can offer a new pathway for the research field of solid rocket motor design.

Multi-thrust solid rocket motors are extensively used in tactical missiles. To effectively achieve the desired multi-thrust performance curve, firstly, the concept of modular grain is introduced. Star grain, slot grain, and end-burning grain are chosen as the fundamental templates, which can be flexibly combined to form an arbitrary multi-thrust performance curve. Secondly, a quadric approximation of the burning perimeter is derived, leading to the establishment of a governing equation for modular grain design. This equation ensures a close match between the resulting performance curve and the target one. Thirdly, the Nelder–Mead optimization algorithm is employed to maximize the propellant loading fraction and reduce the combustion chamber size. Finally, the method successfully produces single-thrust, dual-thrust, and triple-thrust grains. The results show that the relative maximum deviation between the designed and target pressure curves is less than 6.1%. Additionally, the best grain configuration is identified, which maximizes the propellant loading fraction while adhering to the throat-to-port ratio constraints. Consequently, the concept of modular grain offers a valuable approach for creating complex internal ballistic characteristics by combining simpler grain templates. This approach allows for fast, responsive motor conceptual design, prototyping, testing, and even production, thereby advancing the development of solid rocket motors in a more efficient and effective manner.

研究微重力下液氧贮箱内气泡脱离半径是运载火箭推进系统推进剂在轨沸腾与换热计算的基础. 与常重力和低重力环境不同, 微重力下Marangoni效应变得突出. 为了求解气泡脱离半径, 构建包含浮力、惯性力、压差力、表面张力、黏性阻力和Marangoni力的气泡动力学模型. 针对现有Marangoni力计算公式适用范围狭窄的问题, 依托数值仿真方法, 拟合得到了更精确的修正因子计算公式, 进而扩充了Marangoni力计算模型的适用范围. 使用运载火箭液氧贮箱常规工作压力0.3 MPa下的饱和液氧物性参数, 计算得到气泡所受合力随半径的变化关系以及气泡脱离半径随重力的变化关系. 结果表明, 气泡的脱离行为可以由微重力区、过渡区和低重力区三个区域来划分. 微重力区内可以形成厘米级甚至米级的大气泡, 而低重力区内只能形成0.1 mm级的小气泡. 相比之前的模型, 本文模型可以同时适用于三个区, 更全面地揭示了微重力下液氧贮箱内的气泡脱离特性, 可以为液氧贮箱换热特性分析提供理论支撑.