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.

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.

The Multi-Stage Steam Ejector System (MSSES) is essential for High-Altitude Simulation Tests (HASTs) of liquid rocket engines using active ejection. Rapid performance prediction is crucial due to the system’s high energy consumption. In this study, the one-dimensional gas dynamics theory was used to analyze MSSES performance under non-design operational conditions. The analysis is based on the assumption of constant-pressure mixing, considering that the steam and engine exhaust gas start mixing at a certain position in the cylindrical section of the second-throat ejector diffuser. An active ejecting scheme combining a rocket engine, a supersonic engine diffuser, and a four-stage MSSES was employed. Two air flow tests using an orifice plate simulated the mass flow and pressure conditions of rocket engines, providing preliminary MSSES validation before real engine tests. The nominal air mass flow rates of 0.60 and 0.34 kg/s simulated combustion products from NTO/MMH-fueled engines. Through a computational analysis of the results of the first test, we derived four key coefficients essential for characterizing the MSSES behavior under the critical operation modes. These coefficients were then applied to forecast the MSSES performance in the second testing scenario, demonstrating their utility in performance prediction. A comparison with experimental data highlights minor discrepancies, including errors of up to 15.78% in the secondary flow rate predictions and errors of up to 5.25% in the MSSES outlet pressure estimates. This knowledge, applied to an existing HAST facility, may significantly enhance the efficiency and accuracy of pre-test planning for HASTs.

Energetic materials typically lack precise control over energy release rates once ignited. Inverse design of their structures offers a promising solution by enabling regulation of burning surface evolution and energy release profiles, facilitating customizable combustion. Such a capability could significantly enhance energy management in aerospace and chemical industrial systems. While both optimization-driven and data-driven approaches have demonstrated their potential, their optimality, time efficiency, and feasibility cannot be guaranteed simultaneously. Here, we present a dual-model approach combining a deep eikonal auto-decoder and a denoising diffusion probabilistic model to generate novel energetic material structures while accurately representing their entire burning surface evolution. A key to success is a optimization-driven fine-tuning technique that substantially improves the optimality while maintaining high volume loading efficiency and other critical constraints. This combination of optimization-driven and data-driven approaches opens a pathway toward fully customizable combustion of energetic materials, capable of precisely matching complex target performance curves.

To prevent pogo oscillation in liquid launch vehicles, it is essential to install a gas-filled accumulator near the pump with minimum flow inertance and a target-design level of flow resistance, which are the real and imaginary parts of communication port impedance. However, the present approach of estimating the flow impedance of the accumulator communication port based on an orifice flow model introduces a non-negligible error, possibly leading to accumulator failure. In this study, a transient computational fluid dynamics simulation is conducted on a communication port model, where the liquid oxygen is considered incompressible and k-ε turbulence model is used. The results indicate that the formation of a vortex downstream of the communication port leads to the attenuation of its linear resistance. A method for calculating the impedance of the communication port is proposed, where the impact of supply pipeline velocity, oscillatory flow amplitude, and frequency is considered. The results indicate that the quasi-steady assumption is suitable for oscillatory flow frequencies below 14.5 Hz, with a deviation of less than 30%. Above this frequency, a linear frequency correction can be used to reduce the deviation to less than 26.5% within the pogo frequency range. The impedance calculation formulae given in this research can be used in the engineering design of the gas-filled accumulators.