Axial-injection end-burning hybrid rockets (AIEB-HR) operate in a combustion mode called the regression mode, which enables efficient and high-power propulsion. However, under certain conditions, a transition to flame-spreading mode (RtS transition) occurs, leading to operational failure. This study experimentally investigates the RtS transition conditions using three oxidizers: oxygen, nitrous oxide, and 33 vol% oxygen diluted with nitrogen. A total of 183 firing tests were conducted over a pressure range of 0.5–2.0 MPa and a wide range of oxidizer mass fluxes to identify the RtS transition conditions. Unlike previously proposed metrics such as friction velocity and Damköhler number, the stand-off distance showed universal scaling across different oxidizers. It is revealed that a reduction in the stand-off distance and perturbations can trigger the RtS transition, and it successfully explains experimentally observed behaviors. These findings demonstrate that stand-off distance provides a robust and physically meaningful criterion for predicting the RtS transition in AIEB-HRs.

Cusped field thrusters are a promising technology that enables efficient and cost-effective space transportation for small spacecraft platforms owing to their design simplicity, minimal wall erosion, and extended operational lifespan achieved through high plasma confinement with permanent magnets. Due to the restriction on electron movement caused by this high plasma confinement, neutral-electron impact ionization is expected to occur mainly in the vicinity of the channel axis. Multi-objective design optimization is conducted by utilizing an improved global power model coupled with magnetostatic simulation coupled into genetic algorithms with the goal of concurrently refining thruster channel designs to improve thruster performance. An investigation into the interaction between plasma behavior and the magnetic field within low-power cusped field thrusters has identified key design factors that affect their performance. It has been observed that enhancing ionization and thruster performance can be accomplished by adjusting the divergent channel to reshape the magnetic field distribution, hence mirror ratio. Sensitivity analysis indicates that anode current is the primary design factor influencing performance, as it significantly impacts the ionization process.

This paper describes the effect of adding lithium fluoride (LiF), a negative combustion catalyst, into a 0.1N class combustion-controllable solid-propellant microthruster using laser heating. In our previous studies, self-sustaining combustion has been suppressed by using fuel-rich propellant, which decreased theoretical specific impulse. Hence, we attempted to enable combustion-interrupt capability and enlarge the specific impulse by applying LiF to a propellant with a higher theoretical specific impulse. Using HTPB/AP composite propellants with 1.0 wt% LiF, the thrust was measured using a prototype thruster, and the burning surface temperature before ignition was evaluated using thermography. The distribution of LiF particles was varied using sieving; tests were conducted using non-sieved, 50-µm, and 100-µm sieved particles. Thrust measurement revealed that combustion was interrupted by pausing laser irradiation at success rates of 3/5 and 1/5 for non-sieved and 50 µm-sieved LiF, respectively. The thruster using the non-sieved LiF propellant yielded the highest specific impulse of 88.3 ± 10.0 s, and that with 100 µm-sieved LiF presented the shortest ignition delay of 0.6 ± 0.3 s. The thermography on the burning surface showed that the 100 µm-sieved LiF propellant yielded the smallest time variations in temperature and the slowest rise in temperature.

Accurate forecasting of solar cycle onsets is pivotal for space weather preparedness and for advancing our understanding of solar dynamo mechanisms. This study addresses this challenge by employing the optimized Long Short-Term Memory network (LSTM+), tailored for iterative, multi-step ahead prediction, to forecast the commencement of Solar Cycle 26 (SC26). Leveraging historical data from SC18 through SC24, the LSTM+ framework was trained and rigorously validated using three key solar activity indicators - sunspot number (SSN), sunspot area (SSA), and the solar flare index (SFI). The validation across SC23 and SC24 demonstrated the model’s robust capability to capture fundamental cycle dynamics with high fidelity, consistently outperforming baseline statistical and simpler neural network models. Critically, the application of the validated LSTM+ model to the ongoing SC25 yields a convergent prediction across all three solar indices, projecting the onset of SC26 to occur around May 2030. This timing implies a duration of approximately 10.4 years for SC25. This work thus provides a novel, data-driven, and multi-indicator-supported estimate for the next solar minimum, contributing a significant input for long-range solar activity forecasting.

This paper deals with a simultaneous optimization problem of stacking sequence and drop-off placement for a ply drop-off laminate under uni-axial tensional load to maximize the resin pocket strength. As a practical reinforcement design condition, although the stacking sequence of the thin plate side is fixed, only the ply orientation angle ratio for the thick plate side is fixed. That is, the ply drop-off placement and the stacking sequence for the thick plate side should be determined. The strength index evaluated from von Mises stress at the resin pocket for the three-layer laminate is defined as the objective function. For the practical application, empirical stacking constraints such as the number of consecutive layers, the ply orientation angle difference between adjacent layers and so on are imposed for the balanced symmetric laminate consisting of 0°, ±45°, and 90° plies. As the optimization method, the genetic algorithm with gene repair strategy is proposed to satisfy the above constraints. The optimal design solution d tends to cut the 0° plies first, and ensure a uniform distribution of the strength index through layers. The validity of the optimization result is then verified by using the three-dimensional finite element analysis.