Parametric optimization serves as a crucial means for addressing the multi-dimensional, multidisciplinary, nonlinear, and strongly coupled optimization challenges in solid rocket motor design. This research first reviews the application of parametric optimization methods in motor design across five aspects: grain structural design, nozzle contour design, casing design, overall motor design, and motor optimization considering overall rocket performance. Common technical challenges and general technical routes are summarized. Second, the technical characteristics of motor parametric optimization are discussed from three perspectives: parametric structural optimization, engineering practice constraints, and aerospace digitization. Finally, it identifies five existing technical challenges in parametric optimization design for solid motor and proposes four future research directions. The development of parametric optimization design technology is of great significance for enhancing performance limits, shortening development cycles, and promoting technological innovation.
Knowledge graphs and large language models have emerged as cutting-edge technologies in recent years, and leveraging them for fault diagnosis of avionics equipment has become a current research hotspot. Knowledge-graph-based fault diagnosis methods offer strong interpretability but have limited semantic generalization capabilities. In contrast, large language models possess semantic understanding and generation abilities but suffer from poor controllability and are prone to hallucinations, which can reduce fault diagnosis accuracy. To improve the accuracy of avionics equipment fault diagnosis, this paper proposes a fault-diagnosis method driven by the integration of knowledge graphs and large models, centered around u201Cfault elementsu201D. First, at the data layer, a u201Cexpert-guided and large-model-assistedu201D fault-element extraction mechanism is developed to clean and structurally extract the raw data. Subsequently, a structured knowledge graph is constructed based on the extracted fault elements and various entities, with visualization implemented using Neo4j. Second, at the inference layer, the TuckER tensor decomposition model is employed for causal chain modeling and multi-hop reasoning, while a large language model is introduced to perform enhanced reasoning based on semantic subgraphs of the knowledge graph. Finally, at the decision layer, a consistency verification module is designed to cross-validate the subgraph reasoning results from the tensor decomposition model and the large language model. Conclusions are output only when they meet a preset threshold, balancing accuracy and consistency. The proposed method is validated on an avionics equipment dataset, and results show that it outperforms traditional methods in terms of diagnostic accuracy, information richness, and professional consistency, demonstrating good robustness and scalability. These findings provide a new technical approach for building high-reliability, high-trustworthiness intelligent diagnosis systems for avionics equipment.
Initial geometric imperfections arising from manufacturing processes and service conditions serve are critical triggers of dynamic instability of structures. However, the influence pathways of such imperfections on structural dynamic behavior remain unclear. In particular, when it comes to complex nonlinear responses such as bifurcation and chaos, the coupled instability mechanisms involving initial geometric imperfections, structural motion states, and complex physical environments remain largely unexplored. To address this gap, a rotating Functionally Graded Material (FGM) cylindrical shell with initial geometric imperfections is adopted as the subject of investigation. The bifurcation and chaotic responses are systematically examined under coupled multi-physical fields, including thermal environments, magnetic fields, and external excitations. Firstly, a magneto-thermo-elastic nonlinear dynamic model is established via Hamiltonu2019s principle, which comprehensively accounts for geometric nonlinearity, thermoelastic nonlinearity, and magnetization nonlinearity. The proposed model captures the effects of initial geometric imperfections, rotational motion, and spatially graded material properties. Subsequently, the partial differential governing equations are discretized into ordinary differential equations via the Galerkin method, and the critical condition for the onset of Smale horseshoe chaos is analytically derived using the Melnikov method. Finally, a parametric study on the bifurcation and chaotic behaviors is conducted through numerical examples, with the aim of elucidating the influence mechanisms of various control parameters on dynamic system responses. The results indicate that initial geometric imperfections alter the sensitivity of the dynamic system to external multi-field environments by modulating the nonlinear characteristics. Meanwhile, the magnetic field intensity exerts a suppressive effect on chaos through electromagnetic damping, whereas the external excitation promotes the occurrence of chaos via energy injection.
To address the problem of degraded tracking performance in small UAV swarms operating in near-ground environments due to wind speed disturbances, a hybrid controller scheduling strategy based on wind speed grading is proposed to achieve robustness control for the formation system across all operating conditions. First, a wind disturbance model is established, decomposing the composite disturbance into a slowly varying mean wind, turbulence components, and unknown bounded disturbances, and the wind intensity is classified based on the Beaufort Scale. On this basis, a three-level progressive control architecture is designed. For low wind speeds (Levels 1-2), an optimal control law is developed within a reinforcement learning framework to balance energy consumption and tracking accuracy; for moderate wind speeds (Levels 3), a distributed cooperative extended state observer is introduced to improve disturbance estimation accuracy through network collaboration; for high wind speeds (Levels 4-5), adaptive integral sliding mode compensation is integrated to effectively suppress severe disturbances. Based on the leader-follower consensus principle, a unified analytical framework is constructed using Lyapunov theory and input-to-state stability theory, with a hysteresis-based switching strategy designed to avoid controller chattering at wind level boundaries. Theoretical analysis proves that the formation states exhibit global asymptotic stability and bounded convergence. Simulation experiments demonstrate that the proposed method effectively suppresses disturbances under varying wind speeds and communication topologies, while exhibiting superior performance in energy efficiency, disturbance rejection capability, and dynamic response speed. The approach provides an efficient and reliable solution for autonomous navigation and cooperative operations of near-ground UAV swarms, offering strong practical engineering value.
To investigate the influence and underlying mechanisms of thermal effects induced by high total temperatures on the flow and acoustic fields of supersonic underexpanded jets, numerical simulations were conducted on a Mach number 1.95 supersonic underexpanded jet using a hybrid approach that combines Large Eddy Simulation (LES) with the Ffowcs Williams-Hawkings (FW-H) acoustic analogy. By comparing flow and acoustic data under varying total temperature conditions, the impacts of thermal effects on jet evolution and far-field radiated noise were analyzed. The results indicate that thermal effects accelerate the development of the jet shear layer, shorten the potential core length by 14.2%, and enhance turbulent fluctuations along the nozzle lip line upstream of the potential core. Consequently, thermal effects increase the peak far-field overall sound pressure level, shift the peak radiation direction upstream, and move the source locations of broadband Shock-Associated Noise (BBSAN) upstream. Subsequently, by designing comparative cases with controlled jet parameters, the thermal effect was decoupled into a temperature effect (constant-velocity heating) and a velocity effect (constant-temperature accelerating), revealing their differential impacts on BBSAN generation: the temperature effect enhances the shear layer turbulence upstream of the potential core, and by altering the jet Mach number, it shortens the shock-cell length and weakens the shock-wave intensity, leading to a decreased peak sound pressure level and an increased peak frequency of the BBSAN; conversely, the velocity effect also enhances the upstream shear layer turbulence, but it elongates the shock cells and strengthens the shock-wave structure, resulting in a significantly increased peak sound pressure level and a decreased peak frequency. Under the combined influence of these two effects, the overall variations in BBSAN are relatively minor. Furthermore, within the thermal effect, the velocity effect dominates the impact on large-scale turbulent mixing noise, thereby significantly enhancing it.
