The state of charge （SOC） of lithium-ion batteries is a critical parameter in the battery management system of new energy electric vehicles. To address the issue of insufficient SOC prediction accuracy for lithium-ion batteries under complex operating conditions，an intelligent SOC prediction method for electric vehicle lithium-ion batteries based on the Transformer neural network was proposed. Taking the Nissan Leaf battery as the research object， a charging and discharging test platform for new energy electric vehicle lithium-ion batteries was built to simulate the real energy demands of users and the dynamic changes in real-time energy needs. This platform dynamically adjusted the battery’s charging and discharging strategies， collected multi-dimensional battery data， and preprocessed the data. Then， a SOC prediction framework based on the Transformer model was constructed， which extracted complex time series features through neural networks， achieved high-precision predictions of lithium-ion battery SOC. The experimental results indicate that the proposed method outperforms other networks in prediction accuracy， with a mean absolute error of less than 1.51% and a RMSE of less than 0.48%， validating the effectiveness and accuracy of this method.

To reduce the development costs of electric aircraft， a multidisciplinary optimization design approach was investigated based on the entire life cycle. By analyzing the composition of the entire life cycle costs for electric aircraft，an entire life cycle cost estimation model was developed. The research established multidisciplinary analysis models encompassing aerodynamics， flight performance，mass， and economic factors. With the optimization objective of minimizing life entire cycle costs， a comprehensive multidisciplinary optimization design method for electric aircraft was proposed. Taking a four-seat electric aircraft as a case， the parallel subspace optimization algorithm was employed to identify the optimal solution， resulting in an optimized overall design scheme that validates the method's effectiveness. This method can be applied to electric aircraft conceptual design， providing technical support for electric aircraft development.

In order to address the challenges in structural strength analysis caused by limited training samples and extreme environmental conditions while improving analytical efficiency，deep transfer learning methods was applied to investigate the mechanical properties of composite laminates for the RX4E electric aircraft.Based on the analysis of experimental results obtained from composite laminates， multiple deep learning models were compared in terms of their ability to predict the experimental results. Finally， the convolutional long short-term memory （CLSTM） was selected as the optimal deep learning model. Furthermore， a transfer learning （TL） model was introduced to accurately predict the stress-strain relationships of composite laminates under varying temperatures， humidities and layup configurations. The results indicate that the proposed TL-CLSTM network model has exceptional capability in predicting the mechanical properties of composites， particularly in predicting the stress-strain relationship， with a mean squared error and a root-mean-square error of 10^-5and 10^-3 respectively.The proposed model can effectively predict the mechanical properties of composite laminates for electric aircraft， overcoming the complexities and inefficiencies of traditional mechanical properties measurement methods，which providing a novel pathway for the future study of electric aircraft manufacturing.

To address the process route planning problem characterized by dynamic process requirements and intricate process data，a flexible process route planning method was proposed based on deep recurrent q-network （DRQN）.Firstly， leveraging the structural advantages of the long short-term memory （LSTM） network， sequential data features were thoroughly mined to enhance the accuracy and stability of process route planning.Secondly， by integrating the robust dynamic decision-making capability of the deep q-network （DQN） with an adaptive adjustment strategy， the challenges posed by fluctuations in requirements and processing environments were effectively mitigated.Lastly， in response to frequent process changes， a "selective forgetting" mechanism was implemented to improve the response speed of process route planning during step process changes.Simulation results demonstrate that the proposed method can efficiently resolve the process route planning issue associated with part occurrence feature reconstruction.

For manufacturing enterprise managers，mastering digital leadership adapts to the development of the digital era is the key to promoting the transformation and upgrading of manufacturing enterprises. Based on leadership theory，a path was explored for manufacturing enterprise managers to improve digital leadership，which summarized six antecedents affecting digital leadership，and empirically investigated them using the fuzzy set qualitative comparative analysis （fsQCA） method. The research shows that among all antecedent condition variables，no single element could individually contribute to the realisation of high digital leadership in manufacturing enterprises. There are two paths to achieve high digital leadership capabilities for managers in manufacturing companies. There are four paths to achieve non-high digital leadership capabilities for managers in manufacturing enterprises，which are causally asymmetric with respect to the group paths that produced high digital leadership in manufacturing enterprise.

Video person re-identification is a technology for identifying specific person in a multi-camera surveillance network. Compared to the methods based on single-frame images， this type of algorithms can provide more person information， but it also has issues such as model complexity and misalignment in constructing features. To address those issues， a feature fusion-based video person re-identification algorithm was proposed. The proposed algorithm included a global branch and a local branch with spatial transformation. The global branch extracted the global features of person， capturing coarse-grained information and overall contextual information of the person. The local branch with spatial transformation integrated a spatial transformation matrix into the local branch to learn discriminative local regional features and alleviating the issue of feature misalignment. By utilizing a multi-branch structure， the algorithm fused local and global features and aggregated features through temporal average pooling to enhance the diversity of features and improve the robustness of the model. Finally， the model was trained using cross-entropy and a soft boundary triplet loss. The test results on the Mars and DukeMTMC-Video datasets have verified the feasibility of the proposed algorithm. Specifically， the Mars dataset achieves mAP and Rank-1 accuracies of 82.25% and 89.76% respectively， demonstrating excellent practicality.

Aiming at the problem that the forming quality of wingtip fairing and glass fiber rib parts in aircraft body couldn’t be guaranteed in the manufacturing process， the influence of uniformity of temperature field distribution on mechanical properties and solidification deformation of parts was studied by optimizing the curing program， changing the number of parts entering the tank， and monitoring the surface temperature field distribution of parts by thermocouple.The results show that in the curing process， appropriately increasing the curing temperature can effectively increase the heating rate in the lower temperature area， improve the mechanical properties of parts and reduce the deformation during curing. When six glass fiber rib specimens are put into the autoclave， the heating rate in the whole tank will decrease， which will affect the curing effect of the structure. In actual production， the number of specimens in the autoclave should be reasonably arranged to ensure the forming quality.

Regular inspection of transmission lines is an important guarantee for the long-term stable operation.To solve the problems existing in traditional manual inspection methods such as high labor intensity，low work efficiency and high cost，a PLC-based transmission line automatic inspection robot system was designed， focusing on the robot inspection operations of anti-vibration hammer resetting and broken lines repairing control problems. The manual and automatic control mode were used， the classic PID control algorithm was applied to reset the anti-vibration hammer， and repair the broken lines. The experimental results show that the transmission line inspection robot repair control system can accurately achieve the resetting of the anti-vibration hammer and the repairing of broken lines， and get a better control effect. The system designed is of great significance for the inspection robot to replace the traditional manual inspection method， reduces the labor intensity of inspectors and improves the automation level of transmission line management and maintenance.

Based on the theory of planned behavior，the qualitative comparative analysis was used to explore the complex antecedent mechanism affecting the innovation willingness of scientific and technological talents in universities.The results show that internal ecological degree，external ecological degree，directive norm，exemplary norm，self-efficacy and control are not the necessary conditions for the innovation willingness of scientific and technological talents in universities； There are three paths to affect the innovation willingness of scientific and technological talents in universities，namely，innovation attitude-driven，directive-normative attitude-driven，and mixed-driven.The conclusions are helpful to systematically explain the complex antecedents of the innovation willingness of scientific and technological talents in universities，improve the innovation efficiency of scientific and technological talents，and empower the high-quality development of scientific and technological innovation.

To address the control requirements for extending the residual stability margin of adaptive variable-cycle engines， a specific configuration of an adaptive variable-cycle engine was selected as the research object. The basic effect sensitivity analysis method was used to analyze the impact of geometric adjustment on the stability margin under three typical engine operating conditions： ground take-off state， subsonic cruise state and supersonic cruise state. The adjustable mechanisms that should be prioritized when the stability margin of the compression system of the adaptive variable-cycle engine was insufficient were identified. The geometric adjustment stability margin extension law was established for this specific adaptive variable-cycle engine and verified under typical operating conditions. The results show that adjusting the throat area of the outer nozzle is effective for extending the front fan stability margin； adjusting the throat area of the nozzle and the ejector area of the rear bypass is effective for extending the rear fan stability margin； adjusting the compressor inlet guide vane angle is effective for extending the compressor stability margin. When applying the established geometric adjustment strategy under typical conditions， the absolute error in stability margins for all compression components is less than ​​0.5%​​， demonstrating the good applicability of the proposed method.

Addressing the high-precision modeling requirements of unsteady aerodynamics during complex aircraft maneuvers， a method for modeling non-steady aerodynamic forces based on an adaptive genetic algorithm （AGA） optimized long short-term memory （LSTM） neural network was proposed. Computational fluid dynamics （CFD） simulations were conducted to capture maneuver flight data during rapid turns at varying bank angles and rolling and looping maneuvers at different Mach numbers. An AGA-LSTM model was developed using this data to predict aerodynamic coefficients under non-steady conditions. Specifically， predictions for the aerodynamic coefficients during a 60° bank angle rapid turn maneuver were made， demonstrating accurate estimation of lift coefficient， drag coefficient， and pitch moment coefficient that closely matched CFD simulation results. To further validate the proposed model’s accuracy， predictions were compared with CFD simulation data and a traditional LSTM neural network model for Envelopment maneuvers. The results indicate that the AGA-LSTM neural network model provides closer predictions to simulation data compared to traditional LSTM models， thus offering improved prediction accuracy.

In order to improve the hot corrosion resistance of TiAl-based alloys in molten salt， rare earth Ce modified NiCrAlY coating was prepared on the surface of TiAl-based alloy by atmospheric plasma spraying technology. The hot corrosion tests in molten salt of NiCrAlY（Ce） coatings were carried out at 900 ℃ for 60 h. The effect of rare earth element Ce on the hot corrosion behavior and mechanism was further discussed. The results show that the addition of rare earth element Ce can improve the hot corrosion resistance of NiCrAlY coating. After hot corrosion at 900 ℃ for 60 h， the corrosion layer on the surface of the NiCrAlY/TiAl coating system peels off severely， causing the failure of the coating. Therefore， the TiAl alloy has suffered from severe corrosion. For the NiCrAlYCe/TiAl coating system， its surface still covers with a complete corrosion layer and only small cracks generat at the interface between the coating and substrate. The NiCrAlYCe coating still possesses hot corrosion resistance property to a certain degree after corrosion.The addition of rare earth element Ce is beneficial to the selective oxidation of Al element， which hindered the formation of spinel oxides. The dense Al₂O₃ film blocks the internal diffusion of S element， thereby improving the corrosion resistance of NiCrAlY coating and extending service life of TiAl based alloy.

In order to prevent the issue of transmission efficiency decline or even failure in universal joint caused by wear，the wear amount was calculated based on the Archard model and Hertz contact theory.the functional relationship between the variation amplitude of the output angular velocity and the wear amount was given by using dynamic simulations，and a reliability and sensitivity calculation model was established.The results show that greater wear leads to greater fluctuation of the output angular velocity，with a significant increase in fluctuations once the wear amount exceeds a certain threshold.As the wear amount increases，reliability gradually decreases，and the sensitivity of reliability to each random variable is negative，with the parameter K _H having the highest sensitivity.Therefore，while regularly monitoring the wear amount and the variation amplitude of output angular velocity，appropriate process measures should be taken to improve material hardness and use high-quality lubricants to reduce the mean and dispersion of K _H.

In order to investigate the drag reduction characteristics of groove structure，triangular and trapezoidal grooves with different dimensions were selected and simulation comparison on the drag reduction characteristics of flat models with two-dimensional transverse，three-dimensional transverse and three-dimensional longitudinal groove arrangements were carried out. The results show that the groove structure can generate low-speed fluid at the bottom of the groove. The low-speed fluid in the transverse groove can act as a rolling bearing. The drag reduction mechanism of the longitudinal groove can be explained from the perspective of protrusion height theory. The low-speed fluid in the grooves reduces the near-wall velocity gradient，thereby reducing friction drag. The groove structure can effectively reduce the turbulent kinetic energy and shear stress，leading to a reduction in viscous drag. The drag reduction effects of the two-dimensional transverse and the three-dimensional transverse groove model is similar，whereas longitudinal grooves exhibit a higher drag reduction rate than transverse grooves. Moreover，trapezoidal grooves achieve a higher drag reduction rate than triangular grooves. The optimal groove dimensions are a width of 0.1mm and a depth of 0.1mm，yielding a maximum drag reduction rate of 18.57%.

In response to the current problems of relying on manual experience and slow processing speed for ligh electric helicopter faults，a fault diagnosis expert system based on fault tree was proposed to ensure the flight safety and reliability.Fault tree model was constructed based on common faults of a light electric helicopter. Through qualitative and quantitative analysis of the fault tree，the probability importance FVI values of each event in the fault tree were calculated. The FVI values were used to rank each fault event and convert the fault tree into a binary fault tree. An expert knowledge base was established through binary fault tree analysis，and the inference machine was designed based on the production rules and forward reasoning strategies in the knowledge base. Finally，the expert system was developed using QT，and the simulation testing proved that the designed expert system can effectively diagnose faults in light electric helicopters.

Traditional transmission line wire crimping is done manually， and its crimping accuracy and consistency are difficult to ensure. To this end， an automatic crimping control system for transmission line conductors was designed， which took Siemens S7-1200 as the controller， took fuzzy PID as the core control algorithm， and applied the SCL language of Protherm platform for the programming and realization of fuzzy PID control algorithm. The designed system could set crimping parameters through the monitoring interface of the upper computer， realizing the functions of automatic movement of the slide table， automatic crimping of wires， automatic measurement of the distance between wires and edges， etc.， and the real-time change curves， the current variable values and the crimping status in the operation process of the system could be viewed in the monitoring interface. Experimental results show that the designed system can adaptively and dynamically adjust the control parameters to achieve precise control of wire crimping， improving crimping accuracy and reducing the labor costs.

In view of the problem of traditional continuous carbon fiber toughened ultra-high temperature ceramic matrix composites （C_f /UHTCs）， such as long preparation period and high cost， an internal grouting process of carbon fiber based on ultrasonic regulation technology was proposed to realize the uniform distribution of ceramic powder in carbon fiber braided body， and the mechanical properties of the prepared composites were analyzed. The research results show that the mechanical properties of the composites can be effectively improved by using ultrasonic regulation technology to assist the internal grouting of carbon fiber， and it has a remarkable effect in the preparation of ultra-high temperature ceramic matrix composites toughened by continuous carbon fiber， which can improve the material preparation efficiency and improve the material properties at the same time， and plays a very important role in promoting the innovation of composite preparation technology.

In order to improve the spoofing interference detection capability of satellite navigation system， a satellite navigation spoofing interference detection algorithm based on RNN was investigated， and the loss function was designed. In order to improve the accuracy of data prediction， a data preprocessing method was studied， which maped the data in a fixed interval and amplifies the characteristics of the data. The experimental results show that the prediction accuracy of the RNN model for the signal-to-noise ratio of ten satellites is higher than that of the Transformer model. The recurrent neural network model has an average accuracy of 64.76% in predicting the signal-to-noise ratio data， while the Transformer model has only 3%. In the RNN prediction model， the accuracy of the prediction for 7 out of 10 satellites signal-to-noise ratios is above 60%. It can be seen that the RNN model has a better prediction effect when facing the signal-to-noise ratio data of BeiDou satellite navigation signals with the time series data type. Therefore， the RNN model can realize the prediction of 0.08dB error for BeiDou signal-to-noise ratio， and when the difference between the future signal-to-noise ratio value and the predicted value is greater than 0.08 dB， it is considered that the signal is a spoofing signal at this time， so as to realize spoofing interference detection. The research results provide certain reference value for the research of satellite navigation spoofing algorithm.

In order to study the impact of the Reynold number on the performance of wind tunnel heat exchangers，the CFD numerical simulation method was employed to analyze a conventional finned-type heat exchanger in a wind tunnel. Firstly，a three-dimensional geometric model of the finned-type heat exchanger was created through NX12.0，and then the mesh was generated through Ansys-Meshing. Numerical simulations were carried out using Fluent 2021R1. The numerical simulation mainly focused on the impact of the incoming flow Reynolds number on the heat transfer performance and resistance performance of the heat exchanger. The calculations revealed that corresponding to 2mm，4mm，and 6mm，the Reynolds numbers are 676.79，1 353.59，and 2 030.39 respectively. As the Reynolds number increases，the pressure drops and temperature difference between the inlet and outlet of the heat exchanger decreases，and the comprehensive heat transfer performance increases by 72.61% and 28.28% respectively. However，the improvement effect tends to be flat. Under identical structural parameters，as the inlet wind velocity increases，the corresponding Reynolds numbers are 1 355.09，2 710.18，4 065.27，5 420.354，and 6 775.44 respectively. As the Reynolds number increases，the heat transfer factor decreases，and the heat transfer characteristics of the wind tunnel heat exchanger show a downward trend. Meanwhile，the friction factor also decreases，leading to a downward trend in the flow resistance of the wind tunnel heat exchanger. The comprehensive heat transfer factor decreases，which decreases by 9.34%，8.96%，4.79%，and 5.34% respectively. Consequently，the comprehensive performance of the heat exchanger demonstrates progressive deterioration.

Tibetan Jiu Chess， a traditional folk chess game， is a complete information game that carries the profound Tibetan civilization and splendid culture. In view of the complexity of the rule system and the diversity of the game changes， the traditional game search algorithm is unable to cope with the vast game board and complex strategies. In order to improve the intelligence level of Tibetan Jiu Chess， a Monte Carlo tree search （MCTS） algorithm optimization strategy incorporating prior knowledge was proposed. The strategy was based on deep reinforcement learning in the key phases of layout planning and move strategy，and the strategy selection optimization function and evaluation function were designed by integrating the prior knowledge of domain experts. The search process of MCTS was efficiently guided by functions，and the best model for high-quality tessellation could be trained. Experimental results show that the improved MCTS algorithm achieves significant performance in the game.

As one of the critical components of aircraft engine icing，the water droplet impingement characteristics on the rotating fairing surface directly influences the subsequent icing state.To investigate the water droplet impingement behavior under different operating conditions，a three-dimensional water droplet impingement model for the rotating fairing of aero-engine was established by using the Euler method and single rotating coordinate system.The water droplet impingement characteristics of the rotating fairing under stationary and rotating conditions were simulated respectively.The results show that under stationary conditions，as the freestream Mach number increases，both the water droplet collection coefficient and impingement area​ exhibit significant growth； compared to single-diameter droplets，when considering the Langmuir-D distribution​​of water droplets，the collection coefficient at the stagnation point decreases，while the impingement zone increases and the downstream collection coefficient also slightly increases.Under rotating conditions，the​​rotational speed​​ has negligible effects on droplet impingement characteristics due to the streamlined aerodynamic profile of the rotating fairing.

In order to solve the thermo-mechanical fatgue damage problem of trubine blade under the complex multifield coupling service conditions，combining the improved Morrow low-cycle fatigue damage and creep damage models based on the load spectrum of the engine’s service conditions，the prediction of the thermo-mechanical fatgue life of engine turbine blade was achived.Firstly， considering the rotor-stator interference effect between cascades， the three-dimensional flow field modeling and multi-field coupling simulation of the turbine blade were completed. Further， based on the engine load spectrum and multi-field coupling response characteristics under service operating condition， the key assessment positions of fatigue damage of the turbine blade were determined. then， an improved Morrow low-cycle fatigue damage model was developed and compared with traditional models and experimental results for verification. Finally， using the linear cumulative damage criterion， as well as the Morrow low-cycle fatigue damage and L-M creep damage， thermo-mechanical fatigue life of the turbine blade was predicted under service conditions. The results show that the numerical simulation results are in good agreement with the experimental data， verifying the accuracy of the three-dimensional unsteady flow field simulation of the turbine blade and the improved Morrow low-cycle fatigue damage model. Under the engine load spectrum and multi-field coupling effect of service conditions， considering the Morrow low-cycle fatigue damage and L-M creep damage， the thermo-mechanical fatigue life of the turbine blade is 6.028×10³ h. The area with the minimum life is located at the leading edge of the blade root at the air inlet， the assessment position A， which is the key maintenance part of the turbine blade. This study can provide a theoretical reference and basis for the thermo-mechanical fatigue life assessment of turbine blade under complex service operating condition.

Based on the measurement of EEG，the behavioral intention of online shoppers after interacting with shopping website was evaluated and predicted.The research method combining objective electroencephalogram （EEG） experiments and subjective evaluation scales was employed to measure the EEG of online shoppers during the interaction tasks on the homepage of shopping websites， and to predict the behavioral intentions of online shoppers towards shopping websites after completing the tasks. The method of variance analysis was adopted to extract the significant EEG-based indicators which influence users’ behavior intentions. Based on the partial least squares （PLS）method，a relationship model between the users’ electrical indicators and the behavior intentions was established， and the model was verified. The results show that the proposed model can predict the approach trend of online shoppers towards shopping websites well.

To meet the requirements for open-loop trajectory motion of a solar-powered car， a design and fabrication method was proposed for a solar-powered car. Firstly， by using B-spline curves， the characteristic points were fitted to generate an ideal open-loop trajectory curve. MATLAB software was then utilized to analyze and optimize the planned trajectory to produce the cam profile. Next， based on the overall functional requirements， the overall structure， transmission mechanism， and steering fine-tuning mechanism of the solar-powered car were designed. Subsequently， simulation software was employed to perform simulation analysis and optimization of the cam trajectory， and the car’s circuit modules were assembled. Finally， actual driving tests of the car were conducted. To improve the trajectory accuracy of the car， the front wheel offset angle was modified by adjusting the fine-tuning mechanism. Experimental results demonstrate that the car fabricated using this method operates stably and successfully passes through all predetermined characteristic points.

To ensure the normal operation of turbine blades Facing continuously increasing turbine inlet temperatures， the development of more efficient cooling technologies has become particularly urgent. Ceramic matrix composite （CMC） transpiration cooling technology combines the excellent high-temperature resistance of CMC with the efficient heat dissipation potential of transpiration cooling， showing broad application prospects in future thermal protection of turbine blades. However， the strong anisotropy of CMC materials and their complex internal porous structure pose significant challenges for the thermal analysis of the transpiration cooling process， and the underlying flow and heat transfer mechanisms remain unclear. This review summarized recent research progress on the anisotropic thermal conductivity and microporous seepage characteristics of CMC， analyzed key existing problems and challenges in current studies， and offerd suggestions for establishing a comprehensive design framework for CMC turbine blade transpiration cooling structures.

In order to better solve the problem of robust fault-tolerant control for magnetorheological landing gear buffer systems with damper failures， a model reference adaptive fault-tolerant control strategy was proposed，and a mechanical model was established and key parameters were determined by damping experiments. A fault model was established for the magnetorheological landing gear system and fault-tolerant control was introduced. An adaptive law was designed to adjust the control gain in real time， and a fault-tolerant controller was constructed to deal with damper failures. Results show that compared with the traditional passive and model reference adaptive control methods， the buffer efficiency of the damper fault tolerant control method is improved by 4.9% and 0.95% respectively. This control method significantly improves the damping efficiency and quality of the magnetorheological landing gear damping system.

In order to select the characteristic indicator to more effectively characterize the performance degradation of the air compressor bearing，a feature indicator selection method based on game theory combinatorial weighting method was proposed.Through the analysis of the time-domain indicator of the bearing vibration and the preprocessing of redundant information，the time-domain indicator dataset of the bearing was obtained.Three characteristic indicator evaluation methods including monotonicity，robustness and trend were used to complete the selection of performance characteristic indicators.Based on this，the game theory combinatorial weighting method was used to weight three characteristic indicator and sixteen time-domain indicator to complete the selection of bearing performance degradation indicator.The effectiveness of this method was illustrated by example verification.

In the context of cooperative operation between UAV and ground vehicle， rotor UAV often needs to pass through necessary points or avoid obstacles when executing related tasks， which increases the trajectory fluctuation and brings more challenges to path planning. To solve this problem， an improved discrete mechanics and optimal control （DMOC） trajectory optimization algorithm was proposed， which transformed the optimal control problem into a nonlinear programming problem. Considering the endurance time of vehicle-mounted rotor UAV， the shortest time was set as the optimization objective under guaranteed traversal conditions. In the process of the algorithm implementation， an approach was proposed to adjust the distance walk length according to the size of the error factor， and then approximate correlation integral， which effectively solved the problem of trajectory fluctuation. The experimental results show that this method can improve the smoothness of the optimal trajectory while ensuring that all the necessary points are traversed，and also provides a reference for the trajectory optimization of other agents.

In response to the need for rapid iteration in the conceptual design stage of propeller aircraft， low-fidelity aerodynamic evaluation methods， such as the vortex lattice method was a more suitable choice. In order to obtain suitable computation parameters and quantitative errors， the NASA OpenVSP unsteady vortex lattice method （UVLM） was studied using the propeller standard model， and the computation characteristics of the multiple reference frame quasi-steady vortex lattice method （MRF-VLM） were presented for the first time. The computational convergence and error characteristics were analyzed using the APC electric-propeller standard model， and the grid/iterative parameter settings that take into account both computational stability and efficiency were obtained. The comparison with experimental data shows that with appropriate grid and iterative parameters， the computation errors for the propulsion efficiency of the above two methods are within 6.1% and 3.6% respectively， under conditions of low pitch angle and medium advance ratio. The accuracy meets the requirements of the conceptual design stage， and the MRF-VLM computation with 4-thread parallel processing takes only 4 minutes， which is more efficient. The case of NACA 5868-9 propeller standard model further verify the computational reliability of the MRF-VLM method. The above research results can provide a reference for the reliable engineering application of OpenVSP VLM. However， due to the limitations of the linearized potential flow theory of the VLM， the above two methods can not accurately simulate the strong flow nonlinearity under high pitch angle and high/low advance ratio， and the computation accuracy needs to be improved.

Nomex honeycomb materials are widely used in marine，high-speed rail，aerospace and other fields because of the material’s low density，high specific strength and high specific stiffness. However，the non-homogeneous，thin-walled and brittle characteristics of honeycomb structures make them prone to tearing，crushing and other defects during processing. Therefore，scholars both in China and abroad have used ultrasonic machining technology to solve the problem of Nomex honeycomb material processing with the advantages of small cutting force and high surface quality，which have carried out extensive and in-depth research. However，compared with ordinary machining，high-frequency vibration and periodic impact generated in high-quality and efficient ultrasonic machining process put forward higher requirements for tool performance and life. Therefore， focused on tool material selection， tool design， kinematic analysis， cutting force and cutting heat modeling， and surface topography analysis during machining of Nomex honeycomb materials. The research status of machining mechanism，cutting characteristics and ultrasonic tool design for ultrasonic cutting of Nomex honeycomb materials were summarized. The future research and development trend of ultrasonic cutting Nomex honeycomb materials were put forward， which provided a reference for further research on the service performance and processing quality of ultrasonic cutting tools in cutting.

In order to deeply study the driving performance of ionic liquid gel （ILG）as ionic electroactive polymer，developed a novel soft actuator based on ILG and conducted a detailed investigation into the electromechanical coupling model of the ILG soft actuator. According to the material properties and current response law of electroactive polymers，established electromechanical coupling equations and driving equations for ILG soft actuator based on the equivalent transformer model of ionic polymer-metal composite actuators proposed by Claudia Bonomo. The least squares method was used to identify the coupling model of the ILG soft actuator， and the influence of structural parameters on the end displacement and driving force of the soft actuator was analyzed， providing a theoretical basis for the control of soft actuators. The electromechanical coupling model of the ionic liquid gel soft actuator is established， which lays a foundation for the development of ionic liquid gel soft robots.

To address the collaborative search task planning issue for unmanned aerial vehicles （UAV） and unmanned ground vehicles （UGV） in search and rescue scenarios， a method that combines centralized task allocation with independent path planning was proposed. Firstly， tasks were clarified based on requirements and assigned to UAV and UGV. Firstly， the UAV and UGV conducted trajectory planning and path planning independently according to their assigned tasks. For the task allocation problem，a multi-objective area segmentation and allocation model based on task types was proposed，and an adaptive hybrid algorithnm incorporating genetic algorithm and tabu search was developed in solution.For trajectory planning， a “traveling salesman path-area coverage path planning” model was constructed based on the task requirements of the UAV， employing an improved genetic algorithm that introduced an “S”-shaped path as the initial population and designed a fitness function to optimize the selection process. Regarding the path planning for UGV， a method that combines A* algorithm with artificial potential field algorithm was proposed to find the optimal traveling path. Simulation results indicate that the proposed algorithm effectively completes search tasks and shows significant improvements in both task execution efficiency and path planning accuracy compared to commonly used genetic algorithms and A* algorithms.

To enhance the resilience of drone enterprise supply chains and their ability to respond to risks， the impact and mechanism of artificial intelligence technology on supply chain resilience were empirically examined which based on data from A-share listed drone companies from 2016 to approximately 2022. It was found that strengthening artificial intelligence technology could significantly improve supply chain resilience. This conclusion remained valid after a series of robustness tests and was particularly pronounced for small and medium-sized drone companies with lower market share. Mechanism analysis indicated that artificial intelligence mainly operates through two pathways： enhancing supply chain concentration and reducing internal management costs. By optimizing resource allocation， improving operational efficiency， and reducing management expenses， it strengthened the enterprise's ability to cope with market fluctuations and external shocks. This study provided policy and strategic recommendations for governments and companied to use artificial intelligence to optimize supply chain management， as well as the oretical support and practical guidance for enhancing enterprise risk resilience.

Aiming to address the limitations of traditional health monitoring methods of hydraulic oil pump truck testbed， such as reliance on periodic physical inspections and maintenance， low levels of informatization， and insufficient production efficiency， an application development architecture for a hydraulic oil pump truck monitoring system was proposed based on digital twin technology. This architecture integrates data， models， and communication to improved the predictive maintenance level and operational efficiency of the equipment. The research enabled real-time data interaction and fusion between the physical equipment and the virtual model by constructing a digital twin virtual model of the equipment and utilizing the TCP/IP protocol and a unified JSON packet format. On this basis， the system could perform simulation， numerical prediction， fault diagnosis， and early warning for the hydraulic oil pump truck testbed， thereby optimizing the equipment’s operation status.Test results show that the system is able to perform health monitoring and fault diagnosis for the hydraulic oil pump truck， showing broad prospects in the application of aviation testbed.

To investigate the influence of assembly deviations on pipeline sealing performance， a finite element model was established based on the torque-tension relationship. The influence of three assembly deviations （axial，radial， and angular） acting individually on the sealing performance of pipeline connectors was analyzed， and the coupling influence of these three assembly deviations on the sealing performance of pipeline connectors was studied through orthogonal experiment. The results indicate that under single deviation conditions， negative axial deviation，radial deviation，and angular deviationall reduce pipeline sealing performance. When the three assembly deviations are coupled， the sealing performance decreases. Negative axial deviation and angular deviation have significant influences on pipeline sealing performance， while the influence of radial deviation is relatively small. Therefore， in practical engineering， negative axial deviation and angular deviation should be strictly controlled， and positive axial deviation and radial deviation should be minimized as much as possible.

The key to achieving high level of scientific and technological self-reliance is to strengthen the incentives for the innovative behaviors of scientific and technological personnel. Based on the AMO theory， combined with NCA and QCA methods， a driving path of scientific and technological personnel’s innovative behaviors from the perspective of complex interactive configuration between individuals and organizations was explored. The results show that the emergence of a single factor is not a necessary condition for the outcome variable. There are three paths that can drive the high innovative behavior of scientific and technological personnel. It enriches the relevant theoretical research on the scientific and technological personnel’s innovative behavior， providing reliable basis and concrete measures for managers to effectively drive the innovative behavior of scientific and technological personnel.

To accurately capture the aerodynamic characteristics of Z-shaped folding wing at different folding angles， while considering the influence of the structural elastic effect of different wing segments， a parametric aerodynamic-structural coupling model was constructed based on a quasi-steady state environment.Using ANSYS Workbench software with the RNG k - \epsilon turbulence model and the Coupled algorithm，numerical simulation was conducted to analyze the aerodynamic characteristics of the folding wing under the action of elastic effect and the results were compared with those of a rigid wing. Additionally， the deformation laws of the elastic folding wing in different flight environments were systematically explored. The results show that under the influence of elastic effect， the absolute values of lift and pitching moment of the folding wing are slightly lower than those of the rigid wing. As the folding angle increases， the impact of elastic deformation on the overall aerodynamic performance of the wing stabilizes. When the folding angle is 90°， the differences in lift and pitching moment between the elastic and rigid wings are only 0.93% and 0.70% respectively. Furthermore， under the influence of elastic effect， the maximum deformation of the wing occurs at the wingtip. As the folding angle increases， the magnitude of wingtip deformation gradually decreases.

In the highly complex and intensely adversarial air combat environment， current unmanned aerial vehicle intention assessment methods generally face challenges such as strong subjectivity of fusion rules， ignoring the time correlation of relevant attributes， and insufficient defect information processing means. To address these issues， this paper proposes a dynamic evidence network method that integrates defect information correction and subjective and objective rules. Firstly， focusing on the strong correlation of continuous variables in the time dimension， a spatiotemporal fusion evidence network model was modularly constructed. Subsequently， by introducing the LSTM trajectory prediction technology， a defect evidence correction mechanism was established， which significantly improved the accuracy and integrity of information. Finally， objective rules were designed based on statistical calculation methods， and combined with subjective experience， a library of subjective and objective rules was constructed. Based on the above improvements， simulation experiments were conducted. The results confirm that the proposed mechanism of defect information correction， subjective and objective rules， and dynamic fusion have significant advantages in improving the accuracy and credibility of intention recognition results.

In order to improve the accuracy of component performance evaluation under a whole-engine test environment for aero-engine，a performance optimization and evaluation method was proposed for aero-engine components based on the artificial bee colony algorithm. This method took the performance parameters of the components to be evaluated as input variables. It utilized an overall aero-engine performance simulation model combined with the ABC algorithm to optimize and obtain the optimal set of performance parameters for the components to be evaluated， ensuring compliance with the accuracy requirements of the test parameters. This method was applied to evaluate the component performance under the test environment of a core engine. The results show that the deviation between the evaluated parameters and the measured values remains within 0.95%， demonstrating the high effectiveness and engineering practicability of the evaluation method. During the multi-operating condition optimization process， to balance the accuracy of the entire speed range， the maximum deviation increases by approximately 0.1%. But the introduction of solution constraints enhances the authenticity of the performance evaluation and reduces the impact of test uncertainties on the performance evaluations. Meanwhile， the correction characteristics of component performance under different inlet temperature environments of the core engine are obtained in the optimization process.

Automatic drilling technology plays an important role in modern aircraft manufacturing. To improve the efficiency and reduce the cost of aircraft assembly and manufacturing，a hole path planning algorithm of aircraft curved surface components was studied in depth. Based on the situation that the traditional ant colony algorithm was easy to fall into local optimum and converge slowly，the new school-based optimization algorithm combined with the traditional ant colony optimization algorithm was proposed to achieve the optimization of the drilling path of aircraft curved surface components. The simulation results show that the improved school-based optimization ant colony algorithm is superior to the traditional ant colony algorithms in terms of drilling path length and iteration speed.