Using the carbon fiber-reinforced composite tank can remarkably reduce the weight of the launch vehicle.However，the analysis method for cryogenic tanks subjected to mechanical-thermal loads remains to be studied，especially to accurately consider the microthermal stress produced between fiber and matrix in the cryogenic environment.A representative volume model containing multiple fibers was adopted，combined with the matrix and fiber failure criteria，to establish a microscopic stress field and failure prediction model.The k-means clustering method was used for dimensionality reduction calculation，and an efficient and high-fidelity trans-scale analysis method for composite tanks was proposed.The results of illustrative examples show that the proposed method can accurately predict the elastic constants and failure strength of the composite single-layer plate according to the thermal and mechanical constants of fiber and matrix.The leakage failure process of a composite tank subjected to mechanical-thermal loads was simulated，and the critical load and failure state were given.
Based on the NNW-PHengLEI software，for surfaces with uniformly distributed rough elements，rough surface boundary condition corrections and transition momentum thickness Reynolds numbers were introduced into the k-ω SST turbulence model and the γ-Reθt transition model respectively.At the same time，some rough surface settings were added to achieve local/global rough surface boundary layer flow simulation capability.Test results show that when an airfoil is in a fully turbulent state，rough surfaces can reduce the stall angle of attack by 4°，and an equivalent roughness height of 1×10-3 of chord length can cause a decrease in maximum lift coefficient by approximately 36%.Additionally，rough surfaces can advance the boundary layer transition，leading to an increase in skin friction drag.The computational analysis proves that the rough surface boundary condition proposed by Hellsten et al.can reflect the roughness effect more accurately than the dissipation rate boundary condition.The computational aerodynamic forces are consistent with the experimental data，and the flow field simulation results conform to the flow characteristics of rough surfaces.In addition，Hellsten’s model makes it possible to ensure stable coupling simulation with the transition model and its rough surface correlation function.The simulation results for the transition onset location and its trend with roughness level and freestream turbulent intensity are consistent with the experimental data.Still，the transition length needs further verification and correction through more rigorous experiments.
In engine performance analysis，the ratio of the cooling air flow of high-pressure turbine guide vane to compressor inlet flow is a fixed value.However，the actual bleed air flow of the turbine is affected by some factors，such as the pressure difference between the inlet and outlet of the cooling air flow path，flow area and flow resistance，resulting in deviations between the current simulation accuracy and actual performance of the engine.To further improve the simulation accuracy of engine performance，a modeling method based on the flow characteristics of high-pressure turbine guide vane was proposed.The computational model of the core engine performance was improved based on this method，and the performance parameters of one core engine were calculated with this model.The numerical results show that，for this core engine the actual cooling air flow of turbines decreases after considering the flow characteristics of high-pressure turbine guide vane，and the cooling air flow is more sensitive to the change of the total pressure recovery coefficient of the cooling air flow path.In the performance calculation of the core engine，after using the cooling air flow correction of high-pressure turbine guide vane，the total temperature at the outlet of the combustion chamber decreases by 1% to 2%，the temperature ratio of the core engine increases by 0.2% to 0.45%，the unit cycle power increases by 0.24% to 0.48%，and the pressure ratio of the core engine and the fuel consumption rate per unit cycle power change relatively little.
The changes in stiffness and damping of the bolted joint surface can cause the changes of dynamic characteristics of the whole bolted joint structure，so it is of great practical value to accurately obtain the dynamic parameters of the joint surface in engineering.Based on the bolted structure，an improved equivalent model of the stiffness of the joint surface with uneven distribution in the range of bolt preload was proposed，and the distribution of different stiffness matrix elements in the finite element modeling was analyzed.The stiffness parameter identification of bolted joint surface was carried out by combining experiment and finite element analysis method.The results show that the increase of the number of stiffness matrix elements can improve the accuracy of natural frequency solutions to some extent.At the same time，considering the uneven stiffness distribution of the joint surface in the range of bolt preload，the equivalent modeling accuracy of the bolted joint structure can be effectively improved.
In view of the problems of difficulty in extracting gear fault features and low diagnosis accuracy under noisy environment，a gear fault diagnosis method was proposed，which combined VMD parameter optimization based on comprehensive evaluation indicators，KPCA feature fusion and BP network.Firstly，in order to effectively evaluate the IMF components after VMD decomposition and avoid the problem of manually setting relevant parameters for VMD，a comprehensive evaluation index based on envelope entropy and kurtosis was designed to establish a fitness function for VMD parameter optimization and screen the optimal IMF components.Secondly，after performing VMD decomposition according to the optimal parameters，a multi domain feature set was extracted from the optimal IMF component，and then the KPCA model was used to fuse its features.Finally，fault diagnosis was performed using the BP network model.The experiment shows that under the same experimental conditions，compared with other traditional methods，this method improves the recognition rate of gear faults，with an accuracy of up to 98%，proving the effectiveness of this method.
Human action recognition is a key technology for understanding pedestrian intentions from video captured by unmanned aerial vehicles （UAV）.However，UAV platforms have limited computing power，and existing action recognition methods are inefficient.A lightweight spatial grouping attention graph convolutional network （SGA-GCN） was proposed to reduce network depth to improve the efficiency and ensure the accuracy of action recognition.In order to capture body parts that represent global motion，spatial grouping attention was introduced to enhance local features with high similarity to global features.Moreover，since it was impossible to effectively distinguish actions with similar motion trajectories solely based on joint and skeletal features，a high-order feature encoding of skeletal angles was constructed to capture changes in angles between limb joints that better reflected subtle motion differences and improved feature representation capabilities.Finally，to address the low frame rate issue in UAV aerial video，a linear interpolation scheme based on inter-frame differences was proposed to increase sample information quantity.Experimental results demonstrate that compared to the existing state-of-the-art （SOTA） methods，the proposed approach achieves better performance in terms of recognition rate，parameter quantity，training time and execution time on the UAV-Human dataset.
Rocket image target recognition and tracking is an important application field of image target recognition，which is an important support for rocket test launch and flight control，and has great significance for rocket target tracking and attitude analysis and control.Image tracking of rocket target in ascending stage is an important stage of rocket flight measurement and control，but at present，the video image tracking of rocket in ascending stage mainly relies on manual operation of pinion controller to achieve rocket tracking in the image.The tracking image has some phenomena such as tracking lag and picture shaking，and the tracking effect is greatly affected by human factors.Combining full convolution theory with deep learning method，a method of rocket image target recognition and tracking based on full convolution deep neural network was proposed.Images of rocket launch and ascending flight were collected as samples，and a full convolutional network model was constructed and trained.An end-to-end semantic segmentation method was adopted to realize semantic judgment of rocket targets at pixel level on the basis of deep classification network，with good recognition rate and robustness.Based on the recognition of the rocket target，the PTZ control model was established，the high-quality image of the rocket ascent stage was obtained through the intelligent control of the PTZ，and the tracking of the rocket target was realized.
To study the evacuation characteristics in the Airbus 330 cabin crew under different hatch opening conditions，the Pathfinder software was used to simulate the evacuation characteristics.The evacuation characteristics of personnel were explored in four situations： all cabin doors opened， emergency doors could not be opened， front cabin doors could not be opened， and rear cabin doors could not be opened.The results show that the simulation time for evacuation is 63.3 s when all hatches are open，while 118.3 s when the emergency hatch cannot be opened.The per-unit-time flow of people at No.3，No.4，No.7，and No.8 hatches decreases by 0.09 people，0.2 people 0.09 people，0.04 people respectively.It takes 64s to evacuate people when the front cabin door could not be opened.The number of people evacuated from No.3，No.4，No.5，and No.6 doors are 47，48，52，and 50 respectively，indicating that the failure to open the front cabin door does not have no significant impact on the evacuation time.When the rear cabin door could not be opened，the evacuation time is 112.3s.The time interval from the first person to the last person from the No.3，No.4，No.5 and No.6 doors is 67.7 s，67.6 s，109.9 s and 107.1 s，respectively.This indicates that more passengers in the economy class choose to escape from the hatches No.5 and No.6，which increases the evacuation time.The research results provide a reference for understanding the evacuation characteristics of personnel under different hatch opening conditions.
In the flight test measurements of civil aircraft noise airworthiness certification，the influence of meteorology is crucial to the final test results.In order to ensure that applicants correctly measure meteorological parameters，correct noise data for meteorological effects and obtain accurate test results，technical index requirements was put forward for test equipment used by ground weather stations，vehicle-mounted weather stations and meteorological aircraft.The review guidance requirements were given for measurement methods，test flight methods，data validity，etc.The atmospheric stratification method and the quadratic interpolation method of the atmospheric sound attenuation coefficient used to correct the noise measurement data were proposed，analyzed，and verified.In the process of noise airworthiness measurement data correction，the noise results can meet the baseline conditions and airworthiness requirements by calculating the atmospheric sound attenuation coefficient and correcting the measurement data.The meteorological data test flight，measurement，analysis，and data correction methods proposed improve the accuracy and reliability of the final noise results and ensure the comparability and reliability of noise test results under different meteorological conditions.
Based on the data of A-share listed enterprises in Chinese equipment manufacturing industry from 2011 to 2021，the mechanism of digital transformation and dynamic capability on enterprise innovation performance were empirically tested.The results show that digital transformation can promote the innovation performance of equipment manufacturing enterprises by enhancing dynamic capabilities，and organizational inertia has a different degree of moderating effect between digital transformation and dynamic capabilities.The analysis of enterprise heterogeneity shows that the impact of digital transformation on enterprise innovation performance is significantly different between state-owned enterprises and non-state-owned enterprises，as well as among the eastern，central and western regions.The research conclusions remedy the theoretical gap between dynamic capabilities and organizational inertia，and provide practical guidance for the internal mechanism of digital transformation to promote enterprise innovation performance.
