A calculation method of effective power based on heat transfer mathematical simulation of intercooling system was proposed for an aviation piston engine, and the heat transfer simulation model of the intercooling system was developed with VB language. The validity of the simulation model was verified by the test data. The results show that the errors between the simulation values and the test values of the intercooler cold side outlet temperature and the hot side outlet temperature are within 1.5%. Using the simulation model, the influence of fan air flow on the effective power of the aviation piston engine was studied, and the air effective power recovery was studied. The results show that with the increase of fan air volume, the amplification of fan power increases, while the amplification of engine power decreases. Under the combined effect of the two, the effective power of the engine first increases and then decreases with the increase of fan air volume. For the aviation piston engine studied, when the fan air volume is 1 400 m³/h, the effective power of the engine reaches the maximum, which is 101.6 kW. When the flight altitude is below 2 000 m, the engine effective power recovery coefficient increases slightly with the increase of flight altitude, and when the flight altitude is above 2 000 m, the engine effective power decreases significantly with the increase of flight altitude. Under the condition of 50 ℃ at sea level, the effective power recovery coefficient of the engine at 7 000 m is only 92.2%.

A deep learning based T-beam formwork polishing robot was designed for the problems of difficult and time consuming polishing of T-beam formwork for variable cross-section. Firstly, an adaptive polishing structure was proposed to solve the technical problem that the existing polishing device cannot fit the inner variable cross-section of the T-beam formwork, and the polishing roller was easy to get stuck in the T-beam formwork partition. Secondly, in order to realize the quantitative monitoring of the polishing quality, a YOLOv8n-DSE algorithm was proposed to identify concrete dirt and stains on the formwork, the DySample dynamic up-sampling module was introduced to enhance the anti-interference ability of the model and accelerate the calculation speed, to improve the accuracy of small target detection, the SOEP (small object enhance pyramid) module was designed to improve the detection performance of small target detection through the SPDConv(space to depth convolution) to obtain the information features of the small target and give them to the CSP(cross stage partial)-Omni-Kernel for the integration of the features. Finally, the EMA(exponential moving average)-SlideLoss was replaced to make the model more concerned with the quantitative monitoring of the concrete, allowed the model to focus more on difficult targets, which can improve the effect on difficult case detection. The accuracy, recall, and mAP(mean average precision) values are improved by 3.1%, 9.7%, and 3.2%, respectively, compared with those before the improvement. The improved model was deployed to the robot and tested in the field. The results show that the equipment meets the plant's needs for polishing variable-section T-beam formwork.

The principle of particle damping energy consumption and inertial capacity efficiency increase are widely used in structural vibration reduction control. Based on the advantages of particle damping and inertial capacity, a particle damping inertial capacity shock damper (PID) was designed, which mainly contained particle damping unit, inertial mass unit and stiffness unit. Firstly, the working principle of PID was elaborated, the mechanical analysis model of single degree of freedom was established, a small PID mock-up was produced, a variety of working conditions were set up to test the mechanical properties of the PID. Then, the mechanical properties of PID were further explored by the combined simulation method of many-body dynamics software and discrete element software. Finally, to verify its engineering application value, dynamic time-course analysis of the damping structures configured with PID and tuned mass damper (TMD) by finite element structural analysis software SAP2000. The results show that PID has excellent damping performance, when the other conditions are certain, the energy consumption effect of PID increases with the increase of vibration displacement amplitude and vibration frequency. In the building structure, PID shows better damping than TMD, it has a high engineering application value.

Sodium fire accidents in sodium technology room can generate harmful aerosols. To analyze the impact of sodium fire aerosol particle migration, a microchannel grid structure similar to real cracks was constructed using computational fluid dynamics(CFD) method based on the actual concrete crack characteristics to simulate the migration process of aerosol particles in the room wall. A two-dimensional horizontal microchannel flow model was established, considering gravity, inertial force, and the influence of Saffman lift and Brownian diffusion on particle motion was studied, and a microchannel particle motion model was constructed to numerically simulate particle retention characteristics for different gap structures. The results indicate that when the gap size is less than submillimeter, it is considered that there is no risk of causing a large amount of aerosol particle leakage in the gap. The branching corners and uneven micro structures within concrete gaps can effectively reduce the penetration coefficient of particles in the gaps and reduce leakage.

The silica with the highest content in sandstone was taken as the main research object. The SiO₂ surfaces underwent hydroxyl (—OH) and methyl (—CH₃) treatments to represent the hydrophilic and hydrophobic walls, respectively. LAMMPS software was utilized to implement molecular dynamics simulations and replicate the process of supercritical CO₂ extraction of crude oil components. The results indicate that the temperature is 333.15 K and the pressure is 20 MPa. The hydroxylated silica surface extracted 5.07% more saturated hydrocarbon molecules using supercritical CO₂ compared to the methylated silica surface. The interaction energy between the oil components and the two wall surfaces is mutually attractive. The interaction energy of saturated hydrocarbon molecules decreases by 77.52% for hydroxylated silica surface and 46.04% for methylated silica surface, respectively. Additionally, the interaction energy of methylated silica on saturated hydrocarbon is greater than that of the hydroxylated silica surface.It is important to note that carbon dioxide easily extracts saturated hydrocarbons with short molecular chains. The diffusion coefficients of crude oil components under two surface conditions are saturated hydrocarbons > resins > aromatic hydrocarbons > asphaltenes.

In order to improve the accuracy of network traffic classification, a traffic classification method combining an attention mechanism and a convolutional neural network was proposed. An attention mechanism layer was designed and implemented on the basis of the convolutional neural network model, which received the output of the fully connected layer as input, calculated the weight of the input features, and multiplied it by the original features to strengthen the key features. This, in turn, helped to improve the model's ability to capture key information. Secondly, in order to solve the problem that the model was overfitting to the high-proportion category due to the unbalanced sample number of network traffic categories, and it was difficult to identify the small-proportion categories, a method to augment the dataset was proposed. Considering the perspective of hyperparameter combination optimization, a hyperparameter search strategy based on Bayesian optimization and five-fold cross-validation was proposed to optimize the hyperparameter combination of the model. The combination of hyperparameters of the model was determined by the above methods. The public dataset was used for the above experiments and model tests. The results show that compared with other methods, the overall accuracy, precision, and F₁ score are significantly improved, which verifies that the proposed method has better classification performance.

To improve the short-term prediction accuracy of photovoltaic power generation models with multiple input features, a photovoltaic power prediction ensemble model LGGWO-TCN-MHSA based on optimizing TCN hyperparameters was proposed. The model integrated the levy gold grey wolf optimization (LGGWO), temporal convolutional network (TCN), and multi-head self-attention mechanism (MHSA). First, the Spearman correlation coefficient method extracted the main features that significantly affect photovoltaic power, which were then fed into the TCN prediction model. Then, the proposed multi-strategy LGGWO was applied to the TCN for hyperparameter optimization, which improved the model's prediction performance. Finally, the predicted values were input into the multi-head self-attention model to further boost prediction accuracy. The experiment was verified using original Australian photovoltaic data. By comparing with six groups of models including convolutional neural networks (CNN) and long short-term memory neural networks (LSTM), the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model on the test data set were reduced by 2.03%~82.0% and 10.5%~80.1%, respectively. The results show that the proposed method has high prediction accuracy and good stability.

A mathematical model was established to address the multi-constraint, large-scale three-dimensional bin packing problem. A hybrid metaheuristic algorithm combining an improved whale algorithm with simulated annealing was proposed. The algorithm discretized the whale algorithm, including individual encoding and updating mechanisms, and utilized simulated annealing to overcome local optima traps. Moreover, a heuristic loading rule was designed for decoding and optimizing the packing solution. The algorithm was evaluated using standard packing instances from Bischoff and Ratcliff's OR-Library, as well as real-world cargo order data, covering a range of cargo types from weakly heterogeneous to strongly heterogeneous. The proposed algorithm achieved a balance between global and local search capabilities, resulting in high packing efficiency for various types of containers. Specifically, the average container filling rate were 92.24% for weakly heterogeneous cargo, 88.78% for strongly heterogeneous cargo, and an overall average of 91.29%. This result provides valuable insights and references for the study of three-dimensional bin packing problems.

As essential components in power conversion modules, rectifiers are extensively utilized in power supply systems such as inverters, where their operational reliability directly influences the overall system performance. In order to enhance the reliability of rectifiers, it is critical to conduct lifespan predictions for sensitive components, particularly rectifier diodes. A predictive model was proposed that employs an improved grey wolf optimization (GWO) algorithm to optimize the hyperparameters of a simple recurrent unit (SRU) network. Initially, a power cycling accelerated aging test was performed on the diode, followed by an analysis of its characteristic parameters, with forward voltage drop identified as the primary aging indicator. Subsequently, the improved GWO algorithm was applied to optimize SRU hyperparameters—such as learning rate, number of hidden layers, and iteration count—thereby establishing a hybrid predictive model. Finally, the model was trained and validated using aging test data, with predictive accuracy compared against alternative models. The results show that the proposed model achieves superior predictive accuracy, and the data-driven predictive approach enhances the precision of diode lifespan estimation compared to conventional analytical modeling methods, thereby contributing to enhanced operational reliability of rectifiers.

In order to enrich and improve the geohazard-pregnant mechanism of structural mélange, taking the North Lancang River suture zone as an example, the structural petrology characteristics of the structural mélange were dissected, then the physical and mechanical characteristics were identified through field geological investigation and laboratory test analysis, and finally its geohazard effect were revealed. The results show that the tectonic mélange in the northern Lancang River suture zone is sandwiched between granite and granite gneiss on the southwest and volcano-sedimentary rocks on the northeast as a non-abutment layer. The main rock types are phyllite and slate with a small amount of basalt, limestone and siliceous rock blocks, showing obvious “hybrid accumulation” characteristics. Under the coupling process of tectonic uplift, differential weathering and river erosion, it gradually evolved into a mountain deep canyon landform, which is the topographic basis for the development of geohazard. In terms of structural petrology, the structural mélange in the North Lancang River suture zone shows the characteristics of “one weak and three strong”, that is, weak metamorphism, strong deformation, strong alteration and strong orientation, which leads to serious deterioration of rocks and is the source basis of geohazard. In terms of physical and mechanical properties, the tectonic mélange appears as a set of easy to slip soft rock “formation”, rich in clay minerals, with high porosity and water absorption, low compressive and shear strength, which provides structural conditions for the development of geohazards. Tectonic activity and tectonic stress are strong in the North Lancang River suture zone, which provides dynamic conditions for the development of geohazards. The research results are conducive to further enriching and improving the geohazard breeding mechanism of tectonic mélange.