The propagation analysis of high-dimensional structural responses under complex uncertain variables is prone to problems such as low modeling efficiency and poor analytical accuracy. To address this issue, a rapid analysis method for high-dimensional structural response uncertainty based on evidence theory was proposed. Firstly, the basic probability assignment of evidence variables was utilized to generate sample sets efficiently via optimal Latin hypercube sampling.Secondly, principal component analysis was applied to reduce the dimensionality of the high-dimensional structural responses, extracting low-dimensional features and eigenvectors to decrease modeling complexity. Finally, an extreme learning machine was employed to construct the mapping relationship between the uncertainty parameters and the low-dimensional features, enabling the prediction of uncertainty propagation at any position of the high-dimensional responses with respect to the input variables. Validation through the examples demonstrated that the proposed method could effectively quantify the uncertainty distribution at arbitrary positions of both high-dimensional time-domain and spatial responses, achieving high accuracy with relatively high modeling efficiency. The proposed method can significantly reduce the complexity of uncertainty analysis for high-dimensional full-field responses and serve as an effective tool for uncertainty propagation analysis of complex engineering structures.

In order to tackle the technical bottleneck of predicting jamming and empty drilling faults of rock drills in drill-and-blast tunnel construction, a method of fault classification and prediction of the rock drill based on multi-feature time-series labeling Transformer was proposed. By collecting the key high-frequency while-drilling parameters of the rock drill under various working conditions, and integrating the thresholds of these parameters in the faulty states, a labeled dataset of jamming and empty drillin was constructed. A multi-feature time-series labeling strategy was designed to convert raw data into sequences of embedding vectors with temporal relationships. Building upon this, a multi-head self-attention mechanism was employed to mine long-term dependencies among the multiple features. Combined with a feedforward neural network and a dynamic slicing optimization strategy, and enhanced by residual connections and layer normalization, a time-prospective Transformer model was constructed. This model ultimately achieved the dual functions of fault classification and prediction. The experimental results demonstrated that the proposed method achieved an accuracy of 93.233% in the classification and prediction of jamming and empty drilling faults of the rock drill, significantly outperforming comparative models such as CNN (convolutional neural network), LSTM (long short-term memory), CNN-LSTM, RNN (recurrent neural network), and iTransformer. Visualization results of features using t-SNE (t-distribution stochastic neighbour embedding) revealed superior intra-class clustering and inter-class separation characteristics for the proposed model. Furthermore, it exhibited the lowest training loss and an inference time of merely 0.014 6 s, meeting the real-time warning requirements. The research results provide a reliable technical approach for classifying and predicting the faults of rock drills under complex geological conditions.

Spiral bevel gears in the helicopter's main gearbox exhibit pronounced spatial meshing characteristics. Under the combined effects of complex loads as well as manufacturing and assembly errors, the key excitations such as mesh stiffness and transmission error are difficult to obtain online, which limits the high-accuracy prediction of vibration responses. To address this issue, a digital twin method for vibration prediction was proposed by integrating tooth surface contact analysis with mechanism-guided data-driven modeling. The proposed method adopted collaborative modeling along physical and data paths. On the physical path, a tooth surface with geometric errors was constructed based on the three-coordinate measurement data, and the tooth surface contact analysis was carried out to extract time-varying mesh stiffness and composite transmission error. A condition-related vibration intensity index was then obtained through torsional dynamics analysis and used as an interpretable mechanism-informed feature. On the data path, a Bayesian-optimized XGBoost (eXtreme Gradient Boosting) mapping model BO-XGBoost was constructed to fuse the measured operating parameters with the mechanism features for nonlinear prediction of three-dimensional vibration of the output-shaft. The verification results based on the test-rig data of the transmission system of the main gearbox demonstrated that the model had a higher prediction accuracy for the three-dimensional vibration of the output-shaft, with the determination coefficient R² being higher than 0.97. Compared with baseline models such as BO-XGBoost, SVR (support vector regression), LSTM (long short-term memory), and GRU (gated recurrent unit), its prediction accuracy was the highest. The research results provide a physically interpretable modeling approach for vibration monitoring and performance evaluation of the transmission system of the helicopter's main gearbox.

As oil and gas exploration extends to deep earth, deep sea and complex geological environments, the conventional exploration equipment struggles to meet exploration demands for efficiency, cost and adaptability. Therefore, the development of drilling robots is extremely urgent. A drilling robot based on connecting rod-slider support structure was designed. A support unit integrating a connecting rod-slider and a support plate, as well as a propulsion unit driven by a lead screw motor, were proposed. The support structure enabled stable radial anchoring and independent attitude adjustment, while the propulsion structure was simple and efficient. Through the static analysis of the robot, the rationality of the structural design was verified. The rigid body dynamics simulation was conducted using the ADAMS software, which revealed the motion posture of the robot in the pipe and the torque characteristics of the steering gears. A prototype was manufactured using 3D printing technology, and performance tests were conducted in a simulated well pipe environment. The experimental results showed that this robot had excellent crawling forward capability. The single movement cycle lasted for 7 s and the step length was 7.98 mm. Its supporting structure was firmly anchored, the maximum anchoring force within the pipe diameter range of 140-155 mm was 96.4 N, and could also remain stable in inclined pipes. The research results have provided new ideas for the design of pipe robots and have positive implications for promoting the intelligent development of underground exploration equipment.

The multi-segment stacked hybrid mechanism consists of multiple parallel segments connected in series, combining the advantages of serial and parallel configurations. However, such mechanisms feature complex structures and exhibit distinct configurations with different numbers of segments, making it difficult to directly obtain unified kinematic position and velocity solutions valid for any number of segments, thereby hindering their design, analysis and optimization. To solve this problem, the multi-segment stacked hybrid mechanism 3-R₁S(RS) _N-1R₂ is selected as the research object, and its forward kinematics modeling and optimal design are carried out based on the finite and instantaneous screw theory. Firstly, the configuration feature of the hybrid mechanism was described, which achieved multi-segment superimposition through a common motion platform and R joints. Meanwhile, the multi-segment motion superimposition and transmission principle of the mechanism was derived using the finite and instantaneous screw theory. Then, the forward kinematics model of a single segment was deduced. By further extension based on the principle of multi-segment motion superposition and transmission, the unified forward kinematics model and velocity Jacobian matrix valid for any number of segments were obtained, and the correctness of the position and velocity models was verified through simulation. Finally, the optimal number of segments was determined by defining standardized equal-weight performance comparison indicator. The multi-objective optimization was conducted using NSGA-Ⅱ (non-dominated sorting genetic algorithm-II), and the optimal solution was selected from the Pareto frontier by using the entropy-weighted TOPSIS (technique for order preference by similarity to an ideal solution) method. The comparison results before and after optimization indicated that the optimal solution improved significantly in four performance indicators, making the optimized hybrid mechanism more suitable for practical engineering applications. The research results provide new ideas for the analysis and optimization of multi-segment stacked hybrid mechanisms.

Aiming at the contradiction between low resistance and large volume in the shape design of autonomous underwater vehicles (AUVs), an optimization design method taking dolphins as bionic objects is explored to enhance the hydrodynamic performance and mission payload capacity of AUVs. Firstly, nine segments of Myring-type curves were used to parameterize and fit the dolphin contour, thereby establishing a three-dimensional AUV geometric model. Based on the Reynolds-averaged Navier-Stokes (RANS) equation and the standard k-ε model, the total resistance and envelope volume of the initial AUV were obtained through computational fluid dynamics (CFD) simulation. Subsequently, the optimal Latin hypercube sampling method was utilized to generate sample points, and Kriging surrogate models describing the mapping relationship between the total resistance and envelope volume of the AUV and design variables were constructed. Finally, with the objectives of minimizing total resistance and maximizing envelope volume, the Pareto optimal solution set was solved using NSGA-II (non-dominated sorting genetic algorithm-II). After optimization, the total resistance of the AUV decreased by 5.74% and the envelope volume increased by 5.87% at a navigation speed of 2 m/s. Flow field simulation analysis indicated that the optimized shape flattened the pressure gradient at the AUV tail, reducing the pressure difference resistance by 12.56%. At the same time, the velocity gradient at the AUV tail decreased, effectively inhibiting boundary layer separation. The resistance composition showed that the reduction in pressure difference resistance was the main reason for the decrease in total resistance. The horizontal stability index G_H>0 indicated that the optimized AUV had dynamic stability. The multi-objective optimization method that integrates parametric modeling, CFD simulation, Kriging surrogate model and NSGA-II provides a reference for the shape optimization of underwater vehicles.

In order to enhance the service performance of epoxy resin dry-type distribution transformer, taking a 10 kV/0.4 kV epoxy resin dry-type distribution transformer as the research object, a structure optimization design method for dry-type distribution transformer based on the multi-objective starfish optimization algorithm (MOSFOA) was proposed. Firstly, a transformer model coupling electromagnetic-thermal-mechanical fields was established for performance simulation. The cross-sectional areas of the high/low voltage winding conductors, the width of the air ducts, and the radius of the core were determined as the core design variables. Subsequently, high-precision surrogate models linking the design variables to the hotspot temperature rise, loss and short-circuit electromagnetic force per unit volume of the high/low voltage windings were constructed using central composite design and response surface methodology. Finally, collaborative optimization based on the MOSFOA was performed to obtain the optimal combination of structural parameters.The resultsindicate that, while maintaining the insulation safe, compared with the initial plan, the hotspot temperature rise, loss, and short-circuit electromagnetic force per unit volume of the high/low voltage windings were reduced by 22.91%, 33.37%, 9.39%, 42.82%, and 45.52% respectively after optimization. This verified the effectiveness of the proposed optimization method.The research results provide a new method for the optimization design of dry-type transformers, and have significant reference value for improving the design efficiency and operational reliability of dry-type transformers.

To address the casting process problem caused by the convergence of the inscribed circle radius of the axial flow fan blade trailing edge contour to zero, the existing blade trailing edge thickening structure is researched, and a new type of blade trailing edge rounding structure is proposed. Through establishing a numerical simulation model of the axial flow fan, the air flow rate, total pressure, impeller power, total pressure efficiency and noise of the fans with different trailing edge structures were analyzed by the computational fluid dynamics method. The results showed that the maximum air flow rate of the fans with different trailing edge structures gradually decreased with the increase of trailing edge thickness. For every 0.5 mm increase in trailing edge thickness, the maximum air flow rate of the fans with thickening structure and rounding structure was reduced by 0.72% and 0.68%, respectively. Under the designed air flow rate, for every 0.5 mm increase in trailing edge thickness, the impeller power of the fans with thickening structure and rounding structure was reduced by 1.92% and 1.87%, the total pressure was decreased by 3.04% and 2.84%, and the total pressure efficiency was reduced by 0.47% and 0.62%. When the thickness l of the thickening structure was 2.5 mm, the noise near the impeller was the minimum, reaching 105.41 dB, which was 0.79 dB lower than that at l=0 mm. When the fillet diameter D of the rounding structure was 3.0 mm, the noise near the impeller was the minimum, reaching 104.91 dB, which was 1.29 dB lower than that at D=0 mm. By quantifying the impact of trailing edge thickness variations on the performance of axial flow fans, the casting process problem of the blade is solved, which provides a reference for the structural design of the blade trailing edge and the optimization of its aerodynamic performance.

Large injection-molded parts are prone to significant warpage deformation during the injection molding process due to their large size and complex rib structures. Traditional methods based on process parameter optimization and empirical structural design are often insufficient to meet the low deformation requirements of these parts. To effectively reduce such warpage deformation, a structural optimization design approach based on the extension innovation is proposed. This approach began by constructing an extension model of the injection-molded part, followed by extension analysis and extension transformation to generate multiple candidate optimization schemes. Then, injection molding simulations were employed to identify the optimal structural scheme. Simulation results showed that the warpage deformation on the bottom surface of the initial injection-molded building formwork was 3.49 mm, which was reduced to 2.82 mm after optimization using the extension innovation method. Final trial mold validation results demonstrated that the maximum relative error between the simulated value and measured average value of warpage deformation for the optimized injection-molded building formwork was only 5.6%. The extension innovation-based structural optimization design approach enables efficient identification of structural defects and rapid generation of optimized solutions, providing novel optimization insights and practical references for the low warpage design and manufacturing of large injection-molded parts.

To enhance the energy density of high-power vanadium flow battery stacks and address the issues of excessive structural mass coupled with the absence of efficient and precise design methods for end plates, a design method for the end plate of vanadium battery flow stacks is proposed, which integrates deformation prediction, lightweight design and manufacturing process constraints. Firstly, a theoretical method for calculating end plate pressure loads was proposed, and the numerical analysis models for springs and studs were constructed. Then, a numerical simulation method for predicting mechanical deformation of the end plate was established by integrating the complete assembly processes, including pressure loading, spring pre-tightening, stud locking and pressure unloading. Experimental results validated that this method had high prediction accuracy. Subsequently, by introducing manufacturing process and assembly displacement constraints, a topology optimization method enabling efficient iterative computation for lightweight end plate design was developed based on the variable density method. The optimized end plate yielded a 44.00% decrease in mass, maintained stress within the strength requirements, and exhibited a more uniform displacement distribution. Finally, the spatial distribution law of stud preload and the influence law of end plate configuration on stud preload and spring compression deformation were revealed. The results showed that the closer the distance to the end plate symmetry center, the greater the stud preload, with the maximum variation rate being 36.06%; the end plate configuration had a smaller influence on the spring compression deformation. The proposed method can achieve efficient lightweight design of vanadium flow battery stack end plates while meeting strength and stiffness requirements, which has significant engineering application value.

To meet the high-reliability demands of the gear transmission systems in aero-engine blade rolling mills, multi-dimensional simulation and optimization methods were employed to improve the comprehensive performance. A virtual gear transmission system model was built using Romax software, and the high-load gears Z₃ and Z₆ were identified by combining static and dynamic analyses. Internal excitation analysis revealed the gear meshing impact characteristics, and a tooth surface flash temperature model was built based on Blok theory to obtain the temperature distribution along the tooth surface distance and the rolling angle. The second-generation genetic algorithm was used to optimize the composite modification of high-load gears. The results showed that after modification, the contact stress distribution on the tooth surface was improved from a step-like pattern to a uniform arch-shaped pattern, with the maximum stress reduced by 20.47%-44.94%. The fluctuation range of transmission error was reduced from 11.97-14.56 μm to 2.26-4.53 μm, and the amplitude was reduced by 68.89%-84.15%. The maximum tooth surface temperature decreased by 5.3%-13.18%, and the thermal concentration phenomenon was significantly alleviated. This study demonstrates that composite modification can synergistically optimize the mechanical, kinematic, and thermodynamic performance of gear transmission systems, providing a theoretical basis for the reliability design of high-precision gears in aviation rolling mills.

RV (rotate vector) reducers are widely used in complex mechanical systems such as robots. Their reliability directly affects the performance and service life of the entire system, and reliability assessment serves as a crucial foundation for reliability design and optimization. Currently, the reliability assessment for RV reducers is usually based on probability theory. However, due to the complexity of mechanical systems and the multiple sources of factors influencing reliability, it is difficult to obtain the probability distributions of all uncertain factors in practical engineering. Relying solely on probability theory makes it hard to ensure the accuracy of reliability assessment results. To address this issue, the probability-interval hybrid uncertainty theory was innovatively introduced into the reliability assessment of RV reducers. Based on the stress-strength interference theory, the multi-component failure criteria for RV reducers were established, and a new reliability assessment method for RV reducers was proposed. Specifically, a double-layer nested loop solution framework was established to solve the problem of probability-interval hybrid reliability calculation. To tackle the low efficiency of multi-dimensional hybrid reliability calculation, the modified chaos control method and the multiplicative dimensionality reduction method were respectively adopted to improve the solution efficiency of probabilistic reliability and interval uncertainty. The results of numerical examples showed that the characterization method of uncertain factors had a significant impact on the reliability assessment results. Using probability-interval hybrid uncertain parameters to describe the uncertain factors was more in line with the actual service reliability of RV reducers. The proposed method provides a new approach for the reliability evaluation of RV reducers, which can offer valuable support for the reliability design and optimization of complex equipment.

In order to improve the load-bearing capacity and mobility of permanent magnet adhesion wall-climbing robots on ferromagnetic surfaces, a bilateral Halbach permanent magnet wheel structure is proposed, aiming to address the issues of non-adjustable adhesion force, low overall adhesion force, and large adhesion force fluctuations during circumferential rotation of traditional unilateral Halbach permanent magnet wheels. This structure achieved the enhancement of adhesion performance and effectively suppressed adhesion force fluctuations through the collaborative action of the inner and outer permanent magnet wheels. Based on the theory of magnetic dipoles and the Maxwell stress tensor method, an adhesion force theoretical model of the bilateral Halbach permanent magnet wheel was constructed and its accuracy was verified through the finite element analysis. Meanwhile, parameter indicators for evaluating the adhesion performance of the permanent magnet wheel were established. The influence of the relative rotation angle between the inner and outer permanent magnet wheels on the adhesion force regulation effect was analyzed in detail, and the relative rotation angle was optimized. Finally, an experimental platform for testing permanent magnet wheel adhesion performance was built and the practical research was conducted. The results showed that when the relative rotation angle of the bilateral Halbach permanent magnet wheel was 35°, its average adhesion force reached 187.955 N, and the adhesion force fluctuation coefficient dropped to 0.057. Compared with the traditional unilateral permanent magnet wheel structure, its adhesion force increased by 114.004%, and the adhesion force fluctuation coefficient was reduced by 89.366%. The study demonstrates that the bilateral Halbach permanent magnet wheel structure achieves significant enhancement and controllable adjustment of adhesion performance through the regulation of relative rotation angles, providing theoretical support and practical guidance for the design of high-performance permanent magnet adhesion systems, which is of great significance for improving the reliability and safety of industrial automation equipment.

Aiming at the problems of low snow removal efficiency, slow operating speed and inability to throw snow beyond guardrails of the existing highway snow removal equipment, a theoretical model of the high-speed snow removal operation for the snow plow blade is established, and the working parameters during the operation are optimized and analyzed based on the explicit dynamics and discrete element method. Firstly, a simplified snow removal model of the snow plow blade was built using SolidWorks software, and explicit dynamics simulation and discrete element simulation were carried out by ANSYS/LS-DYNA and Rocky DEM softwares to simulate the crushing and throwing of snow by the snow plow blade. The equivalent stress, throwing height and throwing displacement of the snow were obtained. Subsequently, the theoretical analysis and simulation results were fitted and converted into visualized results by MATLAB software, from which the optimal ranges of cutting angle, travel angle and travel speed of the snow plow blade when removing snow were obtained. The simulation results showed that the optimal cutting angle range of the snow plow blade was 35°-36°, the optimal travel angle range was 51°-52° and the optimal travel speed range was 28-29 m/s. Simulated operations under these conditions yielded a minimum snow throwing height of 1.360 m and a minimum snow throwing displacement of 11.700 m, which were 0.160 m and 1.490 m higher than the pre-optimization values, respectively, meeting the industry standards for snow throwing of the snow plow blade. Finally, the response surfaces were obtained by fitting the snow removal test data of the snow plow blade with Design-Expert software, and it was observed that the pairwise interactions among the cutting angle, travel angle and travel speed were significant. To achieve better snow removal effect, a parameter combination of cutting angle of 36°, travel angle of 52° and travel speed of 28.5 m/s was selected for the snow removal test. The test results showed that the snow throwing height of the snow plow blade was 1.450 m and the snow throwing displacement was 12.600 m, which met the requirements of highway snow removal and verified the reliability of the theoretical analysis and simulation results. The research results provide a reference for the optimization design of the structural parameters of highway snow plow blades.