Storm rainfall patterns are critical to infrastructure flood control, but the main design methods are hindered by requiring high resolution data and ignoring the impacts of climate change. Taking the typical small and medium-sized river basin (Liudong River Basin) with relatively high flood control pressure in the Karst area of southwest China over the years as an example, based on the Gamma distribution with flexible distribution characteristics and wide application, and using the duration data of the maximum 24-hour rainstorm in the past 60 years, the model performance was evaluated, the parameters of the model were determined, and the evolution characteristics of the parameters were analyzed. Based on the above results, the changing trends of rain pattern uncertainty, complexity and predictability were calculated. The results show that the model has high accuracy, and the average correlation coefficient is greater than 0.92. The evolution trend of the model parameters indicates that under the background of climate change, the rain pattern of the maximum 24-hour rainstorm shows the characteristics of decreasing shape factor and increasing scale factor. The uncertainty of rain patterns has generally increased, and the complexity and predictability show high spatial heterogeneity. The research results will provide references for optimizing the rainstorm rain pattern model and analyzing its dynamic evolution, thereby better preventing and controlling flood disasters, especially in Karst areas with high precipitation uncertainty and complex underlying surface conditions.

The cathode flow channel of proton exchange membrane fuel cell (PEMFC) serves as the site of oxidant reduction, and the interaction of the flow channel configuration and operating parameters is one of the keys to enhance the performance of the cell. A three-dimensional proton exchange membrane cell model with cathode sidewall shrinkage runner was established to meet the design requirements of PEMFC sidewall shrinkage runner regarding the operating parameters. The changing rules of electrochemical performance, temperature distribution on the membrane surface and water content distribution were investigated under different temperatures, pressures and cathode stoichiometric ratios. It is shown that under the constant operating parameters, the current density curves of the ridge centerline and the flow channel centerline are impulsively fluctuated, and the temperature curves of the membrane surface and the water content curves of the membrane surface are regularly fluctuated. The current density, temperature and water content at the ridge centerline are obviously higher than those at the flow channel centerline. Under the change of operating parameters, when the pressure is increased from 0.1 MPa to 0.3 MPa, the current density is increased from 0.860 A/cm² to 1.500 A/cm², with an increase of 74.4%. When the temperature is increased from 50 ℃ to 80 ℃, the current density is increased from 0.822 A/cm² to 0.856 A/cm², with an increase of 4.1%. And when the cathode stoichiometry ratio is increased from 10 to 90, the current density is increased from 1.502 A/cm² to 1.568 A/cm², with an increase of 4.4%. Furthermore, a PEMFC output performance evaluation method based on the combined assignment method and the improved radar diagram method has been established. The cathode sidewall-retracted proton exchange membrane fuel cell is shown to exhibit excellent output performance under the operating parameters of 0.25~0.3 MPa, 70~80 ℃ and the stoichiometric ratio in the range of 70~90.

As the trend toward more-electric and all-electric aircraft accelerates, multi-electric engines have become a key technology. A coaxial high-torque permanent magnet synchronous motor based on a new type of rotary cylinder disc engine was designed to achieve the integration of the engine and the motor. Firstly, based on the relationship between the engine performance parameters and the drive shaft, the coaxial structure and motor dimensions were determined. Secondly, the inhibitory effect of the number and size of the flat wire winding layers on copper loss was analyzed through the finite element soft analysis. Meanwhile, the motor topology was optimized by using the rotor segmented inclined pole, auxiliary slot and Taguchi algorithm to reduce the cog-slot torque, rated torque ripple, stator iron loss and air-gap magnetic flux density distortion rate of the motor, significantly improving the electromagnetic performance. Finally, the various working conditions of the motor were simulated, the driving and power generation efficiencies were calculated, and it is verified that the motor does not demagnetize under the condition of large current. Results indicate the motor delivers 200 N·m of torque at a rated speed of 6 000 r/min, with a peak torque of 400 N·m and a maximum power output of 250 kW in high-speed generation mode, meeting all design specifications.

Improper tunnel blasting parameters will seriously affect the safety and quality of tunnel construction. Therefore, the determination of appropriate blasting parameters is an important work in tunnel construction. In order to solve this problem, based on deep learning model-whale optimization deep belief network (WO-DBN) and multi-objective optimization algorithm-non-dominated sorting genetic algorithm II (NSGA-II), an intelligent algorithm for tunnel blasting parameters optimization was proposed. Firstly, using the developed deep learning model WO-DBN, an intelligent model for predicting the safety and quality of tunnel blasting construction based on geological parameters and blasting parameters was constructed. The tunnel crown subsidence and overbreak and underbreak area were taken as the index of construction safety and quality evaluation. Secondly, based on the established tunnel blasting construction safety and quality evaluation model, an intelligent algorithm for tunnel blasting parameter optimization was proposed by using NSGA-II to control crown subsidence, overbreak and underbreak area. Finally, taking the blasting construction of Panlongshan highway tunnel as an example, the proposed new algorithm was verified by engineering application. The results show that the construction parameters obtained by the new algorithm can reduce the tunnel crown subsidence and the overbreak and underbreak area by 27.05% and 60.30%, respectively, and the construction effect is greatly improved. Therefore, the proposed intelligent algorithm can provide technical support for the real-time optimization control of tunnel blasting parameters and provide a strong guarantee for the smooth progress of tunnel construction.

In order to solve the defects of poor water stability and easy disintegration of red clay, industrial solid waste [fly ash (FA), phosphogypsum] combined with cement (C) was used to improve red clay. The mechanical properties, water stability and micro-mechanisms of the industrial solid waste-cement amended red clay were investigated through indoor tests. The results show that the strength of the improved soil shows a trend of increasing and then decreasing with the increase of the ratio (R) of phosphogypsum replacing fly ash.When the cement doping is 7% and R=7%, the maximum dry density of improved soil increases by 2.6%, the 7-day unconfined compressive strength (UCS) increases by 11%, and the 28-day UCS increases by 57%, which meets the bearing standard of subgrade for road use. There is no obvious change in the water-filled specimen after the 7-day maintenance, the resistance to disintegration is enhanced, and the water stability coefficient of the specimen reaches 92.9% in the 28-day maintenance. The water stability coefficient of the specimen reaches 92.9%, and the water stability coefficient increases 1.61 times. The microscopic analysis shows that the replacement of fly ash by phosphogypsum promoted the generation of new hydration products of ettringite and calcium-silicate-hydrate (C-S-H), which transforms the soil body from fragmented granular to a denser gel network structure, enhances the bonding between the red clay particles, and fills up the pore space at the same time. The results verifies the feasibility of industrial solid waste-cement-amended red clay as roadbed fill, provides a solid theoretical foundation and basis for engineering practice, and broadened the reuse of industrial solid waste.

To improve the utilization rate of waste clay, a composite curing agent composed of cement, alkali excitation agent, siliceous solid waste material and sulfate solid waste material was used to cure a waste clay. Unconfined compressive strength test (UCS), water stability test, X-ray diffraction test and scanning electron microscope(SEM) test were carried out to capture the mechanical behavior and reinforcing mechanism of the solidified clay. The results show that there is a significant solidification effect on the clay when the dosage of curing agent is from 10% to 12%, and the water stability coefficient of solidified clay is larger than 80%. The UCS of the solidified clay with 12% curing agent content is 2.45 MPa after curing for 28 days, which is 2.33 times the UCS of the solidified clay at a curing time of 7 days (1.05 MPa). A large number of hydration products such as calcium silicate (aluminate) hydrate and ettringite produced in the solidified clay can improve the performance of solidified soil by improving the micro-pore structure of soil and strengthening the cementation between aggregates. The present research can provide reference for the resource utilization of waste clay.

Urban roads are recognized to facilitate daily human activities while simultaneously being observed to shape behavioral patterns. Currently, most studies simply analyze the relationship between roads and crime distribution as part of the built environment, and few studies have thoroughly explored the impact of different structural attributes of roads on crime. In order to further explore this impact mechanism and provide guidance for the optimization of police resource allocation by front-line police departments. The operational mechanisms were investigated by which geometric and topological road attributes influence theft distribution across varied transportation modalities. Using crime data from YC District, JB City, spatial crime hotspots and high-risk road segments were initially identified through geospatial analysis. Next, the road structure was systematically decomposed into two distinct dimensions: geometric attributes and topological properties. A space syntax segment model was created to quantitatively assess geometric characteristics and traffic modality-specific topological configurations. Finally, Statistical relationships were investigated using zero-inflated negative binomial regression complemented by multiple linear regression modeling. Research has found that theft incidence is demonstrated to be positively associated with segment length, angular curvature metrics, and branch road density within the network. Elevated crime probabilities are observed in extended roadway segments exhibiting complex geometric configurations and regions characterized by dense branch road networks. The closeness and betweenness of the road's topological structure exert varying significant effects on theft crime across different traffic modes. Specifically, in pedestrian traffic modes, a significant negative correlation is observed between road closeness and betweenness and the occurrence of theft crime. In bicycle traffic modes, road closeness and betweenness are found to have a positive effect on theft crime. In electric vehicle and motor vehicle traffic modes, road closeness have a positive effect on theft crime, while in electric vehicle traffic mode, betweenness was found to have a negative effect on theft crime. These findings contribute novel insights to environmental criminology theory while offering empirically grounded recommendations for strategic police deployment and urban security management.

The complex diagenetic facies of the tight sandstone reservoir in the Shaximiao Formation, located in the Jinqiu gas field to Tianfu gas area in the central Sichuan region, pose significant challenges to reservoir evaluation and natural gas exploration and development. Traditional diagenetic facies identification methods are often low in accuracy, heavily reliant on specialized personnel, and time-consuming. There is an urgent need for a diagenetic facies identification method that is highly accurate, cost-effective, and fast. Firstly, based on cast thin section identification data, the lithology of the tight sandstone was determined using a ternary plot of components. Image processing techniques were then used to identify the types and proportions of pores and cements, and the diagenetic facies of the tight sandstone were classified. Secondly, the corresponding 1 019 depth-based well log data for core-divided diagenetic facies were analyzed in terms of distribution range, median, uniformity, and skewness. These 6 types of well log data were standardized to a 0-1 range, and data imbalance was addressed using synthetic minority over-sampling technique (SMOTE). Finally, 10 traditional machine learning algorithms and ensemble learning algorithms were selected for model training and performance comparison. The study found that ensemble learning algorithms, especially the extreme randomized trees (ET) algorithm, performs best in diagenetic facies identification, achieving higher accuracy and F₁ scores than traditional machine learning algorithms. This significantly improved identification accuracy and stability. The ET model was then used to predict the diagenetic facies of the JQ8 well, validating the feasibility of the method. This study provides effective technical methods and references for diagenetic facies research in tight sandstones.

The plunger pump is one of the important power conversion components of the hydraulic system, and its performance directly affects the safety and stability of the hydraulic system. In order to accurately evaluate the operating status of the plunger pump, a plunger pump health status assessment method based on a combination of convolutional neural network(CNN) and long short-term memory network(LSTM) was proposed, and a genetic algorithm was introduced to optimize the parameters of the neural network. The vibration signals of the plunger pump at different operating moments were collected. The energy characteristics of the vibration signals were extracted by using wavelet packets. Combined with the time-frequency domain characteristics of the signals, a dataset of the health status characteristics of the plunger pump was constructed. The health status was identified and classified by the CNN-LSTM method, and the classification results were evaluated by sample entropy. To verify the effectiveness of this health assessment method, it was applied to the experimental test of the plunger pump. The results show that the recognition accuracy of this method reaches 99%, which can effectively improve the accuracy of the health status assessment of the plunger pump.

In order to guide the optimal design and daily maintenance of explosive capacity bombs, the response characteristics of 20 L explosive capacity bombs under the explosion action of explosives were studied. Internal explosion tests of different masses of trinitrotoluene (TNT) explosive capacity bombs were designed. The strain distribution and variation law of the outer wall of the explosive capacity bomb were measured, and the internal explosion impact overpressure of the explosive capacity bomb was obtained. According to the test results, the equal-scale simulation calculation model was calibrated, and the propagation law of shock wave inside the explosive capacitor bomb after the explosive explosion was calculated, and the pressure distribution in the explosion field was obtained. The results show that the simulation results of internal pressure of explosive charge are basically consistent with the experimental results. Under the action of explosive explosion, the internal pressure of explosive capacitor presents a complex structure of multi-peaks, the maximum pressure appears at the corner of explosive capacitor, and the maximum strain of the outer wall of explosive capacitor appears at the lower position of the explosion center plane.