A geomagnetic storm is a periodic natural disaster in which the changing geomagnetic field can induce an induced geoelectric field. A geomagnetic induced current (GIC) loop is formed between the transmission line and the earth conductor through the neutral points of grounding transformers. GIC seriously threatens the safe and stable operation of extra-high and ultra-high voltage AC transmission systems. There are many types of terrain and complex structures in our country, which makes the influence of geological landforms on induced geoelectric fields very significant. A finite element calculation method for GIC was proposed based on a three-dimensional earth conductivity model to address the difficulties in modeling and calculating GIC. Firstly, a three-dimensional earth conductivity model was established considering the anisotropy of geological structures. Meanwhile, a calculation model for electromagnetic field penetration depth under multi-layer geological conditions was given. Secondly, a mathematical model based on time-varying electromagnetic fields was established. Combined with the topology of the power grid, an equivalent calculation model for the power grid GIC was derived. Finally, taking the Shache-Turpan 750 kV transmission line in Xinjiang area as an example, a corresponding physical model was built in COMSOL Multiphysics finite element simulation software. The three-dimensional distribution of the induced ground electric field in the power grid was obtained through geometric modeling, boundary condition setting, grid division, and iterative solution. Furthermore, the GIC flowing through the neutral point of the 750 kV transformer was obtained. The research results indicatethat the overall level of GIC obtained by the 3D model is higher than that of the 2D model. Besides, the 3D model considers the geometric angle between the transmission line and different terrains, which can provide a more detailed distribution of induced geoelectric fields. The research results verify the effectiveness of the method proposed, which provides a reference basis for scientific planning of ultra-high and ultra-high voltage transmission corridors.

In recent years, digital class D power amplifiers have attracted widespread attention in the audio electronics field due to their high efficiency and seamless integration with digital audio sources. As one of the crucial digital signal processing modules in digital class D amplifiers, the Sigma-Delta modulator plays a pivotal role for digital audio signal processing. The noise-shaping characteristic of the Sigma-Delta modulator can reduce the implementation cost of the power amplifier system while maintaining or even improving the output signal-to-noise ratio of the system, and can suppress the noise introduced by some signal transmission paths. Firstly, the working principle and mainstream architecture of digital D-class power amplifiers were summarized. Then, based on the basic principle of Sigma-Delta modulators, the design schemes of Sigma-Delta modulators used in digital D-class power amplifiers in recent years were discussed, with a focus on the architecture design and noise transfer function design of Sigma-Delta modulators. Finally, the research and development of Sigma-Delta modulators for digital D-class power amplifiers were summarized.

During growth, crop stalks are prone to bending deformation, posing challenges for computer visualization simulation. A differential analysis method was employed to investigate the physical stress-strain relationship of stalks, and a visualization simulation method was proposed for flexible stalks. Firstly, a mechanical model of the stalk under tensile, bending, shear, and torsional loads was established. Secondly, a geometric model based on a semi-structural approach and surface modeling was constructed. Finally, C++ and OpenGL were utilized to implement the visualization simulation. Experimental analysis of the bending stress-strain characteristics of stalks from different varieties was conducted. The results demonstrate that this method can relatively accurately simulate the deformation process of stalks. The physics-based model ensures the accuracy of bending simulation, providing a novel informatics analysis tool for selecting and breeding lodging-resistant crop varieties.

Aiming at the problem of effective extraction and identification of rolling bearing fault information in complex environments, a fault diagnosis method for rolling bearings based on feature mode decomposition (FMD) combined with multiscale fuzzy dispersion entropy (MFDE) and zebra optimization algorithm (ZOA) optimization support vector machine was proposed. In order to solve the problem that the key parameters in FMD are not adaptive, the minimum envelope entropy was used as the objective function, and the beluga whale optimization (BWO) was used to optimize FMD to find the optimal parameter combination to achieve the optimal decomposition of fault signals. Multiscale fuzzy dispersion entropy was introduced to construct the eigenvectors under different modes after decomposition. Finally, the feature vectors were input into the support vector machine for training and recognition. The effectiveness of the proposed method was verified by the public dataset and the self-made experimental platform dataset.

The effect of water flooding development in low permeability reservoir is generally poor, and CO₂ flooding can effectively improve the degree of crude oil recovery. In order to analyze various factors affecting the effect of CO₂-assisted gravity flooding, based on the research mechanism of gas-injection-assisted gravity flooding, a mechanism model of one injection and one production was established to analyze the influence law of geological factors and development factors, and the orthogonal experimental design method and Shapley value method were used for multi-factor analysis. The results show that the positive rhythm reservoir is more suitable for CO₂-assisted gravity flooding. Reservoir dip angle, average permeability and gas injection velocity are positively correlated with development effect. The ratio of vertical permeability to horizontal permeability and the difference of permeability stage were negatively correlated with the development effect. If the ratio of gas injection and dimensionless horizontal well section length is set to 0.8, the CO₂-assisted gravity flooding effect can be effectively improved. The ratio of vertical permeability to horizontal permeability has the highest influence on CO₂-assisted gravity flooding. The research results provide technical guidance and theoretical support for improving the development effect of CO₂-assisted gravity flooding in low permeability reservoirs.

In order to accurately predict the water richness of the weathered bedrock aquifer, 28 groups of weathered bedrock pumping test borehole data in Zhangjimao minefield were used as training and verification samples, and the lithology combination index, weathering index, thickness, core recovery rate and burial depth of the weathered bedrock were selected as evaluation indexes. Based on whale optimization algorithm-support vector machines (WOA-SVM), a water-rich identification model for weathering bedrock aquifers was proposed. This model can predict the water-rich grade of the weathered bedrock in the area without pumping test data, and realize water-rich zoning of the weathered bedrock in the well field by comprehensive use of the geological information of 249 exploration boreholes. The study shows that the weathered bedrock of Zhangjiamao minefield is weakly water-rich as a whole, and its spatial distribution is uneven. There are strong water-rich areas in the central part of the field and the local area along Wulanbula Gully, but their distribution range is small, there are some moderately water-rich areas in the central-western and southeastern parts, and the northeastern and southwestern areas are weakly and very weakly water-rich almost all the time. The results predicted are more in line with the actual situation, and the research results can provide a reference for the safe production of the mine and a new way of thinking for the prediction of the water-richness of the weathered bedrock.

In order to investigate the properties of seawater mixing alkali-activated materials, the development law of hydration reaction, mechanics and corrosion resistance of seawater alkali-activated materials based on multi-component composite cementitious materials was studied. The results show that seawater mixing has a certain inhibitory effect on the hydration reaction of alkali-activated slag, and the compressive strength of SLCM at different ages also shows a decreasing trend to a certain extent, which is not conducive to the development of strength. Fly ash and silica fume can reduce the hydration reaction rate, early strength and toughness of the seawater alkali-activated materials, but their strength and toughness increase potential is significant in the later period, in which the strength and toughness growth rate of the silica-fly ash-slag terpolymer system from 7 days to 28 days reaches 50.9% and 86.7%, respectively. Compared with alkali-activated slag, adding fly ash and silica fume can improve the electric flux permeability and chloride ion mobility coefficient of alkali excited materials in seawater to a certain extent, which is consistent with their microstructure, but the three still belong to the same chloride ion permeability grade, i.e., medium permeability grade (electric flux method).

Aiming at the problem of short effective prediction time for the movement history of amphibious aircraft in waves, the statistical values of amphibious aircraft movement over a period of time were proposed to predict, and a prediction model for the statistical characteristics of amphibious aircraft movement was constructed based on long short-term memory neural networks(LSTM). Taking the NACA TN 2929 amphibious aircraft as an example, based on its numerical simulation data, the statistical values of the three degrees of freedom motion of heave, roll, and pitch of amphibious aircraft under sea conditions of level 3, 4, and 5 were predicted, and their prediction effects were analyzed in detail. The results show that the LSTM neural network-based model for predicting the statistical characteristics of amphibious aircraft motion has good prediction accuracy. In practical engineering applications, this model can accurately predict the statistical values of amphibious aircraft motion in the future, providing auxiliary decision-making information for offshore operations.

In order to efficiently and accurately predict freight forwarders’ transportation modes preferences between China and Europe during major emergencies, as well as to uncover the relevant factors influencing freight forwarders’ choices, the stated preference method was employed to survey freight forwarders. Additionally, considering the influences of transportation and cargo attributes, decision trees, logistic regressions, and random forest prediction models were constructed to forecast the selection behavior of freight forwarders. The prediction results of the machine learning model and the discrete choice model were comprehensively compared through four evaluation metrics: accuracy, precision, recall, and F₁ score. Furthermore, the random forest algorithm was utilized to rank the importance of attributes influencing freight forwarders’ transportation mode choices during different stages of the pandemic. The study results demonstrate that the prediction accuracy of all three machine learning models is higher than that of the discrete choice model. Among them, the random forest model exhibits superior prediction accuracy compared to the decision tree and logistic regression models in addressing the choice of Sino-Europe container transport modes, making it more suitable for this problem. Regarding influencing factors, during stable periods, cargo attributes are identified as the most important factors. When major emergencies occur, freight forwarders place greater emphasis on the threshold delay time. Furthermore, the destination and value of the cargo are found to have significant impacts on the choice of Sino-Europe container transport modes. The study proposes an accurate analysis of the decision-making mechanisms guiding freight forwarders’ mode choice behavior during major global emergencies. Furthermore, it is utilized by shipping companies and operators of the China Railway Express to gain a deeper understanding of the preferences and decision-making factors influencing freight forwarders. The insights derived from this study are considered a solid basis for effectively responding to similar emergency situations.

In order to study the mechanical properties of negative Poisson’s ratio(NPR) anchor cable under static tension and drop hammer impact load, the self-developed NPR anchor cable tension test system and NPR anchor cable drop hammer impact test system were used to conduct static tensile test and dynamic impact test for a certain batch of NPR anchor cables. Its resistance and absorption of slow deformation and instantaneous impact energy of surrounding rock were verified by the supporting force, elongation and expansion of anchor cable. Secondly, a 3D numerical model of NPR anchor cable was established, and relevant numerical parameters were calibrated according to laboratory test results. The static tensile test of NPR anchor cable and the drop hammer impact test were carried out. The results of the numerical test are in good agreement with those of the laboratory test, which verifies the reliability of the numerical model. The 3D numerical model of NPR anchor cable can be used as an auxiliary analysis tool for the improvement and upgrading of NPR anchor cable in the future.