An Implicit Coupled 1D/2D Model for Unsteady Subcritical Flow in Channel Networks and Embayment

In this study, 1D and 2D shallow-water models were coupled to simulate unsteady flow in channel networks and embayment. The 1D model solved the 1D shallow-water equations (St. Venant) using the Preissmann box method and targeted long narrow reaches of the river networks, while the 2D model targeted broad channels and embayment and solved the 2D shallow-water equations using a semi-implicit scheme applied to an unstructured grid of triangular cells. The 1D and 2D models were solved simultaneously by building a matrix for the free surface elevation at every 1D junction and 2D cell center. Velocities were then computed explicitly based on the results at the previous time step and the updated water level. The originality of the scheme arose from a novel coupling method. The results showed that the coupled 1D/2D model produced identical results as the full 2D model in classical to benchmark problems with considerable savings in computational effort. Application of the model to the Pearl River Estuary in southern China showed that complex patterns of tidal wave propagation could be efficiently modeled.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Artificial Neural Network Model for Optical Fiber Direction Coupler Design

A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time saving. So it is promising in the field of both investigation and application.

Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

Modeling of the Coupling of the Lightning Shock Wave Flow Circuit in the High Voltage Electrical Network

This article focuses on the aggression of lightning overload on the equipment of the electrical network of sites where storm activity is very dense;and the electrocution of people located in the direct environment of the high-voltage substation during the flow of lightning current to the ground through the ground socket. The modeling of the flow circuit of the shock wave consisting of guard wire, lightning arrester and ground socket couple to the transformer of the high voltage substations, thanks to the approach of a servo block, led to the synthesis of a PID regulator (corrector) whose action is to reject the effects of the overvoltage on the network equipment and to significantly reduce or even cancel the effects of the step or touch voltage due to the distribution of the potential around the ground socket;and thus improve the quality of service of the high-voltage transmission and distribution electricity network, especially in stormy times.

Reluctance Network Analysis for Complex Coupled Inductors

The use of reluctance networks has been a conventional practice to analyze transformer structures. Basic transformer structures can be well analyzed by using the magnetic-electric analogues discovered by Heaviside in the 19th century. However, as power transformer structures are getting more complex today, it has been recognized that changing transformer structures cannot be accurately analyzed using the current reluctance network methods. This paper presents a novel method in which the magnetic reluctance network or arbitrary complexity and the surrounding electrical networks can be analyzed as a single network. The method presented provides a straightforward mapping table for systematically linking the electric lumped elements to magnetic circuit elements. The methodology is validated by analyzing several practical transformer structures. The proposed method allows the analysis of coupled inductor of any complexity, linear or non-linear.

COUPLING MODEL OF EXTENDED MANUFACTURING ORGANIZATION AND ITS APPLICATION

For the feature of complex weapon manufacturing on internet,a coupling model is proposed.By using the model,the correlation between manufacturing cells in an extended manufacturing organization can be evaluated quantitatively,so an appropriate control plan is determined.A strategy to improve and reduce the coupling relationship of the organization is studied.A correlation matrix of extended tasks is built to analyze the relationship between sub-tasks and manufacturing resources.An optimization method for manufacturing resource configuration is presented based on the coupling model.Finally,a software system for analyzing coupling model about manufacturing organization on internet is developed,and the result shows that the coupling model is effective.

Characteristic analysis of mechanical thermal coupling model for bearing rotor system of high-speed train

Based on Newton’s second law and the thermal network method,a mechanical thermal coupling model of the bearing rotor system of high-speed trains is established to study the interaction between the bearing vibration and temperature.The influence of lubrication on the vibration and temperature characteristics of the system is considered in the model,and the real-time relationship between them is built up by using the transient temperature field model.After considering the lubrication,the bearing outer ring vibration acceleration and node temperature considering grease are lower,which shows the necessity of adding the lubrication model.The corresponding experiments for characteristics of vibration and temperature of the model are respectively conducted.In the envelope spectrum obtained from the simulation signal and the experimental signal,the frequency values corresponding to the peaks are close to the theoretical calculation results,and the error is very small.In the three stages of the temperature characteristic experiment,the node temperature change of the simulation model is consistent with the experiment.The good agreement between simulation and experiments proves the effectiveness of the model.By studying the influence of the bearing angular and fault size on the system node temperature,as well as the change law of bearing lubrication characteristics and temperature,it is found that the worse the working condition is,the higher the temperature is.When the ambient temperature is low,the viscosity of grease increases,and the oil film becomes thicker,which increases the sliding probability of the rolling element,thus affecting the normal operation of the bearing,which explains the phenomenon of frequent bearing faults of high-speed trains in the low-temperature area of Northeast China.Further analysis shows that faults often occur in the early stage of train operation in the low-temperature environment.

Right on time:measuring Kuramoto model coupling from a survey of wrist-watches

Using a survey of wrist-watch synchronization from a randomly selected group of independent volunteers, we model the system as a Kuramoto-type coupled oscillator network. Based on the phase data both the order parameter and likely size of the coupling are derived and the possibilities for similar research to deduce topology from dynamics are discussed.

Coupling Singular Spectrum Analysis with Artificial Neural Network to Improve Accuracy of Sediment Load Prediction

Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint regions. This research attempts to combine SSA (singular spectrum analysis) with ANN, hereafter called SSA-ANN model, with expectation to improve the accuracy of sediment load predicted by the existing ANN approach. Two different catchments located in the Lower Mekong Basin (LMB) were selected for the study and the model performance was measured by several statistical indices. In comparing with ANN, the proposed SSA-ANN model shows its better performance repeatedly in both catchments. In validation stage, SSA-ANN is superior for larger Nash-Sutcliffe Efficiency about 24% in Ban Nong Kiang catchment and 7% in Nam Mae Pun Luang catchment. Other statistical measures of SSA-ANN are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation. This coupled model is also recommended for the prediction of other water resources variables because extra input data are not required. Only additional computation, time series decomposition, is needed. The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the LMB which is relatively rich in hydrometeorological records.

Nonlinear Control of Magnetically Coupled Rodless Cylinder Position Servo System

Magnetically coupled rodless cylinders are widely used in the coordinate positioning of mechanical arms,electro-static paintings,and other industrial applications.However,they exhibit strong nonlinear characteristics,which lead to low servo control accuracy.In this study,a mass-flow equation through the valve port was derived to improve the control performance,considering the characteristics of the dynamics and throttle-hole flow.Subsequently,a fric-tion model combining static,viscous,and Coulomb friction with a zero-velocity interval was proposed.In addition,energy and dynamic models were set for the experimental investigation of the magnetically coupled rodless cylin-der.A nonlinear mathematical model for the position of the magnetically coupled rodless cylinder was proposed.An incremental PID controller was designed for the magnetically coupled rodless cylinder to control this system,and the PID parameters were adjusted online using RBF neural network.The response results of the PID parameters based on the RBF neural network were compared with those of the traditional incremental PID control,which proved the superiority of the optimization control algorithm of the incremental PID parameters based on the RBF neural network servo control system.The experimental results of this model were compared with the simulation results.The average error between the established model and the actual system was 0.005175054(m),which was approximately 2.588%of the total travel length,demonstrating the accuracy of the theoretical model.

A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network

The power module of the Insulated Gate Bipolar Transistor(IGBT)is the core component of the traction transmission system of high-speed trains.The module's junction temperature is a critical factor in determining device reliability.Existing temperature monitoring methods based on the electro-thermal coupling model have limitations,such as ignoring device interactions and high computational complexity.To address these issues,an analysis of the parameters influencing IGBT failure is conducted,and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory(MMALSTM)recursive neural network is proposed,which takes the forward voltage drop and collector current as features.Compared with the traditional electricalthermal coupling model method,it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process.The simulation model of a highspeed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature.The simulation outcomes,which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model,confirm the reliability of this approach for predicting the temperature of IGBT power modules.

Mathematical Modeling and Control Algorithm Development for Bidirectional Power Flow in CCS-CNT System

As the demand for more efficient and adaptable power distribution systems intensifies, especially in rural areas, innovative solutions like the Capacitor-Coupled Substation with a Controllable Network Transformer (CCS-CNT) are becoming increasingly critical. Traditional power distribution networks, often limited by unidirectional flow capabilities and inflexibility, struggle to meet the complex demands of modern energy systems. The CCS-CNT system offers a transformative approach by enabling bidirectional power flow between high-voltage transmission lines and local distribution networks, a feature that is essential for integrating renewable energy sources and ensuring reliable electrification in underserved regions. This paper presents a detailed mathematical representation of power flow within the CCS-CNT system, emphasizing the control of both active and reactive power through the adjustment of voltage levels and phase angles. A control algorithm is developed to dynamically manage power flow, ensuring optimal performance by minimizing losses and maintaining voltage stability across the network. The proposed CCS-CNT system demonstrates significant potential in enhancing the efficiency and reliability of power distribution, making it particularly suited for rural electrification and other applications where traditional methods fall short. The findings underscore the system's capability to adapt to varying operational conditions, offering a robust solution for modern power distribution challenges.

Hierarchical model updating for high-speed maglev vehicle/guideway coupled system based on multiobjective optimization

The high-speed maglev vehicle/guideway coupled model is an essential simulation tool for investigating vehicle dynamics and mitigating coupled vibration.To improve its accuracy efficiently,this study investigated a hierarchical model updating method integrated with field measurements.First,a high-speed maglev vehicle/guideway coupled model,taking into account the real effect of guideway material properties and elastic restraint of bearings,was developed by integrating the finite element method,multi-body dynamics,and electromagnetic levitation control.Subsequently,simultaneous in-site measurements of the vehicle/guideway were conducted on a high-speed maglev test line to analyze the system response and structural modal parameters.During the hierarchical updating,an Elman neural network with the optimal Latin hypercube sampling method was used to substitute the FE guideway model,thus improving the computational efficiency.The multi-objective particle swarm optimization algorithm with the gray relational projection method was applied to hierarchically update the parameters of the guideway layer and magnetic force layer based on the measured modal parameters and the electromagnet vibration,respectively.Finally,the updated coupled model was compared with the field measurements,and the results demonstrated the model’s accuracy in simulating the actual dynamic response,validating the effectiveness of the updating method.

A COUPLED 1-D AND 2-D CHANNEL NETWORK MATHEMATICAL MODEL USED FOR FLOW CALCULATIONS IN THE MIDDLE REACHES OF THE YANGTZE RIVER

A coupled one-dimensional （1-D） and two-dimensional （2-D） channel network mathematical model is proposed for flow calculations at nodes in a channel network system in this article. For the 1-D model, the finite difference method is used to discretize the Saint-Venant equations in all channels of a looped network. The Alternating Direction Implicit （ADI） method is adopted for the 2-D model at the nodes. In the coupled model, the 1-D model provides a good approximation with small computational effort, while the 2-D model is applied for complex topography to achieve a high accuracy. An Artificial Neural Network （ANN.） method is used for the data exchange and the connectivity between the 1-D and 2-D models. The coupled model is applied to the Jingjiang-Dongting Lake region, to simulate the tremendous looped channel network system, and the results are compared with field data. The good agreement shows that the coupled hydraulic model is more effective than the conventional 1-D model.

Coupled pore network model for the cathode gas diffusion layer in PEM fuel cells

A pore network model(PNM)is developed for gas diffusion layer(GDL)in the cathode side of polymer electrolyte membrane fuel cells(PEMFCs).The model is coupled to network models of reactant oxygen and electron transport inside GDL and also to simple models of catalyst layer and membrane.The coupled model captures the simultaneous effect of reactant and charge access to reaction sites and the resulting water generation,allowing it a transient nature up to reaching the steady state,which is a notable modification to the available PNMs which assume uniform invasion of liquid water from catalyst layer.The results show strongly non-uniform water saturation distributions inside GDL with maxima under the current collector ribs.As an extra feature,the model can predict time evolution of oxygen concentration and water generation rate at catalyst layer as a result of liquid water build-up in GDL.Also included is a dry case coupled model in order to be compared with the main model.The local water blockages in GDL inflict an average of 38.8%loss on the produced limiting current of the fuel cell.Finally,the coupling allows prediction of concentration overvoltages which emerges to be most pronounced in the under-rib region.

Ecological Carrying Capacity Prediction of Huainan City Based on GM–BP Neural Network

Evaluation of ecological carrying capacity is an important method of analyzing regional sustainable development, study on ecological carrying capacity is to settle the contradictions between resource and environment, and it is a significant basis for realizing regional sustainable development. This paper, on the basis of the academician Sun Tiehang's "unification of three" for the eco-city construction, established ecological carrying capacity evaluation indexes for the traditional industrial and mining city—Huainan City; and applied GM–BP neural network coupling model for the dynamic evolution and prediction of ecological carrying capacity of Huainan City in the future decade. The results showed that ecological carrying capacity index of Huainan would be 2.13 by 2025, higher than the loadable state 1, so the ecological carrying capacity would keep in the over-loaded level, but the over-loaded degree would be lower than the current. Carrying capacity of arable land, energy and water resources contribute greatly to the improvement of ecological carrying capacity, thus it is imperative to adjust this unreasonable and unsustainable ecological consumption relationship, enhance environmental protection awareness and high-efficiency utilization of resources, and take an energy-saving and intensive development path.

热液成矿系统构造控矿理论

构造对成矿的控制是热液成矿系统的典型特征之一,系统剖析多重尺度控矿构造的几何学、运动学、动力学、流变学和热力学对认识矿床成因和预测找矿至关重要;而如何实现控矿构造格架、渗透性结构、成矿流体通道和矿化变形网络由静态到多尺度时-空四维动态的转变,查明流体通道和矿床增量生长过程与控制因素,揭示热液成矿系统的构造-流体耦合成矿机制和定位规律是亟待解决的关键科学难题。为此,我们在对已有相关成果系统梳理的基础上,提出了科学构建热液成矿系统构造控矿理论的基本要点与对应方法及应用范畴:(1)流体而非构造是构造控矿理论的中心,热液系统的流体流动与成矿作用受控于断裂带格架及其渗透性结构,其中渗透率是将流体流动与流体压力变化联系起来理解控矿构造的核心;(2)不同控矿构造组合的关键控制是构造差应力和流体压力的大小,而矿化类型的变化可能是由于构造应力场引起的容矿构造方位的不同和赋矿围岩之间的强度差异所致;(3)流体通道的生长始于超压流体储库上游围岩中孤立的微裂隙沿流体压力梯度最大的方向、随裂隙发育且相互连结而形成新的长裂隙,并最终连通形成断裂网络内的流体通道,矿床的增量生长发生在高流体通量的短爆发期,断层反复滑动驱动其内流体压力、流速和应力快速变化,当由此诱发的流体通道生长破坏了流体系统的动态平衡时,随之而来的流体快速降压就成为金属沉淀成矿的关键驱动因素;(4)以热液裂隙-脉系统野外地质观测和构造-蚀变-矿化网络三维填图为基础,通过宏观与微观各级控矿构造相结合、地质历史与构造应力分析相结合、局部与区域点-线-面相结合、浅部与深部相结合、时间与空间相结合、定性和定量相结合,对各种控矿因素开展多学科、多尺度、多层次、全方位综合研究,是应遵循的基本原则;(5)通过构造-蚀变-矿化网络填图,将蚀变-矿化体与控矿构造的类型、形态、规模、产状和间距等几何学特征联系起来,利用热液裂隙-脉系统和断裂网络拓扑学及矿体三维几何结构分析等定量方法查明控矿构造格架和渗透性结构并揭示矿化变形网络的连通性与成矿潜力;(6)合理构建地质模型,选取合适的热力学参数和动力学边界条件,利用HCh和COMSOL等方法,定量模拟成矿过程中的流体流动、热-质传递、应力变形和化学反应等的时-空变化,是揭示构造-流体耦合成矿机理和定位规律、预测矿化中心和确定找矿目标的有效途径。进而提出了构造控矿理论的研究流程:聚焦构造-流体耦合成矿机制和定位规律这一关键科学问题,选择热液裂隙-脉系统和构造-蚀变-矿化网络为重点研究对象;通过几何学描述、运动学判断、流变学分析、动力学解析和热力学综合,厘定控矿构造格架,定位矿化中心,示踪成矿流体通道和多种矿化样式的增量生长过程及其关键控制,揭示渗透性结构的时-空演变规律及构造再活化与成矿定位的成因关联,建立构造-流体耦合成矿模式,服务新一轮战略找矿突破。以胶东焦家金矿田为例,开展控矿构造理论研究和成矿预测应用实践,证实了其科学性和有效性。

平原河网水动力优化调度与水环境改善的响应关系研究

平原河网河道流动性较差是引起水环境污染严重的主要原因,引调水是改善平原河网地区水环境的重要措施之一。本文以佛山市三山围为例,基于实测资料构建了河网水动力水质耦合模型,采用15天的连续水动力水质监测数据对模型进行了验证,并将NSE和RMSE模型评价指标用于模型评价。同时结合地势、潮汐规律和景观水位等设计了4种闸控模式和7种景观控制水位,共28种模拟工况,模拟分析了不同条件下的水动力水质改善与内外江潮位变化、引排水流量及其空间分布、闸控方式和景观水位等综合响应机理。结果表明:构建的模型合理可靠。研究区域受引排水路径的影响,河道流动性差异较为显著。综合考虑河道流量分布、水流路径、分汊河道分流作用和外江污染物浓度等对河道水质的作用效果并有效结合潮汐河网的动态水环境容量和污染源排放的时空分布对水环境的改善作用将非常显著。与低景观控制水位相比,高景观控制水位的内江动态水环境容量相对较大,污染物浓度较低。景观控制水位从0.2 m上升至0.8 m时,不同引调水路径的引水流量上升28.00%~64.70%,断面氨氮浓度削减了0.85~5.50 mg/L,削减比例达到28.89%~67.23%。本研究为平原潮汐河网水环境优化调度研究提供了新的思路,为相关部门对平原河网的水环境改善提供了重要的参考。

中国降碳-减污-扩绿-增长协同发展空间关联网络特征及影响因素研究

协同推进降碳-减污-扩绿-增长已成为我国经济社会发展全面绿色转型的必然选择。基于我国30个省份面板数据(不包含港澳台地区以及西藏自治区数据),运用熵值法、耦合协调度模型和社会网络分析方法,分析各省份间降碳-减污-扩绿-增长协同演变趋势及空间关联网络特征。结果表明:①各省份降碳-减污-扩绿-增长协同效应的变化趋势基本一致,但在空间上呈现东部>东北>西部>中部的区域不均衡特征。②降碳-减污-扩绿-增长协同效应呈现以东部地区为核心的复杂空间网络结构,省际间空间关联性呈上升态势,但网络结构稳定性还有待提高。③北京市、天津市和上海市等地区凭借优越区位,在关联网络中处于主导地位,而宁夏回族自治区、黑龙江省和新疆维吾尔自治区等地区对其他地区的影响较小。④北京市、天津市和上海市等地区属于“主受益”板块,浙江省、广东省等地区属于“经纪人板块”,安徽省、江西省和湖北省等地区属于“净溢出”板块。⑤降碳-减污-扩绿-增长协同效应的空间关联网络受多种因素共同影响,人力资本水平、科技投入、市场化水平和数字经济发展均有利于空间关联关系的建立。研究显示,中国降碳-减污-扩绿-增长协同效应存在空间关联性,需进一步加强省份间的绿色合作与交流,共同推动降碳-减污-扩绿-增长协同发展。