This paper examines the effect of the microstructure and electrical conductivity(EC)on the swelling ratio and pressure in red-bed mudstone sampled from arid areas in the Xining region in the northeastern Tibetan Plate...This paper examines the effect of the microstructure and electrical conductivity(EC)on the swelling ratio and pressure in red-bed mudstone sampled from arid areas in the Xining region in the northeastern Tibetan Plateau.A series of laboratory tests,including swelling experiments,X-ray diffraction(XRD),and scanning electron microscope(SEM),was carried out for mechanical and microstructural analysis.The coupled influence of the EC and microstructural parameters on the expansion ratio and pressure was investigated,and the weight coefficients were discussed by the entropy weight method.The results revealed an increasing exponential trend in EC,and the maximum swelling speed occurred at an EC of approximately 10 μS/cm.In addition,a method for predicting the expansion potential is proposed based on the microstructure,and its reliability is verified by comparing with swelling experimental results.In addition,according to the image analysis results,the ranges of the change in the clay minerals content(CMC),the fractal dimension(FD),the average diameter(AD)of pores,and the plane porosity(PP)are 23.75%-53%,1.08-1.17,7.53-22.45 mm,and 0.62%-1.25%,respectively.Moreover,mudstone swelling is negatively correlated with the plane porosity,fractal dimension and average diameter and is linearly correlated with the clay mineral content.Furthermore,the weight values prove that the microstructural characteristics,including FD,AD,and PP,are the main factors influencing the expansion properties of red-bed mudstones in the Xining region.Based on the combination of macro and micro-analyses,a quantitative analysis of the swelling process of mudstones can provide a better reference for understanding the mechanism of expansion behavior.展开更多
Determining osmotic suction from the electrical conductivity(EC)of soil pore water was widely reported in the literature.However,while dealing with unsaturated soils,they do not have enough soil pore water to be extra...Determining osmotic suction from the electrical conductivity(EC)of soil pore water was widely reported in the literature.However,while dealing with unsaturated soils,they do not have enough soil pore water to be extracted for a reliable measurement of EC.In this paper,the chilled-mirror dew-point hygrometer and contact filter paper method were used to determine the total and matric suctions for low-plasticity soils with different salinities(0.05‰,2.1‰,and 6.76‰).A new piecewise function was proposed to calculate the osmotic suction,with the piecewise point corresponding to the first occurrence of precipitated salt in mixed salt solutions(synthetic seawater).EC,ion and salt concentrations used for osmotic suction calculation were transformed from the established relationships of mixed salt solution instead of experimental measurement.The calculated osmotic suction by the proposed equation and the equations in the literature was compared with the indirectly measured one(the difference between the measured total and matric suctions).Results showed that the calculated osmotic suction,especially the one calculated using the proposed function,was in fair agreement with the indirectly measured data(especially for specimens with higher salinity of 6.76‰),suggesting that the transformation of EC and concentrations from the established relationship is a good alternative to direct measurement for lowplasticity soil.In particular,the proposed method could be applied to unsaturated low-plasticity soils which do not have enough soil pore water for a proper EC measurement.展开更多
具有再生制动功能的电动汽车制动系统与传统燃油汽车的摩擦制动系统不同,在回收部分制动能量的同时其制动稳定性会发生变化.在保证安全制动距离的前提下,制动能量回收率的提高受到制动稳定性的制约和限制.针对电制动和常规摩擦制动组成...具有再生制动功能的电动汽车制动系统与传统燃油汽车的摩擦制动系统不同,在回收部分制动能量的同时其制动稳定性会发生变化.在保证安全制动距离的前提下,制动能量回收率的提高受到制动稳定性的制约和限制.针对电制动和常规摩擦制动组成的机电复合制动系统,建立了电制动力、电制动力矩和电池充电功率计算模型.考虑到电机转矩特性和电池充电功率限制,以最大化回收制动能量为目标,设计3种不同的机电复合制动控制策略.通过在ADVISOR软件中建立嵌入式仿真模块对制动能量回收率、电池荷电状态和纯电动模式的续驶里程进行了仿真计算和分析.计算结果表明:I曲线和ECE(Economic Commissionof Europe safety regulations)法规边界线都不是理想的制动力分配曲线,所提出的制动力分配曲线OABCD综合性能较好,制动能量回收率达到59.56%,且一个循环的荷电状态变化很小,仅降低了4.29%.实车试验表明能量回收能够提高续驶里程.展开更多
Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly...Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly influences the planning of digging trajectories and energy consumption.Load prediction of ECS mainly consists of two types of methods:physics-based modeling and data-driven methods.The former approach is based on known physical laws,usually,it is necessarily approximations of reality due to incomplete knowledge of certain processes,which introduces bias.The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization,which introduces variance.In addition,some parts of load are non-observable and latent,which cannot be measured from actual system sensing,so they can’t be predicted by data-driven methods.Herein,an innovative hybrid physics-informed deep neural network(HPINN)architecture,which combines physics-based models and data-driven methods to predict dynamic load of ECS,is presented.In the proposed framework,some parts of the theoretical model are incorporated,while capturing the difficult-to-model part by training a highly expressive approximator with data.Prior physics knowledge,such as Lagrangian mechanics and the conservation of energy,is considered extra constraints,and embedded in the overall loss function to enforce model training in a feasible solution space.The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset.展开更多
基金the funding support from National Natural Science Foundation of China(Grant No.42077271)Sichuan Science and Technology Program,China(Grant No.2023YFS0364)Chengdu Science and Technology Program(Grant No.2022-YF05-00340-SN).
文摘This paper examines the effect of the microstructure and electrical conductivity(EC)on the swelling ratio and pressure in red-bed mudstone sampled from arid areas in the Xining region in the northeastern Tibetan Plateau.A series of laboratory tests,including swelling experiments,X-ray diffraction(XRD),and scanning electron microscope(SEM),was carried out for mechanical and microstructural analysis.The coupled influence of the EC and microstructural parameters on the expansion ratio and pressure was investigated,and the weight coefficients were discussed by the entropy weight method.The results revealed an increasing exponential trend in EC,and the maximum swelling speed occurred at an EC of approximately 10 μS/cm.In addition,a method for predicting the expansion potential is proposed based on the microstructure,and its reliability is verified by comparing with swelling experimental results.In addition,according to the image analysis results,the ranges of the change in the clay minerals content(CMC),the fractal dimension(FD),the average diameter(AD)of pores,and the plane porosity(PP)are 23.75%-53%,1.08-1.17,7.53-22.45 mm,and 0.62%-1.25%,respectively.Moreover,mudstone swelling is negatively correlated with the plane porosity,fractal dimension and average diameter and is linearly correlated with the clay mineral content.Furthermore,the weight values prove that the microstructural characteristics,including FD,AD,and PP,are the main factors influencing the expansion properties of red-bed mudstones in the Xining region.Based on the combination of macro and micro-analyses,a quantitative analysis of the swelling process of mudstones can provide a better reference for understanding the mechanism of expansion behavior.
文摘Determining osmotic suction from the electrical conductivity(EC)of soil pore water was widely reported in the literature.However,while dealing with unsaturated soils,they do not have enough soil pore water to be extracted for a reliable measurement of EC.In this paper,the chilled-mirror dew-point hygrometer and contact filter paper method were used to determine the total and matric suctions for low-plasticity soils with different salinities(0.05‰,2.1‰,and 6.76‰).A new piecewise function was proposed to calculate the osmotic suction,with the piecewise point corresponding to the first occurrence of precipitated salt in mixed salt solutions(synthetic seawater).EC,ion and salt concentrations used for osmotic suction calculation were transformed from the established relationships of mixed salt solution instead of experimental measurement.The calculated osmotic suction by the proposed equation and the equations in the literature was compared with the indirectly measured one(the difference between the measured total and matric suctions).Results showed that the calculated osmotic suction,especially the one calculated using the proposed function,was in fair agreement with the indirectly measured data(especially for specimens with higher salinity of 6.76‰),suggesting that the transformation of EC and concentrations from the established relationship is a good alternative to direct measurement for lowplasticity soil.In particular,the proposed method could be applied to unsaturated low-plasticity soils which do not have enough soil pore water for a proper EC measurement.
文摘具有再生制动功能的电动汽车制动系统与传统燃油汽车的摩擦制动系统不同,在回收部分制动能量的同时其制动稳定性会发生变化.在保证安全制动距离的前提下,制动能量回收率的提高受到制动稳定性的制约和限制.针对电制动和常规摩擦制动组成的机电复合制动系统,建立了电制动力、电制动力矩和电池充电功率计算模型.考虑到电机转矩特性和电池充电功率限制,以最大化回收制动能量为目标,设计3种不同的机电复合制动控制策略.通过在ADVISOR软件中建立嵌入式仿真模块对制动能量回收率、电池荷电状态和纯电动模式的续驶里程进行了仿真计算和分析.计算结果表明:I曲线和ECE(Economic Commissionof Europe safety regulations)法规边界线都不是理想的制动力分配曲线,所提出的制动力分配曲线OABCD综合性能较好,制动能量回收率达到59.56%,且一个循环的荷电状态变化很小,仅降低了4.29%.实车试验表明能量回收能够提高续驶里程.
基金National Natural Science Foundation of China(Grant No.52075068)Shanxi Provincial Science and Technology Major Project(Grant No.20191101014).
文摘Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly influences the planning of digging trajectories and energy consumption.Load prediction of ECS mainly consists of two types of methods:physics-based modeling and data-driven methods.The former approach is based on known physical laws,usually,it is necessarily approximations of reality due to incomplete knowledge of certain processes,which introduces bias.The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization,which introduces variance.In addition,some parts of load are non-observable and latent,which cannot be measured from actual system sensing,so they can’t be predicted by data-driven methods.Herein,an innovative hybrid physics-informed deep neural network(HPINN)architecture,which combines physics-based models and data-driven methods to predict dynamic load of ECS,is presented.In the proposed framework,some parts of the theoretical model are incorporated,while capturing the difficult-to-model part by training a highly expressive approximator with data.Prior physics knowledge,such as Lagrangian mechanics and the conservation of energy,is considered extra constraints,and embedded in the overall loss function to enforce model training in a feasible solution space.The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset.
