Investigation on microstructures and properties of semi-solid Al_(80)Mg_(5)Li_(5)Zn_(5)Cu_(5)light-weight high-entropy alloy during isothermal heat treatment process

The Al_(80)Mg_(5)Li_(5)Zn_(5)Cu_(5)light-weight high-entropy alloy with globular microstructure was fabricated by isothermal heat treatment.The effects of isothermal temperatures and holding times on the semi-solid microstructure evolution were investigated.The results indicate that,with increase of the isothermal temperature,the average grain size increases and the spheroidization time shortens.With prolongation of holding time,the shape factor increases firstly and then decreases,and the average grain size decreases at first and then increases when the isothermal temperature is below 520°C,however it increases gradually at 540℃.The optimal semi-solid microstructure is obtained at 520℃ for 30 min,whose shape factor and average grain size are 0.90 and 56.4μm,respectively.Compared with as-cast Al_(80)Mg_(5)Li_(5)Zn_(5)Cu_(5) light-weight high-entropy alloy,the compressive strength and plasticity of semi-solid Al_(80)Mg_(5)Li_(5)Zn_(5)Cu_(5) light-weight high-entropy alloy are increased by 36%and 108%,respectively.The formation of semi-solid microstructures includes three stages:melting separation,spheroidization,and coarsening growth.The sluggish diffusion effect of Al_(80)Mg_(5)Li_(5)Zn_(5)Cu_(5) light-weight high-entropy alloy leads to a low coarsening rate,resulting in slow grain growth.

Numerical simulation of irregular section underground structure shaking table test model

Based on the the large shaking table test results on irregular section subway station structure in soft soil,an overall time-history numerical simulation is conducted to study the nonlinear dynamic interaction of the soilirregular underground structure.Typical test results,including the acceleration of the soil,acceleration,and deformation of the structure,were analyzed.Satisfactory consistency between the simulation and test results is verified,and the difference between these results was discussed in detail.The maximum inter-story drift ratio was approximately 1/472 under input PGA=0.54 g.The strain responses of columns were significantly larger than those of the side walls and slabs.The components in the lower layers of the irregular subway station structure,particularly in the central columns,underwent cumulative damage.The research results could provide a simplified analysis method to quantitatively evaluate the damage of irregular underground structures in soft soil.

Helical streamers guided by surface electromagnetic standing waves

The streamer that is driven by the specific pulse DC discharge parameters can stably form a three-dimensional helical plasma channel in a long dielectric tube in the low-temperature plasma experiment,in cases when there were neither external background magnetic field or other factors that destroyed the poloidal symmetry of the tube.The formation mechanism and chirality of helical streamers are discussed according to the surface electromagnetic standing wave theory.The shape of the helical streamers and the characteristics of helical branches are quantitatively analyzed to further expand the application of plasma and streamer theory in the helix problem and chiral catalytic synthesis.

Theoretical research on gas seepage in the formations surrounding bedded gas storage salt cavern

When constructing salt cavern gas or petroleum storage in lacustrine sedimentary salt formations rich in mudstone interlayers, the influence of the sealing performance of interlayers and salt-mud interface on the storage tightness should be considered adequately. In order to reveal the gas seepage in deep formations surrounding bedded salt cavern underground storage, a leakage analysis model was established based on the characteristics of a low dip angle and the interbedded structure of bedded rock salt. The gas seepage governing equations for one-dimensional and plane radial flow were derived and solved. A gas seepage simulation experiment was conducted to demonstrate the accuracy and reliability of the theoretical calculation results. The error of the seepage range was approximately 6.70%, which is acceptable. The analysis and calculation results indicate that the motion equation of gas in deep formations satisfies a non-Darcy's law with a threshold pressure gradient and slippage effect. The sufficient condition for the gas flow to stop is that the pressure gradient is equal to the threshold pressure gradient.The relationship between the leakage range and operating time is a positive power function, that is, the leakage range gradually increases with time and eventually stabilizes. As the seepage range increases, the seepage pressure decreases sharply during the early stage, and then decreases gradually until the flow stops.Combining the research results with engineering applications, three quantitative evaluation indexes named the maximum admissible leakage range, leakage volume and leakage rate are proposed for the tightness evaluation of gas storage salt cavern during their operating stage. These indexes can be used directly in actual engineering applications and can be compared with the key design parameters stipulated in the relevant specifications. This work is expected to provide theoretical and technical support for the gas loss and tightness evaluation of gas storage salt caverns.

Linear active disturbance rejection control of heavy-haul train operation based on an interval type-2 fuzzy logic system model

The heavy-haul train(HHT)has large capacity and high efficiency,which represents the level of freight and makes the amelioration of control performance a trend in various countries.Improving the model reliability and increasing the anti-disturbance ability of the operation controller are two main ways to improve the operation control accuracy of HHTs.Herein,to describe the large nonlinear system more precisely,an interval type-2 fuzzy logic system(IT2FLS)is introduced to obtain a dynamic model.Then,a linear active disturbance rejection controller(LADRC)is designed to achieve precise operational control.In addition,the‘bandwidth method’is combinedwith the sparrowsearch algorithm(SSA)to solve the difficulty of controller parameters adjustment.Afterwards,the stability analysis of the closed-loop control system is given.The simulation experiments are conducted based on data collected from HXD1 locomotives driven by excellent drivers.Results show that the speed tracking error is no more than 0.5 km/h,and demonstrate that the proposed method significantly improves the operational performance of HHTs.

Model-free adaptive robust control method for high-speed trains

Aiming at the robustness issue in high-speed trains(HSTs)operation control,this article proposes a model-free adaptive control(MFAC)scheme to suppress disturbance.Firstly,the dynamic linearization data model of train system under the action of measurement disturbance is given,and the Kalman filter(KF)based on this model is derived under the minimum variance estimation criterion.Then,according to the KF,an anti-interference MFAC scheme is designed.This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance.Finally,the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor.The proposed control algorithm can effectively suppress the measurement disturbance,and obtain smaller tracking error and larger signal to noise ratio with better applicability.

Online Monitoring Method for Insulator Self-explosion Based on Edge Computing and Deep Learning

Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on edge computing and DL(deep learning).In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge,a lightweight SSD(Single Shot MultiBox Detector)target recognition network is designed at the edge,which adopts the MobileNets network to replace VGG16 network in the original model to reduce redundant computing.In the cloud,three detection algorithms(Faster-RCNN,Retinanet,YOLOv3)with obvious differences in detection performance are selected to obtain the coordinates and confidence of the insulator self-explosion area,and then the self-explosion fault detection of the overhead transmission line is realized by a novel multimodel fusion algorithm.The experimental results show that the proposed scheme can effectively reduce the amount of uploaded data,and the average recognition accuracy of the cloud is 95.75%.In addition,it only increases the power consumption of edge devices by about 25.6W/h in their working state.Compared with the existing online monitoring technology of insulator selfexplosion at home and abroad,the proposed scheme has the advantages of low transmission delay,low communication cost and high diagnostic accuracy,which provides a new idea for online monitoring research of power internet of things equipment.

Wind power forecasting based on improved variational mode decomposition and permutation entropy

Due to the significant intermittent,stochastic and non-stationary nature of wind power generation,it is difficult to achieve the desired prediction accuracy.Therefore,a wind power prediction method based on improved variational modal decomposition with permutation entropy is proposed.First,based on the meteorological data of wind farms,the Spearman correlation coefficient method is used to filter the meteorological data that are strongly correlated with the wind power to establish the wind power prediction model data set;then the original wind power is decomposed using the improved variational modal decomposition technique to eliminate the noise in the data,and the decomposed wind power is reconstructed into a new subsequence by using the permutation entropy;with the meteorological data and the new subsequence as input variables,a stacking deeply integrated prediction model is developed;and finally the prediction results are obtained by optimizing the hyperparameters of the model algorithm through a genetic algorithm.The validity of the model is verified using a real data set from a wind farm in north-west China.The results show that the mean absolute error,root mean square error and mean absolute percentage error are improved by at least 33.1%,56.1%and 54.2%compared with the autoregressive integrated moving average model,the support vector machine,long short-term memory,extreme gradient enhancement and convolutional neural networks and long short-term memory models,indicating that the method has higher prediction accuracy.

Piecewise model-based adaptive compensation control for high-speed trains with unknown actuator failures

In order to deal with the uncertainties caused by different operation conditions and unknown actuator failures of high-speedtrains,an adaptive failures compensation control scheme is designed based on the piecewise model.A piecewise constant model is introduced to describe the variable system parameters caused by the variable operation environments,and a multiple-particle plecewise model of high-speed trains,with unknown actuator failures,is then established.An adaptive failure compensation controller is developed for the multiple-particle piecewise constant model,by using a direct model refering to the adaptive control method.Such an adaptive controller can not only compensate the uncertainties from unknown actuator failures,but also effectively deal with the uncertainties caused by different operating conditions.Finally,a CRH380A high-speed train model is taken as the controlled object for the simulation study.The simulation results show that the proposed controller ensures the desired system performance in the presence of unknown actuator failures and uncertain operation conditions.

A novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder for small negative samples

This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder(DCAE)for small negative samples.The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning.In order to reduce the high cost of training Deep Neural Networks,this paper pre-trained the Convolutional Neural Networks(CNN)through open labelled datasets.Through transferring learning,the encoder part of the traditional Convolutional Auto-Encoder was replaced by the first three layers of the CNN,and a small number of defect samples were used to fine-tune the parameters.A threshold discrimination scheme was designed to evaluate the model detection,realising the self-explosion detection of insulator by judging the residual result and abnormal score.The experimental results show that compared with the existing insulator self-explosion detection schemes,the proposed scheme can reduce the model training time by up to 40%,and the recognition accuracy can reach 97%.Moreover,this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.