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Flow Field Simulation and Parameter Analysis of Hydraulic Unbalanced Bionic Self-recovery Actuator for Rotary Equipment

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Journal of Bionic Engineering 2024年第1期21卷 325-343页

作者： Wei Li Xin Pan Dehong Ge Jinji gao Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical TechnologyBeijing 100029China Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing University of Chemical TechnologyBeijing 100029China

The rotor is the most important component of rotating machinery,and the vibration produced by its mass unbalance has a serious influence on the secure and steady operation of the machine,so an effective online suppression technology is urgently needed.A new hydraulic unbalanced bionic self-recovery system is introduced,imitating the way of manually repairing faulty *** accomplish the effect of actuator mass redistribution,the technology employs pressurized air to drive the quantitative transfer of liquid in the reservoir cavity at opposite *** can complete the online adjustment of the equipment's balancing state and suppress the unbalanced vibration of equipment in real time,which gives the equipment the ability to maintain an autonomous health state and improve equipment *** composition and working principle of the system are introduced in detail,and the key performance parameters,such as the minimum running speed and the balancing liquid transfer speed,are analyzed *** fluid-solid coupling model of the actuator was established,and the two-phase flow from inside the hydraulic unbalanced bionic self-recovery actuator was simulated under multiple working conditions and the performance parameters were quantitatively analyzed.A balancing simulation test bed was built,and its effectiveness was verified by performance parameter tests and unbalanced bionic self-recovery *** experimental results show that the mass distribution adjustment of the balancing disk can be achieved using different viscosity balancing liquid,and the response of liquid viscosity 10 cSt is faster than that of liquid viscosity 100 cSt in the process of balancing liquid transfer,and the time is reduced by more than 75%;the system can reduce the simulated rotor amplitude from 18.3μm to 10.6μm online in real time,which provides technical support for the subsequent development of a new generation of bionic intelligent equipment.

关键词： Engineering self-recovery-Bionics Simulation analysis Self-recovery regulation

Device design principles and bioelectronic applications for flexible organic electrochemical transistors

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International Journal of Extreme Manufacturing 2024年第1期6卷 126-153页

作者： Lin gao Mengge Wu Xinge Yu Junsheng Yu State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China(UESTC)Chengdu 610054People’s Republic of China Department of Biomedical Engineering City University of Hong KongHong Kong Special Administrative Region of ChinaPeople’s Republic of China

Organic electrochemical transistors(OECTs) exhibit significant potential for applications in healthcare and human-machine interfaces, due to their tunable synthesis, facile deposition, and excellent biocompatibility. Expanding OECTs to the fexible devices will significantly facilitate stable contact with the skin and enable more possible bioelectronic applications. In this work,we summarize the device physics of fexible OECTs, aiming to offer a foundational understanding and guidelines for material selection and device architecture. Particular attention is paid to the advanced manufacturing approaches, including photolithography and printing techniques, which establish a robust foundation for the commercialization and large-scale fabrication. And abundantly demonstrated examples ranging from biosensors, artificial synapses/neurons, to bioinspired nervous systems are summarized to highlight the considerable prospects of smart healthcare. In the end, the challenges and opportunities are proposed for fexible OECTs. The purpose of this review is not only to elaborate on the basic design principles of fexible OECTs, but also to act as a roadmap for further exploration of wearable OECTs in advanced bio-applications.

关键词： flexible organic electrochemical transistors wearable bioelectronics manufacturing approaches device physics neuromorphic applications

Recorded recurrent deep reinforcement learning guidance laws for intercepting endoatmospheric maneuvering missiles

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Defence Technology（防务技术） 2024年第1期31卷 457-470页

作者： Xiaoqi Qiu Peng Lai Changsheng gao Wuxing Jing Department of Aerospace Engineering Harbin Institute of TechnologyHarbin150001China Shanghai Electro-Mechanical Engineering Institute Shanghai Academy of Spaceflight TechnologyShanghai201100China

This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation *** attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with *** measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance *** training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the *** training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training *** test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.

关键词： Endoatmospheric interception Missile guidance Reinforcement learning Markov decision process Recurrent neural networks

Distributed Source-Load-Storage Cooperative Low-Carbon Scheduling Strategy Considering Vehicle-to-Grid Aggregators

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 440-453页

作者： Xiao Xu Ziwen Qiu Teng Zhang Hui gao School of Automation Nanjing University of Posts and TelecommunicationsNanjing210023China

The vehicle-to-grid(V2G)technology enables the bidirectional power flow between electric vehicle(EV)batteries and the power grid,making EV-based mobile energy storage an appealing supplement to stationary energy storage ***,the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent *** unlock the scheduling potential of EVs,this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G *** uncertainty of EV charging patterns is managed through a rolling-horizon control framework,where the scheduling and control horizons are adaptively adjusted according to the availability periods of ***,a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling *** method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual ***,a Nash bargaining based cooperative scheduling model involving a distribution system operator(DSO),an EV aggregator(EVA),and a load aggregator(LA)is established to maximize the social welfare and improve the low-carbon performance of the *** model is solved by the alternating direction method of multipliers(ADMM)algorithm in a distributed manner,with privacy of participants fully *** proposed strategy is proven to achieve the objective of low-carbon economic operation.

关键词： Electric vehicle(EV) low-carbon scheduling mobile storage system Nash bargaining power flexibility alternating direction method of multipliers(ADMM).

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 886-899页

作者： Jie Xu Hongjun gao Renjun Wang Junyong Liu the College of Electrical Engineering Sichuan UniversityChengduChina

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage *** paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss *** deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL *** to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen ***,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and *** framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus *** results demonstrate the significant real-time regulation capabilities of the proposed strategy.

关键词： Reconfiguration active distribution network distributed energy resource real-time control deep reinforcement learning parameter sharing scalability

The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

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Chinese Journal of Electronics 2024年第1期33卷 274-281页

作者： Juhan WANG Ying gao Yuan CAO Tao TANG School of Electronics and Information Engineering Beijing Jiaotong University Graduate Department China Academy of Railway Science National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University

The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. A fault-tolerant mechanism based on multi-classification support vector machine(SVM) and adaptive network-based fuzzy inference system(ANFIS) is introduced. Multi-classification SVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar,the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

关键词： Support vector machines Fault diagnosis Pressure sensors Fuzzy logic Fault tolerance Adaptive systems Fault tolerant systems

Microstructure evolution and strengthening mechanism of high -performance powder metallurgy TA15 titanium alloy by hot rolling

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International Journal of Minerals,Metallurgy and Materials 2024年第6期31卷 1426-1436页

作者： Ying gao Ce Zhang Jiazhen Zhang Xin Lu Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and MaterialsInstitute for Advanced Ma-terials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Engineering Research Center for Advanced Rolling Technology National Engineering Research Center for Flat Rolling EquipmentInstitute of Engin-eering TechnologyUniversity of Science and Technology BeijingBeijing 100083China

Hot deformation of sintered billets by powder metallurgy(PM)is an effective preparation technique for titanium alloys,which is more significant for high-alloying *** this study,Ti–6.5Al–2Zr–Mo–V(TA15)titanium alloy plates were prepared by cold press-ing sintering combined with high-temperature hot *** microstructure and mechanical properties under different process paramet-ers were *** microscope,electron backscatter diffraction,and others were applied to characterize the microstructure evolution and mechanical properties strengthening *** results showed that the chemical compositions were uniformly dif-fused without segregation during sintering,and the closing of the matrix craters was accelerated by increasing the sintering *** block was hot rolled at 1200℃ with an 80%reduction under only two passes without *** strength and elongation of the plate at 20–25℃ after solution and aging were 1247 MPa and 14.0%,respectively,which were increased by 24.5%and 40.0%,respect-ively,compared with the as-sintered alloy at 1300℃.The microstructure was significantly refined by continuous dynamic recrystalliza-tion,which was completed by the rotation and dislocation absorption of the substructure surrounded by low-angle grain *** hot rolling combined with heat treatment,the strength and plasticity of PM-TA15 were significantly improved,which resulted from the dense,uniform,and fine recrystallization structure and the synergistic effect of multiple slip systems.

关键词： elemental powder powder metallurgy titanium alloy hot rolling strength and plasticity

Neural network modeling and prediction of HfO2 thin film properties tuned by thermal annealing

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High Power Laser Science and Engineering 2024年第2期12卷 79-86页

作者： Min gao Chaoyi Yin Jianda Shao Meiping Zhu School of Microelectronics Shanghai UniversityShanghaiChina Laboratory of Thin Film Optics Key Laboratory of Materials for High Power LaserShanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouChina

Plasma-enhanced atomic layer deposition (PEALD) is gaining interest in thin films for laser applications, and post-annealing treatments are often used to improve thin film properties. However, research to improve thin film properties is often based on an expensive and time-consuming trial-and-error process. In this study, PEALD-HfO2 thin film samples were deposited and treated under different annealing atmospheres and temperatures. The samples were characterized in terms of their refractive indices, layer thicknesses and O/Hf ratios. The collected data were split into training and validation sets and fed to multiple back-propagation neural networks with different hidden layers to determine the best way to construct the process–performance relationship. The results showed that the three-hidden-layer back-propagation neural network (THL-BPNN) achieved stable and accurate fitting. For the refractive index, layer thickness and O/Hf ratio, the THL-BPNN model achieved accuracy values of 0.99, 0.94 and 0.94, respectively, on the training set and 0.99, 0.91 and 0.90, respectively, on the validation set. The THL-BPNN model was further used to predict the laser-induced damage threshold of PEALD-HfO2 thin films and the properties of the PEALD-SiO2 thin films, both showing high accuracy. This study not only provides quantitative guidance for the improvement of thin film properties but also proposes a general model that can be applied to predict the properties of different types of laser thin films, saving experimental costs for process optimization.

关键词： laser-induced damage threshold laser thin film neural network plasma-enhanced atomic layer deposition

Hole cleaning evaluation and installation spacing optimization of cuttings bed remover in extended-reach drilling

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Petroleum Science 2024年第3期21卷 2005-2022页

作者： Shuo Peng Wen-Jun Huang De-Li gao MOE Key Laboratory of Petroleum Engineering China University of Petroleum-BeijingBeijing102249China State Key Laboratory of Petroleum Resources and Engineering China University of Petroleum-BeijingBeijing102249China

In extended-reach or long-horizontal drilling,cuttings usually deposit at the bottom of the *** cuttings accumulate to a certain thickness,complex problems such as excessive torque and drag,tubing buckling,and pipe stuck probably occur,which results in a lot of non-productive time and remedial *** bed remover can efficiently destroy deposited cuttings in time through hydraulic and mechanical stirring *** paper aims to build a method for hole cleaning evaluation and installation spacing optimization of cuttings bed remover to improve the wellbore cleaning ***,a Computational Fluid Dynamics approach with Eulerian—Eulerian multiphase model was utilized to investigate the mechanism of cuttings transportation,and a new type of cuttings bed remover was ***,an evaluation method of hole cleaning effect of remover was *** that,the effects of several drilling parameters on hole cleaning including flow rate of drilling fluid,rotational speed of drillpipe,rate of penetration,wellbore size,rheological property of drilling fluid,and remover eccentricity on the performance of cuttings bed remover were *** results demonstrate that the new type of remover with streamline blade performs better than conventional *** efficiency of hole cleaning is greatly improved by increasing the rotational speed of drillpipe,flow rate of drilling fluid,remover eccentricity,and 6 rpm Fann dial reading for drilling *** higher rate of penetration and large wellbore size result in worse hole *** findings can serve as an important guide for the structure optimization design of cuttings bed remover and installation spacing of removers.

关键词： Extended-reach drlling Drilling hydraulics Cuttings transport Hole cleaning Cuttings bed remover