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Exploring multiple phases and first-order phase transitions in Kármán Vortex Street

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Science China(Physics,Mechanics & Astronomy) 2024年第11期67卷 47-54页

作者： Xu Li Xing Xiang Tingting Xue Limin Wang xiaosong Chen School of Systems Science and Institute of Nonequilibrium Systems Beijing Normal UniversityBeijing 100875China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy ofSciencesBeijing 100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100049China

Kármán Vortex Street, a fascinating phenomenon of fluid dynamics, has intrigued the scientific community for a long time. Many researchers have dedicated their efforts to unraveling the essence of this intriguing flow pattern. Here, we apply the lattice Boltzmann method with curved boundary conditions to simulate flows around a circular cylinder and study the emergence of Kármán Vortex Street using the eigen microstate approach, which can identify phase transition and its order-parameter. At low Reynolds number, there is only one dominant eigen microstate W_(1) of laminar flow. At Re_(c)^(1)= 53.6, there is a phase transition with the emergence of an eigen microstate pair W^(2,3) of pressure and velocity fields. Further at Re_(c)^(2)= 56, there is another phase transition with the emergence of two eigen microstate pairs W^(4,5) and W^(6,7). Using the renormalization group theory of eigen microstate,both phase transitions are determined to be first-order. The two-dimensional energy spectrum of eigen microstate for W^(1), W^(2,3) after Re_(c)^(1), W^(4-7) after Re_(c)^(2) exhibit-5/3 power-law behavior of Kolnogorov's K41 theory. These results reveal the complexity and provide an analysis of the Kármán Vortex Street from the perspective of phase transitions.

关键词： Kármán Vortex Street first-order phase transition eigen microstate approach renormalization group theory of eigen microstate

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Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

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Journal of Energy Chemistry 2024年第5期92卷 605-618页

作者： Qingguang Qi Wenxue Liu Zhongwei Deng Jinwen Li Ziyou Song xiaosong Hu College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina Department of Mechanical Engineering National University of SingaporeSingapore 117575Singapore

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion *** numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery *** challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity *** fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field *** proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction ***,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation ***,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV ***,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 *** results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing *** work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

关键词： Electricvehicle Lithium-ion battery pack Capacity estimation Machine learning Field data

Fractal Study on the Evolution of Micro-Pores in Concrete Under Freeze-Thaw

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Journal of Wuhan University of Technology(Materials Science) 2024年第1期39卷 109-117页

作者：孙浩然邹春霞 XU Deru GUO xiaosong HUANG Kun College of Water Conservancy and Civil Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Xi’an HaiTang Vocational College Xi’an 710038China

After exposure to freeze-thaw cycles, scanning electron microscopy(SEM) and nuclear magnetic resonance(NMR) were used to test the four mixtures. The microstructure is qualitatively analyzed from the 2D SEM image and the 3D pore distribution curve before and after freezing and thawing. The fractal dimension is utilized to characterize the two-dimensional topography image and the three-dimensional pore distribution, quantitatively. The results reveal that the surface porosity and volume porosity increase as the freeze-thaw action increases. Self-similarity characteristics exist in micro-damage inside the concrete. In the fractal dimension, it is possible to characterize pore evolution quantitatively. The fractal dimension correlates with pore damage evolution. The fractal dimension effectively quantitatively characterizes micro-damage features at various scales from the local to the global level.

关键词： fractal dimension freeze-thaw cycle concrete SEM NMR

Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

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Chinese Journal of Mechanical Engineering 2024年第2期37卷 247-263页

作者： Deying Su Shaojie Wang Haojing Lin xiaosong Xia Yubing Xu Liang Hou Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen UniversityXiamen 361102China Xuzhou XCMG Excavation Machinery Co. LtdXuzhou 221000China

The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder *** test data are easily obtained,but it is challenging to emulate actual loads in the research on parameter estimation of valve-controlled cylinder *** the actual load information contained in the operating data of the control valve,its acquisition remains *** paper proposes a method that fuses bench test and operating data for parameter estimation to address the aforementioned *** proposed method is based on Bayesian theory,and its core is a pool fusion of prior information from bench test and operating ***,a system model is established,and the parameters in the model are ***,the bench and operating data of the system are ***,the model parameters and weight coefficients are estimated using the data fusion ***,the estimated effects of the data fusion method,Bayesian method,and particle swarm optimisation(PSO)algorithm on system model parameters are *** research shows that the weight coefficient represents the contribution of different prior information to the parameter estimation *** effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO *** load complexity leads to a decrease in model accuracy,highlighting the crucial role of the data fusion method in parameter estimation studies.

关键词： Valve-controlled cylinder system Parameter estimation The Bayesian theory Data fusion method Weight coefficients

Experimental Investigation on Solution Regeneration Performance and Coefficients in Full-Open System for Heat and Water Recovery of Flue Gas

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Journal of Thermal Science 2024年第3期33卷 1094-1108页

作者： WEI Hongyang HUANG Shifang MA Yuxin CHEN Bo SUN Li ZHANG xiaosong School of Energy and Environment Southeast UniversityNanjing210096China MOE Engineering Research Center for Building Energy Equipment&Environment Southeast UniversityNanjing210096China Jiangsu Frontier Electric Technology Co. LtdNanjing211106China

The recovery of heat and water from low-grade flue gas is of considerable importance for energy conservation and environmental *** the full-open absorption heat pump shows promise as a means of achieving heat and water recovery,the lack of research on heat and mass transfer performance of open-type solution evaporation regeneration represents a significant impediment to its design and *** paper experimentally investigates the regeneration performance of an open-type spaying tower equipped with ceramic structured *** different regeneration modes are proposed,namely ambient air receiver mode and flue gas receiver mode,to utilize air or low-grade flue gas as a driving *** impact of different input parameters on the regeneration characteristics,including heat transfer capacity,water removal rate,thermal efficiency,and humidity effectiveness,are *** findings indicate that the enhancement of regeneration can be achieved through the increase of solution flow rate,solution temperature,and flue gas flow rate in both regeneration ***,high solution concentration and flue gas humidity ratio can weaken water removal rates and reduce thermal *** the regeneration of CaCl_(2)-H_(2)O with a concentration of55%,flue gas around 200℃with a humidity ratio below 44 g/kg can successfully drive the solution regeneration *** the solution concentration or flue gas humidity ratio continues to rise,additional energy is necessary for ***,the coupled heat and mass transfer coefficients are fitted,which can contribute to the design and optimization of the open-type regenerator.

关键词： heat and water recovery solution regeneration flue gas regeneration performance coefficient

A vision system based on CNN-LSTM for robotic citrus sorting Author links open overlay panel

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Information Processing in Agriculture 2024年第1期11卷 14-25页

作者： Yonghua Yu xiaosong an Jiahao Lin Shanjun Li Yaohui Chen College of Engineering Huazhong Agricultural UniversityWuhan 430070China Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River Ministry of Agriculture and Rural AffairsWuhan 430070China National R&D Center for Citrus Preservation Wuhan 430070China Citrus Mechanization Research Base Ministry of Agriculture and Rural AffairsWuhan 430070China

Compared with manual sorting of citrus fruit,vision-based sorting solutions can help achieve higher accuracy and *** this study,we present a vision system based on CNN-LSTM,which can cooperate with robotic grippers for real-time sorting and is readily applicable to various citrus processing plants.A CNN-based detector was adopted to detect the defective oranges in view and temporarily classify them into corresponding types,and an LSTM-based predictor was used to predict the position of the oranges in a future frame based on image sequential *** fusion of CNN and LSTM networks enabled the system to track defective ones during rotation and identify their true types,and their future path was also predicted which is vital for predictive control of visually guided robotic *** detection accuracy of 94.1%was obtained based on experimental results,and the error for path prediction was within 4.33 pixels 40 frames *** average time to process a frame was between 28 and 62 frames per second,which also satisfied real-time *** results proved the potential of the proposed system for automated citrus sorting with good precision and efficiency,and it can be readily extended to other fruit crops featuring high versatility.

关键词： Deep learning Long short-term memory Vision system Online citrus sorting Path prediction

Rapid health estimation of in-service battery packs based on limited labels and domain adaptation

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Journal of Energy Chemistry 2024年第2期89卷 345-354,I0009页

作者： Zhongwei Deng Le Xu Hongao Liu xiaosong Hu Bing Wang Jingjing Zhou School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina School of Sustainability Stanford UniversityStanfordCA 94305USA College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China China Automotive Engineering Research Institute Co.Ltd. Chongqing 401122China

For large-scale in-service electric vehicles(EVs)that undergo potential maintenance,second-hand transactions,and retirement,it is crucial to rapidly evaluate the health status of their battery ***,existing methods often rely on lengthy battery charging/discharging data or extensive training samples,which hinders their implementation in practical *** address this issue,a rapid health estimation method based on short-time charging data and limited labels for in-service battery packs is proposed in this ***,a digital twin of battery pack is established to emulate its dynamic behavior across various aging levels and inconsistency ***,increment capacity sequences(△Q)within a short voltage span are extracted from charging process to indicate battery ***,data-driven models based on deep convolutional neural network(DCNN)are constructed to estimate battery state of health(SOH),where the synthetic data is employed to pre-train the models,and transfer learning strategies by using fine-tuning and domain adaptation are utilized to enhance the model ***,field data of 10 EVs exhibiting different SOHs are used to verify the proposed *** using the△Q with 100 m V voltage change,the SOH of battery packs can be accurately estimated with an error around 3.2%.

关键词： Lithium-ion battery Electric vehicles Health estimation Feature extraction Convolutional neural network Domain adapatation

Failure transition of shear-to-dilation band of rock salt under triaxial stresses

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 56-64页

作者： Jianfeng Liu xiaosong Qiu Jianxiong Yang Chao Liang Jingjing Dai Yu Bian State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan UniversityChengdu610065China CNPC Key Laboratory of Oil and Gas Underground Storage Engineering Langfang065007China College of Water Resource and Hydropower Sichuan UniversityChengdu610065China

Great potential of underground gas/energy storage in salt caverns seems to be a promising solution to support renewable *** the underground storage method,the operating cycle unfortunately may reach up to daily or even hourly,which generates complicated pressures on the salt ***,the mechanical behavior of rock salt may change and present distinct failure characteristics under different stress states,which affects the performance of salt cavern during the time period of full *** reproduce a similar loading condition on the cavern surrounding rock mass,the cyclic triaxial loading/unloading tests are performed on the rock salt to explore the mechanical transition behavior and failure characteristics under different *** results show that the rock salt samples pre-sent a diffused shear failure band with significant bulges at certain locations in low confining pressure conditions(e.g.5 MPa,10 MPa and 15 MPa),which is closely related to crystal misorientation and grain boundary *** the elevated confinement(e.g.20 MPa,30 MPa and 40 MPa),the dilation band dominates the failure mechanism,where the large-size halite crystals are crushed to be smaller size and new pores are *** failure transition mechanism revealed in the paper provides additional insight into the mechanical performance of salt caverns influenced by complicated stress states.

关键词： Rock salt Cyclic mechanical loading Shear band Dilation band Underground gas storage(UGS)

Plastic Crystal Neopentyl Glycol/Multiwall Carbon Nanotubes Composites for Highly Efficient Barocaloric Refrigeration System

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Journal of Thermal Science 2024年第1期33卷 383-393页

作者： DAI Zhaofeng SHE Xiaohui SHAO Bohan YIN Ershuai DING Yulong LI Yongliang ZHANG xiaosong ZHAO Dongliang School of Energy&Environment Southeast UniversityNanjing 210096China School of Mechanical Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Energy and Power Engineering Nanjing University of Science&TechnologyNanjing 210094China Birmingham Centre for Energy Storage School of Chemical EngineeringUniversity of BirminghamBirmingham B152TTUK

Plastic crystal neopentyl glycol(NPG)exhibits colossal barocaloric effect with high entropy ***,their application is restricted in several aspects,such as low thermal conductivity,substantial supercooling effect,and poor springback *** this work,multi-walled carbon nanotubes(MWCNTs)with ultra-high thermal conductivity and high mechanical strength were selected for performance enhancement of *** optimal mixing ratio was determined to be NPG with 3 wt%MWCNTs composites,which showed a 6K reduction in supercooling without affecting the phase change ***,comprehensive performance of the composites with optimal mixing ratio was compared with pure NPG At 40 MPa,390J·K^(-1)·kg^(-1)change in entropy and 9.9 K change in temperature were ***,the minimum driving pressure required to achieve reversible barocaloric effect was reduced by 19.2%.In addition,the thermal conductivity of the composite was increased by approximately 28%,significantly reducing the heat exchange time during a barocaloric refrigeration *** importantly,ultra-high pressure release rate resulted in a73.7%reduction in the springback time of the composites,offering new opportunities for the recovery of expansion work.

关键词： barocaloric refrigeration neopentyl glycol MWCNTs performance enhancement