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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 359-370页

作者： Jianlin Li Zhijin Fang Qian Wang Mengyuan Zhang Yaxin Li Weijun Zhang Beijing Future Technology Innovation centre for Electrochemical Energy Storage System Integration North China University of Technology Beijing China State Grid Fujian Electric Power research Institute Fuzhou China

As renewable energy continues to be integrated into the grid,energy storage has become a vital technique supporting power system development.To effectively promote the efficiency and economics of energy storage,centralized shared energy storage(SES)station with multiple energy storage batteries is developed to enable energy trading among a group of entities.In this paper,we propose the optimal operation with dynamic partitioning strategy for the centralized SES station,considering the day-ahead demands of large-scale renewable energy power plants.We implement a multi-entity cooperative optimization operation model based on Nash bargaining theory.This model is decomposed into two subproblems:the operation profit maximization problem with energy trading and the leasing payment bargaining problem.The distributed alternating direction multiplier method(ADMM)is employed to address the subproblems separately.simulations reveal that the optimal operation with a dynamic partitioning strategy improves the tracking of planned output of renewable energy entities,enhances the actual utilization rate of energy storage,and increases the profits of each participating entity.The results confirm the practicality and effectiveness of the strategy.

关键词： Shared energy storage(SES) dynamic partitioning strategy optimal operation Nash bargaining theory actual utilization rate of energy storage

A novel method for modeling the phase change of iron ore particles in the cohesive zone of a blast furnace

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Particuology 2024年第3期86卷 252-261页

作者： Dianyu E Yingming Wen Jiayun Dan Youyuan Jiang Guangchao Wei Jiaxin Cui Jiangxi Provincial Key Laboratory for simulation and modelling of Particulate Systems Jiangxi University of Science and TechnologyNanchang330013China International Institute for Innovation Jiangxi University of Science and TechnologyNanchang330013China Ironmaking Plant Xiangtan Iron and Steel Corporation of Hunan HualingXiangtan411101China Schoolof Transportation and Vehicle Enginering Shandong University of TechnologyZibo255000China

Cohesive zone plays a vital role in the stable operation of a blast furnace(BF),yet the complex phase change process of iron ore particles in this zone is still not well understood.In this study,a novel one-dimensional(1D)unsteady phase change model was developed to elucidate the heat transfer and melting mechanisms of iron ore particles.After model validation,the effects of several key operating parameters(e.g.,particle diameter,gas velocity,initial temperature)on the phase change behavior of iron ore particles were analyzed,and the joint effect of multiple parameters was discussed.The results show that larger-sized iron ore particles possess lower specific surface areas,which in turn reduces their convective heat absorption capacity.Consequently,the distance from the solid-liquid phase interface to the particle surface increases,thereby slowing down the movement of the phase interface and pro-longing the melting duration of the particles.Increasing the gas velocity and the initial temperature does not have a significant impact on reducing the duration of the complete melting process.Under the specified conditions,it is observed that increasing the gas velocity by 3-fold and 9-fold results in a reduction of the melting duration by 2.4%and 8.3%,respectively.Elevating the initial temperature of iron ore particles results in a decrease in the core-to-surface temperature difference,a slower heating rate,and a shorter duration to achieve melting.Among the factors affecting the melting process,the particle diameter is found to be the most significant in terms of the liquid phase precipitation,mushy zone thickness,and core-to-surface temperature difference of iron ore particles.

关键词： Blast furnace Iron ore melting Cohesive zone Heat transfer Phase change

Comparison of Semi-Parametric Shared Frailty Models for Bees’Survival

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Journal of Data Analysis and Information Processing 2024年第2期12卷 267-288页

作者： Patience Isiaho Daisy Salifu Samuel Mwalili Henri E. Z. Tonnang Department of Statistics and Actuarial Sciences Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Data Management Modelling and Geo-Information Unit International Centre of Insect Physiology and Ecology (ICIPE) Nairobi Kenya

Survival analysis is a fundamental tool in medical science for time-to-event data. However, its application to colony organisms like bees poses challenges due to their social nature. Traditional survival models may not accurately capture the interdependence among individuals within a colony. Frailty models, accounting for shared risks within groups, offer a promising alternative. This study evaluates the performance of semi-parametric shared frailty models (gamma, inverse normal, and positive stable-in comparison to the traditional Cox model using bees’ survival data). We examined the effect of misspecification of the frailty distribution on regression and heterogeneity parameters using simulation and concluded that the heterogeneity parameter was more sensitive to misspecification of the frailty distribution and choice of initial parameters (cluster size and true heterogeneity parameter) compared to the regression parameter. From the data, parameter estimates for covariates were close for the four models but slightly higher for the Cox model. The shared gamma frailty model provided a better fit to the data in comparison with the other models. Therefore, when focusing on regression parameters, the gamma frailty model is recommended. This research underscores the importance of tailored survival methodologies for accurately analyzing time-to-event data in social organisms.

关键词： Correlated Failure Times Frailty Survival Analysis Unobserved Heterogeneity

Correcting Climate Model Sea Surface Temperature simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

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Advances in Atmospheric Sciences 2024年第7期41卷 1299-1312页

作者： Ya WANG Gang HUANG Baoxiang PAN Pengfei LIN Niklas BOERS Weichen TAO Yutong CHEN BO LIU Haijie LI State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Earth System modelling School of Engineering and DesignTechnical University of MunichOttobrunn 85521Germany Potsdam Institute for Climate Impact research Potsdam 14412Germany Department of Mathematics and Global Systems Institute University of ExeterExeter EX44QEUK CMA Earth System Modeling and Prediction centre(CEMC) Chinese Academy of Meteorological SciencesBeijing 100081China

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

关键词： generative adversarial networks model bias deep learning El Niño-Southern Oscillation marine heatwaves

Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

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Journal of Arid Land 2024年第3期16卷 331-354页

作者： XU Wenjie DING Jianli BAO Qingling WANG Jinjie XU Kun College of Geography and Remote Sensing Sciences Xinjiang UniversityUrumqi 830017China Key Laboratory of Smart City and Environment modelling of Higher Education Institute Xinjiang UniversityUrumqi 830017China

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Pre

关键词： precipitation estimates satellite-based and reanalysis precipitation dynamic Bayesian model averaging streamflow simulation Ebinur Lake Basin Xinjiang

Towards data-efficient mechanical design of bicontinuous composites usinggenerative AI

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Theoretical & Applied Mechanics Letters 2024年第1期14卷 57-64页

作者： Milad Masrouri Zhao Qin Department of Civil and Environmental Engineering Syracuse UniversitySyracuseNY 13244USA Laboratory for Multiscale Material modelling Syracuse UniversitySyracuseNY 13244USA The BioInspired Institute Syracuse UniversitySyracuseNY 13244USA

The distribution of material phases is crucial to determine the composite’s mechanical property.While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases,this relationship is difficult to be revealed for complex irregular distributions,preventing design of such material structures to meet certain mechanical requirements.The noticeable developments of artificial intelligence(AI)algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures.It is intriguing how these tools can assist composite design.Here,we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading.We find that generative AI,enabled through fine-tuned Low Rank Adaptation models,can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution.The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness,fracture and robustness of the material with one model,and such has to be done by several different experimental or simulation tests.This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.

关键词： Generative artificial intelligence Stable diffusion Composite design Phase field model Molecular dynamics simulation

A particle-resolved heat-particle-fluid coupling model by DEM-IMB-LBM

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第6期16卷 2267-2281页

作者： Ming Xia Jinlong Fu Y.T.Feng Fengqiang Gong Jin Yu Hunan Key Laboratory of Geomechanics and Engineering Safety Xiangtan UniversityXiangtan411105China Zienkiewicz Institute for modelling Data and AISwansea UniversitySwanseaSA18EPUK School of Civil Engineering Southeast UniversityNanjing211189China Fujian research Center for Tunneling and Urban Underground Space Engineering Huaqiao UniversityXiamen361021China

Multifield coupling is frequently encountered and also an active area of research in geotechnical engineering.In this work,a particle-resolved direct numerical simulation(PR-DNS)technique is extended to simulate particle-fluid interaction problems involving heat transfer at the grain level.In this extended technique,an immersed moving boundary(IMB)scheme is used to couple the discrete element method(DEM)and lattice Boltzmann method(LBM),while a recently proposed Dirichlet-type thermal boundary condition is also adapted to account for heat transfer between fluid phase and solid particles.The resulting DEM-IBM-LBM model is robust to simulate moving curved boundaries with constant temperature in thermal flows.To facilitate the understanding and implementation of this coupled model for non-isothermal problems,a complete list is given for the conversion of relevant physical variables to lattice units.Then,benchmark tests,including a single-particle sedimentation and a two-particle drafting-kissing-tumbling(DKT)simulation with heat transfer,are carried out to validate the accuracy of our coupled technique.To further investigate the role of heat transfer in particle-laden flows,two multiple-particle problems with heat transfer are performed.Numerical examples demonstrate that the proposed coupling model is a promising high-resolution approach for simulating the heat-particle-fluid coupling at the grain level.

关键词： Particle-fluid interaction Heat transfer Discrete element method(DEM) Lattice Boltzmann method(LBM) Dirichlet-type thermal boundary Direct numerical simulation

A framework for dynamic modelling of railway track switches considering the switch blades,actuators and control systems

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Railway Engineering Science 2024年第2期32卷 162-176页

作者： Saikat Dutta Tim Harrison Christopher Ward Roger Dixon Phil Winship Institute of Thermofluids School of Mechanical EngineeringUniversity of LeedsLeedsWest YorkshireUK Wolfson School of Mechanical Electrical and Manufacturing EngineeringLoughborough UniversityLoughboroughLeicestershireUK Birmingham centre for Railway research and Education School of EngineeringUniversity of BirminghamBirminghamWest MidlandsUK Network Rail Milton KeynesBuckinghamshireUK

The main contribution of this paper is the development and demonstration of a novel methodology that can be followed to develop a simulation twin of a railway track switch system to test the functionality in a digital environment.This is important because,globally,railway track switches are used to allow trains to change routes;they are a key part of all railway networks.However,because track switches are single points of failure and safety-critical,their inability to operate correctly can cause significant delays and concomitant costs.In order to better understand the dynamic behaviour of switches during operation,this paper has developed a full simulation twin of a complete track switch system.The approach fuses finite element for the rail bending and motion,with physics-based models of the electromechanical actuator system and the control system.Hence,it provides researchers and engineers the opportunity to explore and understand the design space around the dynamic operation of new switches and switch machines before they are built.This is useful for looking at the modification or monitoring of existing switches,and it becomes even more important when new switch concepts are being considered and evaluated.The simulation is capable of running in real time or faster meaning designs can be iterated and checked interactively.The paper describes the modelling approach,demonstrates the methodology by developing the system model for a novel“REPOINT”switch system,and evaluates the system level performance against the dynamic performance requirements for the switch.In the context of that case study,it is found that the proposed new actuation system as designed can meet(and exceed)the system performance requirements,and that the fault tolerance built into the actuation ensures continued operation after a single actuator failure.

关键词： Railway track switch Mathematical modelling Redundant actuation Finite element analysis

Multi-scale modelling of gas flow in nanoscale pore space with fractures

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Journal of Rock Mechanics and Geotechnical Engineering 2020年第1期12卷 32-40页

作者： Qingrong Xiong Diansen Yang Weizhong Chen research centre for radwaste&decommissioning and modelling&simulation centre Dalton Nuclear InstituteUniversity of ManchesterManchesterM139PLUK School of Mechanical Aerospace and Civil EngineeringUniversity of ManchesterManchesterM139PLUK State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan430071China University of Chinese Academy of Sciences Beijing100049China

In this work,a multi-scale pore network with fractures is developed against experimental data in a wide range of degrees of water saturation.The pore network is constructed based on the measured microstructure information at several length scales.The gas transport is predicted by different gas transport equations(e.g.Javadpour,dusty gas model(DGM),Civan and Klinkenberg),which can consider the fundamental physics mechanisms in tight porous media,such as Knudsen diffusion and viscous flow.Then,the model is applied to simulating the gas permeability of the Callovo-Oxfordian(COx)claystone.The predicted gas permeability is basically in good agreement with the experimental data under different degrees of water saturation.Then the effects of micro-fissures are studied.The results suggest that this model can predict the gas flow in other tight porous media as well and can be applied to other fields such as carbon capture and storage.

关键词： Pore network Nanopores Apparent gas permeability Callovo-Oxfordian(COx)claystone

Molecular Dynamics, Diffusion Coefficients and Activation Energy of the Electrolyte (Anode) in Lithium (Li and Li⁺), Sodium (Na and Na⁺) and Potassium (K and K⁺)

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Modeling and Numerical simulation of Material Science 2024年第1期14卷 39-57页

作者： Alain Second Dzabana Honguelet Timothée Nsongo Bitho Rodongo Earvin Loumbandzila Faculty of Science and Technology Marien Ngouabi University Brazzaville Congo research Group on The Physical and Chemical Properties of Materials Brazzaville Congo Association Alpha Sciences Beta Technologies Brazzaville Congo centre for Geological and Mining research Brazzaville Congo

This work is a simulation modelling with the LAMMPS calculation code of an electrode based on alkali metals (lithium, sodium and potassium) using the MEAM potential. For different multiplicities, two models were studied;with and without gap. In this work, we present the structural, physical and chemical properties of the lithium, sodium and potassium electrodes. For the structural properties, the cohesive energy and the mesh parameters were calculated, revealing that, whatever the chemical element selected, the compact hexagonal hcp structure is the most stable, followed by the face-centred cubic CFC structure, and finally the BCC structure. The most stable structure is lithium, with a cohesion energy of -6570 eV, and the lowest bcc-hcp transition energy of -0.553 eV/atom, followed by sodium. For physical properties, kinetic and potential energies were calculated for each of the sectioned chemical elements, with lithium achieving the highest value. Finally, for the chemical properties, we studied the diffusion coefficient and the activation energy. Only potassium followed an opposite order to the other two, with the quantities with lacunae being greater than those without lacunae, whatever the multiplicity. The order of magnitude of the diffusion coefficients is given by the relationship DLi > DNa > Dk for the multiplicity 6*6*6, while for the activation energy the order is reversed.

关键词： Molecular Dynamics Diffusion Coefficients Activation Energy Lithium Alkali Metals MEAM Potential