The El Niño-Southern Oscillation(ENSO)is a naturally recurring interannual climate fluctuation that affects the global climate system.The advent of deep learning-based approaches has led to transformative changes...The El Niño-Southern Oscillation(ENSO)is a naturally recurring interannual climate fluctuation that affects the global climate system.The advent of deep learning-based approaches has led to transformative changes in ENSO forecasts,resulting in significant progress.Most deep learning-based ENSO prediction models which primarily rely solely on reanalysis data may lead to challenges in intensity underestimation in long-term forecasts,reducing the forecasting skills.To this end,we propose a deep residual-coupled model prediction(Res-CMP)model,which integrates historical reanalysis data and coupled model forecast data for multiyear ENSO prediction.The Res-CMP model is designed as a lightweight model that leverages only short-term reanalysis data and nudging assimilation prediction results of the Community Earth System Model(CESM)for effective prediction of the Niño 3.4 index.We also developed a transfer learning strategy for this model to overcome the limitations of inadequate forecast data.After determining the optimal configuration,which included selecting a suitable transfer learning rate during training,along with input variables and CESM forecast lengths,Res-CMP demonstrated a high correlation ability for 19-month lead time predictions(correlation coefficients exceeding 0.5).The Res-CMP model also alleviated the spring predictability barrier(SPB).When validated against actual ENSO events,Res-CMP successfully captured the temporal evolution of the Niño 3.4 index during La Niña events(1998/99 and 2020/21)and El Niño events(2009/10 and 2015/16).Our proposed model has the potential to further enhance ENSO prediction performance by using coupled models to assist deep learning methods.展开更多
This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of w...This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.展开更多
Tailoring grain size can improve the strength of polycrystals by regulating the proportion of grains to grain boundaries and the interaction area.As the grain size decreases to the nanoscale,the deformation mechanism ...Tailoring grain size can improve the strength of polycrystals by regulating the proportion of grains to grain boundaries and the interaction area.As the grain size decreases to the nanoscale,the deformation mechanism in polycrystals shifts from being primarily mediated by dislocations to deformation occurring within the grains and grain boundaries.However,the mechanism responsible for fine-grain strengthening in ferroelectric materials remains unclear,primarily due to the complex multi-field coupling effect arising from spontaneous polarization.Through molecular dynamics simulations,we investigate the strengthening mechanism of barium titanate(BaTiO3),with extremely fine-grain sizes.This material exhibits an inverse Hall–Petch relationship between grain size and strength,rooting in the inhomogeneous concentration of atomic strain and grain rotation.Furthermore,we present a theoretical model to predict the transition from the inverse Hall–Petch stage to the Hall–Petch stage based on strength variations with size,which aligns well with the simulation results.It has been found that the piezoelectric properties of the BaTiO3 are affected by polarization domain switching at various grain sizes.This study enhances our understanding of the atomic-scale mechanisms that contribute to the performance evolution of fine-grain nano-ferroelectric materials.It also provides valuable insights into the design of extremely small-scale ferroelectric components.展开更多
The utilization of multi-field coupling simulation methods has become a pivotal approach for the investigation of intricate fracture behavior and interaction mechanisms of rock masses in deep strata.The high temperatu...The utilization of multi-field coupling simulation methods has become a pivotal approach for the investigation of intricate fracture behavior and interaction mechanisms of rock masses in deep strata.The high temperatures,pressures and complex geological environments of deep strata frequently result in the coupling of multiple physical fields,including mechanical,thermal and hydraulic fields,during the fracturing of rocks.This review initially presents an overview of the coupling mechanisms of these physical fields,thereby elucidating the interaction processes ofmechanical,thermal,and hydraulic fields within rockmasses.Secondly,an in-depth analysis ofmulti-field coupling is conducted from both spatial and temporal perspectives,with the introduction of simulation methods for a range of scales.It emphasizes cross-scale coupling methodologies for the transfer of rock properties and physical field data,including homogenization techniques,nested coupling strategies and data-driven approaches.To address the discontinuous characteristics of the rock fracture process,the review provides a detailed explanation of continuousdiscontinuous couplingmethods,to elucidate the evolution of rock fracturing and deformationmore comprehensively.In conclusion,the review presents a summary of the principal points,challenges and future directions of multi-field coupling simulation research.It also puts forward the potential of integrating intelligent algorithms with multi-scale simulation techniques to enhance the accuracy and efficiency of multi-field coupling simulations.This offers novel insights into multi-field coupling simulation analysis in deep rock masses.展开更多
Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties.This study applies coupled perturbed density functional theory(DFT)to predict the refractive index(...Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties.This study applies coupled perturbed density functional theory(DFT)to predict the refractive index(RI)and Abbe number of polymers.Using the LorentzLorenz equation,the frequency-dependent polarizability and molecular volume were calculated to estimate RI.Wavelength-dependent RI values were used to derive the Abbe numbers.Our results show a strong correlation with experimental data,with Pearson coefficients of 0.912 for RI and 0.968 for Abbe number,enabling the introduction of linear correction functions to minimize discrepancies between theoretical predictions and experimental results.By categorizing polymers into classes such as poly(methyl methacrylate)(PMMA)-,polyethylene(PE)-,polycarbonate(PC)-,polyimide(PI)-,and polyurethane(PU)-based materials,this method enables precise predictions and reduces discrepancies using linear correction functions.This efficient and direct computational framework avoids the complexity of traditional models and offers a practical tool for the design and optimization of advanced optical materials.展开更多
Sandwich piezoelectric semiconductor(PS)structures have significant applications in multi-functional semiconductor devices.The analysis of multi-field coupling behaviors of PS structures is of fundamental importance i...Sandwich piezoelectric semiconductor(PS)structures have significant applications in multi-functional semiconductor devices.The analysis of multi-field coupling behaviors of PS structures is of fundamental importance in developing novel PS devices.In this paper,we develop a general temperature-deformation-polarization-carrier(TDPC)coupling model for sandwich-type PS beams involving pyroelectricity under thermal loadings,based on three-dimensional(3D)basic equations of the thermo-piezoelectric semiconductor(TPS).We derive analytical solutions for extensional,bending,and buckling deformations of simply-supported sandwich n-type PS beams subjected to open-circuit and electrically isolated boundary conditions.The accuracy of the proposed model in this paper is verified through finite element simulations implemented in the COMSOL software.Numerical results show that the initial electron concentration and the thickness ratio of the PS layer to the beam's total thickness have a significant effect on thermally induced extensional and bending responses,as well as critical buckling mechanical and thermal loadings.This study provides a theoretical framework and guidance for designing semiconductor devices based on sandwich PS beam structures.展开更多
With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher ...With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher and higher.The importance of fast and accurate electromagnetic thermal coupling analysis of such motors becomes more and more prominent.In view of this,the surfacemounted PMSM(SPMSM)equipped with unequally thick magnetic poles is taken as the main object and its electromagnetic thermal coupling analytical model(ETc AM)is investigated.First,the electromagnetic analytical model(EAM)is studied based on the modified subdomain method.It realizes the fast calculation of key electromagnetic characteristics.Subsequently,the 3D thermal analytical model(TAM)is developed by combining the EAM,the lumped parameter thermal network method(LPTNM),and the partial differential equation of heat flux.It realizes the fast calculation of key thermal characteristics in 3D space.Further,the information transfer channel between EAM and TAM is built with reference to the intrinsic connection between electromagnetic field and temperature field.Thereby,the novel ETcAM is proposed to realize the fast and accurate prediction of electromagnetic and temperature fields.Besides,ETcAM has a lot to commend it.One is that it well accounts for the complex structure,saturation,and heat exchange behavior.Second,it saves a lot of computer resources.It offers boundless possibilities for initial design,scheme evaluation,and optimization of motors.Finally,the validity,accuracy,and practicality of this study are verified by simulation and experiment.展开更多
In order to solve the problem of strength instability of cemented tailings backfill(CTB)under low temperature environment(≤20℃),the strength optimization and prediction of CTB under the influence of multiple factors...In order to solve the problem of strength instability of cemented tailings backfill(CTB)under low temperature environment(≤20℃),the strength optimization and prediction of CTB under the influence of multiple factors were carried out.The response surface method(RSM)was used to design the experiment to analyze the development law of backfill strength under the coupling effect of curing temperature,sand-cement ratio and slurry mass fraction,and to optimize the mix proportion;the artificial neural network algorithm(ANN)and particle swarm optimization algorithm(PSO)were used to build the prediction model of backfill strength.According to the experimental results of RSM,the optimal mix proportion under different curing temperatures was obtained.When the curing temperature is 10-15℃,the best mix proportion of sand-cement ratio is 9,and the slurry mass fraction is 71%;when the curing temperature is 15-20℃,the best mix proportion of sand-cement ratio is 8,and the slurry mass fraction is 69%.The ANN-PSO intelligent model can accurately predict the strength of CTB,its mean relative estimation error value and correlation coefficient value are only 1.95%and 0.992,and the strength of CTB under different mix proportion can be predicted quickly and accurately by using this model.展开更多
A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the ...A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The ＂observation＂ of the SST anomaly, which is sampled from a ＂truth＂ model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.展开更多
The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （C...The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.展开更多
HP40Nb steel, used as a candidate material for ethylene cracking furnace tube, suffers creep and carburization damage from the complex environment of high temperature, high carbon potential and low oxygen partial pres...HP40Nb steel, used as a candidate material for ethylene cracking furnace tube, suffers creep and carburization damage from the complex environment of high temperature, high carbon potential and low oxygen partial pressure, and they lead to failure of the furnace tubes ahead of designed life. In order to investigate damage evolution under the complex condition, coupled creep damage and carburization damage constitutive equations were developed according to continuum damage mechanics theory. Based on the finite element ABAQUS code, user subroutines were developed for analyz- ing damage evolution of ethylene furnace tube under the action of coupled creep- carburization. The results show that carburization accelerates the damage process dramatically, damage value reaches the critical value along the inner surface after serving for 75,000 h under the action of creep-carburization, meanwhile the damage value is only 0.53 along the outer surface after operating the same time under the action of creep alone, which means that microcracks are generated along the inner surface under the action of coupled creep-carburization, fracture begins along the outer surface of tube under the action of creep alone.展开更多
The‘Two Oceans and One Sea’area(West Pacific,Indian Ocean,and South China Sea;15°S–60°N,39°–178°E)is a core strategic area for the‘21st Century Maritime Silk Road’project,as well as national ...The‘Two Oceans and One Sea’area(West Pacific,Indian Ocean,and South China Sea;15°S–60°N,39°–178°E)is a core strategic area for the‘21st Century Maritime Silk Road’project,as well as national defense.With the increasing demand for disaster prevention and mitigation,the importance of 10–30-day extended range prediction,between the conventional short-term(around seven days)and the climate scale(longer than one month),is apparent.However,marine extended range prediction is still a‘blank point’in China,making the early warning of marine disasters almost impossible.Here,the authors introduce a recently launched Chinese national project on a numerical forecasting system for extended range prediction in the‘Two Oceans and One Sea’area based on a regional ultra-high resolution multi-layer coupled model,including the scientific aims,technical scheme,innovation,and expected achievements.The completion of this prediction system is of considerable significance for the economic development and national security of China.展开更多
The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichm...The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichment law. This study builds porosity and fracture development and evolution models in different deposition environments, through core observation, casting thin section, SEM, porosity and permeability analysis, burial history analysis, and ＂four-property-relationships＂ analysis.展开更多
Based on a simple coupled Lorenz model,we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics.Four initial perturb...Based on a simple coupled Lorenz model,we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics.Four initial perturbation approaches are used in the ensemble forecasting experiments:the random perturbation(RP),the bred vector(BV),the ensemble transform Kalman filter(ETKF),and the nonlinear local Lyapunov vector(NLLV)methods.Results show that,regardless of the method used,the ensemble averages behave indistinguishably from the control forecasts during the first few time steps.Due to different error growth in different time-scale systems,the ensemble averages perform better than the control forecast after very short lead times in a fast subsystem but after a relatively long period of time in a slow subsystem.Due to the coupled dynamic processes,the addition of perturbations to fast variables or to slow variables can contribute to an improvement in the forecasting skill for fast variables and slow variables.Regarding the initial perturbation approaches,the NLLVs show higher forecasting skill than the BVs or RPs overall.The NLLVs and ETKFs had nearly equivalent prediction skill,but NLLVs performed best by a narrow margin.In particular,when adding perturbations to slow variables,the independent perturbations(NLLVs and ETKFs)perform much better in ensemble prediction.These results are simply implied in a real coupled air–sea model.For the prediction of oceanic variables,using independent perturbations(NLLVs)and adding perturbations to oceanic variables are expected to result in better performance in the ensemble prediction.展开更多
A hybrid coupled ocean-atmosphere model is designed, which consists of a global AGCM and a simple anomaly ocean model in the tropical Pacific. Retroactive experimental predictions initiated in each season from 1979 to...A hybrid coupled ocean-atmosphere model is designed, which consists of a global AGCM and a simple anomaly ocean model in the tropical Pacific. Retroactive experimental predictions initiated in each season from 1979 to 1994 are performed. Analyses indicate that: (1) The overall predictive capability of this model for SSTA over the central-eastern tropical Pacific can reach one year, and the error is not larger than 0.8 degrees C. (2) The prediction skill depends greatly on the season when forecasts start. However, the phenomenon of SPB (spring prediction barrier) is not found in the model. (3) The ensemble forecast method can effectively improve prediction results. A new initialization scheme is discussed.展开更多
Currently,numerical models based on idealized assumptions,complex algorithms and high computational costs are unsatisfactory for ocean surface current prediction.Moreover,the complex temporal and spatial variability o...Currently,numerical models based on idealized assumptions,complex algorithms and high computational costs are unsatisfactory for ocean surface current prediction.Moreover,the complex temporal and spatial variability of ocean currents also makes the prediction methods based on time series data challenging.The deep network model can automatically learn and extract complex features hidden in large amount of complex data,so it is a promising method for high quality prediction of ocean currents.In this paper,we propose a spatiotemporal coupled attention deep network model STCANet that can extract abundant temporal and spatial coupling information on the behavior characteristics of ocean currents for improving the prediction accuracy.Firstly,Spatial Module is designed and implemented to extract the spatiotemporal coupling characteristics of ocean currents,and meanwhile the spatial correlations and dependencies among adjacent sea areas are obtained through Spatial Channel Attention Module(SCAM).Secondly,we use the GatedRecurrent-Unit(GRU)to extract temporal relationships of ocean currents,and design and implement the nearest neighbor time attention module to extract the interdependences of ocean currents between adjacent times,which can further improve the accuracy of ocean current prediction.Finally,a series of comparative experiments on the MediSea_Dataset and EastSea_Dataset showed that the prediction quality of our model greatly outperforms those of other benchmark models such as History Average(HA),Autoregressive Integrated Moving Average Model(ARIMA),Long Short-term Memory(LSTM),Gate Recurrent Unit(GRU)and CNN_GRU.展开更多
The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calcula...The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.展开更多
The shape of strip is calculated by iterative method which combines strip plastic deformation model with rolls elastic deformation model through their calculation results, which can be called results coupling method. ...The shape of strip is calculated by iterative method which combines strip plastic deformation model with rolls elastic deformation model through their calculation results, which can be called results coupling method. Be- cause the shape and rolling force distribution are very sensitive to strip thickness transverse distribution% variation, the iterative course is rather unstable and sometimes convergence cannot be achieved. In addition, the calculating speed of results coupling method is low, which restricts its usable range. To solve the problem, a new model cou- pling method is developed, which takes the force distribution between rolls, rolling force distribution and strip＇s exit transverse displacement distribution as basic unknowns, and integrates strip plastic deformation model and rolls elas- tic deformation model as a unified linear equations through their internal relation, so the iterative calculation between the strip plastic deformation model and rolls elastic deformation model can be avoided. To prove the effectiveness of the model coupling method, two examples are calculated by results coupling method and model coupling method re- spectively. The results of front tension stress, back tension stress, strip^s exit gauge, the force between rolls and rolling force distribution calculated by model coupling method coincide very well with results coupling method. How- ever the calculation course of model coupling method is more steady than results coupling method, and its calculating speed is about ten times as much as the maximal speed of results coupling method, which validates its practicability and reliability.展开更多
In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems： the Lorenz model, which possesses a single characteristic time scale, and the c...In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems： the Lorenz model, which possesses a single characteristic time scale, and the coupled Lorenz model, which possesses two different characteristic time scales. The limit of predictability is defined here as the time at which the error reaches 95% of its saturation level; nonlinear behaviors of the error growth are therefore involved in the definition of the limit of predictability. Our results show that the logarithmic function performs well in describing the relationship between the limit of predictability and initial error in both models, although the coefficients in the logarithmic function were not constant across the examined range of initial errors. Compared with the Lorenz model, in the coupled Lorenz model in which the slow dynamics and the fast dynamics interact with each other--there is a more complex relationship between the limit of predictability and initial error. The limit of predictability of the Lorenz model is unbounded as the initial error becomes infinitesimally small; therefore, the limit of predictability of the Lorenz model may be extended by reducing the amplitude of the initial error. In contrast, if there exists a fixed initial error in the fast dynamics of the coupled Lorenz model, the slow dynamics has an intrinsic finite limit of predictability that cannot be extended by reducing the amplitude of the initial error in the slow dynamics, and vice versa. The findings reported here reveal the possible existence of an intrinsic finite limit of predictability in a coupled system that possesses many scales of time or motion.展开更多
Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses...Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions.展开更多
基金The National Key Research and Development Program of China under contract Nos 2024YFF0808900,2023YFF0805300,and 2020YFA0608804the Civilian Space Programme of China under contract No.D040305.
文摘The El Niño-Southern Oscillation(ENSO)is a naturally recurring interannual climate fluctuation that affects the global climate system.The advent of deep learning-based approaches has led to transformative changes in ENSO forecasts,resulting in significant progress.Most deep learning-based ENSO prediction models which primarily rely solely on reanalysis data may lead to challenges in intensity underestimation in long-term forecasts,reducing the forecasting skills.To this end,we propose a deep residual-coupled model prediction(Res-CMP)model,which integrates historical reanalysis data and coupled model forecast data for multiyear ENSO prediction.The Res-CMP model is designed as a lightweight model that leverages only short-term reanalysis data and nudging assimilation prediction results of the Community Earth System Model(CESM)for effective prediction of the Niño 3.4 index.We also developed a transfer learning strategy for this model to overcome the limitations of inadequate forecast data.After determining the optimal configuration,which included selecting a suitable transfer learning rate during training,along with input variables and CESM forecast lengths,Res-CMP demonstrated a high correlation ability for 19-month lead time predictions(correlation coefficients exceeding 0.5).The Res-CMP model also alleviated the spring predictability barrier(SPB).When validated against actual ENSO events,Res-CMP successfully captured the temporal evolution of the Niño 3.4 index during La Niña events(1998/99 and 2020/21)and El Niño events(2009/10 and 2015/16).Our proposed model has the potential to further enhance ENSO prediction performance by using coupled models to assist deep learning methods.
基金the Key Research&Development Program of Xinjiang(Grant Number 2022B01003).
文摘This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.
基金supported by the National Natural Science Foundation of China(Nos.12172117,12372154)National Science and Technology Major Project(No.J2019-1II-0010-0054)+1 种基金National Numerical Windtunnel(No.NNW2019-JT01-023)High-Performance Computing Center of Hebei University。
文摘Tailoring grain size can improve the strength of polycrystals by regulating the proportion of grains to grain boundaries and the interaction area.As the grain size decreases to the nanoscale,the deformation mechanism in polycrystals shifts from being primarily mediated by dislocations to deformation occurring within the grains and grain boundaries.However,the mechanism responsible for fine-grain strengthening in ferroelectric materials remains unclear,primarily due to the complex multi-field coupling effect arising from spontaneous polarization.Through molecular dynamics simulations,we investigate the strengthening mechanism of barium titanate(BaTiO3),with extremely fine-grain sizes.This material exhibits an inverse Hall–Petch relationship between grain size and strength,rooting in the inhomogeneous concentration of atomic strain and grain rotation.Furthermore,we present a theoretical model to predict the transition from the inverse Hall–Petch stage to the Hall–Petch stage based on strength variations with size,which aligns well with the simulation results.It has been found that the piezoelectric properties of the BaTiO3 are affected by polarization domain switching at various grain sizes.This study enhances our understanding of the atomic-scale mechanisms that contribute to the performance evolution of fine-grain nano-ferroelectric materials.It also provides valuable insights into the design of extremely small-scale ferroelectric components.
基金supported by the National Natural Science Foundation of China(Grant Nos.42477185,41602308)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY20E080005)the Postgraduate Course Construction Project of Zhejiang University of Science and Technology(Grant No.2021yjskj05).
文摘The utilization of multi-field coupling simulation methods has become a pivotal approach for the investigation of intricate fracture behavior and interaction mechanisms of rock masses in deep strata.The high temperatures,pressures and complex geological environments of deep strata frequently result in the coupling of multiple physical fields,including mechanical,thermal and hydraulic fields,during the fracturing of rocks.This review initially presents an overview of the coupling mechanisms of these physical fields,thereby elucidating the interaction processes ofmechanical,thermal,and hydraulic fields within rockmasses.Secondly,an in-depth analysis ofmulti-field coupling is conducted from both spatial and temporal perspectives,with the introduction of simulation methods for a range of scales.It emphasizes cross-scale coupling methodologies for the transfer of rock properties and physical field data,including homogenization techniques,nested coupling strategies and data-driven approaches.To address the discontinuous characteristics of the rock fracture process,the review provides a detailed explanation of continuousdiscontinuous couplingmethods,to elucidate the evolution of rock fracturing and deformationmore comprehensively.In conclusion,the review presents a summary of the principal points,challenges and future directions of multi-field coupling simulation research.It also puts forward the potential of integrating intelligent algorithms with multi-scale simulation techniques to enhance the accuracy and efficiency of multi-field coupling simulations.This offers novel insights into multi-field coupling simulation analysis in deep rock masses.
基金financially supported by the Shenzhen Science and Technology Project(Nos.JCYJ20210324095210028,JSGGZD20220822095201003)the National Natural Science Foundation of China(U21A2087)。
文摘Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties.This study applies coupled perturbed density functional theory(DFT)to predict the refractive index(RI)and Abbe number of polymers.Using the LorentzLorenz equation,the frequency-dependent polarizability and molecular volume were calculated to estimate RI.Wavelength-dependent RI values were used to derive the Abbe numbers.Our results show a strong correlation with experimental data,with Pearson coefficients of 0.912 for RI and 0.968 for Abbe number,enabling the introduction of linear correction functions to minimize discrepancies between theoretical predictions and experimental results.By categorizing polymers into classes such as poly(methyl methacrylate)(PMMA)-,polyethylene(PE)-,polycarbonate(PC)-,polyimide(PI)-,and polyurethane(PU)-based materials,this method enables precise predictions and reduces discrepancies using linear correction functions.This efficient and direct computational framework avoids the complexity of traditional models and offers a practical tool for the design and optimization of advanced optical materials.
基金Project supported by the National Natural Science Foundation of China(No.11672265)。
文摘Sandwich piezoelectric semiconductor(PS)structures have significant applications in multi-functional semiconductor devices.The analysis of multi-field coupling behaviors of PS structures is of fundamental importance in developing novel PS devices.In this paper,we develop a general temperature-deformation-polarization-carrier(TDPC)coupling model for sandwich-type PS beams involving pyroelectricity under thermal loadings,based on three-dimensional(3D)basic equations of the thermo-piezoelectric semiconductor(TPS).We derive analytical solutions for extensional,bending,and buckling deformations of simply-supported sandwich n-type PS beams subjected to open-circuit and electrically isolated boundary conditions.The accuracy of the proposed model in this paper is verified through finite element simulations implemented in the COMSOL software.Numerical results show that the initial electron concentration and the thickness ratio of the PS layer to the beam's total thickness have a significant effect on thermally induced extensional and bending responses,as well as critical buckling mechanical and thermal loadings.This study provides a theoretical framework and guidance for designing semiconductor devices based on sandwich PS beam structures.
基金supported by the Project of National Natural Science Foundation of China under Grant 52077122。
文摘With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher and higher.The importance of fast and accurate electromagnetic thermal coupling analysis of such motors becomes more and more prominent.In view of this,the surfacemounted PMSM(SPMSM)equipped with unequally thick magnetic poles is taken as the main object and its electromagnetic thermal coupling analytical model(ETc AM)is investigated.First,the electromagnetic analytical model(EAM)is studied based on the modified subdomain method.It realizes the fast calculation of key electromagnetic characteristics.Subsequently,the 3D thermal analytical model(TAM)is developed by combining the EAM,the lumped parameter thermal network method(LPTNM),and the partial differential equation of heat flux.It realizes the fast calculation of key thermal characteristics in 3D space.Further,the information transfer channel between EAM and TAM is built with reference to the intrinsic connection between electromagnetic field and temperature field.Thereby,the novel ETcAM is proposed to realize the fast and accurate prediction of electromagnetic and temperature fields.Besides,ETcAM has a lot to commend it.One is that it well accounts for the complex structure,saturation,and heat exchange behavior.Second,it saves a lot of computer resources.It offers boundless possibilities for initial design,scheme evaluation,and optimization of motors.Finally,the validity,accuracy,and practicality of this study are verified by simulation and experiment.
基金the National Key Technology Research and Development Program of China(Nos.2018YFC1900603 and 2018YFC0604604)。
文摘In order to solve the problem of strength instability of cemented tailings backfill(CTB)under low temperature environment(≤20℃),the strength optimization and prediction of CTB under the influence of multiple factors were carried out.The response surface method(RSM)was used to design the experiment to analyze the development law of backfill strength under the coupling effect of curing temperature,sand-cement ratio and slurry mass fraction,and to optimize the mix proportion;the artificial neural network algorithm(ANN)and particle swarm optimization algorithm(PSO)were used to build the prediction model of backfill strength.According to the experimental results of RSM,the optimal mix proportion under different curing temperatures was obtained.When the curing temperature is 10-15℃,the best mix proportion of sand-cement ratio is 9,and the slurry mass fraction is 71%;when the curing temperature is 15-20℃,the best mix proportion of sand-cement ratio is 8,and the slurry mass fraction is 69%.The ANN-PSO intelligent model can accurately predict the strength of CTB,its mean relative estimation error value and correlation coefficient value are only 1.95%and 0.992,and the strength of CTB under different mix proportion can be predicted quickly and accurately by using this model.
基金supported by the National Natural Science Foundation of China(Grant Nos.41490644,41475101 and 41421005)the CAS Strategic Priority Project(the Western Pacific Ocean System+2 种基金Project Nos.XDA11010105,XDA11020306 and XDA11010301)the NSFC-Shandong Joint Fund for Marine Science Research Centers(Grant No.U1406401)the NSFC Innovative Group Grant(Project No.41421005)
文摘A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The ＂observation＂ of the SST anomaly, which is sampled from a ＂truth＂ model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.
基金supported by the National Natural Science Foundation of China (NFSC Grant Nos. 41690122, 41690120, 41490644, 41490640 and 41475101)+5 种基金the Ao Shan Talents Program supported by Qingdao National Laboratory for Marine Science and Technology (Grant No. 2015ASTP)a Chinese Academy of Sciences Strategic Priority Projectthe Western Pacific Ocean System (Grant Nos. XDA11010105, XDA11020306)the NSFC–Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406401)the National Natural Science Foundation of China Innovative Group Grant (Grant No. 41421005)the Taishan Scholarship and Qingdao Innovative Program (Grant No. 2014GJJS0101)
文摘The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.
基金the support of National Natural Science Foundation of China (No. 50775107)National High Technical Research and Development Programme of China (No. 2007AA04Z407)Innovation Program for Graduate Students in Nanjing University of Technology (No. BSCX200816)
文摘HP40Nb steel, used as a candidate material for ethylene cracking furnace tube, suffers creep and carburization damage from the complex environment of high temperature, high carbon potential and low oxygen partial pressure, and they lead to failure of the furnace tubes ahead of designed life. In order to investigate damage evolution under the complex condition, coupled creep damage and carburization damage constitutive equations were developed according to continuum damage mechanics theory. Based on the finite element ABAQUS code, user subroutines were developed for analyz- ing damage evolution of ethylene furnace tube under the action of coupled creep- carburization. The results show that carburization accelerates the damage process dramatically, damage value reaches the critical value along the inner surface after serving for 75,000 h under the action of creep-carburization, meanwhile the damage value is only 0.53 along the outer surface after operating the same time under the action of creep alone, which means that microcracks are generated along the inner surface under the action of coupled creep-carburization, fracture begins along the outer surface of tube under the action of creep alone.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFC1404105,2017YFC1404100,2017YFC1404101,2017YFC1404102,2017YFC1404103 and 2017YFC1404104)
文摘The‘Two Oceans and One Sea’area(West Pacific,Indian Ocean,and South China Sea;15°S–60°N,39°–178°E)is a core strategic area for the‘21st Century Maritime Silk Road’project,as well as national defense.With the increasing demand for disaster prevention and mitigation,the importance of 10–30-day extended range prediction,between the conventional short-term(around seven days)and the climate scale(longer than one month),is apparent.However,marine extended range prediction is still a‘blank point’in China,making the early warning of marine disasters almost impossible.Here,the authors introduce a recently launched Chinese national project on a numerical forecasting system for extended range prediction in the‘Two Oceans and One Sea’area based on a regional ultra-high resolution multi-layer coupled model,including the scientific aims,technical scheme,innovation,and expected achievements.The completion of this prediction system is of considerable significance for the economic development and national security of China.
文摘The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichment law. This study builds porosity and fracture development and evolution models in different deposition environments, through core observation, casting thin section, SEM, porosity and permeability analysis, burial history analysis, and ＂four-property-relationships＂ analysis.
基金jointly supported by the National Natural Science Foundation of China (Grant Nos. 42225501, 42105059)
文摘Based on a simple coupled Lorenz model,we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics.Four initial perturbation approaches are used in the ensemble forecasting experiments:the random perturbation(RP),the bred vector(BV),the ensemble transform Kalman filter(ETKF),and the nonlinear local Lyapunov vector(NLLV)methods.Results show that,regardless of the method used,the ensemble averages behave indistinguishably from the control forecasts during the first few time steps.Due to different error growth in different time-scale systems,the ensemble averages perform better than the control forecast after very short lead times in a fast subsystem but after a relatively long period of time in a slow subsystem.Due to the coupled dynamic processes,the addition of perturbations to fast variables or to slow variables can contribute to an improvement in the forecasting skill for fast variables and slow variables.Regarding the initial perturbation approaches,the NLLVs show higher forecasting skill than the BVs or RPs overall.The NLLVs and ETKFs had nearly equivalent prediction skill,but NLLVs performed best by a narrow margin.In particular,when adding perturbations to slow variables,the independent perturbations(NLLVs and ETKFs)perform much better in ensemble prediction.These results are simply implied in a real coupled air–sea model.For the prediction of oceanic variables,using independent perturbations(NLLVs)and adding perturbations to oceanic variables are expected to result in better performance in the ensemble prediction.
文摘A hybrid coupled ocean-atmosphere model is designed, which consists of a global AGCM and a simple anomaly ocean model in the tropical Pacific. Retroactive experimental predictions initiated in each season from 1979 to 1994 are performed. Analyses indicate that: (1) The overall predictive capability of this model for SSTA over the central-eastern tropical Pacific can reach one year, and the error is not larger than 0.8 degrees C. (2) The prediction skill depends greatly on the season when forecasts start. However, the phenomenon of SPB (spring prediction barrier) is not found in the model. (3) The ensemble forecast method can effectively improve prediction results. A new initialization scheme is discussed.
基金The authors would like to thank the financial support from the National Key Research and Development Program of China(Nos.2020YFE0201200,2019YFC1509100)the partial support by the Youth Program of Natural Science Foundation of China(No.41706010)the Fundamental Research Funds for the Central Universities(No.202264002).
文摘Currently,numerical models based on idealized assumptions,complex algorithms and high computational costs are unsatisfactory for ocean surface current prediction.Moreover,the complex temporal and spatial variability of ocean currents also makes the prediction methods based on time series data challenging.The deep network model can automatically learn and extract complex features hidden in large amount of complex data,so it is a promising method for high quality prediction of ocean currents.In this paper,we propose a spatiotemporal coupled attention deep network model STCANet that can extract abundant temporal and spatial coupling information on the behavior characteristics of ocean currents for improving the prediction accuracy.Firstly,Spatial Module is designed and implemented to extract the spatiotemporal coupling characteristics of ocean currents,and meanwhile the spatial correlations and dependencies among adjacent sea areas are obtained through Spatial Channel Attention Module(SCAM).Secondly,we use the GatedRecurrent-Unit(GRU)to extract temporal relationships of ocean currents,and design and implement the nearest neighbor time attention module to extract the interdependences of ocean currents between adjacent times,which can further improve the accuracy of ocean current prediction.Finally,a series of comparative experiments on the MediSea_Dataset and EastSea_Dataset showed that the prediction quality of our model greatly outperforms those of other benchmark models such as History Average(HA),Autoregressive Integrated Moving Average Model(ARIMA),Long Short-term Memory(LSTM),Gate Recurrent Unit(GRU)and CNN_GRU.
基金supported by the National Natural Science Foundation of China(52372310)the State Key Laboratory of Advanced Rail Autonomous Operation(RAO2023ZZ001)+1 种基金the Fundamental Research Funds for the Central Universities(2022JBQY001)Beijing Laboratory of Urban Rail Transit.
文摘The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe distance.To guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB process.In this case,the minimal safety distance is usually numerically calculated without an analytic formulation.Thus,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing literature.To solve this problem,we propose a Distributed Economic Model Predictive Control(DEMPC)approach with computation efficiency and theoretical guarantee.Specifically,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved efficiently.For theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient tuning.Moreover,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among VCTS.Finally,experimental results demonstrate the performance and advantages of the proposed approaches.
基金Sponsored by National Science and Technology Support Plan of China (2009AA04Z143)Science and Technology Support Plan of Hebei Province of China (10212101D)Important Natural Science Foundation of Hebei Province of China (E2006001038)
文摘The shape of strip is calculated by iterative method which combines strip plastic deformation model with rolls elastic deformation model through their calculation results, which can be called results coupling method. Be- cause the shape and rolling force distribution are very sensitive to strip thickness transverse distribution% variation, the iterative course is rather unstable and sometimes convergence cannot be achieved. In addition, the calculating speed of results coupling method is low, which restricts its usable range. To solve the problem, a new model cou- pling method is developed, which takes the force distribution between rolls, rolling force distribution and strip＇s exit transverse displacement distribution as basic unknowns, and integrates strip plastic deformation model and rolls elas- tic deformation model as a unified linear equations through their internal relation, so the iterative calculation between the strip plastic deformation model and rolls elastic deformation model can be avoided. To prove the effectiveness of the model coupling method, two examples are calculated by results coupling method and model coupling method re- spectively. The results of front tension stress, back tension stress, strip^s exit gauge, the force between rolls and rolling force distribution calculated by model coupling method coincide very well with results coupling method. How- ever the calculation course of model coupling method is more steady than results coupling method, and its calculating speed is about ten times as much as the maximal speed of results coupling method, which validates its practicability and reliability.
基金sprovided jointly by the 973 Program (Grant No.2010CB950400)National Natural Science Foundation of China (Grant Nos. 40805022 and 40821092)
文摘In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems： the Lorenz model, which possesses a single characteristic time scale, and the coupled Lorenz model, which possesses two different characteristic time scales. The limit of predictability is defined here as the time at which the error reaches 95% of its saturation level; nonlinear behaviors of the error growth are therefore involved in the definition of the limit of predictability. Our results show that the logarithmic function performs well in describing the relationship between the limit of predictability and initial error in both models, although the coefficients in the logarithmic function were not constant across the examined range of initial errors. Compared with the Lorenz model, in the coupled Lorenz model in which the slow dynamics and the fast dynamics interact with each other--there is a more complex relationship between the limit of predictability and initial error. The limit of predictability of the Lorenz model is unbounded as the initial error becomes infinitesimally small; therefore, the limit of predictability of the Lorenz model may be extended by reducing the amplitude of the initial error. In contrast, if there exists a fixed initial error in the fast dynamics of the coupled Lorenz model, the slow dynamics has an intrinsic finite limit of predictability that cannot be extended by reducing the amplitude of the initial error in the slow dynamics, and vice versa. The findings reported here reveal the possible existence of an intrinsic finite limit of predictability in a coupled system that possesses many scales of time or motion.
基金supported by the National Key Research&Development Program of China(Grant Nos.2017YFC1404100 and 2017YFC1404104)the National Natural Science Foundation of China(Grant Nos.41775100 and 41830964)。
文摘Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions.
