Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
Water electrolyzers play a crucial role in green hydrogen production.However,their efficiency and scalability are often compromised by bubble dynamics across various scales,from nanoscale to macroscale components.This...Water electrolyzers play a crucial role in green hydrogen production.However,their efficiency and scalability are often compromised by bubble dynamics across various scales,from nanoscale to macroscale components.This review explores multi-scale modeling as a tool to visualize multi-phase flow and improve mass transport in water electrolyzers.At the nanoscale,molecular dynamics(MD)simulations reveal how electrode surface features and wettability influence nanobubble nucleation and stability.Moving to the mesoscale,models such as volume of fluid(VOF)and lattice Boltzmann method(LBM)shed light on bubble transport in porous transport layers(PTLs).These insights inform innovative designs,including gradient porosity and hydrophilic-hydrophobic patterning,aimed at minimizing gas saturation.At the macroscale,VOF simulations elucidate two-phase flow regimes within channels,showing how flow field geometry and wettability affect bubble discharging.Moreover,artificial intelligence(AI)-driven surrogate models expedite the optimization process,allowing for rapid exploration of structural parameters in channel-rib flow fields and porous flow field designs.By integrating these approaches,we can bridge theoretical insights with experimental validation,ultimately enhancing water electrolyzer performance,reducing costs,and advancing affordable,high-efficiency hydrogen production.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of shor...Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of short-term temporal dependencies of lip-shape variations between adjacent frames,which leaves space for further improvement in feature extraction.Methods This article presents a spatiotemporal feature fusion network(STDNet)that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling.Specifically,to distinguish more similar and intricate content,STDNet adds a temporal feature extraction branch based on a 3D-CNN,which enhances the learning of dynamic lip movements in adjacent frames while not affecting spatial feature extraction.In particular,we designed a local–temporal block,which aggregates interframe differences,strengthening the relationship between various local lip regions through multiscale convolution.We incorporated the squeeze-and-excitation mechanism into the Global-Temporal Block,which processes a single frame as an independent unitto learn temporal variations across the entire lip region more effectively.Furthermore,attention pooling was introduced to highlight meaningful frames containing key semantic information for the target word.Results Experimental results demonstrated STDNet's superior performance on the LRW and LRW-1000,achieving word-level recognition accuracies of 90.2% and 53.56%,respectively.Extensive ablation experiments verified the rationality and effectiveness of its modules.Conclusions The proposed model effectively addresses short-term temporal dependency limitations in lip reading,and improves the temporal robustness of the model against variable-length sequences.These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.展开更多
Urban expansion in semi-arid regions poses critical challenges for sustainable land management,ecological resilience,and heritage conservation.Jaipur,India-a United Nations Educational,Scientific and Cultural Organiza...Urban expansion in semi-arid regions poses critical challenges for sustainable land management,ecological resilience,and heritage conservation.Jaipur,India-a United Nations Educational,Scientific and Cultural Organization(UNESCO)World Heritage City located in a semi-arid environment-faces rapid urbanization that threatens agricultural productivity,fragile ecosystems,and cultural assets.This study quantifies past and projects future land use/land cover(LULC)dynamics in Jaipur to support evidence-based planning.Using theDynamicWorld dataset,we generated annual 10-m LULC maps from 2016 to 2025 within the municipal boundary.Temporal change detection was conducted through empirical transition probability analysis,and future scenarios for 2026-2030 were simulated with a Markov chain model coupled with a neighbour-aware cellular automata(CA-Markov)allocation to capture spatial diffusion and terrain constraints.Validation on a 2025 hold-out achieved an Overall Accuracy of 0.79,Cohen’sκof 0.15,and a figure of Merit of 0.073 for built-up gains,confirming credible localization of urban growth.Results reveal that the built-up area expanded from 340.57 km^(2) in 2016 to 387.25 km^(2) in 2025(+13.71%)and is projected to rise by+44.96%by 2030.Over 2016-2025,cropland declined by−40.83%,shrub/scrub by−27.71%,tree cover by−4.12%,and flooded vegetation by−41.28%,while bare ground(+3.14%),grass(−4.22%),and water(~+0.18%)showed minimal change.Forecasts for 2016-2030 indicate severe contractions in crops(−98.40%),shrub/scrub(−93.10%),trees(−80.44%),grass(−95.36%),water(−99.53%),bare ground(−99.51%),and flooded vegetation(−99.80%).These findings highlight an accelerating transformation of Jaipur’s peri-urban landscape,with built-up expansion occurring at the expense of nearly all productive and ecological land classes.The study demonstrates that CA-Markov-based LULC forecasting provides a reproducible and transparent framework for high-frequency monitoring and offers actionable insights for sustainable urban management in heritage cities under rapid growth pressure.展开更多
Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experime...Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.展开更多
A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and...A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and development of the NMI model and then emphasize that the NMI model represents a new tool for identifying the basic physics of how climate change influences mid-to-high latitude weather extremes.The building of the NMI model took place over three main periods.In the 1990s,a nonlinear Schr?dinger(NLS)equation model was presented to describe atmospheric blocking as a wave packet;however,it could not depict the lifetime(10-20 days)of atmospheric blocking.In the 2000s,we proposed an NMI model of atmospheric blocking in a uniform basic flow by making a scale-separation assumption and deriving an eddyforced NLS equation.This model succeeded in describing the life cycle of atmospheric blocking.In the 2020s,the NMI model was extended to include the impact of a changing climate mainly by altering the basic zonal winds and the magnitude of the meridional background potential vorticity gradient(PVy).Model results show that when PVy is smaller,blocking has a weaker dispersion and a stronger nonlinearity,so blocking can be more persistent and have a larger zonal scale and weaker eastward movement,thus favoring stronger weather extremes.However,when PVy is much smaller and below a critical threshold under much stronger winter Arctic warming of global warming,atmospheric blocking becomes locally less persistent and shows a much stronger westward movement,which acts to inhibit local cold extremes.Such a case does not happen in summer under global warming because PVy fails to fall below the critical threshold.Thus,our theory indicates that global warming can render summer-blocking anticyclones and mid-to-high latitude heatwaves more persistent,intense,and widespread.展开更多
Predicting information dissemination on social media,specifcally users’reposting behavior,is crucial for applications such as advertising campaigns.Conventional methods use deep neural networks to make predictions ba...Predicting information dissemination on social media,specifcally users’reposting behavior,is crucial for applications such as advertising campaigns.Conventional methods use deep neural networks to make predictions based on features related to user topic interests and social preferences.However,these models frequently fail to account for the difculties arising from limited training data and model size,which restrict their capacity to learn and capture the intricate patterns within microblogging data.To overcome this limitation,we introduce a novel model Adapt pre-trained Large Language model for Reposting Prediction(ALL-RP),which incorporates two key steps:(1)extracting features from post content and social interactions using a large language model with extensive parameters and trained on a vast corpus,and(2)performing semantic and temporal adaptation to transfer the large language model’s knowledge of natural language,vision,and graph structures to reposting prediction tasks.Specifcally,the temporal adapter in the ALL-RP model captures multi-dimensional temporal information from evolving patterns of user topic interests and social preferences,thereby providing a more realistic refection of user attributes.Additionally,to enhance the robustness of feature modeling,we introduce a variant of the temporal adapter that implements multiple temporal adaptations in parallel while maintaining structural simplicity.Experimental results on real-world datasets demonstrate that the ALL-RP model surpasses state-of-the-art models in predicting both individual user reposting behavior and group sharing behavior,with performance gains of 2.81%and 4.29%,respectively.展开更多
Considering the urban characteristics, a customized multi-scale numerical modeling system is established to simulate the urban meteorological environment. The system mainly involves three spatial scales: the urban sca...Considering the urban characteristics, a customized multi-scale numerical modeling system is established to simulate the urban meteorological environment. The system mainly involves three spatial scales: the urban scale, urban sub-domain scale, and single to few buildings scale. In it, different underlying surface types are employed, the building drag factor is used to replace its roughness in the influence on the urban wind field, the effects of building distribution, azimuth and screening of shortwave radiation are added, and the influence of anthropogenic heating is also taken into account. All the numerical tests indicate that the simulated results are reasonably in agreement with the observational data, so the system can be used to simulate the urban meteorological environment. Making use of it, the characteristics of the meteorological environment from the urban to urban sub-domain scales, even the among-buildings scale, can be recognized. As long as the urban planning scheme is given, the corresponding simulated results can be obtained so as to meet the need of optimizing urban planning.展开更多
The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope ...The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.展开更多
Liquid-metal cooling(LMC)process can offer refinement of microstructure and reduce defects due to the increased cooling rate from enhanced heat extraction,and thus an understanding of solidification behavior in nickel...Liquid-metal cooling(LMC)process can offer refinement of microstructure and reduce defects due to the increased cooling rate from enhanced heat extraction,and thus an understanding of solidification behavior in nickel-based superalloy casting during LMC process is essential for improving mechanical performance of single crystal(SC)castings.In this effort,an integrated heat transfer model coupling meso grain structure and micro dendrite is developed to predict the temperature distribution and microstructure evolution in LMC process.An interpolation algorithm is used to deal with the macro-micro grids coupling issues.The algorithm of cells capture is also modified,and a deterministic cellular automaton(DCA)model is proposed to describe neighborhood cell tracking.In addition,solute distribution is also considered to describe the dendrite growth.Temperature measuring,EBSD,OM and SEM experiments are implemented to verify the proposed model,and the experiment results agree well with the simulation results.Several simulations are performed with a range of withdrawal rates,and the results indicate that 12 mm·min^(-1)is suitable for LMC process in this work,which can result in a fairly narrow and flat mushy zone and correspondingly exhibited fairly straight grains.The mushy zone length is about 4.8 mm in the steady state and the average deviation angle of grains is about 13.9°at the height 90 mm from the casting base under 12 mm·min^(-1)withdrawal process.The competitive phenomenon of dendrites at different withdrawal rates is also observed,which has a great relevant to the temperature fluctuation.展开更多
The human cardiovascular system is a closed- loop and complex vascular network with multi-scaled het- erogeneous hemodynamic phenomena. Here, we give a selective review of recent progress in macro-hemodynamic modeling...The human cardiovascular system is a closed- loop and complex vascular network with multi-scaled het- erogeneous hemodynamic phenomena. Here, we give a selective review of recent progress in macro-hemodynamic modeling, with a focus on geometrical multi-scale model- ing of the vascular network, micro-hemodynamic modeling of microcirculation, as well as blood cellular, subcellular, endothelial biomechanics, and their interaction with arter- ial vessel mechanics. We describe in detail the methodology of hemodynamic modeling and its potential applications in cardiovascular research and clinical practice. In addition, we present major topics for future study： recent progress of patient-specific hemodynamic modeling in clinical applica- tions, micro-hemodynamic modeling in capillaries and blood cells, and the importance and potential of the multi-scale hemodynarnic modeling.展开更多
Successful modeling and/or design of engineering systems often requires one to address the impact of multiple ＂design variables＂ on the prescribed outcome.There are often multiple,competing objectives based on which...Successful modeling and/or design of engineering systems often requires one to address the impact of multiple ＂design variables＂ on the prescribed outcome.There are often multiple,competing objectives based on which we assess the outcome of optimization.Since accurate,high fidelity models are typically time consuming and computationally expensive,comprehensive evaluations can be conducted only if an efficient framework is available.Furthermore,informed decisions of the model/hardware＇s overall performance rely on an adequate understanding of the global,not local,sensitivity of the individual design variables on the objectives.The surrogate-based approach,which involves approximating the objectives as continuous functions of design variables from limited data,offers a rational framework to reduce the number of important input variables,i.e.,the dimension of a design or modeling space.In this paper,we review the fundamental issues that arise in surrogate-based analysis and optimization,highlighting concepts,methods,techniques,as well as modeling implications for mechanics problems.To aid the discussions of the issues involved,we summarize recent efforts in investigating cryogenic cavitating flows,active flow control based on dielectric barrier discharge concepts,and lithium（Li）-ion batteries.It is also stressed that many multi-scale mechanics problems can naturally benefit from the surrogate approach for ＂scale bridging.＂展开更多
Urban faults in Shenzhen are potential threats to city security and sustainable development. In consideration of the importance of the Shenzhen fault zone, the author provide a detailed interpretation on gravity data ...Urban faults in Shenzhen are potential threats to city security and sustainable development. In consideration of the importance of the Shenzhen fault zone, the author provide a detailed interpretation on gravity data model. Bouguer gravity covering the whole Shenzhen City was calculated with a 1-km resolution. Wavelet multi-scale analysis（MSA） was applied to the Bouguer gravity data to obtain the multilayer residual anomalies corresponding to different depths. In addition, 2D gravity models were constructed along three profiles. The Bouguer gravity anomaly shows an NE-striking high-low-high pattern from northwest to southeast, strongly related to the main faults. According to the results of MSA, the correlation between gravity anomaly and faults is particularly significant from 4 to 12 km depth. The residual gravity with small amplitude in each layer indicates weak tectonic activity in the crust. In the upper layers, positive anomalies along most of faults reveal the upwelling of high-density materials during the past tectonic movements. The multilayer residual anomalies also yield important information about the faults, such as the vertical extension and the dip direction. The maximum depth of the faults is about 20 km. In general, NE-striking faults extend deeper than NW-striking faults and have a larger dip angle.展开更多
Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale pr...Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites.An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture.The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions.Three RVE models of cuboid,hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale.Besides,the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models.The prediction results at the mesoscale correspond closely to the experimental values.The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration.The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.展开更多
Eariy-arrival waveform tomography （EWT） is one of the most promising techniques for building near-surface velocity model. Based on finite-frequency wave equation, EWT estimates velocities by matching calculated earl...Eariy-arrival waveform tomography （EWT） is one of the most promising techniques for building near-surface velocity model. Based on finite-frequency wave equation, EWT estimates velocities by matching calculated early-arrival waveforms with the observed ones. However, the objective function of EWT can easily converge to local minimum because of the cycle-skipping phenomenon. In order to reduce the cycle-skipping problem, a hybrid-domain early-arrival waveform tomography （HEWT） is proposed in this paper. The forward modeling of HEWT is realized in the time domain where early-arrival waveforms are easier to be selected from seismic data and less memory is needed than they are in the frequency domain. The inversion is implemented in the frequency domain where multi-scale strategy is more convenient to be realized than that in the time domain. Discrete Fourier transformation （DFT） is used to transform the time-domain wavefield to the frequency-domain wavefield. Test results show that HEWT is more competitive than EWT in both accuracy and computational time.展开更多
Superalloy thin-walled structures are achieved mainly by brazing,but the deformation process of brazed joints is non-uniform,making it a challenging research task.This paper records a thorough investigation of the eff...Superalloy thin-walled structures are achieved mainly by brazing,but the deformation process of brazed joints is non-uniform,making it a challenging research task.This paper records a thorough investigation of the effect of brazing parameters on the microstructure of joints and its mechanical properties,which mainly inquires into the deformation and fracture mechanisms in the shearing process of GH99/BNi-5a/GH99 joints.The macroscopic-microscopic deformation mechanism of the brazing interface during shearing was studied by Crystal Plasticity(CP)and Molecular Dynamics(MD)on the basis of the optimal brazing parameters.The experimental results show that the brazing interface is mainly formed by(Ni,Cr,Co)(s,s)and possesses a shear strength of approximately 546 MPa.The shearing fracture of the brazed joint occurs along the brazing seam,displaying the characteristics of intergranular fracture.MD simulations show that dislocations disassociate and transform into fine twinning with increased strain.CP simulated the shear deformation process of the brazed joint.The multiscale simulation results are consistent with the experimental results.The mechanical properties of thin-walled materials for brazing are predicted using MD and CP methods.展开更多
Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the ro...Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.展开更多
A new method of multi-scale modeling and display of geologic data is introduced to provide information with appropriate detail levels for different types of research. The multi-scale display mode employs a model exten...A new method of multi-scale modeling and display of geologic data is introduced to provide information with appropriate detail levels for different types of research. The multi-scale display mode employs a model extending existing 2D methods into 3D space. Geologic models with different scales are organized by segmenting data into orthogonal blocks. A flow diagram illustrates an octree method for upscaling between blocks with different scales. Upscaling data from the smallest unit cells takes into account their average size and the Burgers vector when there are mismatches. A geocellular model of the Chengdao Reservoir of the Shengli Oilfield, China is taken as an illustrative case, showing that the methods proposed can construct a multi-scale geologic model correctly and display data from the multi-scale model effectively in 3D.展开更多
The complexity of distribution network model mainly depends on the model scale of grid-connected distributed photovoltaic (PV) power generation. Therefore, the simulation performance of multi-scale PV model is the key...The complexity of distribution network model mainly depends on the model scale of grid-connected distributed photovoltaic (PV) power generation. Therefore, the simulation performance of multi-scale PV model is the key factor of the simulation accuracy in the specific operating scenarios of distribution network. In this paper, a multi-scale model of grid connected PV distributed generation system is proposed based on the mathematical model of grid-connected distributed PV power generation. It is analyzed that differences of simulation performance, such as adaptability of simulation step size, accuracy of output and the effect on voltage profile of distribution network, between PV models with different scales in IEEE 33 node example. Simulation results indicate that the multi-scale model is effective in improving the accuracy and efficiency of simulation under different operating conditions of distribution network.展开更多
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
基金supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(Project No.15308024)a grant from Research Centre for Carbon-Strategic Catalysis,The Hong Kong Polytechnic University(CE2X).
文摘Water electrolyzers play a crucial role in green hydrogen production.However,their efficiency and scalability are often compromised by bubble dynamics across various scales,from nanoscale to macroscale components.This review explores multi-scale modeling as a tool to visualize multi-phase flow and improve mass transport in water electrolyzers.At the nanoscale,molecular dynamics(MD)simulations reveal how electrode surface features and wettability influence nanobubble nucleation and stability.Moving to the mesoscale,models such as volume of fluid(VOF)and lattice Boltzmann method(LBM)shed light on bubble transport in porous transport layers(PTLs).These insights inform innovative designs,including gradient porosity and hydrophilic-hydrophobic patterning,aimed at minimizing gas saturation.At the macroscale,VOF simulations elucidate two-phase flow regimes within channels,showing how flow field geometry and wettability affect bubble discharging.Moreover,artificial intelligence(AI)-driven surrogate models expedite the optimization process,allowing for rapid exploration of structural parameters in channel-rib flow fields and porous flow field designs.By integrating these approaches,we can bridge theoretical insights with experimental validation,ultimately enhancing water electrolyzer performance,reducing costs,and advancing affordable,high-efficiency hydrogen production.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金Supported by the National Key Research and Development Program of China(2023YFC3306201)the National Natural Science Foundation of China(61772125)the Fundamental Research Funds for the Central Universities(N2317004).
文摘Background Lip reading uses lip images for visual speech recognition.Deep-learning-based lip reading has greatly improved performance in current datasets;however,most existing research ignores the significance of short-term temporal dependencies of lip-shape variations between adjacent frames,which leaves space for further improvement in feature extraction.Methods This article presents a spatiotemporal feature fusion network(STDNet)that compensates for the deficiencies of current lip-reading approaches in short-term temporal dependency modeling.Specifically,to distinguish more similar and intricate content,STDNet adds a temporal feature extraction branch based on a 3D-CNN,which enhances the learning of dynamic lip movements in adjacent frames while not affecting spatial feature extraction.In particular,we designed a local–temporal block,which aggregates interframe differences,strengthening the relationship between various local lip regions through multiscale convolution.We incorporated the squeeze-and-excitation mechanism into the Global-Temporal Block,which processes a single frame as an independent unitto learn temporal variations across the entire lip region more effectively.Furthermore,attention pooling was introduced to highlight meaningful frames containing key semantic information for the target word.Results Experimental results demonstrated STDNet's superior performance on the LRW and LRW-1000,achieving word-level recognition accuracies of 90.2% and 53.56%,respectively.Extensive ablation experiments verified the rationality and effectiveness of its modules.Conclusions The proposed model effectively addresses short-term temporal dependency limitations in lip reading,and improves the temporal robustness of the model against variable-length sequences.These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.
文摘Urban expansion in semi-arid regions poses critical challenges for sustainable land management,ecological resilience,and heritage conservation.Jaipur,India-a United Nations Educational,Scientific and Cultural Organization(UNESCO)World Heritage City located in a semi-arid environment-faces rapid urbanization that threatens agricultural productivity,fragile ecosystems,and cultural assets.This study quantifies past and projects future land use/land cover(LULC)dynamics in Jaipur to support evidence-based planning.Using theDynamicWorld dataset,we generated annual 10-m LULC maps from 2016 to 2025 within the municipal boundary.Temporal change detection was conducted through empirical transition probability analysis,and future scenarios for 2026-2030 were simulated with a Markov chain model coupled with a neighbour-aware cellular automata(CA-Markov)allocation to capture spatial diffusion and terrain constraints.Validation on a 2025 hold-out achieved an Overall Accuracy of 0.79,Cohen’sκof 0.15,and a figure of Merit of 0.073 for built-up gains,confirming credible localization of urban growth.Results reveal that the built-up area expanded from 340.57 km^(2) in 2016 to 387.25 km^(2) in 2025(+13.71%)and is projected to rise by+44.96%by 2030.Over 2016-2025,cropland declined by−40.83%,shrub/scrub by−27.71%,tree cover by−4.12%,and flooded vegetation by−41.28%,while bare ground(+3.14%),grass(−4.22%),and water(~+0.18%)showed minimal change.Forecasts for 2016-2030 indicate severe contractions in crops(−98.40%),shrub/scrub(−93.10%),trees(−80.44%),grass(−95.36%),water(−99.53%),bare ground(−99.51%),and flooded vegetation(−99.80%).These findings highlight an accelerating transformation of Jaipur’s peri-urban landscape,with built-up expansion occurring at the expense of nearly all productive and ecological land classes.The study demonstrates that CA-Markov-based LULC forecasting provides a reproducible and transparent framework for high-frequency monitoring and offers actionable insights for sustainable urban management in heritage cities under rapid growth pressure.
基金National Key Research and Development Program of China (No.2021YFC3100800)the National Natural Science Foundation of China (Nos.42407235 and 42271026)+1 种基金the Project of Sanya Yazhou Bay Science and Technology City (No.SCKJ-JYRC-2023-54)supported by the Hefei advanced computing center
文摘Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.
基金supported by the National Natural Science Foundation of China(Grant Nos.42150204 and 2288101)supported by the China National Postdoctoral Program for Innovative Talents(BX20230045)the China Postdoctoral Science Foundation(2023M730279)。
文摘A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and development of the NMI model and then emphasize that the NMI model represents a new tool for identifying the basic physics of how climate change influences mid-to-high latitude weather extremes.The building of the NMI model took place over three main periods.In the 1990s,a nonlinear Schr?dinger(NLS)equation model was presented to describe atmospheric blocking as a wave packet;however,it could not depict the lifetime(10-20 days)of atmospheric blocking.In the 2000s,we proposed an NMI model of atmospheric blocking in a uniform basic flow by making a scale-separation assumption and deriving an eddyforced NLS equation.This model succeeded in describing the life cycle of atmospheric blocking.In the 2020s,the NMI model was extended to include the impact of a changing climate mainly by altering the basic zonal winds and the magnitude of the meridional background potential vorticity gradient(PVy).Model results show that when PVy is smaller,blocking has a weaker dispersion and a stronger nonlinearity,so blocking can be more persistent and have a larger zonal scale and weaker eastward movement,thus favoring stronger weather extremes.However,when PVy is much smaller and below a critical threshold under much stronger winter Arctic warming of global warming,atmospheric blocking becomes locally less persistent and shows a much stronger westward movement,which acts to inhibit local cold extremes.Such a case does not happen in summer under global warming because PVy fails to fall below the critical threshold.Thus,our theory indicates that global warming can render summer-blocking anticyclones and mid-to-high latitude heatwaves more persistent,intense,and widespread.
文摘Predicting information dissemination on social media,specifcally users’reposting behavior,is crucial for applications such as advertising campaigns.Conventional methods use deep neural networks to make predictions based on features related to user topic interests and social preferences.However,these models frequently fail to account for the difculties arising from limited training data and model size,which restrict their capacity to learn and capture the intricate patterns within microblogging data.To overcome this limitation,we introduce a novel model Adapt pre-trained Large Language model for Reposting Prediction(ALL-RP),which incorporates two key steps:(1)extracting features from post content and social interactions using a large language model with extensive parameters and trained on a vast corpus,and(2)performing semantic and temporal adaptation to transfer the large language model’s knowledge of natural language,vision,and graph structures to reposting prediction tasks.Specifcally,the temporal adapter in the ALL-RP model captures multi-dimensional temporal information from evolving patterns of user topic interests and social preferences,thereby providing a more realistic refection of user attributes.Additionally,to enhance the robustness of feature modeling,we introduce a variant of the temporal adapter that implements multiple temporal adaptations in parallel while maintaining structural simplicity.Experimental results on real-world datasets demonstrate that the ALL-RP model surpasses state-of-the-art models in predicting both individual user reposting behavior and group sharing behavior,with performance gains of 2.81%and 4.29%,respectively.
基金sponsored by the Key Project(96-920-34-07)of the Ministry of Science and Technology,Chinathe Nationa1 Natura1 Science Foundation of China(40333027).
文摘Considering the urban characteristics, a customized multi-scale numerical modeling system is established to simulate the urban meteorological environment. The system mainly involves three spatial scales: the urban scale, urban sub-domain scale, and single to few buildings scale. In it, different underlying surface types are employed, the building drag factor is used to replace its roughness in the influence on the urban wind field, the effects of building distribution, azimuth and screening of shortwave radiation are added, and the influence of anthropogenic heating is also taken into account. All the numerical tests indicate that the simulated results are reasonably in agreement with the observational data, so the system can be used to simulate the urban meteorological environment. Making use of it, the characteristics of the meteorological environment from the urban to urban sub-domain scales, even the among-buildings scale, can be recognized. As long as the urban planning scheme is given, the corresponding simulated results can be obtained so as to meet the need of optimizing urban planning.
基金Funded by the National Natural Science Foundation of China Academy of Engineering Physics and Jointly Setup"NSAF"Joint Fund(No.U1430119)。
文摘The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.
基金supported by the National Natural Science Foundation of China(No.51904276)Science and Technology Development Program of Henan Province(No.192102210013,202102210080)National Science and Technology Major Project(No.2017-VII-0008-0101)。
文摘Liquid-metal cooling(LMC)process can offer refinement of microstructure and reduce defects due to the increased cooling rate from enhanced heat extraction,and thus an understanding of solidification behavior in nickel-based superalloy casting during LMC process is essential for improving mechanical performance of single crystal(SC)castings.In this effort,an integrated heat transfer model coupling meso grain structure and micro dendrite is developed to predict the temperature distribution and microstructure evolution in LMC process.An interpolation algorithm is used to deal with the macro-micro grids coupling issues.The algorithm of cells capture is also modified,and a deterministic cellular automaton(DCA)model is proposed to describe neighborhood cell tracking.In addition,solute distribution is also considered to describe the dendrite growth.Temperature measuring,EBSD,OM and SEM experiments are implemented to verify the proposed model,and the experiment results agree well with the simulation results.Several simulations are performed with a range of withdrawal rates,and the results indicate that 12 mm·min^(-1)is suitable for LMC process in this work,which can result in a fairly narrow and flat mushy zone and correspondingly exhibited fairly straight grains.The mushy zone length is about 4.8 mm in the steady state and the average deviation angle of grains is about 13.9°at the height 90 mm from the casting base under 12 mm·min^(-1)withdrawal process.The competitive phenomenon of dendrites at different withdrawal rates is also observed,which has a great relevant to the temperature fluctuation.
基金supported by Grant-in-Aid for Scientifi Research(Grant(B)17300141)the Development and Use of the Next Generation Supercomputer Project of the MEXT,Japan+4 种基金Fuyou Liang was supported by the National Natural Science Foundation of China(Grant 81370438)the SJTU Medical Engineering Cross-cutting Research Foundation(Grant YG2012MS24)Ken-iti Tsubota was partly funded by a Grant-in-Aid for Challenging Exploratory Research(Grant 25630046),JSPSsupporting the computing facilities essential for the completion of this studyFinancial support provided by HKUST to JW is acknowledged
文摘The human cardiovascular system is a closed- loop and complex vascular network with multi-scaled het- erogeneous hemodynamic phenomena. Here, we give a selective review of recent progress in macro-hemodynamic modeling, with a focus on geometrical multi-scale model- ing of the vascular network, micro-hemodynamic modeling of microcirculation, as well as blood cellular, subcellular, endothelial biomechanics, and their interaction with arter- ial vessel mechanics. We describe in detail the methodology of hemodynamic modeling and its potential applications in cardiovascular research and clinical practice. In addition, we present major topics for future study： recent progress of patient-specific hemodynamic modeling in clinical applica- tions, micro-hemodynamic modeling in capillaries and blood cells, and the importance and potential of the multi-scale hemodynarnic modeling.
文摘Successful modeling and/or design of engineering systems often requires one to address the impact of multiple ＂design variables＂ on the prescribed outcome.There are often multiple,competing objectives based on which we assess the outcome of optimization.Since accurate,high fidelity models are typically time consuming and computationally expensive,comprehensive evaluations can be conducted only if an efficient framework is available.Furthermore,informed decisions of the model/hardware＇s overall performance rely on an adequate understanding of the global,not local,sensitivity of the individual design variables on the objectives.The surrogate-based approach,which involves approximating the objectives as continuous functions of design variables from limited data,offers a rational framework to reduce the number of important input variables,i.e.,the dimension of a design or modeling space.In this paper,we review the fundamental issues that arise in surrogate-based analysis and optimization,highlighting concepts,methods,techniques,as well as modeling implications for mechanics problems.To aid the discussions of the issues involved,we summarize recent efforts in investigating cryogenic cavitating flows,active flow control based on dielectric barrier discharge concepts,and lithium（Li）-ion batteries.It is also stressed that many multi-scale mechanics problems can naturally benefit from the surrogate approach for ＂scale bridging.＂
基金supported by the National Natural Science Foundation of China (Nos.41504015,41429401)the National 973 Project of China (No.2013CB733302)+2 种基金 China Postdoctoral Science Foundation (No.2015M572146)the National High Technology Research and Development Program of China (No.2011AA060503)the Surveying and Mapping Basic Research Program of National Administration of Surveying,Mapping and Geoinformation (No.15-01-08)
文摘Urban faults in Shenzhen are potential threats to city security and sustainable development. In consideration of the importance of the Shenzhen fault zone, the author provide a detailed interpretation on gravity data model. Bouguer gravity covering the whole Shenzhen City was calculated with a 1-km resolution. Wavelet multi-scale analysis（MSA） was applied to the Bouguer gravity data to obtain the multilayer residual anomalies corresponding to different depths. In addition, 2D gravity models were constructed along three profiles. The Bouguer gravity anomaly shows an NE-striking high-low-high pattern from northwest to southeast, strongly related to the main faults. According to the results of MSA, the correlation between gravity anomaly and faults is particularly significant from 4 to 12 km depth. The residual gravity with small amplitude in each layer indicates weak tectonic activity in the crust. In the upper layers, positive anomalies along most of faults reveal the upwelling of high-density materials during the past tectonic movements. The multilayer residual anomalies also yield important information about the faults, such as the vertical extension and the dip direction. The maximum depth of the faults is about 20 km. In general, NE-striking faults extend deeper than NW-striking faults and have a larger dip angle.
基金Supported by Science Center for Gas Turbine Project of China (Grant No.P2022-B-IV-014-001)Frontier Leading Technology Basic Research Special Project of Jiangsu Province of China (Grant No.BK20212007)the BIT Research and Innovation Promoting Project of China (Grant No.2022YCXZ019)。
文摘Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites.An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture.The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions.Three RVE models of cuboid,hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale.Besides,the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models.The prediction results at the mesoscale correspond closely to the experimental values.The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration.The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.
基金supported by the National Natural Science Foundation of China (Nos. 41230138, 41074077)
文摘Eariy-arrival waveform tomography （EWT） is one of the most promising techniques for building near-surface velocity model. Based on finite-frequency wave equation, EWT estimates velocities by matching calculated early-arrival waveforms with the observed ones. However, the objective function of EWT can easily converge to local minimum because of the cycle-skipping phenomenon. In order to reduce the cycle-skipping problem, a hybrid-domain early-arrival waveform tomography （HEWT） is proposed in this paper. The forward modeling of HEWT is realized in the time domain where early-arrival waveforms are easier to be selected from seismic data and less memory is needed than they are in the frequency domain. The inversion is implemented in the frequency domain where multi-scale strategy is more convenient to be realized than that in the time domain. Discrete Fourier transformation （DFT） is used to transform the time-domain wavefield to the frequency-domain wavefield. Test results show that HEWT is more competitive than EWT in both accuracy and computational time.
基金support from the National Natural Science Foundation of China(Grant Nos.52175307)the Taishan Scholars Foundation of Shandong Province(No.tsqn201812128)+1 种基金the Natural Science Foundation of Shandong Province(No.ZR2023JQ021No.ZR2020QE175).
文摘Superalloy thin-walled structures are achieved mainly by brazing,but the deformation process of brazed joints is non-uniform,making it a challenging research task.This paper records a thorough investigation of the effect of brazing parameters on the microstructure of joints and its mechanical properties,which mainly inquires into the deformation and fracture mechanisms in the shearing process of GH99/BNi-5a/GH99 joints.The macroscopic-microscopic deformation mechanism of the brazing interface during shearing was studied by Crystal Plasticity(CP)and Molecular Dynamics(MD)on the basis of the optimal brazing parameters.The experimental results show that the brazing interface is mainly formed by(Ni,Cr,Co)(s,s)and possesses a shear strength of approximately 546 MPa.The shearing fracture of the brazed joint occurs along the brazing seam,displaying the characteristics of intergranular fracture.MD simulations show that dislocations disassociate and transform into fine twinning with increased strain.CP simulated the shear deformation process of the brazed joint.The multiscale simulation results are consistent with the experimental results.The mechanical properties of thin-walled materials for brazing are predicted using MD and CP methods.
基金funded by National High Technology Research and Development Program of China (863 Program,2012AA092303)Project of Shanghai Science and Technology Innovation (12231203900)+2 种基金Industrialization Program of National Development and Reform Commission (2159999)National Science and Technology Support Program (2013BAD13B01)Shanghai Leading Academic Discipline Project
文摘Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.
基金supported by the National High-Tech Research & Development Program of China (No. 2009AA062802) the Fundamental Research Funds for the Central Universities of China (No. 12CX06001A)Shandong Provincial Natural Science Foundation, China (No. ZR2011DQ011)
文摘A new method of multi-scale modeling and display of geologic data is introduced to provide information with appropriate detail levels for different types of research. The multi-scale display mode employs a model extending existing 2D methods into 3D space. Geologic models with different scales are organized by segmenting data into orthogonal blocks. A flow diagram illustrates an octree method for upscaling between blocks with different scales. Upscaling data from the smallest unit cells takes into account their average size and the Burgers vector when there are mismatches. A geocellular model of the Chengdao Reservoir of the Shengli Oilfield, China is taken as an illustrative case, showing that the methods proposed can construct a multi-scale geologic model correctly and display data from the multi-scale model effectively in 3D.
文摘The complexity of distribution network model mainly depends on the model scale of grid-connected distributed photovoltaic (PV) power generation. Therefore, the simulation performance of multi-scale PV model is the key factor of the simulation accuracy in the specific operating scenarios of distribution network. In this paper, a multi-scale model of grid connected PV distributed generation system is proposed based on the mathematical model of grid-connected distributed PV power generation. It is analyzed that differences of simulation performance, such as adaptability of simulation step size, accuracy of output and the effect on voltage profile of distribution network, between PV models with different scales in IEEE 33 node example. Simulation results indicate that the multi-scale model is effective in improving the accuracy and efficiency of simulation under different operating conditions of distribution network.
