Complexity at Mesoscales:A Common Challenge in Developing Artificial Intelligence

Exploring the physical mechanisms of complex systems and making effective use of them are the keys to dealing with the complexity of the world.The emergence of big data and the enhancement of computing power,in conjunction with the improvement of optimization algorithms,are leading to the development of artificial intelligence(AI)driven by deep learning.However,deep learning fails to reveal the underlying logic and physical connotations of the problems being solved.Mesoscience provides a concept to understand the mechanism of the spatiotemporal multiscale structure of complex systems,and its capability for analyzing complex problems has been validated in different fields.This paper proposes a research paradigm for AI,which introduces the analytical principles of mesoscience into the design of deep learning models.This is done to address the fundamental problem of deep learning models detaching the physical prototype from the problem being solved;the purpose is to promote the sustainable development of AI.

Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

The Predictability Limit of Oceanic Mesoscale Eddy Tracks in the South China Sea

Employing the nonlinear local Lyapunov exponent (NLLE) technique, this study assesses the quantitative predictability limit of oceanic mesoscale eddy (OME) tracks utilizing three eddy datasets for both annual and seasonal means. Our findings reveal a discernible predictability limit of approximately 39 days for cyclonic eddies (CEs) and 44 days for anticyclonic eddies (AEs) within the South China Sea (SCS). The predictability limit is related to the OME properties and seasons. The long-lived, large-amplitude, and large-radius OMEs tend to have a higher predictability limit. The predictability limit of AE (CE) tracks is highest in autumn (winter) with 52 (53) days and lowest in spring (summer) with 40 (30) days. The spatial distribution of the predictability limit of OME tracks also has seasonal variations, further finding that the area of higher predictability limits often overlaps with periodic OMEs. Additionally, the predictability limit of periodic OME tracks is about 49 days for both CEs and AEs, which is 5-10 days higher than the mean values. Usually, in the SCS, OMEs characterized by high predictability limit values exhibit more extended and smoother trajectories and often move along the northern slope of the SCS.

Spatiotemporal features and vertical structures of four types of mesoscale eddies in the Kuroshio Extension region

Except for conventional mesoscale eddies,there are also abundant warm cyclonic eddies(WCEs)and cold anticyclonic eddies(CAEs)in the global ocean.Based on the global mesoscale eddy trajectory atlas product,satellite altimetric and remote sensing datasets,and three-dimensional temperature/salinity dataset,spatiotemporal features of WCEs and CAEs are compared with traditional cold cyclonic eddies and warm anticyclonic eddies in the Kuroshio Extension(KE;28°−43°N,140°−170°E)region.Characteristics of abnormal eddies like radius,amplitude,eddy kinetic energy,and proportion in all eddies behave in significant asymmetry on the north and south sides of the KE jet.Unlike eddies in the general sense,temporal feature analysis reveals that it is more favorable to the formation and maintenance of WCEs and CAEs in summer and autumn,while winter is the opposite.The spatiotemporal variation of abnormal eddies is likely because the marine environment varying with time and space.Statistically,proportion of abnormal eddies increases rapidly in decaying stage during the whole eddy lifespan,resulting in smaller average radius,amplitude,sea surface temperature anomaly and sea surface height anomaly compared to normal ones.The three-dimensional composite structures for four types of eddies expose that the difference between abnormal and conventional eddies is not just limited to the sea surface,but also exists within the water below the sea surface.Vertical structures also indicate that the anomalous temperature signal is confined in the water from the sea surface to layers at about 30 m in the KE region.

Experimental and numerical analyses of the effect of fibre content on the close-in blast performance of a UHPFRC beam

Limited research has been conducted on the influences of fiber content on close-in blasting characteristics for ultrahigh-performance fiber-reinforced concrete(UHPFRC)beams.This paper aims to address this knowledge gap through experimental and mesoscale numerical methods.Experiments were conducted on ten UHPFRC beams built with varying steel fiber volumetric fractions subjected to close-in explosive conditions.Additionally,this study considered other parameters,such as the longitudinal reinforcement type and ratio.In the case of UHPFRC beams featuring normal-strength longitudinal reinforcement of diametersΦ12,Φ16,andΦ20,a reduction in maximum displacement by magnitudes of19.6%,19.5%,and 17.4%was observed,respectively,as the volumetric fractions of fiber increased from1.0%to 2.5%.In addition,increasing the longitudinal reinforcement ratio and using high-strength steel longitudinal reinforcement both significantly reduced the deformation characteristics and increase the blasting resistances of UHPFRC beams.However,the effects on the local crushing and spalling damage were not significant.A mesoscale finite element model,which considers the impacts of fiber parameters on UHPFRC beam behaviors,was also established and well correlated with the test findings.Nevertheless,parametric analyses were further conducted to examine the impacts of the steel fiber content and length and the hybrid effects of various types of microfibers and steel fibers on the blasting performance of UHPFRC beams.

Seasonal variation of mesoscale eddy intensity in the global ocean

Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.

Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518).

Contrasts of bimodal tropical instability waves(TIWs)-induced wind stress perturbations in the Pacific Ocean among observations,ocean models,and coupled climate models

The coupling between wind stress perturbations and sea surface temperature(SST)perturbations induced by tropical instability waves(TIWs)in the Pacific Ocean has been revealed previously and proven crucial to both the atmosphere and ocean.However,an overlooked fact by previous studies is that the loosely defined“TIWs”actually consist of two modes,including the Yanai wave-based TIW on the equator(hereafter eTIW)and the Rossby wave-based TIW off the equator(hereafter vTIW).Hence,the individual feedbacks of the wind stress to the bimodal TIWs remain unexplored.In this study,individual coupling relationships are established for both eTIW and v TIW,including the relationship between the TIW-induced SST perturbations and two components of wind stress perturbations,and the relationship between the TIW-induced wind stress perturbation divergence(curl)and the downwind(crosswind)TIW-induced SST gradients.Results show that,due to different distributions of eTIW and vTIW,the coupling strength induced by the eTIW is stronger on the equator,and that by the vTIW is stronger off the equator.The results of any of eTIW and vTIW are higher than those of the loosely defined TIWs.We further investigated how well the coupling relationships remained in several widely recognized oceanic general circulation models and fully coupled climate models.However,the coupling relationships cannot be well represented in most numerical models.Finally,we confirmed that higher resolution usually corresponds to more accurate simulation.Therefore,the coupling models established in this study are complementary to previous research and can be used to refine the oceanic and coupled climate models.

Faster AMEDA-A Hybrid Mesoscale Eddy Detection Algorithm

Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial,while the academia has invented many traditional physical methods with accurate detection capability,but their detection computational efficiency is low.In recent years,with the increasing application of deep learning in ocean feature detection,many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean data.But it is difficult for them to precisely fit some physical features implicit in traditional methods,leading to inaccurate identification of ocean eddies.In this study,to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models,we propose a solution that combines the target detection model Faster Region with CNN feature(Faster R-CNN)with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm(AMEDA).We use Faster R-CNN to detect and generate bounding boxes for eddies,allowing AMEDA to detect the eddy center within these bounding boxes,thus reducing the complexity of center detection.To demonstrate the detection efficiency and accuracy of this model,this paper compares the experimental results with AMEDA and the deep learningbased eddy detection method eddyNet.The results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA.

Quantifying the Role of the Eddy Transfer Coefficient in Simulating the Response of the Southern Ocean Meridional Overturning Circulation to Enhanced Westerlies in a Coarse-resolution Model

This study assesses the capability of a coarse-resolution ocean model to replicate the response of the Southern Ocean Meridional Overturning Circulation(MOC) to intensified westerlies,focusing on the role of the eddy transfer coefficient(κ).κ is a parameter commonly used to represent the velocities induced by unresolved eddies.Our findings reveal that a stratification-dependent κ,incorporating spatiotemporal variability,leads to the most robust eddy-induced MOC response,capturing 82% of the reference eddy-resolving simulation.Decomposing the eddy-induced velocity into its vertical variation(VV) and spatial structure(SS) components unveils that the enhanced eddy compensation response primarily stems from an augmented SS term,while the introduced VV term weakens the response.Furthermore,the temporal variability of the stratification-dependent κ emerges as a key factor in enhancing the eddy compensation response to intensified westerlies.The experiment with stratification-dependent κ exhibits a more potent eddy compensation response compared to the constant κ,attributed to the structure of κ and the vertical variation of the density slope.These results underscore the critical role of accurately representing κ in capturing the response of the Southern Ocean MOC and emphasize the significance of the isopycnal slope in modulating the eddy compensation mechanism.

Recognition of Organizational Morphology of Mesoscale Convective Systems Using Himawari-8 Observations

The onset,evolution,and propagation processes of convective cells can be reflected by the organizational morphology of mesoscale convective systems(MCSs),which are key factors in determining the potential for heavy precipitation.This paper proposed a method for objectively classifying and segmenting MCSs using geosynchronous satellite observations.Validation of the product relative to the classification in radar composite reflectivity imagery indicates that the algorithm offers skill for discriminating between convective and stratiform areas and matched 65%of convective area identifications in radar imagery with a false alarm rate of 39%and an accuracy of 94%.A quantitative evaluation of the similarity between the structures of 50 MCSs randomly obtained from satellite and radar observations shows that the similarity was as high as 60%.For further testing,the organizational modes of the MCS that caused the heavy precipitation in Northwest China on August 21,2016(hereinafter known as the“0821”rainstorm)were identified.It was found that the MCS,accompanied by the“0821”rainstorm,successively exhibited modes of the isolated cell,squall line with parallel stratiform(PS)rain,and non-linear system during its life cycle.Among them,the PS mode might have played a key role in causing this flooding.These findings are in line with previous studies.

Mesoscale and Microphysical Characteristics of a Double Rain Belt Event in South China on May 10–13,2022

A second rain belt sometimes occurs ahead of a frontal rain belt in the warm sector over coastal South China,leading to heavy precipitation.We examined the differences in the mesoscale characteristics and microphysics of the frontal and warm sector rain belts that occurred in South China on May 10–13,2022.The southern rain belt occurred in an environment with favorable mesoscale conditions but weak large-scale forcing.In contrast,the northern rain belt was related to low-level horizontal shear and the surface-level front.The interaction between the enhanced southeasterly winds and the rainfall-induced cold pool promoted the persistent growth of convection along the southern rain belt.The convective cell propagated east over the coastal area,where there was a large temperature gradient.The bow-shaped echo in this region may be closely related to the rear-inflow jet.By contrast,the initial convection of the northern rain belt was triggered along the front and the region of low-level horizontal shear,with mesoscale interactions between the enhanced warm-moist southeasterly airflow and the cold dome associated with the earlier rain.The terrain blocked the movement of the cold pool,resulting in the stagnation of the frontal convective cell at an early stage.Subsequently,a meso-γ-scale vortex formed during the rapid movement of the convective cell,corresponding to an enhancement of precipitation.The representative raindrop spectra for the southern rain belt were characterized by a greater number and higher density of raindrops than the northern rain belt,even though both resulted in comparable hourly rainfalls.These results help us better understand the characteristics of double rain belts over South China.

Analysis of Characteristics of a Heavy Rainstorm Process in Nanchang City on July 7,2020

Based on the conventional observation data,dual polarization radar data and NCEP reanalysis data,the large-scale circulation background field,mesoscale conditions and formation causes of a heavy rainstorm in Nanchang on July 7,2020 were studied.It was found that this heavy rainstorm occurred under the weather background of the confrontation between the northward air flow behind the trough and the strong southwest warm and humid air flow to the northwest of the subtropical high.The divergence at the upper level,the shear in the middle and low levels,the southward movement of cold air at the low level,unusually abundant water vapor and high unstable energy caused the heavy rainstorm weather.In this process,under the influence of continuous eastward movement of several strong echo cells,an obvious"train effect"was formed in Nanchang,so that the local rainfall was continuous and intense.Moreover,the average of VIL was about 17 kg/m 2,and its variation characteristics were consistent with the variation trend of 5-min rainfall intensity,which had a certain indicator effect on short-term heavy precipitation.The topography of the Meiling Mountain in the west of Nanchang had a great influence on the formation and precipitation distribution of the heavy rain process.There was a strong rainstorm center near the mountain,and the precipitation was obviously larger than that in the plain area.

Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper-level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700 hPa and 400 hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes is also witnessed in the WRF model.

On the Key Dynamical Processes Supporting the 21.7 Zhengzhou Record-breaking Hourly Rainfall in China

An extremely heavy rainfall event occurred in Zhengzhou,China,on 20 July 2021 and produced an hourly rainfall rate of 201.9 mm,which broke the station record for China's Mainland.Based on radar observations and a convection-permitting simulation using the WRF-ARW model,this paper investigates the multiscale processes,especially those at the mesoscale,that support the extreme observed hourly rainfall.Results show that the extreme rainfall occurred in an environment characteristic of warm-sector heavy rainfall,with abundant warm moist air transported from the ocean by an abnormally northward-displaced western Pacific subtropical high and Typhoon In-Fa(2021).However,rather than through back building and echo training of convective cells often found in warm-sector heavy rainfall events,this extreme hourly rainfall event was caused by a single,quasi-stationary storm in Zhengzhou.Scale separation analysis reveals that the extreme-rainproducing storm was supported and maintained by the dynamic lifting of low-level converging flows from the north,south,and east of the storm.The low-level northerly flow originated from a mesoscale barrier jet on the eastern slope of the Taihang Mountain due to terrain blocking of large-scale easterly flows,which reached an overall balance with the southerly winds in association with a low-level meso-β-scale vortex located to the west of Zhengzhou.The large-scale easterly inflows that fed the deep convection via transport of thermodynamically unstable air into the storm prevented the eastward propagation of the weak,shallow cold pool.As a result,the convective storm was nearly stationary over Zhengzhou,resulting in record-breaking hourly precipitation.

Mesoscale study on explosion-induced formation and thermochemical response of PTFE/Al granular jet

The dynamic formation,shock-induced inhomogeneous temperature rise and corresponding chemical reaction behaviors of PTFE/Al reactive liner shaped charge jet(RLSCJ)are investigated by the combination of mesoscale simulation,reaction kinetics and chemical energy release test.A two-dimensional granular model is developed with the randomly normal distribution of aluminum particle sizes and the particle delivery program.Then,the granular model is employed to study the shock-induced thermal behavior during the formation and extension processes of RLSCJ,as well as the temperature history curves of aluminum particles.The simulation results visualize the motion and temperature responses of the RLSCJ at the grain level,and further indicate that the aluminum particles are more likely to gather in the last two-thirds of the jet along its axis.Further analysis shows that the shock,collision,friction and deformation behaviors are all responsible for the steep temperature rise of the reactive jet.In addition,a shock-induced chemical reaction extent model of RLSCJ is built based on the combination of the Arrhenius model and the Avrami-Erofeev kinetic model,by which the chemical reaction growth behavior during the formation and extension stages is described quantitatively.The model indicates the reaction extent highly corresponds to the aluminum particle temperature history at the formation and extension stages.At last,a manometry chamber and the corresponding energy release model are used together to study the macroscopic chemical energy release characteristics of RLSCJ,by which the reaction extent model is verified.

Forecast Performance of the Pre-operational CMA-TRAMS(EPS)in South China During April-September 2020

The mesoscale ensemble prediction system based on the Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS(EPS))has been pre-operational since April 2020 at South China Regional Meteorological Center(SCRMC),which was developed by the Guangzhou Institute of Tropical and Marine Meteorology(GITMM).To better understand the performance of the CMA-TRAMS(EPS)and provide guidance to forecasters,we assess the performance of this system on both deterministic and probabilistic forecasts from April to September 2020 in this study through objective verification.Compared with the control(deterministic)forecasts,the ensemble mean of the CMATRAMS(EPS)shows advantages in most non-precipitation variables.In addition,the threat score indicates that the CMA-TRAMS(EPS)obviously improves light and heavy rainfall forecasts in terms of the probability-matched mean.Compared with the European Center for Medium-range Weather Forecasts operational ensemble prediction system(ECMWF-EPS),the CMA-TRAMS(EPS)improves the probabilistic forecasts of light rainfall in terms of accuracy,reliability and discrimination,and this system also improves the heavy rainfall forecasts in terms of discrimination.Moreover,two typical heavy rainfall cases in south China during the pre-summer rainy season are investigated to visually demonstrate the deterministic and probabilistic forecasts,and the results of these two cases indicate the differences and advantages(deficiencies)of the two ensemble systems.

Overview of the identification of traffic accident-prone locations driven by big data

Effective identification of traffic accident-prone points can reduce accident risks and eliminate safety hazards.This paper first systematically compares the research in Chinese and foreign literature,and proposes three types of identification indicators,namely absolute,relative and comprehensive,according to different reference standards.According to the evaluation indicators and modelling methods,the current status of research and problems in identification theory and methods are systematically summarised in terms of mathematical statistics,cluster analysis,machine learning and conflict technology.The study shows that the foreign literature focuses on the innovation of data and indicators and changes from accident point safety management to road network safety management,while the research in Chinese literature focuses on the integration of multiple identification methods and theoretical innovation.Driven by big data,the identification of traffic accident-prone points has been further developed at the meso-micro scale.Morphological image processing methods are widely used,combined with GIS platforms,to accurately mine the spatial attributes and correlations of accidents.Also,considering the spatial and temporal distribution of accidents,the identification results are also transformed from regions to specific road sections and points to achieve more accurate identification.

Review on the mesoscale characterization of cement-stabilized macadam materials

The base layer constructed by cement-stabilized macadam(CSM)has been widely used in highway construction due to its low elasticity deformation and high carrying capacity.As a bearing layer,the CSM base is not exempt from fatigue cracking under cyclic loading in the service process.Cracks in the base will create irreversible structural and functional deficiencies,such as the potential for reflective cracking of subsequently placed asphalt concrete overlays.The fracture of the base will shorten the service life of the pavement.The quality of the CSM base is directly related to the bearing capacity and integrity of the whole pavement structure.It is of practical significance to further study the fatigue failure behavior of CSM material for the long-term performance of the pavement.The CSM material is a typical heterogeneous multiphase composite.On the mesoscale,CSM consists of aggregate,cement mortar,pores,and the interface transitional zone(ITZ).On the microscale,the hardened mortar contains a large number of capillary pores,unhydrated particles,hydrated crystals,etc.,which makes the spatial distribution of its material properties stochastic.In addition,cement hydration,dry shrinkage,and temperature shrinkage can also produce micro-crack defects in cement mortar.These microcracks will have crossscale evolution under load,resulting in structural fracture.Macroscopic complex deformation and mechanical response are the reflections of its microscopic and even mesoscale composition and structure.This study summarized the existing studies on the mesoscopic properties of CSM materials,respectively from the three aspects of mesostructure,structural characterization,and mesoscale fatigue damage analysis,to help the development of long-life pavement.The future research direction is to explore the mesoscale characteristics of CSM using multiscale representation and analysis methods,to establish the connection between mesoscale characteristics and macroscopic mechanical properties.

Multiscale Theories and Applications:From Microstructure Design to Macroscopic Assessment for Carbon Nanotubes Networks

Carbon nanotube(CNT)networks enable CNTs to be used as building blocks for synthesizing novel advanced materials,thus taking full advantage of the superior properties of individual CNTs.Multiscale analyses have to be adopted to study the load transfer mechanisms of CNT networks from the atomic scale to the macroscopic scale due to the huge computational cost.Among them,fully resolved structural features include the graphitic honeycomb lattice(atomic),inter-tube stacking(nano)and assembly(meso)of CNTs.On an atomic scale,the elastic properties,ultimate stresses,and failure strains of individual CNTs with distinct chiralities and radii are obtained under various loading conditions by molecular mechanics.The dependence of the cohesive energies on spacing distances,crossing angles,size and edge effects between two CNTs is analyzed through continuum modeling in nanoscale.The mesoscale models,which neglect the atomic structures of individual CNTs but retain geometrical information about the shape of CNTs and their assembly into a network,have been developed to study the multi-level mechanism of material deformation and microstructural evolution in CNT networks under stretching,from elastic elongation,strengthening to damage and failure.This paper summarizes the multiscale theories mentioned above,which should provide insight into the optimal assembling of CNT network materials for elevated mechanical performance.