Numerical Analysis of the Influence of Turbulence Intensity on Iced Conductors Gallop Phenomena

Turbulence is expected to play a relevant role in the so-called conductor gallop phenomena,namely,the high-amplitude,low-frequency oscillation of overhead power lines due to the formation of ice structures and the ensu-ing effect that wind can have on these.In this work,the galloping time history of a wire with distorted(fixed in time)shape due to the formation of ice is analyzed numerically in the frame of afluid-solid coupling method for different wind speeds and levels of turbulence.The results show that the turbulence intensity has a moderate effect on the increase of the conductor’s aerodynamic lift and drag coefficients due to ice accretion;nevertheless,the corresponding changes in the torsion coefficient are very significant and complicated.A high turbulence intensity can affect the torsion coefficient in a certain range of attack angles and increase the torsion angle of the conductor.Through comparison of the galloping phenomena for different wind velocities,it is found that the related amplitude grows significantly with an increase of the wind speed.For a relatively large wind speed,the galloping amplitude is more sensitive to the turbulence intensity.Moreover,the larger the turbulence intensity,the larger the conductor’s vertical and horizontal galloping amplitudes after icing.The torsion angle also increases with an increase in the wind speed and turbulence intensity.

Nonlinear numerical simulation method for galloping of iced conductor

Based on the principle of virtual work, an updated Lagrangian finite element formulation for the geometrical large deformation analysis of galloping of the iced conductor in an overhead transmission line is developed. In numerical simulation, a three-node isoparametric cable element with three translational and one torsional degrees-of-freedom at each node is used to discretize the transmission line. The nonlinear dynamic system equation is solved with the Newmark time integration method and the Newton-Raphson nonlinear iteration. Numerical examples demonstrate the efficiency of the presented method and the developed finite element program. A new possible galloping mode, which may reflect the saturation phenomenon of a nonlinear dynamic system, is discovered under the condition that the lowest order of vertical natural frequency of the transmission line is approximately two times of the horizontal one.

Nonlinear vibration of iced cable under wind excitation using three-degree-of-freedom model

High-voltage transmission line possesses a typical suspended cable structure that produces ice in harsh weather.Moreover,transversely galloping will be excited due to the irregular structure resulting from the alternation of lift force and drag force.In this paper,the nonlinear dynamics and internal resonance of an iced cable under wind excitation are investigated.Considering the excitation caused by pulsed wind and the movement of the support,the nonlinear governing equations of motion of the iced cable are established using a three-degree-of-freedom model based on Hamilton's principle.By the Galerkin method,the partial differential equations are then discretized into ordinary differential equations.The method of multiple scales is then used to obtain the averaged equations of the iced cable,and the principal parametric resonance-1/2 subharmonic resonance and the 2:1 internal resonance are considered.The numerical simulations are performed to investigate the dynamic response of the iced cable.It is found that there exist periodic,multi-periodic,and chaotic motions of the iced cable subjected to wind excitation.

看见音乐 JBL Pulse NFC便携蓝牙音箱阳泰IceDiamond^+蓝牙音箱

蓝牙无线音箱如铺天盖地之势充斥在市场之中，它们便携的特性、良好的音质让许多人爱不释手。不过，如果把声音和光结合起来会怎么样昵？本文介绍的这两款蓝牙便携音箱，不仅在声音方面表现出众，在视觉方面的表现同样令人惊艳。

基于设计哲学视角的技术功能理论

技术哲学经数次转向后形成的设计哲学注重技术人工物本身,而技术人工物作为物理实体具有结构—功能双重属性。人工物作为设计的结果,其设计的过程既包括物理结构的实现,又包括功能的实现,因此可以说设计就是技术人工物两重属性之间的连接机制。胡克斯与弗玛斯提出的使用—计划进路下的ICE理论,通过给出设计者和使用者视角的功能归属定义及其阐释,一方面化解了人工物的结构—功能两重性难题,另一方面也克服了单纯的意向理论、因果—角色理论和进化理论各自的理论不足。尽管如此,ICE功能理论对于技术人工物的本体论地位的解释仍然是单薄的,功能归属并不能为技术人工物提供个体化标准。技术人工物的双重属性要求更深入完整地理解设计、使用与功能之间的关系。

典型插电混动车型高压系统的原理及检修(一)

一、典型插电式混合动力汽车高压部件1.典型插电式混合动力汽车架构插电式混合动力电动汽车(PHEV)使用电池为电机提供动力,使用另一种燃料(如汽油)为内燃机(ICE)提供动力。PHEV电池可以通过壁装插座或充电桩设备、ICE或再生制动进行充电。车辆通常依靠电力运行,直到电池几乎耗尽,然后汽车自动切换到使用ICE。本文以2018款路虎揽胜运动型P400e为例,来介绍典型PHEV,2015-2023年间生产的很多车型,都采用了这一架构。初期生产的车辆目前已到了社会维修期.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

毛竹ICE基因家族的全基因组鉴定及低温胁迫下的表达模式分析

【目的】对毛竹Phyllostachys edulis ICE基因家族进行鉴定及分析,找出响应毛竹抗寒关键家族成员,研究毛竹ICE基因的生物学功能、响应低温胁迫的分子机制及遗传转化,为提高毛竹抗寒性奠定理论基础。【方法】利用生物信息学方法分析毛竹ICE基因家族成员,并对4、0、−2℃低温处理0(对照)、0.5、1.0、24.0、48.0 h的毛竹生理指标和ICE基因的表达模式进行分析。【结果】共鉴定了4个毛竹ICE基因。保守结构域和多重序列比对分析表明:PeICE基因结构高度相似。系统发育关系及启动子顺式作用元件分析显示:PeICE基因与水稻Oryza sativa亲缘关系更近,同时存在大量与非生物胁迫相关的顺式作用元件。活性氧自由基(ROS)染色发现随着处理时间增长,ROS染色逐渐加深,但是其0℃处理24.0 h、−2℃处理1.0 h后染色逐渐减弱。脯氨酸(Pro)质量摩尔浓度、超氧化物歧化酶(SOD)活性显示:4和0℃条件下,Pro质量摩尔浓度和SOD活性整体增加,但−2℃时低于对照。过氧化物酶(POD)活性显示:在3个低温处理下均增加。ICE基因表达模式分析发现:4、0℃处理时PeICE表达量整体增加,且都以PeICE3增量最明显;而−2℃处理下PeICE整体表达量水平低于对照。【结论】随着温度降低和处理时间增强,毛竹受到的损伤不断增强,其内酶活系统以及ICE基因积极响应低温胁迫,其中,PeICE3对低温胁迫最为敏感,但在−2℃时,ICE基因表达量并未增加,推测该基因家族响应了寒冷胁迫而非冷冻胁迫。

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Joint Probability Analysis and Prediction of Sea Ice Conditions in Liaodong Bay

Sea ice conditions in Liaodong Bay of China are often described by sea ice grades,which classify annual sea ice conditions based on the annual maximum sea ice thickness(AM-SIT)and annual maximum floating ice extent(AM-FIE).The joint probability distribution of AM-SIT and AM-FIE was established on the basis of their data pairs from 1949/1950 to 2019/2020 in Liaodong Bay.The joint intensity index of the sea ice condition in the current year is calculated,and the joint classification criteria of the sea ice grades in past years are established on the basis of the joint intensity index series.A comparison of the joint criteria with the 1973 and 2022 criteria revealed that the joint criteria of the sea ice grade match well,and the joint intensity index can be used to quantify the sea ice condition over the years.A time series analysis of the sea ice grades and the joint intensity index sequences based on the joint criteria are then performed.Results show a decreasing trend of the sea ice condition from 1949/1950 to 2019/2020,a mutation in 1990/1991,and a period of approximately 91 years of the sea ice condition.In addition,the Gray-Markov model(GMM)is applied to predict the joint sea ice grade and the joint intensity index of the sea ice condition series in future years,and the error between the results and the actual sea ice condition in 2020/2021 is small.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

Quantifying freeze-melt dynamics of lakes on the Tibetan Plateau using Sentinel-1 synthetic aperture radar imagery

The ice phenology of alpine lakes on the Tibetan Plateau(TP)is a rapid and direct responder to climate changes,and the variations in lake ice exhibit high temporal frequency characteristics.MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution.However,the low spatial resolutions make it difficult to effectively quantify the freeze-melt dynamics of lakes.This work used Sentinel-1 synthetic aperture radar(SAR)data to derive high-resolution ice maps(about 6 days),then with the aid of Sentinel-2 optical images to quantify freeze-melt processes in three typical lakes on the TP(e.g.Selin Co,Ayakekumu Lake,and Nam Co).The results showed that three lakes had an average annual ice period of 125-157 days and a complete ice cover period of 72-115 days,from 2018 to 2022.They exhibit different ice phenology patterns.Nam Co is characterized by repeated episodes of freezing,melting,and refreezing,resulting in a prolonged freeze-up period.Meanwhile,the break-up period of Nam Co lasts for a longer duration(about 19 days),and the break-up exhibits a smooth process.Similarly,Ayakekumu Lake showed more significant inter-annual fluctuations in the freeze-up period,with deviations of up to 28 days observed among different years.Compared to the other two lakes,Selin Co experienced a relatively short freeze-up and break-up period.In short,Sentinel-1 SAR data can effectively monitor the weekly and seasonal variations in lake ice on the TP.Particularly,this data facilitates quantification of the freeze-melt dynamics.

Preface to the Special Topic on Ocean, Sea Ice and Northern Hemisphere Climate:In Remembrance of Professor Yongqi GAO's Key Contributions

This special issue commemorates the life work of Prof. Yongqi GAO who passed away in July 2021, his time cut short by illness. He had many great achievements, but still much more to contribute. The seven articles in this special issue are from research areas where he contributed, and they illustrate how his close colleagues are continuing his work.

Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.

Snow and regolith albedo variations using CRISM data at McMurdo crater,Mars

The cryosphere component provides the most reliable and insightful indications of any planet’s climate dynamics.Using data from the Compact Reconnaissance Imaging Spectrometer for Mars(CRISM),we develop a novel approach to determining the broadband Visible and Near Infrared(VNIR)albedo of the Martian surface.This study focuses on albedo changes in the McMurdo crater,part of Mars’s south polar layer deposits.We compare seasonal and interannual variations of the McMurdo surface albedo before,during,and after the Global Dust Storm(GDS)of Martian Year(MY)34.As the seasons progressed from spring to summer,the mean albedo in MY 32 and 34 plunged by over 40%,by about 35%in MY 33,and by slightly more than 30%in MY 35.Compared interannually,however,mean albedo values within both seasons(spring and summer)exhibited no significant differences in those same years.Notably,interannual albedo difference maps reveal albedo variation of more than±0.3 in certain regions of the crater.Considering only snow-covered pixels,interannual albedo differences suggest that Mars dust had a pervasive impact on Mars’s cryosphere.Variations in maximum and minimum albedo values as high as 0.5 were observed,depending upon differences in the dust levels in Martian snow/ice.The maximum and the minimum snow albedo values were lowest in MY 34,indicating the effect of the intense dust storm event that year.The average snow albedo decreased from 0.45 in MY 32 to 0.40 in MY 33 and to 0.33 in MY 34,and then rose back to 0.40 in MY 35.This trajectory suggests a temporary deposition of dust,partially reversed after the GDS by self-cleaning mechanisms(local aeolian process and CO_(2)sublimation/deposition cycle).

Advances in ice avalanches on the Tibetan Plateau

As some of the greatest natural disasters in the cryosphere,ice avalanches(IAs)seriously threaten lives and cause catastrophic damage to the resource environment,but a comprehensive overview of the state of knowledge on IAs remains lacking.We summarized 63 IAs on the Tibetan Plateau(TP)since the 20th century,of which,over 20 IAs occurred after the 21st century.The distributions of IAs are mainly concentrated in the southeastern and northwestern TP,and the occurrence time of IAs is mostly concentrated from July to September.We highlight recent advances in mechanical properties and genetic mechanisms of IAs and emphasize that temperature,rainfall,and seismicity are the inducing factors.The failure modes of IAs are summarized into 6 categories by examples:slip pulling type,slip toppling type,slip breaking type,water level collapse type,cave roof collapse type,and wedge failure type.Finally,we deliver recommendations concerning the risk assessment and prediction of IAs.The results provide important scientific value for addressing climate change and resisting glacier-related hazards.

Wintertime Arctic Sea-Ice Decline Related to Multi-Year La Niña Events

Arctic sea ice has undergone a significant decline in the Barents-Kara Sea(BKS)since the late 1990s.Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes(mainly referring to La Niña events).The occurrence of multi-year La Niña(MYLA)events has increased significantly in recent decades,and their impact on Arctic sea ice needs to be further explored.In this study,we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña(OTLA)years.The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years.This is because MYLA events tend to be accompanied by a warm Arctic-cold continent pattern with a barotropic high pressure blocked over the Urals region.Consequently,more frequent northward atmospheric rivers intrude into the BKS,intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice.However,in the early winter of OTLA years,a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere,which obstructs the atmospheric rivers to the south of Iceland.We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes.Considering the rapid climate changes in the past,more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades.

An improved algorithm for retrieving thin sea ice thickness in the Arctic Ocean from SMOS and SMAP L-band radiometer data

The aim of this study was to develop an improved thin sea ice thickness(SIT)retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data.This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort,Chukchi,East Siberian,Laptev and Kara seas and utilized the microwave polarization ratio(PR)at incidence angle of 40°.The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact,reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature.The relationship between the SIT and PR was found to be almost stable across the five selected regions.The SIT retrievals were then compared to other two existing algorithms(i.e.,UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen)and validated against independent SIT data obtained from moored upward looking sonars(ULS)and airborne electromagnetic(EM)induction sensors.The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error(RMSE)being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data.The proposed algorithm can be used for thin sea ice thickness(<1.0 m)estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical.