Research on the Icing Diagnosis ofWind Turbine Blades Based on FS–XGBoost–EWMA

In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.

Optimizing the LSTM Deep Learning Model for Arctic Sea Ice Melting Prediction

The National Oceanic and Atmospheric Administration reports a 95% decline in the oldest Arctic ice over the last 33 years [1], while the National Aeronautics and Space Administration states that summer Arctic Sea Ice Extent (SIE) is shrinking by 12.2% per decade since 1979 due to warmer temperatures [2]. Given the rapidly changing Arctic conditions, accurate prediction models are crucial. Deep learning models developed for Arctic forecasts primarily focus on exploring convolutional neural networks (CNN) and convolutional Long Short-Term Memory (LSTM) networks, while the exploration of the power of LSTM networks is limited. In this research, we focus on enhancing the performance of an LSTM network for predicting monthly Arctic SIE. We leverage five climate and atmospheric variables, validated for their correlation with SIE in prior studies [3]. We utilize the Spearman’s rank correlation and ExtraTrees regressor to enhance our understanding of the importance of the five variables in predicting SIE. We further enhance our predictor variables with seasonal information, lagged time steps, and a linear regression simulated SIE that accounts for the influence of past SIE on current SIE. Statistical methods guide our selection of data scalers and best evaluation metrics for our model. By experimenting with hyperparameter optimization and advanced deep learning training techniques, such as batch sizes, number of neurons, early stopping, and model checkpoint, our model achieved a Mean Absolute Error (MAE) of 0.191 and R2 of 0.996, underscoring its ability to account for nearly all the variance in our data and holds great promise for the prediction of SIE.

Influence of Surface Ice Roughness on the Aerodynamic Performance of Wind Turbines

The focus of this research was on the equivalent particle roughness height correction required to account for the presence of ice when determining the performances of wind turbines.In particular,two icing processes(frost ice and clear ice)were examined by combining the FENSAP-ICE and FLUENT analysis tools.The ice type on the blade surfaces was predicted by using a multi-time step method.Accordingly,the influence of variations in icing shape and ice surface roughness on the aerodynamic performance of blades during frost ice formation or clear ice formation was investigated.The results indicate that differences in blade surface roughness and heat flux lead to disparities in both ice formation rate and shape between frost ice and clear ice.Clear ice has a greater impact on aerodynamics compared to frost ice,while frost ice is significantly influenced by the roughness of its icy surface.

3D Ice Shape Description Method Based on BLSOM Neural Network

When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.

3D IC系统架构概述

随着芯片制造工艺接近物理极限,使用多Die堆叠的三维集成电路(3D IC)已经成为延续摩尔定律的最佳途径之一。利用3D IC将芯片垂直堆叠集成,可以极大程度降低互联长度,提升互联带宽。详细介绍了一些常见的3D IC系统架构方案,说明了使用不同3D架构对于整体芯片系统在性能、功耗等方面的优势,也列举了在物理实现、封装测试、工艺能力等方面的挑战。最后综述了一些业内使用3D IC的典型产品,并介绍了这些产品的系统架构、典型参数、适用领域,以及使用3D IC后给产品带来的竞争力提升情况。针对业界现状,认为应该把握机遇,不惧挑战,实现弯道超车。

3D IC中全铜互连热应力分析

三维集成电路(Three-Dimensional Integrated Circuit,3D IC)技术相比于二维封装形式具有互连长度短、异构集成度高、功耗低以及封装尺寸小等特点.因为铜基体具有优异的导电性、抗电迁移性和机械性能,全铜互联结构替代了焊球作为连接结构应用于3D IC中.本文通过数值模拟研究了含有全铜互连和微流道结构的3D IC模型在循环温度载荷下的热可靠性,分析了全铜互联高度对模型内部热应力的影响.结果表明,全铜互连部分的最大热应力与铜柱所处的空间位置相关,离模型中心越远,铜柱内的变形越大.同时,最危险铜柱内部应力分布和变形情况表明,由于铜柱上下端面所受载荷性质不同,铜柱在热载荷作用下的Mises应力大致呈左右及上下对称分布.这会导致铜柱的潜在失效模式是轴向压缩和剪切共同作用下的断裂或损伤.另外,最大Mises应力随铜柱高度的增加而逐渐减小,当铜柱高度为300 gm时最大Mises应力趋于稳定,可以为全铜互连可靠性设计提供参考.

3D IC-TSV技术的散热特性研究

基于3D IC—TSV互连技术,提出了考虑硅通孔的温度解析模型,Matlab分析表明:在芯片堆叠层数及芯片工作状态相同的情况下,考虑硅通孔之后的芯片温度比未考虑硅通孔时要低;在通孔直径不变的情况下,最高层芯片温度随间距P的增大而增大;在间距P不变的情况下,最高层芯片的温度随通孔直径D的减小而增大。通过热分析软件Icepak件对模型进行仿真,所得结果与Matlab仿真结果相对比,误差相差甚小,充分说明了硅通孔对芯片散热的有效性。

紫外辐射(UVB)胁迫下南极硅藻Phaeodactylumtricornutum ICE-H的生理生化与抗氧化活性响应

对分离自南极海冰的南极硅藻Phaeodactylum tricornutum ICE-H实施一定强度的中波紫外线(UVB)(70μw/cm2)辐射(0~7 d),研究了其生理生化指标的变化情况,并了解了UVB辐射对其生长及生物组成的影响。结果表明:(1)在UVB胁迫下,该南极硅藻生长速率下降,干重减少,总蛋白含量降低,总脂含量增加;(2)总单不饱和脂肪酸(MUFA)含量降低,总多不饱和脂肪酸(PUFA)含量升高;(3)叶绿素a、叶绿素b及总叶绿素含量均降低;(4)抗氧化系统作用相应明显,活性氧自由基(ROS)生成速率增加,丙二醛(MDA)大量产生及总超氧化物歧化酶(SOD)大量累积。本研究为进一步认识南极硅藻Phaeodactylum tricornutum ICE-H应对UVB辐射的机理以及南极生态系统对UV-B辐射响应提供启示。

Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter,where this affects their capacity for power generation as well as their safety.Accurately identifying the icing of the blades of wind turbines in remote areas is thus important,and a general model is needed to this end.This paper proposes a universal model based on a Deep Neural Network(DNN)that uses data from the Supervisory Control and Data Acquisition(SCADA)system.Two datasets from SCADA are first preprocessed through undersampling,that is,they are labeled,normalized,and balanced.The features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset.A middle feature is proposed to show how a given feature is correlated with icing on the blade.Performance indicators for the model,including a reward function,are also designed to assess its predictive accuracy.Finally,the most suitable model is used to predict the testing data,and values of the reward function and the predictive accuracy of the model are calculated.The proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle feature.The results here show that an integrated indicator systemis superior to a single indicator of accuracy when evaluating the prediction model.

眼球参数对iCare IC100眼压计和Goldmann压平式眼压计测量结果的影响

目的:探讨中央角膜厚度、角膜曲率、角膜散光、白到白、前房深度、晶体厚度和眼轴长度对iCare IC100眼压计与Goldmann压平式眼压计(Goldmann applanation tonometry, GAT)测量结果的影响。方法:前瞻性研究。选取47例(73眼)门诊患者作为研究对象。采用IOL Master700测量患者中央角膜厚度、角膜曲率、角膜散光、白到白、前房深度、晶体厚度和眼轴长度,使用iCare IC100眼压计和GAT对所有患者进行眼压测量。采用线性相关分析比较iCare IC100和GAT的测量结果以及iCare IC100和GAT与中央角膜厚度、角膜曲率、角膜散光、白到白、前房深度、晶体厚度和眼轴长度的相关性,并进一步采用多重线性回归法分析相关性好的眼球参数与两种眼压计测量结果的关系。结果:使用iCare IC100和GAT测量眼压的平均值分别为(19.01 ± 7.68) mmHg和(18.99 ± 7.58) mmHg (r = 0.942, P = 0.000)。中央角膜厚度与iCare IC100和GAT测量值均明显相关(r = 0.527, P = 0.000;r = 0.485, P = 0.000)。中央角膜厚度每增加1 μm,iCare IC100测量值增加0.094 mmHg,GAT测量值增加0.085 mmHg。中央角膜厚度对iCare IC100测量值的影响较其对于GAT测量值的影响大。角膜散光与iCare IC100和GAT的测量值均明显相关(r = 0.246, P = 0.036;r = 0.309, P = 0.008)。角膜散光每增加1 D,iCare IC100测量值增加2.045 mmHg,GAT测量值增加2.662 mmHg。角膜散光对iCare IC100测量值的影响较其对于GAT测量值的影响小。眼轴每增加1 mm,iCare IC100测量值增加0.593 mmHg,GAT测量值增加0.489 mmHg。眼轴对iCare ic100测量值的影响较其对于GAT测量值的影响大。角膜曲率、白到白、前房深度、晶体厚度和眼轴长度与iCare IC100和GAT的测量值均无显著相关性(P均 > 0.05)。结论:中央角膜厚度、角膜散光和眼轴长度对iCare IC100和GAT的测量值均有影响,中央角膜厚度和眼轴长度对iCare IC100测量值的影响较其对于GAT的影响大,角膜散光对iCare IC100测量值的影响较其对于GAT的影响小。

Interaction between sea ice/iceberg and ship structures: A review

For ship structural design and good maneuverability in an ice-covered sea, the local and global load of ice cover on ships should be well understood. This paper reviews the extensive work done on ice loads on ships, including： （a） Ice pressure and local load determination based on field and model tests; （b） Global ice loads on ships from full-scale field observations, model tests and numerical models under different ice conditions （level ice and pack ice） and ship operations （maneuvering and mooring）. Spe- cial attention is paid to the discrete element simulation of global ice loads on ships; and （c） Analytical solutions and numerical models of impact loads of icebergs on ships for polar navigation. Finally, research potential in these areas is discussed.

Variation of sea ice extent in different regions of the Arctic Ocean

Sea ice in the Arctic has been reducing rapidly in the past half century due to global warming. This study analyzes the variations of sea ice extent in the entire Arctic Ocean and its sub regions. The results indicate that sea ice extent reduction during 1979-2013 is most significant in summer, following by that in autumn, winter and spring. In years with rich sea ice, sea ice extent anomaly with seasonal cycle removed changes with a period of 4-6 years. The year of 2003-2006 is the ice-rich period with diverse regional difference in this century. In years with poor sea ice, sea ice margin retreats further north in the Arctic. Sea ice in the Fram Strait changes in an opposite way to that in the entire Arctic. Sea ice coverage index in melting-freezing period is an critical indicator for sea ice changes, which shows an coincident change in the Arctic and sub regions. Since 2002, Region C2 in north of the Pacific sector contributes most to sea ice changes in the central Aarctic, followed by C1 and C3. Sea ice changes in different regions show three relationships. The correlation coefficient between sea ice coverage index of the Chukchi Sea and that of the East Siberian Sea is high, suggesting good consistency of ice variation. In the Atlantic sector, sea ice changes are coincided with each other between the Kara Sea and the Barents Sea as a result of warm inflow into the Kara Sea from the Barents Sea. Sea ice changes in the central Arctic are affected by surrounding seas.

Observations and modeling of the ice-ocean conditions in the coastal Chukchi and Beaufort Seas

The Chukchi and Beaufort Seas include several important hydrological features： inflow of the Pacific water, Alaska coast current （ ACC ）, the seasonal to perennial sea ice cover, and landfast ice ＇along the Alaskan coast. The dynamics of this coupled ice-ocean system is important for both regional scale oceanography and large-scale global climate change research. A mumber of moorings were deployed in the area by JAMSTEC since 1992, and the data revealed highly variable characteristics of the hydrological environment. A regional high-resolution coupled ice-ocean model of the Chukchi and Beaufort Seas was established to simulate the ice-ocean environment and unique seasonal landfast ice in the coastal Beaufort Sea. The model results reproduced the Beaufort gyre and the ACC. The depthaveraged annual mean ocean currents along the Beaufort Sea coast and shelf hreak compared well with data from four moored ADCPs, but the simulated velocity had smaller standard deviations, which indicate small-scale eddies were frequent in the region. The model resuits captured the sea,real variations of sea ice area as compared with remote sensing data, and the simulated sea ice velocity showed an ahnost stationary area along the Beaufort Sea coast that was similar to the observed landfast ice extent. It is the combined effects of the weak oceanic current near the coast, a prevailing wind with an onshore component, the opposite direction of the ocean current, and the blocking hy the coastline that make the Beaufort Sea coastal areas prone to the formation of landfast ice.

Terrestrial Temperature, Sea Levels and Ice Area Links with Solar Activity and Solar Orbital Motion

This paper explores the links between terrestrial temperature, sea levels and ice areas in both hemispheres with solar activity indices expressed through averaged sunspot numbers together with the summary curve of eigenvectors of the solar background magnetic field (SBMF) and with changes of Sun-Earth distances caused by solar inertial motion resulting from the gravitation of large planets in the solar system. Using the wavelet analysis of the GLB and HadCRUTS datasets two periods: 21.4 and 36 years in GLB, set and the period of about 19.6 years in the HadCRUTS are discovered. The 21.4-year period is associated with variations in solar activity defined by the summary curve of the largest eigenvectors of the SBMF. A dominant 21.4-year period is also reported in the variations of the sea level, which is linked with the period of 21.4 years detected in the GLB temperature and the summary curve of the SBMF variations. The wavelet analysis of ice and snow areas shows that in the Southern hemisphere, it does not show any links to solar activity periods while in the Northern hemisphere, the ice area reveals a period of 10.7 years equal to a usual solar activity cycle. The TSI in March-August of every year is found to grow with every year following closely the temperature curve, because the Sun moves closer to the Earth orbit owing to gravitation of large planets (solar inertial motion, SIM), while the variations of solar radiation during a whole year have more steady distribution without a sharp TSI increase during the last two centuries. The additional TSI contribution caused by SIM is likely to secure the additional energy input and exchange between the ocean and atmosphere.

RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection

Nowadays,industrial control system(ICS)has begun to integrate with the Internet.While the Internet has brought convenience to ICS,it has also brought severe security concerns.Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts.They are not aimed at the original network data,nor can they capture the potential characteristics of network packets.Therefore,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network traffic.Instead of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater depth.It regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted feature.Experimental results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%.

RECRYSTALLIZATION BEHAVIORS OF ALLOY IC10 AT ELEVATED TEMPERATURE

To investigate recrystallization behaviors of the alloy IC10,tensile experiments are conducted over a wide range of strain rates（10-4—10-2 s-1）at 900°C by using a material testing system（MTS809）.Experimental results show that：（1）The flow stress is sensitive to the strain rate while the stress-strain curve at various strain rates exhibit the similar features;（2）The flow stress,the critical stress and the critical strain increase with strain rates.And the mechanisms of these properties are studied based on the examinations of transmission electron microscopy（TEM）and scanning electron microscopy（SEM）.In order to study the flow features of IC10,a new phenomenological constitutive model is developed.The effectiveness of the model is verified by extensive experiments on IC10.

The effect of single-horn glaze ice on the vortex structures in the wake of a horizontal axis wind turbine

The present study experimentally investigated the effect of a simulated single-horn glaze ice accreted on ro- tor blades on the vortex structures in the wake of a hori- zontal axis wind turbine by using the stereoscopic particle image velocimetry （Stereo-PIV） technique. During the ex- periments, four horizontal axis wind turbine models were tested, and both ＂free-run＂ and ＂phase-locked＂ Stereo-PIV measurements were carried out. Based on the ＂free-run＂ measurements, it was found that because of the simulated single-horn glaze ice, the shape, vorticity, and trajectory of tip vortices were changed significantly, and less kinetic en- ergy of the airflow could be harvested by the wind turbine. In addition, the ＂phase-locked＂ results indicated that the pres- ence of simulated single-horn glaze ice resulted in a dramatic reduction of the vorticity peak of the tip vortices. Moreover, as the length of the glaze ice increased, both root and tip vortex gaps were found to increase accordingly.

Heat Transfer Analysis on Wire Icing and the Current Preventing from Icing

This study concerns the heat transfer processes during ice accretion on wires. The steady state heat balance equation assumed to describe the thermodynamics at the surface of a current heated wire subjected to icing is obtained by analyzing and computing each terms of heat flux. The surface temperature of wire is derived from the heat balance equation, which gives out a proposed estimation of the current intensity to prevent the wire icing

A Novel SPICE Macro-Model for Power ICs

A novel macro-model of high-voltage DMOS for power ICs is proposed according to the canonical piecewiselinear model technique. The method describes nonlinear characteristics directly as functions of node voltage. We employ the Powell algorithm, which gives higher accuracy without the convergence problem and with lower analysis time. Finally, a Comparison of simulation results and measurement results in application to power ICs is reported.

An Arctic sea ice thickness variability revealed from satellite altimetric measurements

A modified algorithm taking into account the first year（FY） and multiyear（MY） ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat（2003–2008）. Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns（2005–2008） leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.