核电站控制棒驱动机构Canopy焊缝焊接温度场和应力-应变场模拟

目的研究机加工和拉拔2种成形方式下得到的填充环对Canopy焊缝的影响,获取焊接焊缝成形、焊接残余应力和变形的相关数据,以指导Canopy焊缝焊接工艺。方法采用数值模拟的方法,建立Canopy焊缝焊接数值分析模型,模拟焊接温度场、焊接残余应力和焊接残余变形。结果拉拔成形环焊接熔池高度为9 mm,机加工成形环焊接熔池高度为8.3 mm;机加工成形环焊接最大残余应力为255.6 MPa,而拉拔成形环焊接最大残余应力为277.8 MPa,均出现在管座紧贴焊缝的位置;机加工成形环焊接残余变形为0.19 mm,拉拔成形环焊接残余变形为0.186 mm,最大残余变形均出现在焊接起始位置附近,在焊缝与管座交接的位置。结论熔池形貌直接影响了热影响区域的大小,拉拔Y型环焊接熔池高度更大,焊接的热影响区域更大;拉拔Y型环焊接残余应力略大于机加工Y型环焊接残余应力;机加工成形环和拉拔成形环焊接残余变形相近。

Drought events influence nutrient canopy exchanges and green leaf partitioning during senescence in a deciduous forest

The increase in the frequency and intensity of drought events expected in the coming decades in Western Europe may disturb forest biogeochemical cycles and create nutrient deficiencies in trees.One possible origin of nutrient deficiency is the disturbance of the partitioning of the green leaf pool during the leaf senescence period between resorption,foliar leaching and senesced leaves.However,the effects of drought events on this partitioning and the consequences for the maintenance of tree nutrition are poorly documented.An experiment in a beech forest in Meuse(France)was conducted to assess the effect of drought events on nutrient canopy exchanges and on the partitioning of the green leaf pool during the leaf senescence period.The aim was to identify potential nutritional consequences of droughts for trees.Monitoring nutrient dynamics,including resorption,chemistry of green and senesced leaves,foliar absorption and leaching in mature beech stands from 2012 to 2019 allowed us to compare the nutrient exchanges for three nondry and three dry years(i.e.,with an intense drought event during the growing season).During dry years,we observed a decrease by almost a third of the potassium(K)partitioning to resorption(i.e.resorption efficiency),thus reducing the K reserve in trees for the next growing season.This result suggests that with the increased drought frequency and intensity expected for the coming decades,there will be a risk of potassium deficiency in trees,as already observed in a rainfall exclusion experiment on the same study site.Reduced foliar leaching and higher parititioning to the senesced leaves for K and phosphorus(P)were also observed.In addition,a slight increase in nitrogen(N)resorption efficiency occurred during dry years which is more likely to improve tree nutrition.The calcium(Ca)negative resorption decreased,with no apparent consequence in our study site.Our results show that nutrient exchanges in the canopy and the partitioning of the green leaf pool can be modified by drought events,and may have consequences on tree nutrition.

Rice canopy temperature is affected by nitrogen fertilizer

Canopy temperature strongly influences crop yield formation and is closely related to plant physiological traits.However, the effects of nitrogen treatment on canopy temperature and rice growth have yet to be comprehensively examined. We conducted a two-year field experiment with three rice varieties(HD-5, NJ-9108, and YJ-805) and three nitrogen treatments(zero-N control(CK), 200 kg ha~(–1)(MN), and 300 kg ha~(–1)(HN)). We measured canopy temperature using a drone equipped with a high-precision camera at the six stages of the growth period. Generally,canopy temperature was significantly higher for CK than for MN and HN during the tillering, jointing, booting, and heading stages. The temperature was not significantly different among the nitrogen treatments between the milky and waxy stages. The canopy temperature of different rice varieties was found to follow the order: HD-5>NJ-9108>YJ-805, but the difference was not significant. The canopy temperature of rice was mainly related to plant traits, such as shoot fresh weight(correlation coefficient r=–0.895), plant water content(–0.912), net photosynthesis(–0.84), stomatal conductance(–0.91), transpiration rate(–0.90), and leaf stomatal area(–0.83). A structural equation model(SEM) showed that nitrogen fertilizer was an important factor affecting the rice canopy temperature.Our study revealed:(1) A suite of plant traits was associated with the nitrogen effects on canopy temperature,(2) the heading stage was the best time to observe rice canopy temperature, and(3) at that stage, canopy temperature was negatively correlated with rice yield, panicle number, and grain number per panicle. This study suggests that canopy temperature can be a convenient and accurate indicator of rice growth and yield prediction.

Fuzzy C-Means Algorithm Based on Density Canopy andManifold Learning

Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced by the random selection of initial cluster centers,and the performance of Euclid distance in complex high-dimensional data is poor.To solve the above problems,the improved FCM clustering algorithm based on density Canopy and Manifold learning(DM-FCM)is proposed.First,a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers,which improves the self-adaptability and stability of the algorithm.Then,considering that high-dimensional data often present a nonlinear structure,the manifold learning method is applied to construct a manifold spatial structure,which preserves the global geometric properties of complex high-dimensional data and improves the clustering effect of the algorithm on complex high-dimensional datasets.Fowlkes-Mallows Index(FMI),the weighted average of homogeneity and completeness(V-measure),Adjusted Mutual Information(AMI),and Adjusted Rand Index(ARI)are used as performance measures of clustering algorithms.The experimental results show that the manifold learning method is the superior distance measure,and the algorithm improves the clustering accuracy and performs superiorly in the clustering of low-dimensional and complex high-dimensional data.

不同Y形环应用于核电站控制棒驱动机构Canopy焊缝焊接工艺及焊缝性能的研究

试验采用两种机械加工Y形环和一种拉拔Y形环应用于核电站控制棒驱动机构Canopy焊缝,研究不同Y形环对Canopy焊缝焊接工艺及焊缝性能的影响。结果表明:机械加工Y形环为整圈环,相比拉拔Y形环,更有利于定位焊;机械加工Y形环(控公差)与拉拔Y形环装配间隙范围相当,比机械加工Y形环装配间隙范围大,更有利于工程应用;3种Y形环熔成的Canopy焊缝组织主要由铁素体和柱状奥氏体组成,无明显差异;3种Y形环熔成的Canopy焊缝经23.1~23.3MPa水压试验,焊缝表面均无渗漏,无可见变形。该研究为机械加工Y形环应用于核电站控制棒驱动机构Canopy焊缝提供了宝贵的经验。

核电站控制棒驱动机构Canopy焊缝组织和性能研究

试验采用专用自动TIG焊机焊接核电站控制棒驱动机构Canopy焊缝,选用机械加工Y形环作为填充材料,研究Canopy焊缝组织和性能。结果表明:通过自动TIG焊,机械加工Y形环熔成的Canopy焊缝,成形良好,宽度适中,焊缝与母材冶金熔合良好;焊缝组织主要由板条状或骨架状铁素体与快速凝固的柱状奥氏体组成;经23.1~23.3MPa水压试验,Canopy焊缝表面无渗漏,无可见变形;维氏硬度结果表明:焊缝硬度在210HV左右,焊缝硬度分布均匀。该研究为核电站控制棒驱动机构Canopy焊缝用机械加工Y形环提供了宝贵的经验。

屏蔽主泵Canopy密封环自动焊接技术研究

为了降低作业人员在AP1000机组屏蔽主泵在役检修拆装过程中的辐照剂量,研究了与实际作业工序相同的Canopy密封环自动焊接工艺,开发了可远程控制、对称施焊的自动焊接装置,并在模拟体上进行了焊接试验。试验结果表明,在优化的焊接参数下,Canopy密封环打底焊成形良好,多层多道焊缝无层间未熔合等缺陷,起弧、收弧过渡良好。该自动焊接装置结构紧凑,可实现模拟件的稳定焊接,焊接质量良好,能够满足屏蔽主泵在役检修更换时的自动焊接需求。

基于加权密度Canopy的K-means文本聚类

针对现有文本聚类性能不高的问题,提出了一种改进质心初始化的K-means文本聚类算法.该算法首先利用Canopy算法进行文本预聚类,并且对Canopy算法的阈值选取策略进行改进,定义加权密度进行Canopy中心的选取,得到更准确的聚类数以及初始聚类中心;然后将所得结果作为K-means算法的初始化参数进行后续迭代聚类,有效解决了传统算法因随机选取初始聚类中心而陷入局部最优解的问题,减少了算法的迭代次数,提高了聚类准确性.实验结果表明:与其他同类型算法相比,该算法在文本聚类分析中更具优势.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Effect of the L-D_(1)alleles on leaf morphology,canopy structure and photosynthetic productivity in upland cotton(Gossypium hirsutum L.)

One of the most important objectives for breeders is to develop high-yield cultivars.The increase in crop yield has met with bottlenecks after the first green revolution,and more recent efforts have been focusing on achieving high photosynthetic efficiency traits in order to enhance the yield.Leaf shape is a significant agronomic trait of upland cotton that affects plant and canopy architecture,yield,and other production attributes.The major leaf shape types,including normal,sub-okra,okra,and super-okra,with varying levels of lobe severity,are controlled by a multiple allelic series of the D-genome locus L-D_(1).To analyze the effects of L-D_(1)alleles on leaf morphology,photosynthetic related traits and yield of cotton,two sets of near isogenic lines(NILs)with different alleles were constructed in Lumianyan 22(LMY22)and Lumianyan 28(LMY28)backgrounds.The analysis of morphological parameters and the results of virus-induced gene silencing(VIGS)showed that the regulation of leaf shape by L-D_(1)alleles was similar to a gene-dosage effect.Compared with the normal leaf,deeper lobes of the sub-okra leaf improved plant canopy structure by decreasing the leaf area index(LAI)and increasing the light transmittance rate(LTR),and the mid-range LAI of sub-okra leaf also guaranteed the accumulation of cotton biomass.Although the chlorophyll content(SPAD)of sub-okra leaf was lower than those of the other two leaf shapes,the net photosynthetic rate(Pn)of sub-okra leaf was higher than those of okra leaf and normal leaf at most stages.Thus,the improvements in canopy structure,as well as photosynthetic and physiological characteristics,contributed to optimizing the light environment,thereby increasing the total biomass and yield in the lines with a sub-okra leaf shape.Our results suggest that the sub-okra leaf may have practical application in cultivating varieties,and could enhance sustainable and profitable cotton production.

Visual rating and the use of image analysis for assessing canopy density in a pecan provenance collection during leaf fall

A collection representing the native range of pecan was planted at the US DA-ARS Southeastern Fruit and Tree Nut Research Station,Byron,GA.The collection(867 trees)is a valuable genetic resource for characterizing important horticultural traits.Canopy density during leaf fall is important as the seasonal canopy dynamics provides insights to environmental cues and breeding potential of germplasm.The ability of visual raters to estimate canopy density on a subset of the provenance collection(76 trees)as an indicator of leaf shed during autumn along with image analysis values was explored.Mean canopy density using the image analysis software was less compared to visual estimates(11.9%vs 18.4%,respectively).At higher canopy densities,the raters overestimated foliage density,but overall agreement between raters and measured values was good(ρc=0.849 to 0.915),and inter-rater reliability was high(R^(2)=0.910 to 0.953).The provenance from Missouri(MOL),the northernmost provenance,had the lowest canopy density in November,and results show that the higher the latitude of the provenance,the lower the canopy density.Based on regression,the source provenance latitude explained 0.609 of the variation using image analysis,and0.551 to 0.640 when based on the rater estimates of canopy density.Visual assessment of pecan canopy density due to late season leaf fall for comparing pecan genotypes provides accurate and reliable estimates and could be used in future studies of the whole provenance collection.

Remote sensing of subtropical tree diversity:The underappreciated roles of the practical definition of forest canopy and phenological variation

Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in spaceborne remote sensing provide unprecedented opportunities to map and monitor tree diversity more efficiently.Here we built partial least squares regression models using the multispectral surface reflectance acquired by Sentinel-2 satellites and the inventory data from 74 subtropical forest plots to predict canopy tree diversity in a national natural reserve in eastern China.In particular,we evaluated the underappreciated roles of the practical definition of forest canopy and phenological variation in predicting tree diversity by testing three different definitions of canopy trees and comparing models built using satellite imagery of different seasons.Our best models explained 42%–63%variations in observed diversities in cross-validation tests,with higher explanation power for diversity indices that are more sensitive to abundant species.The models built using imageries from early spring and late autumn showed consistently better fits than those built using data from other seasons,highlighting the significant role of transitional phenology in remotely sensing plant diversity.Our results suggested that the cumulative diameter(60%–80%)of the biggest trees is a better way to define the canopy layer than using the subjective fixeddiameter-threshold(5–12 cm)or the cumulative basal area(90%–95%)of the biggest trees.Remarkably,these approaches resulted in contrasting diversity maps that call attention to canopy structure in remote sensing of tree diversity.This study demonstrates the potential of mapping and monitoring tree diversity using the Sentinal-2 data in species-rich forests.

Assessing the severity of cotton Verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks

Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

平邑甜茶高位嫁接丽嘎拉苹果树冠层内枝类结构和果实品质、产量的分布

【目的】掌握以平邑甜茶为砧木,采用高位嫁接方式的丽嘎拉苹果树冠层内枝类结构、果实产量和品质的分布特点,为辽宁寒地苹果园建设中应用该种栽培模式,提升果品质量、优化栽培措施提供参考。【方法】以丽嘎拉/GM256/平邑甜茶、丽嘎拉/平邑甜茶(高接)和丽嘎拉/平邑甜茶(低接)苹果树为试材,按照冠层分格的方法,将试验树从地面到顶部分成4个冠层,比较不同冠层高度内枝类结构、果实产量和品质的分布差异。【结果】丽嘎拉/平邑甜茶(高接)苹果树高、冠径、覆盖率生长速度高于丽嘎拉/GM256/平邑甜茶,丽嘎拉/平邑甜茶(高接)树体外观表现为“小脚”现象,而丽嘎拉/GM256/平邑甜茶表现为“大脚”现象。丽嘎拉/平邑甜茶(高接)苹果树枝量、短枝比例高于丽嘎拉/平邑甜茶(低接)和丽嘎拉/GM256/平邑甜茶。丽嘎拉/平邑甜茶(高接)、丽嘎拉/平邑甜茶(低接)和丽嘎拉/GM256/平邑甜茶枝量分布集中区域为冠层高度>2.00~3.00 m、>3.00 m和>1.00~2.00 m,占全树枝量比例的43.51%、45.37%和47.96%。相同冠层高度内,丽嘎拉/平邑甜茶(高接)果实单果质量、果形指数、硬度、可滴定酸含量、色差L和a值高于丽嘎拉/GM256/平邑甜茶。随着冠层高度的增加,丽嘎拉/平邑甜茶(高接)、丽嘎拉/平邑甜茶(低接)和丽嘎拉/GM256/平邑甜茶果实硬度、可滴定酸含量呈现下降趋势,而固酸比和色差值(L、a、b)呈现上升趋势。丽嘎拉/平邑甜茶(高接)和丽嘎拉/平邑甜茶(低接)果实产量主要分布在冠层高度>2.00 m的空间区域,占全树产量的69.79%和75.55%,而丽嘎拉/GM256/平邑甜茶为>1.00~3.00 m,占比全树产量的74.59%。【结论】以平邑甜茶为砧木,采用高位嫁接的丽嘎拉苹果树枝条数量多、短枝比例高、外观色泽好,树体枝量和果实产量分布集中区域为冠层高度>2.00~3.00 m,该冠层生产的苹果单果质量大、果形指数适中、可溶性固形物含量高。

盘锦地区不同品种水稻品质及其冠层高光谱反射率特征分析

对2022年盘锦地区8种水稻进行品质特征分析,并探讨不同品种水稻籽粒加工品质、外观品质、蒸煮品质、营养品质与水稻成熟期冠层原始光谱、一阶导数光谱及连续统去除光谱的相关关系。结果表明:盐粳939的加工品质较好,锦稻106(9系)的外观品质较好,锦稻香103的蒸煮品质较好,盘粳968(3系)的营养品质较好。原始光谱反射率与糙米率指标的相关关系较好,在731~1131 nm和1150~1360 nm两个波段内,相关系数高达0.767;糙米率与一阶导数光谱的相关关系最好,在1459 nm处达到极显著相关,相关系数为0.867;垩白度和垩白粒率均在1804 nm处与连续统去除光谱的相关关系较好,并达到极显著负相关,相关系数分别为-0.979和-0.983;长宽比与原始光谱的相关关系较好,相关系数在659 nm处达到最大值,为0.849;蒸煮品质所包含的食味值、直链淀粉含量和营养品质所包含的蛋白质含量均与连续统去除光谱有较好的相关关系。

基于变量灌溉动态分区管理的冬小麦产量与节水效果

喷灌机机载式红外温度传感器系统是动态监测农田作物水分亏缺状况、构建变量灌溉(variable rate irrigation,VRI)决策支持系统的重要工具。为了评估圆形喷灌机机载式红外温度传感器系统在变量灌溉动态分区管理中的应用效果,该研究以气象参数和土壤水分传感器网络构建的均一灌溉(uniform rate irrigation, URI)决策方法为对照,评估了基于气象参数、土壤水分传感器网络和作物冠层温度的变量灌溉决策方法对华北平原冬小麦灌溉制度、土壤含水率空间分布均匀性和节水增产效果的影响。在河北省邢台市大曹庄中国水利水电科学研究院智慧灌溉技术与装备创新示范推广基地开展试验,试验区为三跨加悬臂圆形喷灌机控制灌溉面积7.07 hm^(2),2021年试验区等分为2个子区,布置URI和VRI处理,2022年试验区等分为4个子区,布置URI处理、基于等间隔法进行管理区聚类划分的VRI(T1)处理、基于“Jenks”自然断点法进行管理区聚类划分的VRI(T2)处理和基于几何间隔断点法进行管理区聚类划分的VRI(T3)处理。结果表明,在冬小麦生育期内,URI和VRI处理灌水7~10次,2 a平均灌水量分别为201和173 mm。开展VRI管理后,冬小麦主根区的土壤含水率空间分布均匀性和产量均匀性提高。2021年URI和VRI处理的冬小麦产量分别为9 470和9 574 kg/hm^(2),2022年的冬小麦产量较2021年分别降低6.7%和6.0%。变量灌溉处理的管理区聚类划分方法未对灌溉制度和产量产生显著影响。与URI处理相比,VRI处理能够减少灌溉水量,且对产量和水分利用效率无显著影响。研究结果可为基于喷灌机机载式红外温度传感器系统的变量灌溉动态分区管理方法的建立提供指导,为变量灌溉决策支持系统的开发升级提供技术支持。

珊瑚礁冠层水动力学问题研究综述

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