Optimum Profiles of Endwall Contouring for Enhanced Net Heat Flux Reduction and Aerodynamic Performance

Successfully utilized non-axisymmetric endwalls to enhance turbine efficiencies(aerodynamic and turbine inlet temperatures)by controlling the characteristics of the secondary flow in a blade passage.This is accomplished by steady-state numerical hydrodynamics and deep knowledge of the field of flow.Because of the interaction between mainstream and purge flow contributing supplementary losses in the stage,non-axisymmetric endwalls are highly susceptible to the inception of purge flow exit compared to the flat and any advantage rapidly vanishes.The conclusions reveal that the supreme endwall pattern could yield a lowering of the gross pressure loss at the design stage and is related to the size of the top-loss location being productively lowered.This has led to diminished global thermal exchange lowered in the passage of the vane alone.The reverse flow adjacent to the suction side corner of the endwall is migrated farther from the vane surface,as the deviated pressure spread on the endwall accelerates the flow and progresses the reverse flow core still downstream.The depleted association between the tornado-like vortex and the corner vortex adjacent to the suction side corner of the endwall is the dominant mechanism of control in the contoured end wall.In this publication,we show that the non-axisymmetric endwall contouring by selective numerical shape change method at most prominent locations is advantageous in lowering the thermal load in turbines to augment the net heat flux reduction as well as the aerodynamic performance using multi-objective optimization.

Real-time instance segmentation based on contour learning

Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial contour.To solve these problems,a real-time instance segmentation algorithm based on contour learning was proposed.Firstly,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution kernel.Secondly,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more comprehensively.Thirdly,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real contour.Finally,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour.The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU.The proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.

Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm:A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC).This method innovatively combines contour edge tracking with affinity propagation(AP)clustering for peak detection in GC×GC fingerprints,the first in this field.Contour edge tracking signif-icantly reduces false positives caused by“burr”signals,while AP clustering enhances detection accuracy in the face of false negatives.The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin.PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples.Furthermore,this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins.The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues.Each sample exhibits unique characteristic components alongside common ones,and vari-ations in content may influence their therapeutic effectiveness.This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional(2D)fingerprint analysis of GC×GC data.

Contour Farming Suitability of the Black Soil Region in Northeast China and Its Spatial Characteristics

Contour farming technology plays a key role in reducing soil erosion,enhancing water use efficiency,and fostering sustain-able agricultural development,Despite being a straightforward yet efficacious farming technique,it has not seen widespread implement-ation in China.Considering the deteriorating quality of arable lands in the Black Soil Region of Northeast China(BSR-NEC),it is ne-cessary to investigate spatial patterns and identify suitable areas for contour farming in this region.To achieve this objective,spatial autocorrelation and grouping analysis methods were employed to classify the land into four categories of suitability for contour farming:highly suitable,moderately suitable,generally suitable,and unsuitable.The results reveal that:1)the contour farming suitable area in BSR-NEC covers 89861.32 km^(2),accounting for 21.59%of arable land as of 2020.Heilongjiang Province owns the largest suitable area of 32853.68 km^(2),and Inner Mongolia has the highest proportion of 28.89%.2)In terms of the spatial distribution,regions with higher suitability for contour farming are concentrated in the Da Hinggan Mountains region,particularly Nenjiang City(Heilongjiang Province),which has the highest area of 2593.07 km^(2).Areas with a high proportion of suitable arable lands for contour farming are mainly found in the Da Hinggan Mountains and Changbai Mountains regions,with Ergun City(Inner Mongolia)having the highest pro-portion at 47.2%.Regions with higher suitability and proportion are concentrated in the Da Hinggan Mountains region,primarily cover-ing the Inner Mongolia and Heilongjiang.3)Regarding spatial clustering,both the area and proportion of suitable contour farming areas exhibit noticeable clustering effects,though not entirely consistent.4)Group analysis results designate 148 counties in BSR-NEC as highly suitable areas,predominantly located in the Changbai Mountains region,Liaodong Peninsula,Hulun Buir Plateau,and the north and south regions of the Da Hinggan Mountains.The zoning of suitable areas for contour farming in BSR-NEC informs the strategic de-velopment of policies and measures,allowing for the implementation of targeted policies in distinct areas suitable for contour farming.This provides a valuable reference for promoting contour farming technology more effectively and efficiently.re effectively and effi-ciently.

Body Contour in the Post-Bariatric Male Patient

Background: Obesity is currently considered a public health problem. Bariatric procedures have become an important part of obesity management and, consequently, the number of male patients seeking post-bariatric reconstructive procedures have increased. Therefore, the clinical approach and understanding of the body contour of this population have become more relevant. The goal of post-bariatric reconstruction is to enhance the male silhouette through removal of skin and adipose tissue excess, and abdominal rectus diastasis repair. Material and Methods: We conducted a retrospective study at the Plastic and Reconstructive Surgery Department of the National Medical Center “20 de Noviembre”. All male patients referred to our department to start a post-bariatric reconstruction protocol from January 2018 to December 2022 were included in this study. Results: In total, 15 patients who underwent corporal contouring procedures were included;median age was 49.2 years with minimum of 33 years, and a maximum of 57 years. Median Body mass index was 28.4 kg/m2 with minimum of 22 kg/m2 and maximum of 38 kg/m2. All patients were treated 18 months after their bariatric surgery. All patients underwent an abdominoplasty as a body contouring procedure. 4 (26.7%) patients presented complications related to the surgery. Conclusion: We described a comprehensive and systematic approach to massive weight loss for male patients, suggesting an abdominal marking based on the patient’s clinical features and the expected results avoiding feminization of the abdominal body contour.

Clinical Efficacy of GBR Technique Combined with Temporary Bridgework-Guided Gingival Contouring in Treating Upper Anterior Tooth Loss with Labial Bone Defects

Objective:To investigate the clinical effect of the guided bone regeneration(GBR)technique combined with temporary bridgework-guided gingival contouring in treating upper anterior tooth loss with labial bone defects.Methods:From July 2023 to April 2024,80 patients with upper anterior tooth loss and labial bone defects were admitted to the hospital and selected as evaluation samples.They were divided into an observation group(n=40)and a control group(n=40)using a numerical table lottery scheme.The control group received treatment with the GBR technique,while the observation group received treatment with the GBR technique combined with temporary bridges to guide gingival contouring.The two groups were compared in terms of clinical red aesthetic scores(PES),labial alveolar bone density,labial bone wall thickness,gingival papillae,gingival margin levels,and patient satisfaction.Results:The PES scores of patients in the observation group were higher than those in the control group after surgery(P<0.05).The bone density of the labial alveolar bone and the thickness of the labial bone wall in the observation group were higher than those in the control group.The levels of gingival papillae and gingival margins were lower in the observation group after surgery(P<0.05).Additionally,patient satisfaction in the observation group was higher than in the control group(P<0.05).Conclusion:The GBR technique combined with temporary bridge-guided gingival contouring for treating upper anterior tooth loss with labial bone defects can improve the aesthetic effect of gingival soft tissue,increase alveolar bone density and the thickness of the labial bone wall,and enhance patient satisfaction.This approach is suitable for widespread application in healthcare institutions.

Embedded System Development for Detection of Railway Track Surface Deformation Using Contour Feature Algorithm

Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each year.For this purpose,a proper condition monitoring system is essential to avoid accidents and heavy losses.Generally,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment.Therefore,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway track.The proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition process.The contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy regions.It works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black color.We have used the multiprocessing technique to overcome the massive processing and memory issues.This embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/h.The functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faults.An unhealthy frame’s onsite detection processing time was recorded at approximately 25.6ms.The proposed system can synchronize the acceleration data on specific railway track deformation.The proposed novel embedded system may be beneficial for detecting faults to overcome the conventional manual railway track condition monitoring,which is still being practiced in various developing or underdeveloped countries.

In-situ 3D contour measurement for laser powder bed fusion based on phase guidance

In-situ layerwise imaging measurement of laser powder bed fusion(LPBF)provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity.Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers,which successfully monitored the contours of components and evaluated their accuracy.However,research for the methods of in-situ 3D contour measurement or component edge warping identification is rare.In this study,a 3D contour mea-surement method combining gray intensity and phase difference is proposed,and its accuracy is verified by designed experiments.The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function.This method can detect the deviations of common ge-ometric features as well as warpage at LPBF component edges,and provides fundamental data for in-situ quality monitoring tools.

CE-EEN-B0:Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

A brain tumor is the uncharacteristic progression of tissues in the brain.These are very deadly,and if it is not diagnosed at an early stage,it might shorten the affected patient’s life span.Hence,their classification and detection play a critical role in treatment.Traditional Brain tumor detection is done by biopsy which is quite challenging.It is usually not preferred at an early stage of the disease.The detection involvesMagneticResonance Imaging(MRI),which is essential for evaluating the tumor.This paper aims to identify and detect brain tumors based on their location in the brain.In order to achieve this,the paper proposes a model that uses an extended deep Convolutional Neural Network(CNN)named Contour Extraction based Extended EfficientNet-B0(CE-EEN-B0)which is a feed-forward neural network with the efficient net layers;three convolutional layers and max-pooling layers;and finally,the global average pooling layer.The site of tumors in the brain is one feature that determines its effect on the functioning of an individual.Thus,this CNN architecture classifies brain tumors into four categories:No tumor,Pituitary tumor,Meningioma tumor,andGlioma tumor.This network provides an accuracy of 97.24%,a precision of 96.65%,and an F1 score of 96.86%which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI image.This model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.

Experimental Research on the Surface Quality of Milling Contour Bevel Gears

Contour bevel gears have the advantages of high coincidence,low noise and large bearing capacity,which are widely used in automobile manufacturing,shipbuilding and construction machinery.However,when the surface quality is poor,the effective contact area between the gear mating surfaces decreases,affecting the stability of the fit and thus the transmission accuracy,so it is of great significance to optimize the surface quality of the contour bevel gear.This paper firstly analyzes the formation process of machined surface roughness of contour bevel gears on the basis of generating machining method,and dry milling experiments of contour bevel gears are conducted to analyze the effects of cutting speed and feed rate on the machined surface roughness and surface topography of the workpiece.Then,the surface defects on the machined surface of the workpiece are studied by SEM,and the causes of the surface defects are analyzed by EDS.After that,XRD is used to compare the microscopic grains of the machined surface and the substrate material for diffraction peak analysis,and the effect of cutting parameters on the microhardness of the workpiece machined surface is investigated by work hardening experiment.The research results are of great significance for improving the machining accuracy of contour bevel gears,reducing friction losses and improving transmission efficiency.

A Novel Contour Tracing Algorithm for Object Shape Reconstruction Using Parametric Curves

Parametric curves such as Bézier and B-splines, originally developedfor the design of automobile bodies, are now also used in image processing andcomputer vision. For example, reconstructing an object shape in an image,including different translations, scales, and orientations, can be performedusing these parametric curves. For this, Bézier and B-spline curves can be generatedusing a point set that belongs to the outer boundary of the object. Theresulting object shape can be used in computer vision fields, such as searchingand segmentation methods and training machine learning algorithms. Theprerequisite for reconstructing the shape with parametric curves is to obtainsequentially the points in the point set. In this study, a novel algorithm hasbeen developed that sequentially obtains the pixel locations constituting theouter boundary of the object. The proposed algorithm, unlike the methods inthe literature, is implemented using a filter containing weights and an outercircle surrounding the object. In a binary format image, the starting point ofthe tracing is determined using the outer circle, and the next tracing movementand the pixel to be labeled as the boundary point is found by the filter weights.Then, control points that define the curve shape are selected by reducing thenumber of sequential points. Thus, the Bézier and B-spline curve equationsdescribing the shape are obtained using these points. In addition, differenttranslations, scales, and rotations of the object shape are easily provided bychanging the positions of the control points. It has also been shown that themissing part of the object can be completed thanks to the parametric curves.

Data Augmentation Using Contour Image for Convolutional Neural Network

With the development of artificial intelligence-related technologies such as deep learning,various organizations,including the government,are making various efforts to generate and manage big data for use in artificial intelligence.However,it is difficult to acquire big data due to various social problems and restrictions such as personal information leakage.There are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning technology.Therefore,this study proposes a mixed contour data augmentation technique,which is a data augmentation technique using contour images,to solve a problem caused by a lack of data.ResNet,a famous convolutional neural network(CNN)architecture,and CIFAR-10,a benchmark data set,are used for experimental performance evaluation to prove the superiority of the proposed method.And to prove that high performance improvement can be achieved even with a small training dataset,the ratio of the training dataset was divided into 70%,50%,and 30%for comparative analysis.As a result of applying the mixed contour data augmentation technique,it was possible to achieve a classification accuracy improvement of up to 4.64%and high accuracy even with a small amount of data set.In addition,it is expected that the mixed contour data augmentation technique can be applied in various fields by proving the excellence of the proposed data augmentation technique using benchmark datasets.

An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

With the improvement of current online communication schemes,it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate.Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission.Both of the techniques mentioned above aid in the confidentiality of feature content.The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red,Green,and Blue.The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet.The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers.The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit(LSB)substitution technique to build the secret Content embedded image known as the stego image,which is subsequently transmitted across the internet medium to the server end.The experimental results are computed using the inputs from“Open-Access Medical Image Repositories(aylward.org)”and demonstrate the scheme’s impudence as the Content concealing procedure progresses.

基于双通路视觉系统的自适应轮廓检测模型

在轮廓检测领域,背景纹理的干扰容易造成轮廓提取不完整。针对这一问题,本文提出了一种基于双通路视觉系统的自适应轮廓检测模型。首先从皮层下通路的信息采集与评估过程出发,对图像整体的显著性进行评估,以此获得轮廓信息的可能性分布;然后采用自适应尺度的高斯导函数对经典视觉通路中感受野的动态特性进行模拟,加强了模型对轮廓细节的捕获;最后在外周抑制算法的基础上,结合像素的空间分布对所有边缘的稀疏性进行度量,更加准确地区分了轮廓和纹理边缘。实验结果表明,本文模型可以有效抑制背景纹理,提升轮廓连续性,具有较好的轮廓检测性能。

基于形色筛选的苹果园羽化害虫粘连图像分割方法

针对苹果园害虫识别过程中的粘连问题,提出了一种基于形色筛选的害虫粘连图像分割方法。首先,采集苹果园害虫图像,聚焦于羽化害虫。害虫在羽化过程中已完成大部分生长发育,其外部形态、颜色、纹理更为稳定显著。因此,基于不同种类害虫的形色特征信息分析,来获取害虫HSV分割阈值和模板轮廓。其次,利用形状因子判定分割粘连区域,通过颜色分割法和轮廓定位分割法来实现非种间与种间粘连害虫的分割。最后,对采集的苹果园害虫图像进行了试验分析,采用基于形色筛选的分割法对单个害虫进行分割,结果表明,本文方法的平均分割率、平均分割错误率和平均分割有效率分别为101%、3.14%和96.86%,分割效果优于传统图像分割方法。此外,通过预定义的颜色阈值,本文方法实现了棉铃虫、桃蛀螟与玉米螟的精准分类,平均分类准确率分别为97.77%、96.75%与96.83%。同时,以Mask R-CNN模型作为识别模型,平均识别精度作为评价指标,分别对已用本文方法和未用本文方法分割的害虫图像进行识别试验。结果表明,已用本文方法分割的棉铃虫、桃蛀螟和玉米螟害虫图像平均识别精度分别为96.55%、94.80%与95.51%,平均识别精度分别提高16.42、16.59、16.46个百分点。这表明该方法可为果园害虫精准识别提供理论和方法基础。

鄂尔多斯盆地西北缘奥陶系克里摩里组等深流沉积

等深流沉积是深水沉积中的常见类型之一。古代地层记录中等深流沉积的鉴别及形成机理研究相对薄弱。在鄂尔多斯盆地西缘奥陶系克里摩里组发现了一种新的等深流沉积类型,即透镜状等深流沉积。研究表明:(1)研究区发育4种岩相及组合。岩相1为透镜状泥晶灰岩,岩相2以透镜状粉晶灰岩为主,岩相3为透镜状泥晶及亮晶方解石胶结砾屑-砂屑灰岩,岩相4以层状砾屑灰岩最为常见。(2)透镜状泥晶、粉晶及砾屑-砂屑灰岩相单层厚度相对较小,波状界面,呈透镜状,局部见泥质纹层、条带及斑点,粉晶方解石多呈斑块状,整体呈细—粗—细沉积序列,为等深流持续作用而成;层状砾屑灰岩相中砾屑见叠瓦状构造,局部见倒“小”字排列,可能与风暴相关。(3)碳氧同位素反映相对海平面及古温度具有2个升高—降低旋回及1个持续上升半旋回。(4)研究区等深流为相对低能的温盐环流。等深流活动强度与海平面升降、盐度变化呈正相关。(5)从下至上,等深流活动逐渐加强,局部存在短暂极强现象,并可能受风暴影响。

基于深度神经网络的树木伐桩轮廓提取及匹配方法

为了及时准确地找到被盗树木,公安机关需要比对被盗伐树木伐桩的上下截面,寻找共同点,并依此确认两者是否属于同一树木。但是由于存放环境不同,伐桩上下截面的颜色、纹理存在巨大差异,由于锯伐方式和树皮的影响,伐桩上下表面的轮廓也存在很大差异,伐桩下表面还很容易受木屑等影响,背景复杂。针对这些难点,本研究在前面的研究基础上,继续把棋盘格作为特征物放置在伐桩表面,用PPYOLO_MobileNetV3卷积神经网络检测图像中的棋盘格,对棋盘格中的角点进行检测、排序,然后进行透视变换,恢复伐桩的原始面积和轮廓等特征,接着用PP-LiteSeg网络在复杂背景下提取伐桩轮廓,然后用CAE_ViT_base网络对轮廓进行匹配,实现了伐桩轮廓匹配的全流程,从而极大程度节约了人工。理论分析和试验结果都表明,基于局部梯度的匹配法、基于局部点集拓扑特征的匹配法和基于轮廓的全局特征匹配方法等,在伐桩图像的匹配中都是不可行的。CAE_ViT_base网络的解码器将输入图像分割为大小一致的图像块,解码器的训练过程需要关注每一个块的特征,伐桩轮廓的匹配难点在于轮廓有局部缺失,局部梯度误差较大。CAE_ViT_base网络的自监督预训练机制很好地弥补了上述缺点;同时,采用对样本图像随机多角度旋转的方法,使得图像的特征提取能够保持旋转不变性。CAE_ViT_base网络提取出来的特征在尺度上高于基于梯度的特征,也高于基于局部点集拓扑的特征,但低于全局特征。因此,只要少部分图像块高度匹配,则CAE_ViT_base网络给出的最终匹配度就比较高;同时,这种工作方式和人工对比2个伐桩轮廓是否匹配的方法也是一致的。在本研究的344幅伐桩图像上进行试验,结果表明:本研究算法对整个测试集的检测成功率为100%,棋盘格检测成功率100%,轮廓提取精度达到98.8%,轮廓匹配准确率100%,无一错检和误匹配;和基于梯度的轮廓提取方法及基于特征描述子的轮廓匹配方法相比,本研究方法具有全方位的优势。本方法中,伐桩检测匹配全流程计算耗时不足30 s,完全满足实际应用需要,具有较好的推广应用价值。

基于线结构光扫描的工件高精度三维测量方法

为了提高工业中对复杂轮廓工件的测量精度和效率,设计了一套高精度非接触三维测量系统,并提出了一种基于线结构光扫描的工件轮廓三维测量方法。首先,利用高精度相机和三轴移动平台采集线结构光图像。然后,通过基于差分区间的灰度质心算法,精确而高效地提取出线结构光中心线,并生成原始点云模型。接着,对采集到的点云数据进行必要的点云滤波和精简预处理。最后,将预处理后的点云数据与CAD模型精确配准,进行工件表面轮廓的测量与误差评定。实验结果表明:测量工件轮廓高度的绝对误差小于0.07 mm,相对误差小于0.5%。所提出的三维测量系统及方法测量误差较低,能够实现工件的高精度三维测量,具有一定的工业应用价值。

新一代高技术宽带钢轧机电工钢高精度板形控制研究进展

自由规程轧制是实现柔性一体化生产组织和追求最大生产效率的必要途径,板形控制一直是制约电工钢自由规程轧制的瓶颈难题。阐述了国际上对新一代高技术宽带钢轧机机型的不断探索与板形控制技术特征及其日趋复杂化的进展研究;基于热模拟与数学模型构建了电工钢完整轧制过程的高温本构关系,建立了电工钢热塑性变形过程集成仿真模型,原创构建了电工钢自由规程轧制完整过程中可同时控制不均匀变形和不均匀磨损的非对称自补偿轧制作用机制、提出了一种由数据与机理融合驱动的电工钢自由规程轧制形性协同的非对称自补偿轧制轧辊辊形、液压窜辊和液压弯辊的高精度融合控制方法。结合生产实际提出了新一代高技术热连轧自由规程轧制过程的全板形融合π机型与板形控制创新技术,突破性实现了高效低成本对新一代高技术宽带钢轧机自由规程轧制的高精度板形控制,为现场工业生产提供了理论基础和创新实现路径。最后展望了宽幅电工钢高精度板形控制的创新发展趋势。

海洋中示踪物等值线的分形长度及其与混合效率的关系

涡致混合扩散是物理海洋研究中的热点和难点问题。本文基于“有效扩散”理论,研究示踪物等值线在海表地转湍流的多尺度搅拌作用下,发生拉伸、扭曲、变形、折叠等改变其几何拓扑结构的现象,并探讨了等值线分形长度的变化与混合效率的关系。研究结果表明,在地转流场的搅拌下,示踪物的等值线会被迅速拉长,并产生丰富的精细结构。这种分形式的增长可达原长度的10~20倍,是混合效率提高的主要原因;而涡丝和锋面伴随的梯度增强虽然也有贡献,但为次要因素。另一方面,在示踪物模拟过程中,小尺度扩散会通过不可逆混合对示踪物进行均匀化,从而抹平等值线的精细结构,抑制等值线的增长,限制混合效率的提高。基于“数盒子”算法计算了等值线的分形维度,其数值在1.4到1.6之间,介于一维和二维之间。但由于地转湍流数据分辨率的限制,无法考虑更小尺度(次中尺度过程)的搅拌作用,可能低估了等值线的分形长度和混合效率。本研究将海洋混合与等值线几何特征联系了起来,初步得到了分形长度和混合效率两者的经验关系式,未来可以利用图像识别等成熟遥感技术将海洋示踪物等值线的几何特征直接转换为混合效率,为诊断分析海洋混合及其参数化提供了一种新的思路。