Methodology for production logging in oil-in-water flows under low flow rate and high water-cut conditions

This study aimed to obtain the production profiles of oil-in-water flow under low flow rate and high water-cut conditions in oil wells.A combination production profile logging composed of an arc-type conductance sensor(ATCS)and a cross-correlation flow meter(CFM)with a center body is proposed and experimentally evaluated.The ATCS is designed for water holdup measurement,whereas the CFM with a center body is proposed to obtain the mixture velocity.Then,a drift-flux model based on flow patterns is established to predict the individual-phase superficial velocity of oil-in-water flows.Results show that the ATCS possesses high resolution in water holdup measurement and that flow pattern information can be deduced from its signal through nonlinear time series analysis.The CFM can enhance the correlation of upstream and downstream signals and simplify the relationship between the cross-correlation velocity and mixture velocity.On the basis of the drift-flux model,individual-phase superficial velocities can be predicted with high accuracy for different flow patterns.

Emotional inference by means of Choquet integral and λ-fuzzy measurement in consideration of ambiguity of human mentality

Research on human emotions has started to address psychological aspects of human nature and has advanced to the point of designing various models that represent them quantitatively and systematically. Based on the findings, a method is suggested for emotional space formation and emotional inference that enhance the quality and maximize the reality of emotion-based personalized services. In consideration of the subjective tendencies of individuals, AHP was adopted for the quantitative evaluation of human emotions, based on which an emotional space remodeling method is suggested in reference to the emotional model of Thayer and Plutchik, which takes into account personal emotions. In addition, Sugeno fuzzy inference, fuzzy measures, and Choquet integral were adopted for emotional inference in the remodeled personalized emotional space model. Its performance was evaluated through an experiment. Fourteen cases were analyzed with 4.0 and higher evaluation value of emotions inferred, for the evaluation of emotional similarity, through the case studies of 17 kinds of emotional inference methods. Matching results per inference method in ten cases accounting for 71% are confirmed. It is also found that the remaining two cases are inferred as adjoining emotion in the same section. In this manner, the similarity of inference results is verified.

Assessing Watershed Vulnerability in Bernalillo County, New Mexico Using GIS-Based Fuzzy Inference

Watershed vulnerability was assessed for Bernalillo County, New Mexico using a multi-criteria Fuzzy Inference System (FIS) implemented in a Geographic Information System (GIS). A vulnerability map was produced by means of a weighted overlay analysis that combined soil erosion and infiltration maps derived from the FIS methodology. Five vulnerability classes were stipulated in the model: not vulnerable (N), slightly vulnerable (SV), moderately vulnerable (MV), highly vulnerable (HV), and extremely vulnerable (EV). The results indicate that about 88% of the study area is susceptible to slight (SV) to moderate vulnerability (MV), with 11% of the area subject to experience high or extreme vulnerability (HV/EV). For land use and land cover (LULC) classifications, shrub land was identified to experience the most vulnerability. Weighted overlay output compared similarly with the results predicted by Revised Universal Soil Loss Equation (RUSLE) model with the exception of the not vulnerable (N) class. The eastern portion of the county was identified as most vulnerable due to its high slope and high precipitation. Herein, structural stormwater control measures (SCMs) may be viable for managing runoff and sediment transport offsite. This multi-criteria FIS/GIS approach can provide useful information to guide decision makers in selection of suitable structural and non-structural SCMs for the arid Southwest.

An Adaptive Learning Method for the Generation of Fuzzy Inference System from Data

Designing a fuzzy inference system(FIS)from data can be divided into two main phases:structure identification and parameter optimization.First,starting from a simple initial topology,the membership functions and system rules are defined as specific structures.Second,to speed up the convergence of the learning algorithm and lighten the oscillation,an improved descent method for FIS generation is developed.Furthermore, the convergence and the oscillation of the algorithm are system- atically analyzed.Third,using the information obtained from the previous phase,it can be decided in which region of the in- put space the density of fuzzy rules should be enhanced and for which variable the number of fuzzy sets that used to partition the domain must be increased.Consequently,this produces a new and more appropriate structure.Finally,the proposed method is applied to the problem of nonlinear function approximation.

Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference

This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise.Based on the cubature Kalman filter,we propose a new nonlinear filtering algorithm that employs a skew t distribution to characterize the asymmetry of the measurement noise.The system states and the statistics of skew t noise distribution,including the shape matrix,the scale matrix,and the degree of freedom(DOF)are estimated jointly by employing variational Bayesian(VB)inference.The proposed method is validated in a target tracking example.Results of the simulation indicate that the proposed nonlinear filter can perform satisfactorily in the presence of unknown statistics of measurement noise and outperform than the existing state-of-the-art nonlinear filters.

Adaptive cubature Kalman filter based on variational Bayesian inference under measurement uncertainty

A novel variational Bayesian inference based on adaptive cubature Kalman filter(VBACKF)algorithm is proposed for the problem of state estimation in a target tracking system with time-varying measurement noise and random measurement losses.Firstly,the Inverse-Wishart(IW)distribution is chosen to model the covariance matrix of time-varying measurement noise in the cubature Kalman filter framework.Secondly,the Bernoulli random variable is introduced as the judgement factor of the measurement losses,and the Beta distribution is selected as the conjugate prior distribution of measurement loss probability to ensure that the posterior distribution and prior distribution have the same function form.Finally,the joint posterior probability density function of the estimated variables is approximately decoupled by the variational Bayesian inference,and the fixed-point iteration approach is used to update the estimated variables.The simulation results show that the proposed VBACKF algorithm considers the comprehensive effects of system nonlinearity,time-varying measurement noise and unknown measurement loss probability,moreover,effectively improves the accuracy of target state estimation in complex scene.

Instantaneous Creep in Face-centered Cubic Metals at Ultra- low Strain Rates by a High-resolution Strain Measurement

Instantaneous creep in face-centered cubic metals, 5N Al（99.999%）, 2N Al （99%） and 4N Cu （99.99%） with different grain sizes, was firstly investigated by sudden stress-change experiments at ultra- low strain rates ε ≤10-10 s-1 and temperature T 〈 0.32 Tn. The experimental results indicate that the observed instantaneous creep is strongly dependent on grain size, the concentration of impurity, and stacking fault energy. Creep in high-purity aluminum, 5N Al, with a very large grain size, d 〉 1600μm, shows non-viscous behavior, and is controlled by the recovery of dislocations in the boundary of dislocation cells. On the other hand, for 5N A1 with a small grain size, d=30μm, and low-purity aluminum, 2N A1, with d8= 25μm, creep shows viscous behavior and may be related to ＇low temperature grain boundary sliding＇. For high-purity copper, 4N Cu, with d= 40 grn and lower stacking fault energy, creep shows a non-viscous behavior, and is controlled by the recovery process of dislocations. For all of the samples, creep shows anelastic behavior.

低温共烧陶瓷激光测厚系统设计与误差分析

基于提升低温共烧陶瓷(low temperature co-fired ceramic,LTCC)厚度的检测精度、测量效率和可溯源性的需求,设计一套LTCC激光测厚系统。针对测量精度难以满足需求的问题,对存在误差进行分析,采用厚度测量用调整夹具消除同轴度误差和线性度误差,优化设计结构降低倾角误差,循环传感器标定消除重复度误差,通过数据优化和滤波处理降低机械振动误差,提高了系统测量精度。与实际产品厚度数据对比,最终精度误差≤5μm,符合产品的应用要求,具有广阔的应用市场。

低温高速永磁电机硅钢片铁心电磁性能变化机理研究

由于低温泵用高速电机工作在低温环境中,探究低温环境对构件材料性能的影响、低温条件对电机特性的作用机理以及形成统一的选材原则已经成为该类电机亟待解决的问题。采用实验法在常温(25℃)和低温(-196℃)条件下对不同系列的冷轧无取向硅钢片的电磁性能进行了研究,分析了低温环境与频率对硅钢片电磁性能的影响情况。基于实测数据并采用有限元法计算了电机定子铁耗,得到了电机采用不同系列硅钢片的定子铁耗分布以及变化情况。为了探究低温环境下采用不同系列硅钢片时电机定子铁耗变化幅度不同的原因,采用低温冲击法对硅钢片的矫顽力进行测量,验证了有限元计算所得规律的正确性。探明低温条件对硅钢片电磁性能的影响机理,提出低温高速高频电机铁心材料的选材建议,为低温工作环境下低温电机铁心材料选材提供理论借鉴与参考。

高低温试验箱温度偏差校准结果的测量不确定度评定

高低温试验箱可以为仪表及材料等提供相应的高低温环境,其温度场性能及测量不确定度是影响温度试验箱测量结果的重要指标,通过对温度偏差校准结果的不确定度评定和影响量分析,进一步了解高低温试验箱的计量性能、误差来源及减小误差影响的方法。依据GB/T 10592-2023《高低温试验箱技术条件》规定的校准方法选取满足测量要求的温度传感器,对高低温试验箱温度偏差进行校准。校准确认高低温试验箱温度场性能满足温度偏差±2℃的允差要求,测量不确定度满足1/3最大允许误差的技术指标要求。高低试验箱校准温度场性能良好,测量标准器选取短支A级铂热电阻满足校准要求且稳定性良好符合长时间交替温度的测量使用。

中国数字乡村发展统计测度与时空异质性研究

中国政府积极推动着数字技术与乡村发展融合发展的数字乡村建设。引入因果推断方法研究影响因素对数字乡村建设在不同时期中的平均处理效应(ATE)的重要程度,并通过构建空间杜宾模型对其时空特征与溢出效应进行探析。研究表明:第一,科技创新水平对数字乡村建设的影响,由前期的负向作用(ATE=-1.908)转为后期的正向作用(ATE=1.936),后期中低科技创新水平地区的ATC>ATT,说明中低科技创新水平地区创新效果会优于科技创新水平高的地区;第二,数字乡村发展具有较强的时间积累性,受到经济要素流动等影响,呈现出明显的“棘轮效应”特征和空间依赖性,本地科技创新水平、产业结构升级将对本地数字乡村发展产生正面影响,邻近地区的科技创新水平、产业结构升级对本地数字乡村发展并未产生实际影响。由此可见,加强数字化农业技术创新供给能力、稳步推进产业结构升级对中国数字乡村的区域协调发展具有重要意义。

华北东部晚古生代过渡相煤系地层低阻成因、控制因素与地质意义

华北东部晚古生代过渡相煤系地层中低阻层发育,明确其成因类型、控制因素以及地质意义,可以充分揭示其中所包含的地质信息,对于相关层系油气资源评价和勘探部署具有积极意义。本文以华北东部太原组和山西组中低阻层为研究对象,综合运用岩心、薄片、扫描电镜、碳氧同位素和测录井资料,围绕其成因类型、控制因素和地质意义开展研究。结果表明:华北东部晚古生代过渡相煤系低阻层主要发育在太1段和山2段,成因类型包括砂泥岩薄互层、高束缚水体积和发育导电矿物,沉积作用和成岩作用控制了低阻的形成;太1段低阻的成因为砂泥岩薄互层和高束缚水体积,潮汐作用导致的潮汐成因层理和微细孔隙发育促进了障壁海岸背景下太1段低阻的形成,山2段低阻的成因为菱铁矿的密集发育,稳定的覆水还原环境和沉积有机物的成岩演化共同控制了三角洲背景下山2段低阻的形成;太1段的低阻和高毛细管束缚水体积指示着潮坪沉积,说明太原组自下而上为一个海退的过程,是对早二叠世早期冈瓦纳冰川迅速扩张和全球海平面快速下降的响应,山2段的低阻和高光电吸收截面指数指示着三角洲前缘沉积,说明山西组由一期三角洲沉积组成。

绿色低碳循环经济高质量发展水平测度及时空演变分析

建立健全绿色低碳循环发展经济体系是解决我国资源环境生态问题的基础之策。采用熵权法与综合指数法,对我国2016-2021年绿色低碳循环经济发展进行测度,从时空维度对我国31个地区绿色低碳循环发展水平进行演变分析。研究结果表明:(1)我国绿色低碳循环经济高质量发展水平整体呈现稳步上升的趋势,经济发展模式的转型升级效果已经显现;(2)我国绿色低碳循环经济高质量发展评价指标体系五维指标体系发展并不均衡,其中科技创新发挥着最重要的作用,能源利用发展指数逐渐超过产业结构;(3)我国绿色低碳循环经济高质量发展水平区域差异明显,东部地区增速持续加快,中部地区上升缓慢,西部地区则比较平稳。

机动车零部件HV-LV耦合衰减测量装置

为了评估新能源汽车的电磁兼容性(EMC)性能,准确测量HV与LV系统间的耦合衰减显得尤为重要。通过研究HV-LV耦合衰减测量中的矢量网络分析法,研制一种HV-LV耦合衰减测量的专用装置,并探讨了装置的性能优化方案。通过实验验证,该装置在0.15 MHz至108 MHz的频率范围内实现了小于1dB的耦合衰减,且接地电阻值控制在2.5 mΩ以内,证明了装置的有效性和准确性,为新能源汽车的电磁兼容测试提供了有力技术支撑。

基于潮汐计算的水深获取方法

针对船舶高速航行、离靠码头倒车、舰艇摇摆幅度较大等情况下测深仪可能会出现明显测深度不准的情况,论文对基于潮汐计算的水深获取方法进行研究。分析了如何已知高低潮潮高潮时求任意时刻潮高潮时,已知主港求副港潮汐,并通过验潮站及主副港影响范围求任意地潮高潮时。通过潮汐值的计算,可作为测量水深的一种备用手段,并对测深仪水深信息的准确性进行检验。

基于cSVB算法的DME脉冲干扰抑制方法

针对测距仪(distance measure equipment,DME)信号严重干扰L频段数字航空通信系统(L-band digital aviation communication system,L-DACS)前向链路接收机的问题,提出基于相关稀疏变分贝叶斯(correlated sparse variational Bayesian,cSVB)算法的DME脉冲干扰抑制方法。所提方法利用L-DACS系统正交频分复用(orthogonal frequency division multiplexing,OFDM)接收机的空子载波信息构建接收信号的压缩感知方程;然后,根据cSVB算法进行三层次贝叶斯信号建模,最后选择了两种变体算法重构DME干扰信号,并将其从时域接收信号中去除。理论分析与仿真结果表明,所提出的干扰抑制方法可以充分利用信号先验信息,进一步降低DME干扰信号估计的归一化均方误差,有效改善L-DACS系统的误码性能,提高传输可靠性。

基于ADS1256的地电阻率仪数据采集系统设计

地震地电阻率监测目的是观测特定区域的对地电阻率随时间的变化,对测量精度要求很高。文章介绍的基于24位模数转换器ADS1256设计的地电阻率仪数据采集系统,具有高精度、低噪声、大动态和抗干扰能力强的特点,实验室测试结果表明其电压分辨力达到10μV,峰峰噪声小于7μV,动态范围达到120 dB,数据采集系统主要技术性能指标满足地震地电阻率监测预测研究的需求,也可应用于其他需要高精度、低噪声直流电压测量的领域。

热带海岛典型地貌风场特性的无人机实测

利用双无人机测风系统实测海岛三类典型场地的风场特性,并与多国风荷载规范对比。结果表明,近海岸、低矮温室基地、多高层建筑群实测风剖面分别与中国规范A类、日本规范Ⅲ类、美国规范B类剖面较吻合。近海岸湍流强度小于各国规范,低矮温室、多高层建筑群实测值分别与日本规范Ⅲ类、Ⅳ类场地值较吻合。阵风因子随粗糙度、湍流强度提高而增大,近海岸、多高层建筑群阵风因子与湍流强度相关性分别接近Ishizaki、Cao建议模型。实测积分尺度随高度而增大,随粗糙度而减小,提出热带海岛典型地貌的积分尺度公式。同一地貌的风速竖向、水平相关性随高差、水平间距增大而减弱;同一高差的风速竖向相关性随粗糙度提高而减弱。von Karman谱能较好地描述热带海岛脉动风在频域内的能量分布特性。

“双碳”背景下供热行业高质量发展的改革措施

研究以存在实际问题为研究导向,兼顾供热行业发展和减排、整体和局部、长期目标和短期目标、政府和市场的关系,探究国内供热行业的改革方向,提出一系列能够解决困扰政府、企业生存发展的改革措施,从而引导供热行业走上安全可靠、高效节能、绿色低碳及智慧化高质量发展道路。

IP网端到端性能测量技术研究的进展

本文介绍了Internet端到端测量技术的最新进展 ,包括性能拓扑推测、时延测量、丢包率测量 .最后讨论了未来Internet端到端测量的研究趋势和应用前景 .