An analytical solution to wellbore stability using Mogi-Coulomb failure criterion

Deep wellbores/boreholes are generally drilled into rocks for oil and gas exploration, monitoring of tectonic stresses purposes. Wellbore and tunnel in depth are generally in true triaxial stress state, even if the ground is under axisymmetric loading condition. Stability of such wellbores is very critical and collapse of wellbore must be avoided. Mogi-Coulomb failure criterion is a better representation of rock strength under true triaxial condition. In this paper, an analytical solution is proposed using Mogi-Coulomb failure criterion. The solution is obtained for rock mass exhibiting elastic-perfectly plastic or elastic-brittle-plastic behaviour considering in-plane isotropic stresses. The proposed solution is then compared with exact analytical solution for incompressible material and experimental results of thick-wall cylinder. It is shown that the results obtained by the proposed analytical solution are in good agreement with the experimental results and exact analytical solution. A reduction of about 13%–20% in plastic zone from the proposed closed-form solution is observed, as compared to the results from the finite element method (FEM) based Mohr-Coulomb criterion. Next, the influences of various parameters such as Poisson's ratio, internal pressure (mud weight), dilation angle, and out-of-plane stress are studied in terms of stress and deformation responses of wellbore. The results of the parametric study reveal that variation in the out-of-plane stress has an inverse relation with the radius of plastic zone. Poisson's ratio does not have an appreciable influence on the tangential stress, radial stress and radial deformation. Dilation angle has a direct relation with the deformation. Internal pressure is found to have an inverse relation with the radial deformation and the radius of plastic zone.     

Regional forecasts of photovoltaic power generation according to different data availability scenarios: a study of four methods

The development of methods to forecast photovoltaic (PV) power generation regionally is of utmost importance to support the spread of such power systems in current power grids. The objective of this study is to propose and to evaluate methods to forecast regional PV power 1 day ahead of time and to compare their performances. Four forecast methods were regarded, of which two are new ones proposed in this study. Together, they characterize a set of forecast methods that can be applied in different scenarios regarding availability of data and infrastructure to make the forecasts. The forecast methods were based on the use of support vector regression and weather prediction data. Evaluations were performed for 1 year of hourly forecasts using data of 273 PV systems installed in two adjacent regions in Japan, Kanto, and Chubu. The results show the importance of selecting the proper forecast method regarding the region characteristics. For Chubu, the region with a variety of weather conditions, the forecast methods based on single systems' forecasts and the one based on stratified sampling provided the best results. In this case, the best annual normalized root mean square error (RMSE) and mean absolute error (MAE) were 0.25 and 0.15 kWh/kWh_avg, respectively. For Kanto, with homogeneous weather conditions, the four methods performed similarly. In this case, the lowest annual forecast errors were 0.33 kWh/kWh_avg for the normalized RMSE and 0.202 kWh/kWh_avg for the normalized MAE. Copyright © 2014 John Wiley & Sons, Ltd.     

Mixture semisupervised principal component regression model and soft sensor application

Traditionally, data‐based soft sensors are constructed upon the labeled historical dataset which contains equal numbers of input and output data samples. While it is easy to obtain input variables such as temperature, pressure, and flow rate in the chemical process, the output variables, which correspond to quality/key property variables, are much more difficult to obtain. Therefore, we may only have a small number of output data samples, and have much more input data samples. In this article, a mixture form of the semisupervised probabilistic principal component regression model is proposed for soft sensor application, which can efficiently incorporate the unlabeled data information from different operation modes. Compared to the total supervised method, both modeling efficiency and soft sensing performance are improved with the inclusion of additional unlabeled data samples. Two case studies are provided to evaluate the feasibility and efficiency of the new method. © 2013 American Institute of Chemical Engineers AIChE J 60: 533–545, 2014     

基于主成分分析法的燃料乙醇行业清洁生产水平综合评价

燃料乙醇行业是可再生能源的重要组成部分,但是燃料乙醇行业具有高能耗、高污染等特点,环保压力巨大。文章基于主成分分析法对调研的8家燃料乙醇企业9条生产线进行清洁生产综合评价,找到了不同指标之间的相关性和信息重叠之处,提炼出了不同燃料乙醇企业清洁生产评价中的主要控制指标,得到了主成分表达式及主成分综合评价模型,评价结果与百分制法评价结果基本一致,表明主成分分析法在燃料乙醇清洁生产中的综合评价中是一种客观科学的方法。     

Improving automotive dimensional quality by using principal component analysis

Dimensional quality is a measure of conformance of the actual geometry of products with the designed geometry. In the automotive body assembly process, maintaining good dimensional quality is very difficult and critical to the product. In this paper, a dimensional quality analysis and diagnostic tool is developed based on principal component analysis (PCA). In quality analysis, the quality loss due to dimensional variation can be partitioned into a mean deviation and piece-to-piece variation. By using PCA, the piece-to-piece variation can be further decomposed into a set of independent geometrical variation modes. The features of these major variation modes help in identifying the underlying causes of dimensional variation in order to reduce the variation. The variation mode chart developed in this paper provides the explicit and exact geometrical interpretation of variation modes, making PCA easily understood. A case study using an automotive body assembly dimensional quality analysis will illustrate the value and power of this methodology in solving actual engineering problems in a practical manner.     

基于多任务委托的工程监理委托-代理分析

工程监理制度下的业主与工程监理之间的关系属于多任务的委托-代理关系.根据多任务委托-代理模型分析了工程监理多任务委托-代理问题.分析表明:多任务委托-代理情况下的工程监理具有不同于单任务委托-代理情况下的激励机制;工程监理承担任务被观测的难易程度和任务间努力成本的相互依存性是影响激励强度系数的重要因素;为防止对工程监理激励效能的弱化,激励机制的设计必须充分考虑这两种因素的影响.     

基于主成分分析与偏最小二乘法建立芒果成熟度判定指标体系

为建立“台农1号”芒果成熟度判定指标体系,并确定代表性成熟度判定指标,以不同采收期、不同产地的“台农1号”芒果为研究对象,分析果实后熟生理指标果肉和果皮色泽、质构（硬度、胶着性、弹性、咀嚼性）、可溶性固形物、可滴定酸的变化,基于主成分分析与偏最小二乘法方法进行果实后熟生理指标系统研究。结果表明：在以商户初始采收成熟度相同的条件下,各批次“台农1号”芒果果实后熟生理指标间存在差异。应用偏最小二乘法方法与主成分分析方法确定了果肉b*、可滴定酸、硬度作为评判果实成熟度的一、二、三级最终指标,通过三个指标的成熟度判定准确率验证一级指标为94.34%,二级指标为87.21%,三级指标为68.09%。综上所述,基于主成分分析与偏最小二乘法建立芒果成熟度判定指标体系是可行的,该研究为各地区“台农1号”芒果适时采收及销售提供理论指导和实用价值。     

关于《财务会计》中资产减值准备的教法分析

本文针对财务会计学中资产减值准备这一重要内容,从其基本理论、主要内容、会计处理等四个方面进行全面分析,弥补了教材中对此内容缺乏总体说明,没有分析对比的不足,帮助初学者更准确、更快捷地掌握这一知识点。     

银行系统效益评价研究

目前中国银行系统正在进行力度很大的改革,除机制改革外,重要的是扩大服务范围、增强业务来往、提高业绩及素质,但效益是最主要的,因为它是生存的关键.能否按综合指标体系对银行系统进行排序是投资者非常关心的.为了克服传统效益评估方法的不足,文中首先通过选择有代表性的指标和比率,建立起一个银行系统综合效益评价指标体系,在此基础上采用主成分分析法、熵权法和等权法综合构建了一个评价银行系统效益的数学模型,对建立的模型进行数值计算,并由计算结果进行综合排序,研究经济含义等,并使用该模型对7家上市银行进行了实证分析.