双螺杆泵Ω形对称型螺杆流场数值分析

由于螺杆泵几何结构及流体流动状态较为复杂,其压力分布和速度等特性很难用数学解析法来分析计算。本文利用参数化设计模块建立双螺杆泵Q形对称型螺杆三维模型,构建双螺杆流道的流场分析模型,运用有限元分析方法得出了双螺杆泵流道内压力场和速度场分布规律。结果表明：流体在流道中的流速分布具有规律性,整个流道的流场速度高低交替变化,在螺杆与衬套间隙处的流动为层流,螺旋面接触区内的流动为复杂的湍流,离螺杆啮合区近的螺旋槽内易发生涡流。本研究结果对于了解双螺杆泵螺杆工况下的性能情况具有重要的参考价值。     

Understanding Estimators of Treatment Effects in Regression Discontinuity Designs

In this paper, we propose two new estimators of treatment effects in regression discontinuity designs. These estimators can aid understanding of the existing estimators such as the local polynomial estimator and the partially linear estimator. The first estimator is the partially polynomial estimator which extends the partially linear estimator by further incorporating derivative differences of the conditional mean of the outcome on the two sides of the discontinuity point. This estimator is related to the local polynomial estimator by a relocalization effect. Unlike the partially linear estimator, this estimator can achieve the optimal rate of convergence even under broader regularity conditions. The second estimator is an instrumental variable estimator in the fuzzy design. This estimator will reduce to the local polynomial estimator if higher order endogeneities are neglected. We study the asymptotic properties of these two estimators and conduct simulation studies to confirm the theoretical analysis.     

Factor screening in nonregular two-level designs based on projection-based variable selection

In this paper, we focus on the problem of factor screening in nonregular two-level designs through gradually reducing the number of possible sets of active factors. We are particularly concerned with situations when three or four factors are active. Our proposed method works through examining fits of projection models, where variable selection techniques are used to reduce the number of terms. To examine the reliability of the methods in combination with such techniques, a panel of models consisting of three or four active factors with data generated from the 12-run and the 20-run Plackett–Burman (PB) design is used. The dependence of the procedure on the amount of noise, the number of active factors and the number of experimental factors is also investigated. For designs with few runs such as the 12-run PB design, variable selection should be done with care and default procedures in computer software may not be reliable to which we suggest improvements. A real example is included to show how we propose factor screening can be done in practice.     

Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints

This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.     

船舶反弯扭螺旋桨水力特性的数值模拟与分析

运用计算流体力学方法(CFD)对船舶反弯扭螺旋桨的水力特性进行数值模拟计算,获得了螺旋桨的推力系数、扭矩系数及敞水效率等水力特性参数,与试验结果进行了比较,两者吻合良好.模拟计算提供了详细的三维流场分布,可对反弯扭桨叶的工作特性和叶梢涡、尾涡及气蚀的分布状态进行深入的分析.在叶面邻近区域采用层叠式的半结构网格以适应边界层的计算要求,在边界层外的流动区域采用非结构网格,取得了较高的计算精度.     

基于熵权法的财务危机预警指标选择研究

合理选择财务指标是财务危机预警研究中的重要内容,用定性和定量相结合的方法进行选择是科学有效的方法之一。首先根据初选原则对财务指标进行了初步选择,然后提出了一种基于熵权的变量选择方法,对初选的财务指标进行定量筛选,进行实证研究,最终确定了应用于财务危机预警的财务指标。     

Robust signed-rank estimation and variable selection for semi-parametric additive partial linear models

A fully nonparametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known. This becomes even more challenging when the data contain gross outliers or unusual observations. However, in practice the true covariates are not known in advance, nor is the smoothness of the functional form. A robust model selection approach through which we can choose the relevant covariates components and estimate the smoothing function may represent an appealing tool to the solution. A weighted signed-rank estimation and variable selection under the adaptive lasso for semi-parametric partial additive models is considered in this paper. B-spline is used to estimate the unknown additive nonparametric function. It is shown that despite using B-spline to estimate the unknown additive nonparametric function, the proposed estimator has an oracle property. The robustness of the weighted signed-rank approach for data with heavy-tail, contaminated errors, and data containing high-leverage points are validated via finite sample simulations. A practical application to an economic study is provided using an updated Canadian household gasoline consumption data.     

Variable selection in elliptical linear mixed model

Variable selection in elliptical Linear Mixed Models (LMMs) with a shrinkage penalty function (SPF) is the main scope of this study. SPFs are applied for parameter estimation and variable selection simultaneously. The smoothly clipped absolute deviation penalty (SCAD) is one of the SPFs and it is adapted into the elliptical LMM in this study. The proposed idea is highly applicable to a variety of models which are set up with different distributions such as normal, student-t, Pearson VII, power exponential and so on. Simulation studies and real data example with one of the elliptical distributions show that if the variable selection is also a concern, it is worthwhile to carry on the variable selection and the parameter estimation simultaneously in the elliptical LMM.     

Regression from another angle

This paper presents a simply viewed framework that brings together various concepts of regression, prediction, and principal components. Several new concepts related to prediction are introduced, and then the interrelationships of these concepts are established. The generalizations are examined in detail and are illustrated in the context of a well known data set.     

基于可信性理论下的EPQ问题的期望值模型

基于可信性理论,本文给出了不允许缺货的经济生产量(EPQ)问题的期望值模型;研究了当单位生产准备费、单位储存费均为模糊变量且相互独立的情况下EPQ 问题的最佳期望单位存储费用、最佳期望单位生产批量及最佳期望生产周期,并得出了一些重要的结论;特殊情况下,讨论了当模型中的模糊参数分别为三角模糊变量、梯形模糊变量及正态模糊变量情况下的EPQ 问题的最优存储策略;最后,针对上述三种情况本文分别给出了一些数值例子验证了模型的有效性和合理性;本文所得到的结果为求解EPQ 问题设计算法提供了理论依据.