A novel approach to the Poisson’s ratio of the yarn

Abstract

The Poisson’s ratio is a fundamental and inherent property of the materials in their reaction to loading. Determining the correct value of the Poisson’s ratio leads to more accurate predictions of the mechanical behavior of the material, especially in employing mechanical or numerical finite element modeling methods. In this study, seven different types of medium and coarse yarn were digitally filmed in seven sections from three various angles while tensile strength testing. Then, longitudinal and transverse strains and consequently, momentary deformation index and Poisson’s ratio of the yarns were calculated. Significance level of the mean Poisson’ ratio was also determined at different angles and sections using statistical comparison test. The results showed that the transverse strain has a convergent state at the end of the test and the start of the yarn breaking occurs at the convergence point. Additionally, in upper sections of yarn near to the movable jaw of the strength tester, the amount of the longitudinal strain increases which leads to reduce in momentary deformation index and Poisson’s ratio of the yarn. Moreover, a sectional mode to measure the Poisson's ratio was presented.     

Experimental determination of elastic modulus of elasticity and Poisson’s coefficient of date palm tree fiber

The present study is an attempt to valorize local vegetable fibers by evaluating thire effectiveness as a new composite biomaterial. Our aim was to determine the mechanical characteristics, namely the elastic modulus and Poisson’s coefficient, of a fiber, locally called “Lif,” that is a natural material extracted from different date palm tree varieties in Biskra, a region situated in southeastern Algeria. This study compares the mechanical characteristcs of the Lif date palm tree fiber with other synthetic and plant fibers studied previously.     

Regression-adjusted Poisson EWMA control chart

Exponentially weighted moving average (EWMA) control chart has a significant effect in improving product quality and is widely used in various fields. In addition to continuous data, there are many Count Data in life that need to be monitored. Poisson distribution is one of the models that study the probability distribution of discrete data, and has a wide range of applications. In previous monitoring, it was considered that the mean value of Poisson distribution in normal state was a constant value after it was determined. But in the actual situation, there are many unavoidable objective conditions that will affect the final results. We cannot monitor all situations according to the same criteria. If we ignore the conditions that affect the occurrence of the event and directly monitor the final result, on the one hand, it will increase the probability of false alarms from the control chart. On the other hand, the control chart will not be able to detect problems in time due to the untimely update of conditions. In response to this situation, this paper proposes a regression-adjusted EWMA control chart to monitor the Poisson process. The control chart can continuously adjust and update the expected values according to the actual situation. It can make the monitoring process more reasonable and the monitoring results more valuable.     

烧结页岩煤矸石多孔砖砌体的受压变形性能

介绍了20组(80个)烧结页岩煤矸石多孔砖砌体受压试件的受压变形性能试验研究,分别采用对数型表达式、一段式抛物线型表达式和两段式表达式三种模式回归分析了其受压应力-应变关系,并讨论和推荐了砌体的弹性模量、泊松比及峰值应变的取值。     

灌芯砌体的变形性能试验研究

系统地研究了灌芯砌体的变形性能 ,对灌芯砌体的受压和受剪变形进行了试验研究。提出了灌芯砌体的受压和受剪本构关系 ,根据试验研究成果还给出了弹性模量、泊松比和剪变模量的计算公式 ,为灌芯砌体结构的应用提供了理论依据。     

现浇空心板宏观基本本构关系

研究宏观等效泊松比和弹性模量。首先通过解析方法,推导空心板作为双向肋板的泊松比的计算公式。其次根据应力分布特点,提出圆孔空心板横管向弯曲、轴压弹性模量的有效截面计算方法,确定竖向倾角0α=22.5°的孔半径所在水平线为有效截面的边界线,并给出计算公式。此外研究还发现,双向肋板中存在横肋引起刚度增大的“横肋效应”现象,给出该现象的量化表达式。所提方法和公式均通过理论、数值验证,是现浇空心板结构计算的理论基础。     

路基沉降预测中泊松曲线模型参数的拟合方法

针对路基沉降预测中的泊松曲线模型,提出一种模型参数的拟合方法。将泊松曲线方程化为线性方程,通过线性相关系数取极值求出最终沉降,再由线性最小二乘拟合即可求取另外两个参数。通过公路路堤填筑高度与时间关系的实例分析表明,所提出的模型参数拟合方法与三点法、三段法的预测曲线相比较,具有较高的精度。因此在工程实际中具有一定的应用前景。     

Infinitely Divisible Distributions in Integer‐Valued Garch Models

We propose an integer‐valued stochastic process with conditional marginal distribution belonging to the class of infinitely divisible discrete probability laws. With this proposal, we introduce a wide class of models for count time series that includes the Poisson integer‐valued generalized autoregressive conditional heteroscedastic (INGARCH) model (Ferland et al., 2006) and the negative binomial and generalized Poisson INGARCH models (Zhu, 2011, 2012a). The main probabilistic analysis of this process is developed stating, in particular, first‐order and second‐order stationarity conditions. The existence of a strictly stationary and ergodic solution is established in a subclass including the Poisson and generalized Poisson INGARCH models.