HE130合金激光焊缝弹性模量预测

利用嵌入原子法(EAM)及多晶体弹性性能Hill近似表征所获得的合金元素对二元钛合金弹性模量的影响,预测HE130合金激光焊缝中α相的弹性模量;利用修正的脉冲回波法计算公式预测β相的弹性模量;采用经典上下限理论的上限线性加和公式预测焊缝整体的弹性模量.通过理论预测最终获得的HE130合金不同状态激光焊缝的弹性模量与实测结果相吻合,证明了该预测方法的可靠性.

人工冻土单轴弹性模量灰色理论预测

人工冻土弹性模量是冻结法建井计算的重要参数之一。为提高人工冻土单轴弹性模量预测的精确度与稳定性,利用灰色理论在处理小样本数据的优势,考虑影响人工冻土单轴弹性模量因素,提出了人工冻土单轴弹性模量的灰色理论预测方法,由于传统的GM(1,1)模型在预测时产生的较大误差,所以增加了原始序列的光滑度,优化了时间响应函数,由此建立了改进的GM(1,1)模型;并用改进的GM(1,1)模型进行预测,结果表明,改进的GM(1,1)模型的预测精度较传统模型高。该改进模型为人工冻土单轴弹性模量的预测提供了一种新的途径,同时也为进一步了解冻土力学特性提供了理论依据。

基于复合材料模型的橡胶颗粒沥青混合料弹性模量预测

弹性模量是决定橡胶颗粒弹性除冰路面除冰性能的主要参数,为了不通过试验就能预知橡胶颗粒沥青混合料的弹性模量,采用适当方法建立合理的力学模型,提出了一种基于复合材料细观力学模型的弹性模量预测方法,从而能预先评估橡胶颗粒弹性路面的除冰能力。

落叶松抗弯弹性模量的细观尺度建模方法

针对木材的非匀质、各向异性特点,从木材细观结构出发,应用数字图像处理技术在板材上顺序采集、提取生长轮早晚材细胞数据,完成各层生长轮弹性模量计算,结合板材截面图像构建出落叶松试件三维层合材料模型,并通过有限元预测其抗弯弹性模量。首先,应用高倍光学显微镜采集试件截面细胞图像,分别测定试件截面中各个生长轮内胞壁率、胞壁厚、径向宽度、切向宽度等参数;根据预测模型,以密度为单一变量,推导出各层早晚材的弹性模量;将试件按早材、晚材依次叠加的层合材料处理,并利用试件截面图像的早晚材分布信息构建试件三维层合材料模型,利用UG软件建立试件的三维模型,在有限元软件中施加载荷预测木材抗弯弹性模量。按照木材抗弯弹性模量的《GB1936—1980》国家测试标准,加工、测量了10组试件的抗弯弹性模量真值,分别运用本方法和匀质材料预测方法对试件的抗弯弹性模量进行了预测,平均误差分别为5.7%和17.3%。实验结果表明,该方法较匀质材料预测方法更为准确。

短玻璃纤维增强PA66的各向异性拉伸模量和本构方程研究

对30%玻璃纤维增强PA66,应用Mori-Tanaka单向短纤维复合材料弹性模量预测模型,引入纤维混杂因子δ,建立了弹性模量的半经验预测模型,模型预测结果与试验结果吻合,该模型可以对材料的弹性性能进行很好的预估计算;应用Weibull三参数模型建立了材料在0°和90°方向的拉伸应力-应变关系;应用45°拉伸试验结果给出了材料面内剪切应力-应变关系;由此建立了一种短玻璃纤维增强PA66的拉伸本构方程。

有一定取向性的碳纤维增强尼龙6复合材料弹性模量的实验和理论研究

纤维增强热塑性聚合物基复合材料注塑成型后往往被认为是各向同性复合材料。然而,注塑成型后纤维会具有一定的取向性,从而使复合材料试样呈现各向异性的特点。为了合理预测此类复合材料的弹性模量,本文对碳纤维增强尼龙6复合材料注塑试样内部的纤维长度和取向分布情况进行了测试和分析,得出了纤维取向的分布规律。随后结合单向纤维增强聚合物基复合材料力学模型和层叠理论,构造出了适用于有一定取向性的纤维增强树脂基复合材料弹性模量预测理论模型,其理论结果和拉伸实验结果吻合较好,表明该预测模型的准确性比较高。

多基复合管在制备中的管芯混凝土收缩性能分析

对多基复合管成型过程中的管芯混凝土的固化收缩变形与弹性模量变化等开展了相关试验研究,由此建立了管芯混凝土收缩与变弹性模量预测模型。通过迭代算法,对多基复合管成型过程中管芯混凝土的收缩性能进行了研究与分析。结果表明:该分析模型与方法能合理地描述管芯混凝土收缩过程中对内筒体产生的附加外力随时间变化的关系,为多基复合管结构优化设计提供理论指导。

Rock critical porosity inversion and S-wave velocity prediction

A critical porosity model is often used to calculate the dry frame elastic modulus by the rock critical porosity value which is affected by many factors. In practice it is hard for us to obtain an accurate critical porosity value and we can generally take only an empirical critical porosity value which often causes errors. In this paper, we propose a method to obtain the rock critical porosity value by inverting P-wave velocity and applying it to predict S-wave velocity. The applications of experiment and log data both show that the critical porosity inversion method can reduce the uncertainty resulting from using an empirical value in the past and provide the accurate critical porosity value for predicting S-wave velocity which significantly improves the prediction accuracy.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

An investigation of surface deformation after fully mechanized,solid back fill mining

The surface deformation after fully mechanized back filling mining was analyzed.The surface deformation for different backfill materials was predicted by an equivalent mining height model and numerical simulations.The results suggest that:(1) As the elastic modulus,E,of the backfill material increases the surface subsidence decreases.The rate of subsidence decrease drops after E is larger than 5 GPa;(2) Fully mechanized back fill mining technology can effectively control surface deformation.The resulting surface deformation is within the specification grade I,which means surface maintenance is not needed.A site survey showed that the equivalent mining height model is capable of predicting and analyzing surface deformation and that the model is conservative enough for engineering safety.Finally,the significance of establishing a complete error correction system based on error analysis and correction is discussed.

Simplification and improvement of prediction model for elastic modulus of particulate reinforced metal matrix composite

In this paper, we proposed a five-zone model to predict the elastic modulus of particulate reinforced metal matrix composite. We simplified the calculation by ignoring structural parameters including particulate shape, arrangement pattern and dimensional variance mode which have no obvious influence on the elastic modulus of a composite, and improved the precision of the method by stressing the interaction of interfaces with pariculates and maxtrix of the composite. The five- zone model can reflect effects of interface modulus on elastic modulus of composite. It overcomes limitations of expressions of rigidity mixed law and flexibility mixed law. The original idea of five zone model is to put forward the particulate/interface interactive zone and matrix/interface interactive zone. By organically integrating the rigidity mixed law and flexibility mixed law, the model can predict the engineering elastic constant of a composite effectively.