解非定常不可压缩N-S方程的迭代压力Poisson方程法

该文将压力Poisson方程法改进为多步迭代计算,Poisson方程中未知量改为压力的增量.称这样的方法为迭代压力Poisson方程法.其优点如下:1.能保证离散的连续方程成立(达到要求的精度,);2.Poisson方程中~2_H不必用高精度的算子,例如对二维四阶紧致格式,可取~2_H为五点中心差.Chorin方法相当于取Poisson方程中~2_H为－λ/Δt;3.与Chorin方法相比,收敛速度要快得多;4.可直接应用于三维问题.(对三维问题,~2_H可用七点中心差);5.可以推广到有限元格式.为了提高计算精度,利用三次样条函数插值的思想构造差分格式,可以在不增加网格点的情况下提高差分精度.

Thermodynamical versus log-Poisson distribution in turbulence

The thermodynamical model of intermittency in fully developed turbulence due to Castaing (B. Castaing, J. Phys. II France 6 (1996) 105) is investigated and compared with the log-Poisson model (Z-S, She, E. Leveque, Phys. Rev. Lett. 72 (1994) 336). It is shown that the thermodynamical model obeys general scaling laws and corresponds to the degenerate class of scale-invariant statistics. We also find that its structure function shapes have physical behaviors similar to the log-Poisson's one. The only difference between them lies in the convergence of the log-Poisson's structure functions and divergence of the thermodynamical one. As far as the comparison with experiments on intermittency is concerned, they are indifferent.

Effect Of A Negative Poisson Ratio In The Tension Of Ceramics

The effect of a negative Poisson ratio is experimentally revealed in the tension deformation of a natural layered ceramic. This effect can increase the volume strain energy per unit volume by 1100% and, simultaneously, decrease the deformation strain energy per unit volume by about 44%, so that it effectively enhances the deformation capacity by about 1 order of magnitude in the tension of the material. The present study also shows that the physical mechanisms producing the effect are attributed to the climbing on one another of the nanostructures in the natural material, which provides a guide to the design of synthetic toughening composites.

交错网格紧致差分格式和满足等价性的压力Poisson方程

首先给出四阶精度交错网格紧致差分格式; 其次讨论了满足等价性的压力Poisson方程; 然后给出了一种新的解压力Poisson方程的ADI迭代法; 最后, 讨论了驱动方腔流动数值计算.

加卸载响应比在Poisson模型下的随机分布

本文在地震的发生时间服从Ｐｏｉｓｏｎ过程，而地震震级服从ＧｕｔｅｎｂｅｒｇＲｉｃｈｔｅｒ关系的前提下，对不同定义的加卸载响应比Ｙ值的随机分布进行了探讨。结果表明：当在计算窗口的地震发生的期望数目较大（＞４０）时，Ｙ１～Ｙ５值的分布基本稳定，出现高加卸载响应比的概率极低。然而当计算窗口的地震期望数目过小时，Ｙ２～Ｙ５值则变得不太稳定。也就是说，服从Ｐｏｉｓｏｎ过程的地震序列，在计算窗口的地震期望数目过小时，也可能产生Ｙ值较高的结果。为了使利用加卸载响应比预测地震更加可靠，文中给出了Ｙ１、Ｙ３在Ｐｏｉｓｏｎ模型下的９０％、９５％和９９％的置信区间，这对判别加卸载响应比异常是非常有用的。

A Simplified Evaluation Procedure of the Scale of Fluctuation Combined Recurrence Averaging and Weighted Linear Regression

The divergence of properties from one location to another within a soil mass is termed spatial variability, which traditionally includes three parameters the mean, the standard deviation, and the scale of fluctuation, in order to stochastically describe a soil property. Among them, determining the scale of fluctuation in the evaluation of spatial variability of soil profiles is not easy due to soil condition complexity. A simplified procedure is presented in the paper to determine the scale of fluctuation combined recurrence averaging and weighted linear regression. The alternative approach utilizes widely usable spreadsheet to solve the problem more directly and efficiently.

The mean value concept in mono-linear regression of multi-variables and its application to trace studies in geosciences

IEECAS SKLLQG

CO2 Flux Estimation by Different Regression Methods from an Alpine Meadow on the Qinghai-Tibetan Plateau

National Key Research and Development Program [2010CB833500]; National Natural Science Foundation of China [30590381]; Chinese Academy of Sciences [KZCX2-YW-432]

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

Selection for variables in quantitative structure-activity/property relationship studies - Comparison between methods of orthogonal descriptors and leaps-and-bounds regression analysis

In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.