A Lagrangian lattice Boltzmann method for Euler equations

A Lagrangian lattice Boltzmann method for solving Euler equations is proposed. The key step in formulating this method is the introduction of the displacement distribution function. The equilibrium distribution function consists of macroscopic Lagrangian variables at time steps n and n + 1. It is different from the standard lattice Boltzmann method. In this method the element, instead of each particle, is required to satisfy the basic law. The element is considered as one large particle, which results in simpler version than the corresponding Eulerian one, because the advection term disappears here. Our numerical examples successfully reproduce the classical results.

Recovery of the solitons using a lattice Boltzmann model

We formulate a lattice Boltzmann model which simulates Korteweg-de Vries equation by using a method of higher moments of lattice Boltzmann equation. Using a series of lattice Boltzmann equations in different time scales and the conservation law in time scale to, we obtain equilibrium distribution function. The numerical examples show that the method can be used to simulate soliton.

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.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.

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.

Euler方程的双分布函数格子Boltzmann Godunov方法

应用双分布函数系统，通过Ｇｏｄｕｎｏｖ分解，构造了一维Ｅｕｌｅｒ方程的格子Ｂｏｌｔｚｍａｎｎ算法。解决了传统格子气固有的ＧＣ问题与能量方程之间的矛盾，实现了分布函数与宏观物理量之间的一一对应。

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。

等离子体效应对类氦氖Kα线系电偶极辐射的影响

采用离子球模型，通过自洽求解Boltzmann方程和Poisson方程，得到类氦氖离子Kα线系的两条电偶极辐射光谱能量随等离子体环境的漂移．结果显示，Kα线系电偶极谱线随等离子体电子密度增大发生红移，红移量与等离子体电子密度有近似的正比关系；随着等离子体电子温度的降低，光谱红移对等离子体电子密度的敏感性增大。另外，所研究的两条谱线间的能量间隔随等离子体电子密度的增大而减小，减小量随等离子体电子密度的变化也呈现出近似的线性规律。值得注意的是，类氦氖Kα线系中两条电偶极谱线分别为互组合线与共振谱线，而其能量差