计算材料科学中桥域多尺度方法的若干进展

材料科学中存在固有的多尺度特性,桥域多尺度方法是在宏观尺度(如连续介质力学)中引入不同的细微观尺度的计算区域,乃至纳米尺度的分子动力学、量子力学计算区域,将不同尺度的研究方法通过一定的数学模型耦合在一起。该方法既能节约计算成本,又能保证所研究问题的物理特性。本文对多尺度方法的基本概念、跨尺度桥域多尺度方法的发展、基本原理、耦合方法和离散方程进行了讨论,给出了几个应用算例,并在最后进行了总结,展望了今后的可能发展方向。     

基于粗糙集模糊神经网络的爆破振动危害预测

为了探索一种能克服单因素预测的局限性、提高爆破振动危害预测精度的方法,基于粗糙集模糊神经网络理论,建立了综合考虑爆破振动幅值、主频率、主频率持续时间及结构动力特性等10个因素的民房破坏程度预测模型;用铜绿山矿爆破振动和民房破坏情况观测数据,对该模型进行了训练和测试,测试结果与现场观测结果具有良好的一致性。研究表明:粗糙集理论可将现场数据进行属性约简,简化输入变量,缩小神经网络的搜索空间,改善爆破振动的预测性能;基于粗糙集模糊神经网络理论的爆破振动危害预测模型,能更好地考虑各种因素对危害程度的综合影响,避免了单因素预测的局限性。     

光通量法在SHPSB剪切应变测量中的应用

利用光通量方法对分离式霍普金森压剪杆实验技术中的剪切应变测量进行了实验研究。实验装 置主要包括:准直激光器、光电接收器、光学挡板。由装置性能验证实验结果以及不确定度分析结果表明:利 用基于光通量法的剪切应变测量技术能够可靠地测量SHPSB(splitHopkinsonpressurebar)试样的剪切应 变。并对比了基于光通量法测量的剪切应变值与理论近似结果,结果显示前者小于后者,分析并讨论了原因。     

k-ε双方程湍流模型对制退机内流场计算的适用性分析

为研究不同双方程湍流模型对制退机内复杂流场计算的适用性,以某火炮制退机为研究对象,建立了实际结构下的三维计算模型,利用动网格与滑移网格技术,实现了火炮实际后坐速度下的制退机内部三维运动流场的数值计算。分别采用标准k-ε模型、RNGk-ε模型和Realizable k-ε模型计算制退机内部各腔室压力,与实验曲线对比,结果表明,应用标准k-ε模型对后坐冲击过程的制退机内部压力计算的误差最小,与实验结果吻合最好。     

Stochastic Theory of Discrete Binary Fragmentation—Kinetics and Thermodynamics

We formulate binary fragmentation as a discrete stochastic process in which an integer mass k splits into two integer fragments j, k−j, with rate proportional to the fragmentation kernel Fj,k−j. We construct the ensemble of all distributions that can form in fixed number of steps from initial mass M and obtain their probabilities in terms of the fragmentation kernel. We obtain its partition function, the mean distribution and its evolution in time, and determine its stability using standard thermodynamic tools. We show that shattering is a phase transition that takes place when the stability conditions of the partition function are violated. We further discuss the close analogy between shattering and gelation, and between fragmentation and aggregation in general.     

The Use of the Statistical Entropy in Some New Approaches for the Description of Biosystems

Some problems of describing biological systems with the use of entropy as a measure of the complexity of these systems are considered. Entropy is studied both for the organism as a whole and for its parts down to the molecular level. Correlation of actions of various parts of the whole organism, intercellular interactions and control, as well as cooperativity on the microlevel lead to a more complex structure and lower statistical entropy. For a multicellular organism, entropy is much lower than entropy for the same mass of a colony of unicellular organisms. Cooperativity always reduces the entropy of the system; a simple example of ligand binding to a macromolecule carrying two reaction centers shows how entropy is consistent with the ambiguity of the result in the Bernoulli test scheme. Particular attention is paid to the qualitative and quantitative relationship between the entropy of the system and the cooperativity of ligand binding to macromolecules. A kinetic model of metabolism. corresponding to Schrödinger’s concept of the maintenance biosystems by “negentropy feeding”, is proposed. This model allows calculating the nonequilibrium local entropy and comparing it with the local equilibrium entropy inherent in non-living matter.     

Generalized Einstein manifolds

A Finslerian manifold is called a generalized Einstein manifold (GEM) if the Ricci directional curvature R(u,u) is independent of the direction. Let F⁰(M, g_t) be a deformation of a compact n-dimensional Finslerian manifold preserving the volume of the unitary fibre bundle W(M). We prove that the critical points g₀ F⁰(g_t) of the integral I(g_t) on W(M) of the Finslerian scalar curvature (and certain functions of the scalar curvature) define a GEM. We give an estimate of the eigenvalues of Laplacian Δ defined on W(M) operating on the functions coming from the base when (M, g) is of minima fibration with a constant scalar curvature H admitting a conformal infinitesimal deformation (CID). We obtain λ ≥ H/(n − 1) (Δf = λf). If M is simply connected and λ = H/(n − 1), then (M, g) is Riemannian and is isometric to an n-sphere. We first calculate, in the general case, the formula of the second variationals of the integral I (g_t) for G = g₀, then for a CID we show that for certain Finslerian manifolds, I″(g₀) > 0. Applications to the gravitation and electromagnetism in general relativity are given. We prove that the spaces characterizing Einstein-Maxwell equations are GEMs.     

广义变系数模型的Bayesian B样条估计

提出了广义变系数模型函数系数的一种新的估计方法．我们用B样条函数逼近函数系数,不具体选择节点的个数,而是节点个数取均匀的无信息先验,样条函数系数取正态先验,用Bayesian模型平均的方法估计各个函数系数．这种估计方法一个主要特点是允许各个函数系数所需节点个数的后验分布不同,因此允许不同函数系数使用不同的光滑参数．另外,本文还给出了Bayesian B样条估计的计算方法,并通过模拟例子,说明广义变系数模型的函数系数可以由Bayesian B样条估计方法得到很好的估计．     

Convergent sequences of sparse graphs: A large deviations approach

Models based on sparse graphs are of interest to many communities: they appear as basic models in combinatorics, probability theory, optimization, statistical physics, information theory, and more applied fields of social sciences and economics. Different notions of similarity (and hence convergence) of sparse graphs are of interest in different communities. In probability theory and combinatorics, the notion of Benjamini‐Schramm convergence, also known as left‐convergence, is used quite frequently. Statistical physicists are interested in the the existence of the thermodynamic limit of free energies, which leads naturally to the notion of right‐convergence. Combinatorial optimization problems naturally lead to so‐called partition convergence, which relates to the convergence of optimal values of a variety of constraint satisfaction problems. The relationship between these different notions of similarity and convergence is, however, poorly understood. In this paper we introduce a new notion of convergence of sparse graphs, which we call Large Deviations or LD‐convergence, and which is based on the theory of large deviations. The notion is introduced by “decorating” the nodes of the graph with random uniform i.i.d. weights and constructing corresponding random measures on and . A graph sequence is defined to be converging if the corresponding sequence of random measures satisfies the Large Deviations Principle with respect to the topology of weak convergence on bounded measures on . The corresponding large deviations rate function can be interpreted as the limit object of the sparse graph sequence. In particular, we can express the limiting free energies in terms of this limit object. We then establish that LD‐convergence implies the other three notions of convergence discussed above, and at the same time establish several previously unknown relationships between the other notions of convergence. In particular, we show that partition‐convergence does not imply left‐ or right‐convergence, and that right‐convergence does not imply partition‐convergence. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 51, 52–89, 2017     

一个择一定理及对广义凸规划的应用

本文在实线性空间中得出了广义凸函数的择一定理 ,利用这一定理 ,我们获得广义凸规划的最优性条件