单电子器件的仿真

该文介绍了一种利用正统理论与Ｍｏｎｔｅ Ｃａｒｌｏ方法模拟单电子隧空器件的认真程序。该程序可模拟电子通过包含小隧道结、电容和理想电压源的电路的输运过程,利用该程序,对单库仓岛和多库仓同的单电子晶体管（ＳＥＴ）系统进行了模拟。     

Interacting sequential Monte Carlo samplers for trans-dimensional simulation

The methodology of interacting sequential Monte Carlo (SMC) samplers is introduced. SMC samplers are methods for sampling from a sequence of densities on a common measurable space using a combination of Markov chain Monte Carlo (MCMC) and sequential importance sampling/resampling (SIR) methodology. One of the main problems with SMC samplers when simulating from trans-dimensional, multimodal static targets is that transition kernels do not mix which leads to low particle diversity. In such situations poor Monte Carlo estimates may be derived. To deal with this problem an interacting SMC approach for static inference is introduced. The method proceeds by running SMC samplers in parallel on, initially, different regions of the state space and then moving the corresponding samples onto the entire state space. Once the samplers reach a common space the samplers are combined and allowed to interact. The method is intended to increase the diversity of the population of samples. It is established that interacting SMC admit a Feynman-Kac representation; this provides a framework for the convergence results that are developed. In addition, the methodology is demonstrated on a trans-dimensional inference problem in Bayesian mixture modelling and also, using adaptive methods, a mixture modelling problem in population genetics.     

Monte Carlo方法与计算机模拟应用研究

介绍了Monte Carlo方法,提出其在模拟Buffer问题时存在的一个问题,并给出改进的方法;提出了用Monte Carlo方法产生任意分布随机变量的原理及方法,并对Beta分布和标准正态分布随机变量进行了计算机模拟和效果检验。     

出租车上客区规模的Monte Carlo仿真计算方法

为确定出租车上客区的合理规模,分析其运行特征,同时存在乘客和出租车相互等待的情况,而不能采用停车场模型或排队论模型计算。采用Monte Carlo仿真方法,利用随机原理来拟合乘客与出租车的相互作用过程,给出了合理规模的确定流程。实例应用证明该方法具有很好的适用性;同时分析结果表明：（1）当出租车上客区泊位规模达到一定程度时,再增加并不能减少乘客等待时间;（2）高峰时段适当限制乘客的排队长度能够在较小比例的乘客损失下,极大地降低乘客的等待时间。     

A fortran program for Monte Carlo simulation of oil-field discovery sequences

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios.     

Modeling and Monte Carlo simulation of photon transmission in glass-packaged WLEDs

Luminous efficiency is a key factor affecting the luminous performance of white light-emitting diodes (WLEDs). However, the mechanism and photon transport process in WLEDs encapsulated with multilayer films have not been elucidated. In this work, the effect of five different encapsulated inorganic glasses on the luminescence efficiency of WLEDs was investigated by using the Monte Carlo method. Compared with the conformal coating method, the away-from-coating method is more beneficial to improve the light output of WLEDs. The results show that the optical property of WLEDs is closely related to the concentration of phosphor and the thickness of the fluorescent glass. In addition, compared with other fluorescent glasses, fluorophosphate fluorescent glass has higher blue and yellow light transmittance and lower yellow light loss rate, which is suitable to be used as packaging materials to effectively improve the light output efficiency of WLEDs.     

大分子蠕动的Monte Carlo模型及计算方法

高分子链分子量巨大而且分散,结构复杂多变,存在大量不确定的问题,给理论和实验研究造成许多困难。本文建立了一种基于高分子Monte Carlo模拟算法,分析分子蠕动阻力的模型。用链动力学法构造8配位点键长涨落格子链多链体系,通过高分子链的运动使任意的初始有序态演化到平衡态,可同时获得体系的动态和静态性质。将重要性抽样法和高分子多链体系的链动力学构造法,在键长涨落模型中结合起来,形成多链体系的链动力学求解法。引入空格扩散算法,用以研究高浓度态的高分子体系。统计平衡后元胞内高分子多链体系在链动力学算法作用下蠕动失败机率,对高聚物多链体系内分子蠕动的困难程度的定量模拟,能更直观分析高分子玻璃化转变的现象。     

Optimizations and OpenMP implementation for the direct simulation Monte Carlo method

Parallel implementation of a three-dimensional direct simulation Monte Carlo (DSMC) code employing complex data structures and dynamic memory allocation is detailed for shared memory systems using Open Multi-Processing (OpenMP). Several techniques to optimize the serial implementation of the DSMC method are first discussed. Specifically for a 3-level Cartesian grid, a Cartesian-based movement technique including particle indexing is demonstrated to result in a modest decrease in overall simulation expense of 34% compared with a ray-tracing technique combined with stored cell-connectivity. Two strategies for data localization leading to optimal usage of cache memory are demonstrated to speed up certain cell-based functions (such as collision computations) by a factor of 3.38–4.36. The shared-memory parallel implementation using OpenMP is then described in detail. Synchronization points and related critical sections are identified as major factors that impact the OpenMP parallel performance. Techniques to remove all such synchronization points in the OpenMP implementation of the DSMC method are outlined. For dual-core and quad-core systems, speedups of 1.99 and 3.74, respectively, are obtained for a (free-stream flow) test simulation with low granularity. Finally, the parallel performance of identical source code employing OpenMP is shown to be strongly correlated to the underlying computer architecture. Both Symmetric Multiprocessor (SMP) and non-uniform memory access (NUMA) systems are studied in order to achieve a better understanding of their impacts on parallel scalability when using OpenMP.     

Rejuvenation effects on the grid environment performance with response time delays using Monte Carlo simulation

Grid computing is an evolutionary technological achievement that takes advantages of wide area communication and large scale resource sharing. Although there are several advantages, grid does not guaranty stableness of resources due to their resources’ nature, diverse failures and error conditions that may appear. Consequently, grid dependability issues arise. In this paper, a grid computing environment with star topology and direct access to resources is considered, which consists of a Resource Management System (RMS) and distributed Root Nodes (RNs). The distributed RNs are considered either as operational or as failed, though the RMS is susceptible to resource exhaustion, which can lead to software failures. The response waiting time from a distributed RN is also taken under consideration. A software rejuvenation to counteract RMS resource exhaustion is adopted, and its effect on grid environment performance is studied extensively. A non-Markovian approach is considered to model the system’s evolution in time and to evaluate the proposed performance measures. Due to the complicated structure of the system, analytic formulas for the proposed measures are not available, thus Monte Carlo simulation methods are employed.     

Efficient Monte Carlo simulation method of GERT-type network for project management

This paper proposes a new method to estimate network characteristics such as project time, project cost, etc. There are many kinds of projects such as new product development and plant construction. Since these projects consist of various uncertain jobs, we can model those processes as stochastic networks such as GERT network. Only simulation technique can analyze the networks’ characteristics of practical project model. However, the method is extremely time-consuming. Our method can estimate the network characteristics rapidly and accurately by analyzing one set of Monte Carlo simulation results based on the theory of probability.     

Spare parts allocation by improved genetic algorithm and Monte Carlo simulation

A combined Monte Carlo (MC) simulation and Genetic Algorithm (GA) method was proposed by other researchers for the optimisation of spare parts allocation. From case studies, it was found that the number of simulation trials of the existing method tended to be either excessive or inadequate. Thus, a simulation replication number control method making full use of the advance simulation effort is proposed and implemented into the existing method. A numerical example shows significant improvement on overall simulation efficiency and that at the same time the required accuracy is guaranteed. Furthermore, it is argued that application-specific knowledge should be embedded into the general GA procedure so that the evolution process can be more efficient. Heuristic methods for initial population preparation for GA with and without considering component cost difference are proposed and illustrated for spare parts allocation. A computing experiment was designed and performed to examine the influence of parameters for replication number control and initial population preparation. The generation of availability–cost curve further indicates the necessity to adopt heuristic methods to improve searching efficiency in GA.     

基于序贯蒙特卡洛算法的交通流事件重构

针对交通数据重构应用性差、缺乏对交通事件重构的研究等问题,结合交通流非线性非高斯的特点,提出一个基于序贯蒙特卡洛方法的交通流堵塞事件重构模型。该模型不断同化道路上的传感器数据,使仿真中的交通状态不断逼近真实路况,通过分析仿真数据以探测真实路网中存在的堵塞事件。模型能够对探测到的堵塞进行多粒子模拟来实现对真实道路上堵塞事件的重构。实验结果表明,该模型能够推测并重构出道路上的堵塞事件,对堵塞起始位置重构的平均误差为17m,对堵塞范围重构的平均覆盖率为82%。     

基于CVaR和Monte Carlo仿真的贷款组合决策模型

提供了一种新的贷款组合决策优化方法,该模型用更能反映贷款组合信用风险特征的CVaR作为风险度量。由于在实际中很难获取各笔贷款的历史数据,为此给出了一种基于Matlab语言的Monte Carlo仿真方法。从而使谊模型可以通过线性规划技术有效的进行求解。最后给出了一个例子。     

A rigorous sequential update strategy for parallel kinetic Monte Carlo simulation

The kinetic Monte Carlo (kMC) method is used in many scientific fields in applications involving rare-event transitions. Due to its discrete stochastic nature, efforts to parallelize kMC approaches often produce unbalanced time evolutions requiring complex implementations to ensure correct statistics. In the context of parallel kMC, the sequential update technique has shown promise by generating high quality distributions with high relative efficiencies for short-range systems. In this work, we provide an extension of the sequential update method in a parallel context that rigorously obeys detailed balance, which guarantees exact equilibrium statistics for all parallelization settings. Our approach also preserves nonequilibrium dynamics with minimal error for many parallelization settings, and can be used to achieve highly precise sampling.     

极性电介质极化规律的蒙特卡罗模拟研究

使用蒙特卡罗方法模拟了极性电介质的极化现象,结果显示:系统达到平衡态后,倾向于电场方向的分子数增多,逆着电场方向的分子数减少,每个分子电矩的取向仍不断发生变化;电场越强,沿电场方向的分子数越多,电极化强度越大,电极化强度与场强成线性关系;场强不变时,温度越高,分子电矩取向越杂乱,电极化强度越小,说明电介质的极化率和介电常量随温度升高而减小。我们还对20℃、1 atm条件下CH₃Cl、HBr、HCl、CH₃Br、CH₃I、CH₃F、C₂H₅Cl、C₂H₃Cl 8种气体的相对介电常量作了估算,估算值和相应的实验值符合得较好,说明本文的模拟方法可较好地用来估算电介质气体的相对介电常量。     

基于蒙特卡罗模拟修正的随机矩阵去噪方法

针对蕴含噪声信息较少的小组合股票市场,提出使用蒙特卡罗模拟修正的随机矩阵去噪方法。首先通过数据模拟生成随机矩阵,然后利用大量的模拟数据来同时修正噪声下界和上界,最终对噪声范围进行精确测定。运用道琼斯中国88指数和香港恒生50指数的数据进行实证分析,结果表明,与LCPB法、PG+法和KR法相比,在特征值、特征向量和反比参率方面, 蒙特卡罗模拟去噪方法修正后噪声范围的合理性及有效性得到很大的提升;对去噪前后的相关矩阵进行投资组合,得知在相同的期望收益率下,蒙特卡罗模拟去噪方法具有最小的风险值,能够为资产组合选择和风险管理等金融应用提供一定的参考。     

亚胺培南和左氧氟沙星治疗方案蒙特卡洛模拟的比较

依据抗菌药物的药动学/药效学原理,本文建立蒙特卡洛模拟模型,优化比较亚胺培南/西司他丁(剂量以亚胺培南计)和左氧氟沙星抗感染治疗的给药方案。模拟试验表明,亚胺培南0.5 g/8 h静脉滴注对鲍曼不动杆菌、肺炎克雷伯菌感染的疗效较好,累积反应分数(CFR)分别为97.28%和99.71%;对铜绿假单孢菌,低剂量的CFR不够理想,增大剂量可以提高CFR,但低于90%,提示经验治疗时须考虑联合用药。左氧氟沙星静脉滴注0.2g/d对卡他莫拉菌有效,CFR为100%;对肺炎克雷伯菌,0.2g/d、0.3 g/d、0.5g/d和0.75 g/d各治疗方案的CFRs相近,约为55%,增加剂量未能增强效果;对肺炎链球菌,0.5g/d的日剂量CFR达100%,可具有治疗效果。蒙特卡洛模拟可预测和优化抗菌药的用药设计,帮助临床选择合适的抗感染治疗方案。     

LTA型沸石吸附CO2的蒙特卡罗模拟研究

采用巨正则系综的蒙特卡罗(GCMC)方法研究了CO2在LTA型沸石上的吸附.研究表明经典力场不适合CO2/LTA沸石吸附体系.通过拟合实验数据得到一组优化力场参数.基于优化力场的模拟结果表明:二氧化碳主要吸附在4A沸石超笼的中心和超笼中Na 离子附近朝向中心的位置,能量分布图的相应位置也显示了较强的作用力.而对于5A沸石,由于8元环孔口处没有阳离子,二氧化碳主要吸附在八元环孔道中和八面沸石笼的中心.用优化力场计算了4A与5A沸石吸附二氧化碳的吸附热(273K),计算结果与文献值比较吻合.     

Improved algorithm for tolerance allocation based on Monte Carlo simulation and discrete optimization

The allocation of design and manufacturing tolerances has a significant effect on both manufacturing cost and quality. This paper considers nonlinearly constrained tolerance allocation problems. The purpose is to minimize the ratio between the sum of the manufacturing costs (tolerances costs) and the risk (probability of the respect of geometrical requirements). The techniques of Monte Carlo simulation and genetic algorithm are adopted to solve these problems. As the simplest and the popular method for non-linear statistical tolerance analysis, the Monte Carlo simulation is introduced into the frame. Moreover, in order to make the frame efficient, the genetic algorithm is improved according to the features of the Monte Carlo simulation. An illustrative example (hyperstatic mechanism) is given to demonstrate the efficiency of the proposed approach.