基于蒙特卡洛方法的触发器聚类布局算法

针对触发器聚类的布局问题,提出了一种基于蒙特卡洛方法的触发器聚类布局算法。该方法的核心思想为设计合理的评分函数,并通过蒙特卡洛方法模拟触发器聚类在版图上的随机游走过程,获取样本,再通过评分函数评价样本质量,逐步地在可行域中搜索最优解。实验结果表明,相较于现有的方法,该方法在总线长与时序性能两方面,分别得到平均约9%与17%的改善与提升。此外,该方法具有较强的健壮性,适用于复杂度较高或者版图利用率较高的设计,并能够快速收敛,以较小的时间代价完成优化。     

A spectral representation based model for Monte Carlo simulation

A new model is proposed for generating samples of real-valued stationary Gaussian processes. The model is based on the spectral representation theorem stating that a weakly stationary process can be viewed as a superposition of harmonics with random properties. The classical use of this theorem for Monte Carlo simulation is based on models consisting of a superposition of harmonics with fixed frequencies but random amplitude and phase. The resulting samples have the same period depending on the discretization of the frequency band. In contrast, the proposed model consists of a superposition of harmonics with random amplitude, phase, and frequency so that different samples have different periods depending on the particular sample values of the harmonic frequencies.

A band limited Gaussian white noise process is used to illustrate the proposed Monte Carlo simulation algorithm and demonstrate that the estimates of the covariance function based on the samples of the proposed model are not periodic.     

光子在闪烁晶体中传输的蒙特卡罗模拟

为了找到构筑闪烁晶体探测器的优化方法，使用蒙特卡罗方法对闪烁晶体BGO（Bi4Ge3O12，锗酸铋）的光收集效率进行了模拟研究。模拟结果表明：入射面为粗糙面，其余为抛光面，同时外层包装上高反射率的材料，可得到最大的光输出（约59．1％的光子被收集）；耦合剂的折射率的得到高的光输出也起着非常重要的作用。     

磁性多层膜系统自旋重取向行为的Monte Carlo模拟

依据Heisenberg模型,利用Monte　Carlo方法模拟了磁性多层膜系统的自旋重取向行为,研究了各向异性、偶极相互作用以及外磁场对系统自旋取向的影响。通过模拟计算,获得了系统组态、磁分量等随偶极相互作用、外加磁场和温度的变化规律,重点研究了磁性多层膜系统在外磁场作用下的磁滞现象。     

Monte Carlo simulation of low density flows

The necessity for adopting a kinetic-theoretical approach to obtain aerodynamic characteristics in low density flow past space vehicles is highlighted in this paper; it is shown how long-standing difficulties in theoretically handling such flows can be circumvented by adopting a Monte Carlo technique. The principles underlying the technique are briefly described, and are first illustrated by applying the technique to the evaluation of the drag of cylinders and cones in collisionless flow. The Markoff process underlying the Monte Carlo simulation of the full Boltzmann equation with collisions is then described in detail. Instead of the time-counter strategy of Bird, a theoretically sounder ‘Random Collision Number’ (RCN) strategy has been adopted in the present simulation. In this strategy the number of collisions in each time-step in the computation is a random number drawn from an appropriate distribution. Computer programs using this strategy have been developed for calculating aerodynamic characteristics like drag and heat transfer for a cone in the transition regime between free molecule and continuum flow. The results obtained from these programs show that both time-counter and RCN strategies require almost the same computer time.     

Genetic algorithms and Monte Carlo simulation for optimal plant design

We present an approach to the optimal plant design (choice of system layout and components) under conflicting safety and economic constraints, based upon the coupling of a Monte Carlo evaluation of plant operation with a Genetic Algorithms-maximization procedure. The Monte Carlo simulation model provides a flexible tool, which enables one to describe relevant aspects of plant design and operation, such as standby modes and deteriorating repairs, not easily captured by analytical models. The effects of deteriorating repairs are described by means of a modified Brown–Proschan model of imperfect repair which accounts for the possibility of an increased proneness to failure of a component after a repair. The transitions of a component from standby to active, and vice versa, are simulated using a multiplicative correlation model. The genetic algorithms procedure is demanded to optimize a profit function which accounts for the plant safety and economic performance and which is evaluated, for each possible design, by the above Monte Carlo simulation.In order to avoid an overwhelming use of computer time, for each potential solution proposed by the genetic algorithm, we perform only few hundreds Monte Carlo histories and, then, exploit the fact that during the genetic algorithm population evolution, the fit chromosomes appear repeatedly many times, so that the results for the solutions of interest (i.e. the best ones) attain statistical significance.     

A Monte Carlo simulation model for stationary non-Gaussian processes

A class of stationary non-Gaussian processes, referred to as the class of mixtures of translation processes, is defined by their finite dimensional distributions consisting of mixtures of finite dimensional distributions of translation processes. The class of mixtures of translation processes includes translation processes and is useful for both Monte Carlo simulation and analytical studies. As for translation processes, the mixture of translation processes can have a wide range of marginal distributions and correlation functions. Moreover, these processes can match a broader range of second order correlation functions than translation processes. The paper also develops an algorithm for generating samples of any non-Gaussian process in the class of mixtures of translation processes. The algorithm is based on the sampling representation theorem for stochastic processes and properties of the conditional distributions. Examples are presented to illustrate the proposed Monte Carlo algorithm and compare features of translation processes and mixture of translation processes.     

Procedures of Monte Carlo transport simulation for applications in system engineering

Monte Carlo (MC) simulation is the most promising tool for performing realistic reliability and availability analysis of complex systems. Yet, the efficient use of MC simulation technique is not trivial in large scale applications.This paper considers the two commonly adopted approaches to MC simulation: the direct, component-based approach and the indirect, system-based approach. The mathematical details of the two approaches are worked out in detail, so as to show their probabilistic equivalence. The proper formulation for biasing the simulation is introduced, thus leading to the correct expressions for the statistical weights.Both approaches are applied, in an analog as well as in a biased scheme, to a simple system of the literature and comparisons are made with respect to the computing time and the goodness of the estimate, as measured by the variance of the results.     

Monte Carlo simulation of nucleation and growth of thin films

We study thin film growth using a lattice-gas, solid-on-solid model employing the Monte Carlo technique. The model is applied to chemical vapour deposition (CVD) by including the rate of arrival of the precursor molecules and their dissociation. We include several types of migration energies including the edge migration energy which allows the diffusive movement of the monomer along the interface of the growing film, as well as a migration energy which allows for motion transverse to the interface. Several well-known features of thin film growth are mimicked by this model, including some features of thin copper films growth by CVD. Other features reproduced are—compact clusters, fractal-like clusters, Frank-van der Merwe layer-by-layer growth and Volmer-Weber island growth. This method is applicable to film growth both by CVD and by physical vapour deposition (PVD).     

Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation

Efficient maintenance policies are of fundamental importance in system engineering because of their fallbacks into the safety and economics of plants operation. When the condition of a system, such as its degradation level, can be continuously monitored, a Condition-Based Maintenance (CBM) policy can be implemented, according to which the decision of maintaining the system is taken dynamically on the basis of the observed condition of the system.In this paper, we consider a continuously monitored multi-component system and use a Genetic Algorithm (GA) for determining the optimal degradation level beyond which preventive maintenance has to be performed. The problem is framed as a multi-objective search aiming at simultaneously optimizing two typical objectives of interest, profit and availability. For a closer adherence to reality, the predictive model describing the evolution of the degrading system is based on the use of Monte Carlo (MC) simulation. More precisely, the flexibility offered by the simulation scheme is exploited to model the dynamics of a stress-dependent degradation process in load-sharing components and to account for limitations in the number of maintenance technicians available. The coupled (GA[plus ]MC) approach is rendered particularly efficient by the use of the ‘drop-by-drop’ technique, previously introduced by some of the authors, which allows to effectively drive the combinatorial search towards the most promising solutions.     

Monte Carlo simulation of grating-based neutron phase contrast imaging at CPHS

Since the launching of the Compact Pulsed Hadron Source (CPHS) project of Tsinghua University in 2009, works have begun on the design and engineering of an imaging/radiography instrument for the neutron source provided by CPHS. The instrument will perform basic tasks such as transmission imaging and computerized tomography. Additionally, we include in the design the utilization of coded-aperture and grating-based phase contrast methodology, as well as the options of prompt gamma-ray analysis and neutron-energy selective imaging. Previously, we had implemented the hardware and data-analysis software for grating-based X-ray phase contrast imaging. Here, we investigate Geant4-based Monte Carlo simulations of neutron refraction phenomena and then model the grating-based neutron phase contrast imaging system according to the classic-optics-based method. The simulated experimental results of the retrieving phase shift gradient information by five-step phase-stepping approach indicate the feasibility of grating-based neutron phase contrast imaging as an option for the cold neutron imaging instrument at the CPHS.     

Multiobjective spare part allocation by means of genetic algorithms and Monte Carlo simulation

The management of spare parts is a major concern for several industrial organizations, due to the significant amount of resources invested every year for holding spares inventories.In this paper, we explore the possibility of using genetic algorithms for the task of optimizing the number of spare parts required by a multi-component system. To address the question of how many spares should be kept in inventory for each component kind, the analyst is required to define objective functions with respect to which the optimization is sought. In our work we will look at multiple objectives such as, for example, the maximization of system revenues and the minimization of the total spares volume. The modeling of the system failure, repair and replacement stochastic processes is done by means of Monte Carlo simulation, whose flexibility allows a closer adherence to reality.     

应用Monte Carlo算法研究大分子蠕动阻力的影响因素

采用一种基于高分子Monte Carlo模拟算法分析分子蠕动阻力的软件,借助于二维空间中的8配位点键长涨落的格子链模型,对不同温度、分子量、共混小分子时的大分子蠕动行为进行了模拟分析,考察了温度、分子长、共混小分子等因素对蠕动阻力的影响,得到了随着温度增加、分子长减小和共混浓度增加,大分子蠕动阻力减小的结果,这与传统经验理论相符合。这些结果为选择大分子量添加剂黄原胶作为抗结块剂,以及选择β-环糊抑制玻璃化转变等提供了理论依据。     

三维泡泡布点方法及网格生成

本文研究如何在三维区域上生成高质量的节点集并基于节点集进行网格生成.根据区域边界的几何描述和理想间隔控制函数,先后对曲面及区域内部用泡泡布点法进行节点布置.节点布置结束后,对区域边界的节点集运用高质量点集的局部网格生成算法(BLMG)进行网格剖分,对区域内部的点集直接进行Delaunay三角剖分.通过计算节点集生成Delaunay网格单元的质量来评价区域节点集的质量.泡泡均匀分布与非均匀分布的算例均表明,该算法生成的节点具有较高的质量并且在泡泡非均匀分布时具有很好的渐进性.     

Prediction of micro-channel flows using direct simulation Monte Carlo

The direct simulation Monte Carlo (DSMC) method is a particle-based numerical modeling technique. It is recently used for simulating gaseous flow in micro-electro-mechanical-systems (MEMS) where micron-scale features become important. In this paper, numerical simulations of fluid flow in micro-channels are carried out using the DSMC method. The details in determining the parameters critical for DSMC applications in micro-channels are provided. Streamwise velocity distributions in the slip-flow regime are compared with the analytical solution based on the Navier–Stokes equations with slip velocity boundary condition. Satisfactory agreements have been achieved. Effects of the entrance and exit regions on simulation results are discussed. Simulations are then extended to transition flow regime (Kn>0.1) and compared with the analytical solution. It is shown that the results are distinguished with the analytical solutions, which fail to predict the flow due to the break down of continuum assumption. It is indicated that the gradient of the pressure along the channel direction dominates the motion of the fluid flow.     

Numerical simulation of point-to-plane corona discharge using a Monte Carlo method

Monte Carlo simulation techniques are used to study the dynamical properties of charged particles in point-to-plane corona discharge. The numerical model includes the release of electron-ion pairs by photoionization and secondary electron emission from cathode as well as the first Townsend ionization. The simulation results of negative corona discharge in nitrogen show that electron avalanche takes place in the region of high electric field near pin electrode and the photoionization is the essential mechanism to sustain the discharge as well as electron impact ionization.     

Defining the surface binding energy in dynamic Monte Carlo simulation for reactive sputtering of compounds

In Monte Carlo simulations of reactive sputtering, it is commonly assumed that the surface binding energy (SBE) for the different phases in the target exhibits a linear behaviour in the transition between the metal mode and the compound mode. In this work we study how the transition between the two modes takes place, and more specifically attempt to experimentally identify how the SBE for the different phases behaves in the transition between the two modes. In essence, this is done by comparing XPS measurements of the aluminium 2p binding energy on samples comprising pure aluminium, stoichiometric aluminium nitride and aluminium oxide with the corresponding measurements on understoichiometric aluminium nitride samples. In this work, it is assumed that the binding energy of the core level is directly correlated to the SBE of the phase in question. That is to say, if the aluminium 2p binding energy in aluminium nitride exhibits a constant and discrete value independent of the nitrogen concentration, the SBE for the compound exhibits a constant and discrete value independent of the surface concentration of nitrogen. It was found by the XPS measurement that the aluminium 2p binding energy in aluminium nitride exhibits a constant and discrete value independent of the nitrogen concentration in the samples and it was, therefore, concluded that the SBE for the different phases exhibits constant and discrete values independent of the surface concentration of nitrogen. The discrete behaviour of the SBE was implemented in the TRIDYN program and the results from these simulations were compared with simulations in which it is assumed that the SBE of the different phases exhibits a linear behaviour in the transition between the metal mode and the compound mode.     

Semi-analytical Monte Carlo simulation for time-resolved light propagating in multilayered turbid media

In this study, a Monte Carlo (MC) method for time-resolved light scattering from multilayered turbid media (SMCML) has been developed. This method is particularly suitable for simulating light backscattering from layered media and receiving the time-resolved signal in a finite sensor area, such as ocean detection, photomedicine and photobiology. The classical semi-analytical MC method requires the scattering events to be located in a single-layer medium. To address the multilayer problem, the energy loss mechanism of photons propagating in tissue was analyzed in this study. According to the energy contribution to the detector, only photons that contribute significantly were considered. Simulations were conducted for stochastic turbid media with different optical parameters. Temporal profiles of the echo signal were obtained with a satisfactory convergence. Compared to the classical MC method, the SMCML method can dramatically reduce the computation time by more than two orders of magnitude.     

Monte Carlo模拟薄膜生长的研究

阐述了Monte Carlo方法在薄膜生长中的应用和最新进展;简要论述了Monte Carlo算法的类型及各自的特点;结合MonteCarlo方法的特点,提出了模拟薄膜生长的模型以及处理方法.同时,归纳出MonteCarlo模拟薄膜生长需要解决的主要问题.