基于禁忌搜索的车联网蒙特卡洛定位算法

在蒙特卡洛定位算法中引入禁忌搜索算法以提高车联网中快速定位的性能。自组织车联网高速移动的车辆和快速变化的网络拓扑结构,使用传统的蒙特卡洛定位算法,不能迅速的收敛位置信息。在滤波阶段引入禁忌搜索算法对传统蒙特卡洛定位算法进行改进, 优化滤波排除可能性较小的位置点,获得近似最优估计位置采样集。仿真结果表明,改进后的算法在样本采集数、计算时间、定位精度等方面有了显著提升,改进后的算法能更好地解决车联网的定位问题。     

Investigation of nucleation and grain growth in 2-dimensional systems by using generalized Monte Carlo simulations

In this study, nucleation and grain growth was studied by using 2-dimensional generalized Monte Carlo simulations and experiments. As an attempt to improve the JMAK model, we proposed a new differential equation to be able to model nucleation and growth phenomena using nonextensive thermostatistics. One of the reasons that we would like to perform generalized Monte Carlo simulations in studying of nucleation and grain growth phenomena is that the generalized Monte Carlo algorithm was shown to be more effective than the standard Monte Carlo algorithm and also than the standard Molecular Dynamic algorithm in locating the minimum energy configuration. Therefore, for a given temperature, the fact that a configuration of the system with lower energy could be obtained by using the generalized Monte Carlo simulation means that a different textural configuration of grain growth could be also expected. In this respect, it is possible to say that the nonextensive statistics might be an appropriate tool in studying of nucleation and growth phenomena.     

A benchmark comparison of Monte Carlo particle transport algorithms for binary stochastic mixtures

We numerically investigate the accuracy of two Monte Carlo algorithms originally proposed by Zimmerman [1] and Zimmerman and Adams [2] for particle transport through binary stochastic mixtures. We assess the accuracy of these algorithms using a standard suite of planar geometry incident angular flux benchmark problems and a new suite of interior source benchmark problems. In addition to comparisons of the ensemble-averaged leakage values, we compare the ensemble-averaged material scalar flux distributions. Both Monte Carlo transport algorithms robustly produce physically realistic scalar flux distributions for the benchmark transport problems examined. The base Monte Carlo algorithm reproduces the standard Levermore-Pomraning model [3] and [4] results. The improved Monte Carlo algorithm generally produces significantly more accurate leakage values and also significantly more accurate material scalar flux distributions. We also present deterministic atomic mix solutions of the benchmark problems for comparison with the benchmark and the Monte Carlo solutions. Both Monte Carlo algorithms are generally significantly more accurate than the atomic mix approximation for the benchmark suites examined.     

非定常粒子输运蒙特卡罗散射源分层抽样方法

定常粒子输运蒙特卡罗并行计算是成功的,因为粒子游动是独立的,可以把模拟的粒子数等分到每个处理器去.然而,对非定常问题,由于每个时间步涉及散射源和几何网格的通讯,它严重的制约了并行规模,导致并行不可扩展.研究了两种算法,采用自适应分配处理器,提高了加速比和处理器的利用率;采用蒙特卡罗分层抽样大大降低了处理器之间散射源的通讯量,并行可扩展性显著改善,取得了理想的加速比.     

Coarse-grained kinetic scheme-based simulation framework for solution growth of ZnO nanowires

Kinetic Monte Carlo (KMC)-based stochastic model is used to understand the growth of zinc oxide nanowires from aqueous solution containing chemical precursors and capping agent. Through a hydrothermal growth mechanism, the average diameter of zinc oxide wires obtained is around 300 nm, whereas the length is order of several micrometers. Our Monte Carlo algorithm is based on the continuous-time Monte Carlo algorithm of Bortz, Kalos and Lebowitz (BKL) methodology. Both reactions and diffusion mechanisms assigning stochastic probabilities have been simulated. In algorithm, the ZnO atoms were treated as individual particles which diffuse in solution substrate and interact with other type of atoms. Once attached with growing nanowires, the diffusion rate of ZnO atom is considerably reduced. Since in a KMC algorithm each atom can be represented individually therefore, internal noise is automatically incorporated.     

Monte Carlo Hamiltonian:Inverse Potential

The Monte Carlo Hamiltonian method developed recently allows to investigate the ground state and low-lying excited states of a quantum system, using Monte Carlo (MC) algorithm with importance sampling. However, conventional MC algorithm has some difficulties when applied to inverse potentials. We propose to use effective potential and extrapolation method to solve the problem. We present examples from the hydrogen system.     

Monte Carlo simulations in the isothermal—isobaric ensemble: the requirement of a ‘shell’ molecule and simulations of small systems

A modification of the widely used Monte Carlo method for determining thermophysical properties in the isothermal-isobaric ensemble is presented. The new Monte Carlo method, now consistent with recent derivations describing the proper statistical mechanical formulation of the constant pressure ensemble for small systems, requires a ‘shell’ molecule to uniquely identify the volume of the system, thereby avoiding the redundant counting of configurations. Ensemble averages obtained with the new algorithm differ from averages calculated with the old Monte Carlo method, particularly for small system sizes, although both sets of averages become equal in the thermodynamic limit. Monte Carlo simulations in the constant pressure ensemble applied to the Lennard-Jones fluid demonstrate these differences for small systems. Peculiarities of small systems are also discussed, revealing that ‘shape’ is an important thermodynamic variable. Finally, an extension of the Monte Carlo method to mixtures is presented.     

Reverse Monte Carlo iterative algorithm for quantification of X-ray fluorescence analysis based on MCNP6 simulation code

Reverse Monte Carlo iterative algorithm has been developed for quantification of energy-dispersive X-ray fluorescence analysis in order to calculate the concentrations of the elementary composition in solid substances. The core of the simulation code was the MCNP6 that is a well-established and widely applied software package in the nuclear research and practice for simulation of nuclear systems or the full process of gamma- or X-ray spectrometry. The reverse Monte Carlo algorithm and the full analytical procedure was tested by quantitative XRF analysis of reference alloy samples. The atomic compositions of the reference samples were determined by reverse Monte Carlo technique and also fundamental parameter method and by spark emission atomic spectroscopy. The agreement between the results of these three analytical methods was found within the standard deviations of the major elements of the samples. The total duration of the reverse Monte Carlo numerical computation was minimized to a few minutes using the variance reduction procedures available in the MCNP6.     

Quantative measure of efficiency of Monte Carlo simulations

An easily applied, physically motivated algorithm for determining the efficiency of Monte Carlo simulations is introduced. The theoretical basis for the algorithm is developed. As an illustration we apply the method to the Lennard-Jones liquid near the triple point. We show that an acceptance ratio of 0.2 is twice as efficient for the purpose of generating a satisfactory sample as is an acceptance ratio of 0.5. There is a strong correlation between the efficiency measure and the diffusion rate of liquid particles during the simulation. We argue that the optimal value of the acceptance ratio is calculable from short Monte Carlo simulations. The method is very general and is applicable to Monte Carlo simulations involving arbitrary potentials.     

非弹性核反应对质子束能量沉积的影响

提出了一种比较精确且又省时的质子穿透的解析算法,并给出了考虑核反应后50—250MeV质子在水中的能量沉积,与Monte-Carlo方法的结果进行了比较.结果表明:1)非弹性核反应对质子的能量沉积有明显的影响,特别是对较高能量的质子束入射情况;2)该方法计算效率比Monte Carlo方法高百倍以上.因此,本文方法解决了质子能量沉积计算中效率和精度之间的矛盾,有可能在质子束放射治疗中得到实际应用. 关键词： 质子能量沉积 核反应 截面     

Accelerating dynamical-fermion computations using the rational hybrid Monte Carlo algorithm with multiple pseudofermion fields

There has been much recent progress in the understanding and reduction of the computational cost of the hybrid Monte Carlo algorithm for lattice QCD as the quark mass parameter is reduced. In this letter we present a new solution to this problem, where we represent the fermionic determinant using n pseudofermion fields, each with an nth root kernel. We implement this within the framework of the rational hybrid Monte Carlo algorithm. We compare this algorithm with other recent methods in this area and find it is competitive with them.     

Analysis of communication costs for domain decomposed Monte Carlo methods in nuclear reactor analysis

A domain decomposed Monte Carlo communication kernel is used to carry out performance tests to establish the feasibility of using Monte Carlo techniques for practical Light Water Reactor (LWR) core analyses. The results of the prototype code are interpreted in the context of simplified performance models which elucidate key scaling regimes of the parallel algorithm.     

考虑中子泄漏修正的快堆蒙特卡罗少群截面参数制作与验证方法

一种基于B1均匀化方程的泄漏修正模型在连续能量蒙特卡罗程序TRIPOLI4中得以实现并且用于制作少群截面参数。此蒙卡泄漏修正模型通过在连续能量的蒙卡模拟以及求解B1均匀化方程之间迭代,最终得到蒙卡模拟下的临界状态。通过此方法得到的少群截面参数较其他蒙卡以及确定论方法有两点显著优势:用于求解B1均匀化方程的少群常数是用通过临界状态的通量谱得到的;考虑了泄漏效应的蒙卡模拟可以更真实地反映组件计算时的能谱状态。为验证此泄漏修正模型,一个由连续能量的TRIPOLI4模拟而得到的数值临界实验被用于分析与比较。通过与其他蒙卡程序SERPENT以及确定论程序ECCO进行结果对比,可证明此B1泄漏修正方法能够给出更精确的用于堆芯计算的少群截面参数。     

一种适于光学实现的非线性神经网络模型及其蒙特卡洛学习算法

本文用计算机仿真研究了一种适于光学实现的非线性神经网络模型的存储客量α_c和寻址能力,提出了一个改进其触突互联矩阵的蒙特卡洛学习算法.数值研究表明,经过学习修正后的神经网络模型的寻址能力及存储容量都有较大的改进.     

A boundary-based net-exchange Monte Carlo method for absorbing and scattering thick media

A boundary-based net-exchange Monte Carlo method was introduced (JQSRT 74 (2001) 563) that allows to bypass the difficulties encountered by standard Monte Carlo algorithms in the limit of optically thick absorption (and/or for quasi-isothermal configurations). With the present paper, this method is extended to scattering media. Developments are fully 3D, but illustrations are presented for plane parallel configuration. Compared to standard Monte Carlo algorithms, convergence qualities have been improved over a wide range of absorption and scattering optical thicknesses. The proposed algorithm still encounters a convergence difficulty in the case of optically thick, highly scattering media.     

Study on the efficiency of Eb/No algorithm for BER estimation over 112-Gb/s PDM CO-OFDM system with QPSK mapping

We research an efficient BER estimation method for 112-Gb/s polarization division multiplexing coherent optical OFDM (PDM CO-OFDM) with QPSK mapping over 800 km SSMF based on Eb/No algorithm in this paper. We first lay out the related formulas’ derivation to set up the theoretical basis for the feasibility of Eb/No algorithm and then we implement a series of simulations to illustrate the relationship of BER (and Q factor) versus OSNR, launch power of transmitter, chromatic dispersion and nonlinearity in optical fiber link via both Eb/No algorithm and Monte Carlo algorithm. The simulations demonstrate that BER estimated by Eb/No algorithm can achieve a precision up to 10⁻¹⁶ just with 10⁵ bits while Monte Carlo algorithm needs at least 10¹⁸ bits to get the same level. Therefore, Eb/No allows a quick and reliable method for BER estimation instead of the time consuming Monte Carlo method, which can be used to support simulations with various conditions.     

巨正则量子Monte Carlo方法和二维Hubbard模型的研究

用巨正则量子Monte Carlo方法,计算了二维单带Hubbard模型的局域磁矩、磁化率、交错磁化率和内能等物理量.结果表明,用巨正则量子Monte Carlo方法能够统一地研究Hubbard模型的关联强度从弱至强区域的性质,它是处理强关联多体系统的有效方法.     

(3 1)-Dimensional Quantum Mechanics from Monte Carlo Hamiltonian: Harmonic Oscillator

In Lagrangian formulation, it is extremely difficult to compute the excited spectrum and wavefunctions ora quantum theory via Monte Carlo methods. Recently, we developed a Monte Carlo Hamiltonian method for investigating this hard problem and tested the algorithm in quantum-mechanical systems in 1 1 and 2t1 dimensions. In this paper we apply it to the study of thelow-energy quantum physics of the (3 1)-dimensional harmonic oscillator.``     

Efficient assignment of the temperature set for Parallel Tempering

We propose a simple algorithm able to identify a set of temperatures for a Parallel Tempering Monte Carlo simulation, that maximizes the probability that the configurations drift across all temperature values, from the coldest to the hottest ones, and vice versa. The proposed algorithm starts from data gathered from relatively short Monte Carlo simulations and is straightforward to implement. We assess its effectiveness on a test case simulation of an Edwards–Anderson spin glass on a lattice of 12³ sites.