计算动态系统网格通量场的驿站重要抽样方法

对与时间有关输运问题计算条件下,全系统不同网格体通量计算统计涨落较大,甚至个别网格很难得到计数的现象,提出驿站重要抽样方法.研究可以选择的重要性函数,给出抽样方法,推导重要性偏倚抽样的源粒子纠偏权重公式.经过推导证明连续多时间步抽样计算不存在粒子纠偏权发散问题.数值模拟结果表明,采用驿站重要抽样方法,全系统精细网格通量场计算精度基本一致,整体计算结果得到明显改善.     

解深穿透问题的小区域蒙特卡罗方法

对于深穿透问题则一般蒙特卡罗方法存在一定的困难。本文提出了一个新的蒙特卡罗计算深穿透问题的小区域方法。在此基础上给出了两个小区域方法,即平几何小区域方法和球几何小区域方法,通过例子的实际计算表明,小区域方法是比较好的和可行的,克服了一般蒙特卡罗方法解深穿透问题的缺点。     

聚变堆深穿透屏蔽计算方法研究

为了提高具有复杂孔道的聚变堆深穿透屏蔽问题的计算精度,研究了蒙特卡罗法和离散纵标法(MC-S_N)的耦合方法。同时,在权重差分方法的基础上引入具有自适应特性的定向权重和指数定向权重方法,并基于勒让德-切比雪夫求积组和极角细化技术研究了偏倚求积组,有效解决聚变中子穿透能力强、强各向异性散射等屏蔽计算难点。通过与MCNP程序模拟结果的比较分析发现,这些研究方法能有效提高聚变堆深穿透屏蔽计算的精度。     

基于共轭离散纵标的减方差方法

对于深穿透类型的屏蔽计算,为了得到较为可信的统计结果,蒙特卡罗方法(MC方法)需要模拟大量的粒子,巨大的计算时间是其存在的主要问题。源偏倚和权窗技巧能够有效降低深穿透问题的计数误差。开展了基于共轭离散纵标(SN)的MC减方差方法研究,根据SN方法的共轭注量率计算并生成了源偏倚和权窗参数,编写了JMCT程序的源抽样子程序,并且在秦山一期测量值基础上进行了验证,成功应用到CAP1400压力容器快中子注量率和堆腔中子和光子剂量率计算中。数值结果表明,对于深穿透屏蔽计算问题,和无偏的MC方法相比,基于共轭SN的MC减方差方法能够在保证结果精度的前提下,提高计算效率1~2个量级。     

SN共轭函数用于蒙特卡罗粒子输运自动减方差的研究

基于S_N输运计算平台ARES编制了三维共轭输运计算模块,根据一致性共轭驱动重要性抽样方法自动生成减方差参数,用于加速MCNP5计算.数值结果表明,自动生成的减方差参数可有效提高蒙特卡罗计算效率,并保证结果无偏.自动减方差技术利用S_N共轭函数可更经济准确的估计粒子重要性,避免手动估算减方差参数的复杂工作,对于复杂屏蔽问题的蒙特卡罗计算具有较好的应用前景.     

非定常辐射输运问题的蒙特卡罗自适应偏倚抽样

用蒙特卡罗(MC)方法模拟高温、高压、多介质、大变形辐射输运问题时,由于网格体积悬殊,导致各网格通量的统计误差涨落很大,随着时间步的增加,误差积累甚至会导致计算结果失真.为此,发展了针对全局网格计算的源偏倚抽样技巧.用于源偏倚抽样的价值函数基于上个时间步各网格通量及误差,通过加权构造产生,它比传统MC通过解伴随方程获取价值的性价比要高得多.数值试验表明,全局源偏倚抽样通过自适应分配当前时间步各网格的粒子数,有效地降低了当前步重要网格通量误差. 关键词： 非定常 辐射输运 蒙特卡罗 源偏倚抽样     

蒙特卡罗粒子输运问题的全局降方差方法

精确获得中子通量的时间-空间-能量精细分布对于一类非定常粒子输运蒙特卡罗模拟至关重要.以零方差理论为基础,采取化整为零的策略,把蒙特卡罗方法与离散纵标方法相耦合,用近似重要函数指导蒙特卡罗模拟,给出一种实现蒙特卡罗全局降方差的计算方案.数值算例表明,该计算方案获得了全局降方差的效果.     

光衍射数值模拟中不同抽样方法的适用性分析

基于标量衍射理论和快速傅里叶变换,分析了光衍射的数值模拟时可以采用的不同抽样方法及其适用性。理论分析表明,按照抽样对象的不同,存在三种适用范围不同的算法:传递函数抽样法、点扩展函数抽样法和加权函数抽样法。其适用范围分别为衍射距离小于、大于和等于特征距离的情况。在Matlab软件环境下给出了三种算法的模拟计算实例,并与相应的实验结果进行了比较,证明了理论分析的正确性。最后还给出了一种无需改变抽样点数和抽样间隔就可计算任意距离衍射过程的自适应抽样算法及其在数字全息再现中的应用实例。     

蒙特卡罗输运模拟软件JMCT的深穿透屏蔽计算

核设施辐射屏蔽计算,由于其大规模计算及深穿透等特性,一直是蒙特卡罗方法工程应用的难点之一。采用我国自主研发的三维中子-光子蒙特卡罗粒子输运模拟软件JMCT,结合可视化建模工具JLAMT,对OECD国际基准例题Winfrith Iron/Water Benchmark Experiment(ASPIS)两例实验装置进行建模与计算分析, 并将计算结果与实验值及MCNP计算值进行对比。结果表明,JMCT计算值与MCNP计算值符合较好,其中Winfrith Iron Benchmark Experiment(ASPIS)最大偏差不超过7%,平均偏差1.3%;Winfrith Water Benchmark Experiment(ASPIS)最大偏差小于20%,平均偏差小于10%,证明了JMCT在屏蔽计算以及深穿透问题的可靠性与工程应用性。     

重要抽样在阻塞分布蒙特卡罗模拟中的应用

本文用蒙特卡罗方法计算铝单晶中0.2微米深度发射的α粒子沿<110>轴的阻塞角分布,α粒子能量约为2MeV。文中分析了有限发射角的困难,引入了重要抽样函数,对粒子初始发射方向采用了重要抽样。结果表明,发射方向的重要抽样可以提高计算精度并得到物理上合理的分布曲线。     

One-dimensional stochastic Levy-lorentz gas

We introduce a Levy-Lorentz gas in which a light particle is scattered by static point scatterers arranged on a line. We investigate the case where the intervals between scatterers xi(i) are independent random variables identically distributed according to the probability density function &mgr;(xi) approximately xi(-(1+gamma)). We show that under certain conditions the mean square displacement of the particle obeys /=Ct3-gamma for 1相似文献   

A unified gas kinetic scheme with moving mesh and velocity space adaptation

There is great difficulty for direct Boltzmann solvers to simulate high Knudsen number flow due to the severe steep slope and high concentration of the gas distribution function in a local particle velocity space. Local mesh adaptation becomes necessary in order to make the Boltzmann solver to be a practical tool in aerospace applications. The present research improves the unified gas-kinetic scheme (UGKS) in the following two aspects. First, the UGKS is extended in a physical space with moving mesh. This technique is important to study a freely flying object in a rarefied environment. Second, the adaptive quadtree method in the particle velocity space is implemented in the UGKS. Due to the new improvements in the discretization of a gas distribution function in the six dimensional phase space, the adaptive unified gas kinetic scheme (AUGKS) is able to deal with a wide range of flow problems under extreme flying conditions, such as the whole unsteady flying process of an object from a highly rarefied to a continuum flow regime. After validating the scheme, the capability of AUGKS is demonstrated in the following two challenge test cases. The first case is about the free movement of an ellipse flying at initial Mach number 5 in a rarefied flow at different Knudsen numbers. The force on the ellipse and the unsteady trajectory of the ellipse movement are fully captured. The gas distribution function around the ellipse is analyzed. The second case is about the study of unsteady flight of a nozzle under a bursting process of the compressed gas expanding into a rarefied environment. Due to the strong expansion wave and the huge density difference between interior and exterior regions around the nozzle, the particle distribution function changes dramatically in the particle velocity space. The use of an adaptive velocity space in the AUGKS becomes necessary to simulate such a flow and to control the computational cost to a tolerable level. The second test is a challenge problem for any existing rarefied flow solver.     

鲸群优化的粒子滤波算法研究

针对标准的粒子滤波存在粒子贫化问题,提出了一种鲸群优化的粒子滤波算法。用粒子表征鲸鱼个体, 模拟鲸鱼群体搜寻猎物的过程,引导粒子向高似然区域移动。将粒子滤波中粒子的状态值作为鲸鱼群的个体位置,将粒子的状态估计转化为对鲸鱼群的寻优;通过鲸群的螺旋运动方式优化粒子的重要性采样过程,使粒子分布更加合理,对鲸群算法中的全局最优值引入最优邻域随机扰动策略,并在鲸鱼位置更新过程中加入自适应权重因子;选用一种典型的单静态非增长模型进行仿真测试。测试结果表明:提出的方法与传统的粒子滤波以及引力场优化的粒子滤波相比,在保证相同粒子数的前提下,算法的均方误差分别降低了28%和9%,证明了鲸群优化的粒子滤波算法具有更高的估计精度,并且在粒子数较少的情况下,可实现更准确的状态估计。     

Settling statistics of hard sphere particles

Direct imaging of settling, non-Brownian, hard sphere, particles allows measurement of particle occupancy statistics as a function of time and sampling volume dimension. Initially random relative particle number fluctuations, (2)>/ = 1, become suppressed, anisotropic, and dependent. Fitting to a simple Gaussian pair correlation model suggests a minute long ranged correlation leads to strong if not complete suppression of number fluctuations. Calflisch and Luke predict a divergence in velocity fluctuations with increasing sample volume size based on random (Poisson) statistics. Our results suggest this is not a valid assumption for settling particles.     

自适应分层采样辅助粒子滤波在视频跟踪中的应用研究

以视频目标跟踪中粒子滤波的粒子采样优化设计为研究内容,提出一种自适应分层采样辅助粒子滤波算法,以实现保证跟踪准确度和兼顾跟踪鲁棒性的要求.以Bhattacharyya系数为参量设计了粒子数调节函数,能够根据跟踪质量在粒子集中自适应分配用于保证准确度的粒子数和维持鲁棒性的粒子数.以最小二乘法对目标运动的预测点作为产生新粒子集的均值偏移操作起点,使新粒子集更准确的描述目标似然分布并提高算法效率.不同场景下的跟踪实验表明,算法能很好的应用于遮挡和运动方向渐变等情况下的跟踪,处理时间满足实时性要求.     

Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI

We present a fast method for generating random samples according to a variable density poisson-disc distribution. A minimum parameter value is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.     

基于辅助粒子滤波算法的红外目标跟踪

针对红外目标在跟踪中计算复杂的问题,构建辅助粒子滤波算法。利用贝叶斯重要性采样算法,在权值大的粒子基础上引入辅助粒子变量,然后重新定义重要采样分布函数,防止重采样后粒子概率密度变化。两次加权计算,使粒子权值比仅用重采样的粒子权值变化更稳定,采样点最接近真实状态;同时不同权值粒子的概率阈值可作为粒子滤波是否完成的判断准则。在二维平面构造红外运动目标模型中,系统为零均值高斯白噪声。仿真数据表明:该算法在x,y方向的均方误差、画面处理时间、RM SE性能上优于粒子滤波算法和重采样粒子滤波算法。     

Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations

The present paper proposes an adaptive biasing potential technique for the computation of free energy landscapes. It is motivated by statistical learning arguments and unifies the tasks of biasing the molecular dynamics to escape free energy wells and estimating the free energy function, under the same objective of minimizing the Kullback–Leibler divergence between appropriately selected densities. It offers rigorous convergence diagnostics even though history dependent, non-Markovian dynamics are employed. It makes use of a greedy optimization scheme in order to obtain sparse representations of the free energy function which can be particularly useful in multidimensional cases. It employs embarrassingly parallelizable sampling schemes that are based on adaptive Sequential Monte Carlo and can be readily coupled with legacy molecular dynamics simulators. The sequential nature of the learning and sampling scheme enables the efficient calculation of free energy functions parametrized by the temperature. The characteristics and capabilities of the proposed method are demonstrated in three numerical examples.     

Decision-directed Kalman particle filter with application to the MIMO phase noise channel

Demodulation of data transmitted over time-varying channels with a free running hidden Markov state, like the phase noise channel or the fading channel, requires that the receiver tracks the hidden channel state. The tracking technique adopted in the paper is based on non-data-aided sequential importance sampling, also known as particle filtering.The paper proposes a new particle filtering framework for data communication receivers based on an importance distribution such that each individual particle becomes a decision-directed Kalman filter relying upon its local symbol-by-symbol hard decisions. In this framework, different particles are left free to take different sequences of decisions. This leaves to the receiver the possibility of exploring different sequences of transmitted modulation symbols. The weight of the particle will be high for those particles that took in the past the correct sequence of decisions, while will be low for those particles that took wrong decisions. In the resampling procedure, particles with high weight will survive, while particles with low weight will be terminated, leaving space to the birth of new particles resampled from the surviving ones.The crucial point in importance sampling is the choice of the importance distribution and the main novelty of the paper is the proposal of an importance distribution such that the particles of the particle filter become decision-directed Kalman filters. One important benefit brought by our proposed method is that, being non-data-aided, it does not need pilot symbols, thus allowing to preserve the transmission rate. A significant application example, presented and developed in the paper, is constituted by MIMO systems affected by phase noise, where the channel state vector consists of many parameters.     

基于量子粒子群算法的自适应随机共振方法研究

为提升随机共振理论在微弱信号检测领域中的实用性,以随机共振系统参数为研究对象,提出了基于量子粒子群算法的自适应随机共振方法.首先将自适应随机共振问题转化为多参数并行寻优问题,然后分别在Langevin系统和Duffing振子系统下进行仿真实验.在Langevin系统中,将量子粒子群算法和描点法进行了寻优结果对比;在Duffing振子系统中,Duffing振子系统的寻优结果则直接与Langevin系统的寻优结果进行了对比.实验结果表明:在寻优结果和寻优效率上,基于量子粒子群算法的自适应随机共振方法要明显高于描点法;在相同条件下,Duffing振子系统的寻优结果要优于Langevin系统的寻优结果;在两种系统下,输入信号信噪比越低就越能体现出量子粒子群算法的优越性.最后还对随机共振系统参数的寻优结果进行了规律性总结.