The study of a neutron spectrum unfolding method based on particle swarm optimization combined with maximum likelihood expectation maximization

The neutron spectrum unfolding by Bonner sphere spectrometer(BSS) is considered a complex multidimensional model,which requires complex mathematical methods to solve the first kind of Fredholm integral equation. In order to solve the problem of the maximum likelihood expectation maximization(MLEM) algorithm which is easy to suffer the pitfalls of local optima and the particle swarm optimization(PSO) algorithm which is easy to get unreasonable flight direction and step length of particles, which leads to the invalid iteration and affect efficiency and accuracy, an improved PSO-MLEM algorithm, combined of PSO and MLEM algorithm, is proposed for neutron spectrum unfolding. The dynamic acceleration factor is used to balance the ability of global and local search, and improves the convergence speed and accuracy of the algorithm. Firstly, the Monte Carlo method was used to simulated the BSS to obtain the response function and count rates of BSS. In the simulation of count rate, four reference spectra from the IAEA Technical Report Series No. 403 were used as input parameters of the Monte Carlo method. The PSO-MLEM algorithm was used to unfold the neutron spectrum of the simulated data and was verified by the difference of the unfolded spectrum to the reference spectrum. Finally, the 252Cf neutron source was measured by BSS, and the PSO-MLEM algorithm was used to unfold the experimental neutron spectrum.Compared with maximum entropy deconvolution(MAXED), PSO and MLEM algorithm, the PSO-MLEM algorithm has fewer parameters and automatically adjusts the dynamic acceleration factor to solve the problem of local optima. The convergence speed of the PSO-MLEM algorithm is 1.4 times and 3.1 times that of the MLEM and PSO algorithms. Compared with PSO, MLEM and MAXED, the correlation coefficients of PSO-MLEM algorithm are increased by 33.1%, 33.5% and 1.9%, and the relative mean errors are decreased by 98.2%, 97.8% and 67.4%.

Direct Monte Carlo Method Simulation of the Synthesis of Carbon Particle Through Coagulation in the Detonation of Explosives

A model is constructed and used in computing the coagulation probability of free carbon during the detonation of explosives. A direct simulation Monte Carlo (DSMC) program is constructed to simulate the coagulation of free carbon particles. The evaluation of the distribution spectrum of particles in the system is obtained. The simulation result is consistent with the experimental curve.

Calculation of Neutral Particle Energy Spectra in Tokamak by Using the Monte Carlo Method

Neutral particle energy spectra in the HT-7 tokamak are calculated by using the Monte Carlo method. It can reproduce the spectra measured in experiment. Differences of neutral particle energy spectra in higher and lower electron density plasma are discussed. Results show that the ion temperature given by neutral particle energy spectra is lower than the real ion temperature, but the deviation is within 10% if the ion temperature is less than 800 eV and thecentral chord-averaged electron density does not exceed 3 ×1013 cm-3. But for ion temperature higher than 1000 eV at the central chord-averaged density limit up to 5 ×1013 cm-3, the neutral particle energy spectra can still give the ion temperature within 10% deviation.

Study of nonlinear filter methods: particle filter

Extended Kalman filter （EKF） is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering （PF） is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems, that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probabifity distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.

Neutral Particle Transport in Cylindrical Plasma Simulated by a Monte Carlo Code

A Monte Carlo code （MCHGAS） has been developed to investigate the neutral particle transport. The code can calculate the radial profile and energy spectrum of neutral particles in cylindrical plasmas. The calculation time of the code is dramatically reduced when the Splitting and Roulette schemes are applied. The plasma model of an infinite cylinder is assumed in the code, which is very convenient in simulating neutral particle transports in small and middle-sized tokamaks. The design of the multi-channel neutral particle analyser （NPA） on HL-2A can be optimized by using this code.

Monte Carlo Method for the Uncertainty Evaluation of Spatial Straightness Error Based on New Generation Geometrical Product Specification

Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the results should be given together when the measurement result is given. Nowadays most researches on straightness focus on error calculation and only several research projects evaluate the measurement uncertainty based on ＂The Guide to the Expression of Uncertainty in Measurement（GUM）＂. In order to compute spatial straightness error（SSE） accurately and rapidly and overcome the limitations of GUM, a quasi particle swarm optimization（QPSO） is proposed to solve the minimum zone SSE and Monte Carlo Method（MCM） is developed to estimate the measurement uncertainty. The mathematical model of minimum zone SSE is formulated. In QPSO quasi-random sequences are applied to the generation of the initial position and velocity of particles and their velocities are modified by the constriction factor approach. The flow of measurement uncertainty evaluation based on MCM is proposed, where the heart is repeatedly sampling from the probability density function（PDF） for every input quantity and evaluating the model in each case. The minimum zone SSE of a shaft measured on a Coordinate Measuring Machine（CMM） is calculated by QPSO and the measurement uncertainty is evaluated by MCM on the basis of analyzing the uncertainty contributors. The results show that the uncertainty directly influences the product judgment result. Therefore it is scientific and reasonable to consider the influence of the uncertainty in judging whether the parts are accepted or rejected, especially for those located in the uncertainty zone. The proposed method is especially suitable when the PDF of the measurand cannot adequately be approximated by a Gaussian distribution or a scaled and shifted t-distribution and the measurement model is non-linear.

Elbow precision machining technology by abrasive flow based on direct Monte Carlo method

The investigation was carried out on the technical problems of finishing the inner surface of elbow parts and the action mechanism of particles in elbow precision machining by abrasive flow.This work was analyzed and researched by combining theory,numerical and experimental methods.The direct simulation Monte Carlo(DSMC)method and the finite element analysis method were combined to reveal the random collision of particles during the precision machining of abrasive flow.Under different inlet velocity,volume fraction and abrasive particle size,the dynamic pressure and turbulence flow energy of abrasive flow in elbow were analyzed,and the machining mechanism of particles on the wall and the influence of different machining parameters on the precision machining quality of abrasive flow were obtained.The test results show the order of the influence of different parameters on the quality of abrasive flow precision machining and establish the optimal process parameters.The results of the surface morphology before and after the precision machining of the inner surface of the elbow are discussed,and the surface roughness Ra value is reduced from 1.125μm to 0.295μm after the precision machining of the abrasive flow.The application of DSMC method provides special insights for the development of abrasive flow technology.

Online AUV Path Replanning Using Quantum-Behaved Particle Swarm Optimization with Selective Differential Evolution

This paper presents an online AUV(autonomous underwater vehicle)path planner that employs path replanning approach and the SDEQPSO(selective differential evolution-hybridized quantum-behaved particle swarm optimization)algorithm to optimize an AUV mission conducted in an unknown,dynamic and cluttered ocean environment.The proposed path replanner considered the effect of ocean currents in path optimization to generate a Pareto-optimal path that guides the AUV to its target within minimum time.The optimization was based on the onboard sensor data measured from the environment,which consists of a priori unknown dynamic obstacles and spatiotemporal currents.Different sensor arrangements for the forward-looking sonar and horizontal acoustic Doppler current profiler(H-ADCP)were considered in 2D and 3D simulations.Based on the simulation results,the SDEQPSO path replanner was found to be capable of generating a time-optimal path that offered up to 13%reduction in travel time compared to the situation where the vehicle simply followed a path with the shortest distance.The proposed replanning technique also showed consistently better performance over a reactive path planner in terms of solution quality,stability,and computational efficiency.Robustness of the replanner was verified under stochastic process using the Monte Carlo method.The generated path fulfilled the vehicle’s safety and physical constraints,while intelligently exploiting ocean currents to improve the vehicle’s efficiency.

Sequential Monte Carlo Method Toward Online RUL Assessment with Applications

Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear & non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.

Dosimetric Analyses of Single Particle Microbeam in Cell Irradiation Experiment

Single particle microbeam （SPM） is uniquely capable of delivering precisely the predefined number of charged particles to determined individual cells or sub-cellular targets in situ. It has been recognized as a powerful technique for unveiling ionization irradiation mechanisms of organism. This article describes some investigations on the irradiation quality of SPM of major world laboratories by means of Monte Carlo method based on dosimetry and microdosimetry. Those parameters are helpful not only to improve SPM irradiating cell experiments but also to study the biological effects of cells irradiated by SPM.

深穿透跨尺度辐射场分析软件NECP-MCX研发及应用

西安交通大学核工程计算物理实验室自主研发了深穿透跨尺度辐射场分析软件NECP-MCX。针对大空间伽马射线辐射输运模拟、聚变堆停堆剂量模拟和点源屏蔽问题等新应用场景下的新问题与新挑战,在NECP-MCX中研发了对应的新方法与新功能。针对km尺度的伽马射线辐射输运问题,提出一致性共轭驱动重要性抽样(CADIS)-下次事件估计器(NEE)耦合方法,该方法能够精确高效地获得km尺度距离处的光子通量密度,计算效率比传统的NEE高6.8倍;针对聚变堆停堆剂量问题,采用粒子输运-燃耗-活化-源项耦合分析方法,获得PF线圈、TF线圈、真空室和偏滤器处停堆剂量随停堆时间的变化;对于点源屏蔽问题,提出首次碰撞源(FCS)-CADIS方法,解决CADIS方法对点源进行源偏倚的局限性,FCS-CADIS方法的计算效率比CADIS方法高2倍。

风电数据的不确定性建模及在电网规划的应用

在我国经济高速发展的同时,矿物资源的使用持续增长,对环境的污染也在不断加剧,发展风力发电是我国实现低碳转型的一项重要措施。然而,由于风力发电具有较强的不稳定性,这给电网的运行带来了较大的不确定性。因此,考虑风力发电过程中的不确定性因素,并对其进行建模,开展含风力发电的电网规划研究。首先对风电出力的不确定性进行建模,建立了风电机组出力的数学模型。其次,提出了以总成本、总网损最小为目标函数的考虑风电不确定性的最优潮流模型,并给出一种采用局部模型并引入动态惯性权重系数改进的粒子群优化求解算法。经采用实际的风电数据进行实验,结果表明与传统的粒子群优化算法相比,改进的粒子群优化算法在求解速度、收敛性以及稳健性方面均具有更优性能。

基于神经网络的粒子输运问题高效计算方法

蒙特卡罗方法是求解粒子输运问题的有力工具之一,其局限性在于为达到精度要求需模拟大量粒子,计算耗时长,这阻碍了该方法的进一步应用,尤其在需快速响应的情形.本文结合神经网络和若干蒙特卡罗方法基本原理发展了一种计算方法,能够实现源分布可变,几何、材料和目标计数不变的中子输运问题的快速准确求解.首先,为高效生成用于神经网络训练的数据,利用重要性原理实现在同样模拟次数基础上有效扩充训练数据集容量,在一定程度上克服了使用蒙特卡罗计算获取训练数据耗时长的缺点.进而,基于目标计数是源分布与重要性函数乘积积分的事实,设计了利用神经网络实现快速输运计算的策略.该网络的输入是中子源项,输出是目标计数,在几何、材料和目标计数固定的情况下,该神经网络可重复使用,根据新的源项快速准确得到目标计数.本文所提出方法的原理和框架同样适用于其他种类粒子的同类型输运问题.基于若干基准模型的验证表明,训练得到的神经网络能在不到1 s的时间内得到目标计数,且与蒙特卡罗大样本模拟得到基准结果的平均相对偏差均低于5%.

A simulation study for a cost-effective PET-like detector system intended to track particles in granular assemblies

Since many industrial applications rely on the processing of densely packed and moving granular ma-terial,obtaining bulk internal information on the particle movement inside the reactors is of great importance.Such information can be delivered by Positron Emission Particle Tracking(PEPT).By marking pellets with a positron-emitting radioisotope,the position of these tracer particles can be determined via the time-of-flight differences of the emitted gamma-ray pairs.The current paper proposes a PET-like detector system based on cost-effective organic plastic scintillators instead of the more common but expensive inorganic scintillators.This system is currently under construction and was tested for its resolution and efficiency in this simulation study.Using Monte Carlo simulations and the software toolkit Geant4,three different geometries(an empty glass box,a generic grate system,and a cubic box of 1 m3 completely filled with pellets)were investigated,leading to a spatial resolution in the millimeter range and an efficiency,defined as the ratio of reconstructed decay locations to simulated decays,of 2.7%,1.4%,and 0.3%.

考虑颗粒碰撞的多重Monte Carlo算法

从减少计算代价和改进碰撞算法出发,提出了考虑颗粒碰撞的多重MonteCarlo算法,它采用直接模拟MonteCarlo算法来考虑颗粒碰撞,并与求解颗粒拉氏Langevin方程的MonteCarlo算法耦合起来,跟踪比实际颗粒数目小得多的虚拟颗粒.提出了时间步长选定标准、虚拟碰撞伙伴所在控制容积的判断准则、颗粒碰撞发生的判断准则、虚拟碰撞伙伴的选择、基于随机碰撞角度的碰撞动力学,构成了考虑颗粒碰撞的完整多重MonteCarlo算法.对理想工况的细微颗粒流和粗重颗粒流进行了数值模拟,颗粒碰撞率的模拟结果与理论分析解和DNS结果均符合很好,颗粒场演变的细节信息,如时间平均和特定时刻的颗粒数密度,速度和颗粒湍动能等,均与DNS结果符合很好.数值模拟结果证明该算法不仅具有较低的计算代价,而且能够达到足够的计算精度.

基于特征粒子的Monte Carlo自定位方法

提出了一种基于Monte Carlo方法的多机器人自定位方法.该方法在机器人进行自定位时,对用来估计机器人位置的MCL(Monte Carlo Localization)粒子空间进行栅格划分,然后采用可变栅格法获得能代表所有粒子整体特性的特征粒子集.因为特征粒子的数量较粒子总数大大减少,该方法能避免直接将Monte Carlo方法应用于多机器人定位中产生的维数灾的问题,可以在保证精度的情况下降低运算复杂度.仿真结果表明,该方法能较好地满足多机器人自定位的要求.

凝并和成核机理下颗粒尺度分布的Monte Carlo求解

颗粒的凝并和成核现象影响其尺度分布,现有的Monte Carlo方法描述颗粒尺度分布的时间演变过程存在若干困难.提出了一种新的多重Monte Carlo(MMC)算法,基于时间驱动,利用加权的虚拟颗粒的思想,在模拟过程中保持虚拟颗粒总数不变和计算区域体积不变.利用该算法对“常凝并核,一阶成核”的情况下颗粒尺度分布的时间演变过程进行了数值求解,所得结果与数值解相符,表明MMC算法具有高且稳定的计算精度.另外,MMC算法由于跟踪比实际颗粒数目少得多的虚拟颗粒而具有较低的计算代价.

气固两相流中颗粒碰撞的Monte-Carlo数值模拟

利用颗粒碰撞动力学模型和颗粒几何碰撞率模型,采用Monte-Carlo算法来模拟颗粒之间碰撞,把该算法与求解雷诺应力-概率密度函数模型的有限差分-MonteCarlo算法耦合起来,对轴对称突扩通道内的两相旋流场进行了数值模拟,模拟结果表明,由于颗粒碰撞使颗粒的动能和湍动能在三个坐标方向上进行了再分配,从而导致颗粒的动能和湍动能在三个坐标方向上趋于各向同性;另外,由于颗粒碰撞破坏了颗粒-颗粒、颗粒-流体微团之间的速度关联,从而造成颗粒湍动能及两相速度脉动关联的降低。

模拟颗粒凝并过程的快速Monte Carlo方法

对常规异权值Monte Carlo(MC)方法进行改进,基于强核函数思想,通过颗粒群单重遍历即可求得强核函数最大值,采用接受-拒绝法随机搜寻凝并对,并利用搜寻过程中拒绝和接受的所有凝并对的信息来估计凝并事件的等待时间(时间步长),从而避免颗粒群的双重遍历,以提高MC的效率.对典型工况的模拟结果显示该快速方法计算代价仅为O(Ns),能够显著提高计算效率,同时保持足够的计算精度,较好地协调计算代价与计算精度之间的矛盾.

用差分和Monte Carlo求解相结合的方法计算有化学反应的活塞流

用差分和Monte Carlo求解相结合的方法对有化学反应的标量几率密度函数方程进行了求解,讨论了随机颗粒数和时间步长对计算结果的影响。计算结果表明,当化学反应速率是标量的线性函数时,结果与精确解很吻合;当反应速率满足Arrhenius定律时,结果也是合理的。