基于形状误差不确定度的大尺寸测量系统优化配置方法

针对大尺寸测量系统部署问题,提出了面向测量任务的以形状误差不确定度为评价指标的优化配置测量系统的方法。对于基于蒙特卡罗仿真方法的测量点仿真和多测量仪器数据融合进行了研究。在建立形状误差评定模型基础上,提出并实现了基于粒子群算法的形状误差评定模型的求解及基于蒙特卡罗法的形状误差不确定度计算方法。通过某卫星舱段端框的仿真试验,验证了以不确定度为指标进行大尺寸测量系统配置方法的有效性,可为大尺寸测量系统现场快速部署提供方案指导。     

On the effectiveness of Monte Carlo simulation and heuristic search for solving large-scale block layout problems

The effectiveness of Monte Carlo simulation relative to intelligent search strategies for solving block layout problems is investigated. For testing purposes, 810 block layout problems are constructed to span a wide range of problem sizes, material flow variation levels, work centre space requirements distributions, and work centre shape distributions. Contrary to preliminary results reported in earlier studies, greedy search and simulated annealing consistently outperform Monte Carlo Simulation across the full range of test problems and sample sizes. This divergence is explained through a comparison based on probabilistic derivations between the proportion of good solutions sampled by the Monte Carlo method and those found by the heuristic search methods. Conditions for the superiority of either method are identified. Therefore, the current study complements earlier studies by providing analytical arguments and additional experimental evidence for the effectiveness of simple Monte Carlo method and intelligent search heuristics on solving layout problems.     

自适应蒙特卡洛法评定全站仪测距不确定度

全站仪测距精度的校准需要在标准基线场上进行,由于野外环境不可控和气象条件波动剧烈,因此判断全站仪的测量结果的可靠程度具有重要意义。为了解决全站仪测距不确定度评定模型的非线性和输入量强相关等问题,本文首先采用了自适应蒙特卡洛法进行不确定度评定,然后与GUM的不确定度评定结果进行对比,当测距距离为1 176 m时,自适应蒙特卡洛法评定的不确定度结果为2.2 mm,GUM为2.6 mm,结果显示两种不确定度评定方法的测量结果均在合理预期之内,且自适应蒙特卡洛法评定的不确定度置信区间更窄。自适应蒙特卡洛法结合了大量数据样本和自适应优化仿真次数的优势,不仅对全站仪测距过程中的各项误差源引入的不确定度分量评估更为全面,而且在保证了全站仪测距不确定度评定结果准确的同时,相比于蒙特卡洛法节约了70%的样本数量。     

Monte Carlo simulation of a mammographic test phantom

A test phantom, including a wide range of mammographic tissue equivalent materials and test details, was imaged on a digital mammographic system. In order to quantify the effect of scatter on the contrast obtained for the test details, calculations of the scatter-to-primary ratio (S/P) have been made using a Monte Carlo simulation of the digital mammographic imaging chain, grid and test phantom. The results show that the S/P values corresponding to the imaging conditions used were in the range 0.084-0.126. Calculated and measured pixel values in different regions of the image were compared as a validation of the model and showed excellent agreement. The results indicate the potential of Monte Carlo methods in the image quality-patient dose process optimisation, especially in the assessment of imaging conditions not available on standard mammographic units.     

Monte Carlo reliability model for microwave monolithic integrated circuits

A Monte Carlo simulation is reported for analog integrated circuits and is based on the modification of the failure rate of each component due to interaction effects of the failed components. The Monte Carlo technique is the methodology used to treat such circuits, since they are independent of the number of components and the degree of system complexity. The reliability model is applicable over a wide temperature and bias range and may be used to predict reliability of microwave systems. The model is compared with accelerated test results of two analog microwave circuits. Excellent agreement has been obtained for a low noise amplifier as well as for a transimpedence amplifier. Copyright © 2007 John Wiley & Sons, Ltd.     

The use of Monte Carlo simulations for accurate dose determination with thermoluminescence dosemeters in radiation therapy beams

The energy responses of LiF-TLDs irradiated in megavoltage electron and photon beams have been determined experimentally by many investigators over the past 35 years but the results vary considerably. General cavity theory has been used to model some of the experimental findings but the predictions of these cavity theories differ from each other and from measurements by more than 13%. Recently, two groups or investigators using Monte Carlo simulations and careful experimental techniques showed that the energy response of 1 mm or 2 mm thick LiF-TLD irradiated by megavoltage photon and electron beams is not more than 5% less than unity for low-Z phantom materials like water or Perspex. However, when the depth of irradiation is significantly different from dmax and the TLD size is more than 5 mm, then the energy response is up to 12% less than unity for incident electron beams. Monte Carlo simulations of some of the experiments reported in the literature showed that some of the contradictory experimental results are reproducible with Monte Carlo simulations. Monte Carlo simulations show that the energy response of LiF-TLDs depends on the size of detector used in electron beams, the depth of irradiation and the incident electron energy. Other differences can be attributed to absolute dose determination and precision of the TL technique. Monte Carlo simulations have also been used to evaluate some of the published general cavity theories. The results show that some of the parameters used to evaluate Burlin's general cavity theory are wrong by factor of 3. Despite this, the estimation of the energy response for most clinical situations using Burlin's cavity equation agrees with Monte Carlo simulations within 1%.     

Monte Carlo unfolding of pulse-height spectra

A Monte Carlo unfolding method is developed for the unfolding of pulse-height spectra. The method utilizes linear programming to know the range where solutions are allowed to exist. Then the distribution of a possible solution family is evaluated by Monte Carlo process by starting from a flat distribution. The obtained distribution is used to evaluate a new distribution. The process is repeated till a converging distribution is obtained.The proposed method is tested through unfolding of synthesized spectra. The spectra unfolded by the Monte Carlo method are smooth in shape. In all the test runs, the method well reproduces the real spectra even with errors introduced in a random manner in both the pulse-height data and the response matrix.     

Reliability Estimation for Accelerated Life Tests Based on a Cox Proportional Hazard Model with Error Effect

In this paper, a Cox proportional hazard model with error effect applied on the study of an accelerated life test is investigated. Statistical inference under Bayesian methods by using the Markov chain Monte Carlo techniques is performed in order to estimate the parameters involved in the model and predict reliability in an accelerated life testing. The proposed model is applied to the analysis of the knock sensor failure time data in which some observations in the data are censored. The failure times at a constant stress level are assumed to be from a Weibull distribution. The analysis of the failure time data from an accelerated life test is used for the posterior estimation of parameters and prediction of the reliability function as well as the comparisons with the classical results from the maximum likelihood estimation. Copyright © 2017 John Wiley & Sons, Ltd.     

Neutron shielding verification measurements and simulations for a 235-MeV proton therapy center

The neutron shielding at the Massachusetts General Hospital's 235-MeV proton therapy facility was investigated with measurements, analytical calculations, and realistic three-dimensional Monte Carlo simulations. In 37 of 40 cases studied, the analytical calculations predicted higher neutron dose equivalent rates outside the shielding than the measured, typically by more than a factor of 10, and in some cases more than 100. Monte Carlo predictions of dose equivalent at three locations are, on average, 1.1 times the measured values. Except at one location, all of the analytical model predictions and Monte Carlo simulations overestimate neutron dose equivalent.     

Mathematical Theory of Probability and Statistics

Two test statistics are suggested for discriminating between the exponential model and the more general Weibull or gamma models, and these are compared to some previously used test statistics by Monte Carlo methods. The results of estimating reliability under an exponential assumption when the true model is Weibull is also investigated. These results as well as the tests mentioned above indicate that the exponential model is often not adequate when the more general models hold. In contrast to this result it was found that the Weibull model was quite robust relative to the generalized gamma distribution with regard to reliability estimation. Some general pivotal function properties are presented for the maximum likelihood estimator of reliability for the generalized gamma distribution and similar results also hold for the Weibull procedure under a generalized gamma assumption. These results made a Monte Carlo study of this problem feasible. Since the maximum likelihood estimators are apparently ill-behaved for smaller sample sizes and since the Weibull model is robust it appears little is gained by using the generalized gamma distribution for samples of size less than 200 to 400.     

Optimizing maintenance and repair policies via a combination of genetic algorithms and Monte Carlo simulation

In this paper we present an optimization approach based on the combination of a Genetic Algorithms maximization procedure with a Monte Carlo simulation. The approach is applied within the context of plant logistic management for what concerns the choice of maintenance and repair strategies. A stochastic model of plant operation is developed from the standpoint of its reliability/availability behavior, i.e. of the failure/repair/maintenance processes of its components. The model is evaluated by Monte Carlo simulation in terms of economic costs and revenues of operation. The flexibility of the Monte Carlo method allows us to include several practical aspects such as stand-by operation modes, deteriorating repairs, aging, sequences of periodic maintenances, number of repair teams available for different kinds of repair interventions (mechanical, electronic, hydraulic, etc.), components priority rankings. A genetic algorithm is then utilized to optimize the components maintenance periods and number of repair teams. The fitness function object of the optimization is a profit function which inherently accounts for the safety and economic performance of the plant and whose value is computed by the above Monte Carlo simulation model. For an efficient combination of Genetic Algorithms and Monte Carlo simulation, only few hundreds Monte Carlo histories are performed for each potential solution proposed by the genetic algorithm. Statistical significance of the results of the solutions of interest (i.e. the best ones) is then attained exploiting the fact that during the population evolution the fit chromosomes appear repeatedly many times. The proposed optimization approach is applied on two case studies of increasing complexity.     

基于蒙特卡罗模拟模型的投资项目风险分析

运用蒙特卡罗模拟模型和程序,结合实际工程项目,分析评估了项目的主要风险因素,借助EXCEL软件对项目风险进行了模拟和测试,给出了项目风险模拟的结果.由结果可以看出,项目总体上有较高的抗风险能力,并且能够达到预期的投资收益目标.同时,项目风险评估中的蒙特卡罗模拟方法占用的资源少、操作性强,对于项目风险评估是有用的.     

Estimation of flux distributions with Monte Carlo functional expansion tallies

Monte Carlo methods provide a powerful technique for estimating the average radiation flux in a volume (or across a surface) in cases where analytical solutions may not be possible. Unfortunately, Monte Carlo simulations typically provide only integral results and do not offer any further details about the distribution of the flux with respect to space, angle, time or energy. In the functional expansion tally (FET) a Monte Carlo simulation is used to estimate the functional expansion coefficients for flux distributions with respect to an orthogonal set of basis functions. The expansion coefficients are then used in post-processing to reconstruct a series approximation to the true distribution. Discrete event FET estimators are derived and their application in estimating radiation flux or current distributions is demonstrated. Sources of uncertainty in the FET are quantified and estimators for the statistical and truncation errors are derived. Numerical results are presented to support the theoretical development.     

Calculation of eigenfunction fluxes in nuclear systems

A new Monte Carlo method is being developed to calculate eigenfunction fluxes in critical or near-critical nuclear systems. The correct estimation of fluxes is essential for radiation protection and shielding near these systems, in addition to isotope production, isotope depletion, nuclear criticality and other applications. The proposed method applies to Monte Carlo criticality eigenvalue calculations in which the fission sites in one generation are used as fission sources in subsequent generations. The usual Monte Carlo power iteration method for such problems often calculates fluxes (eigenfunctions) that are inaccurate and very different in symmetric parts of a problem geometry. The proposed method calculates flux distributions by estimating an approximate fission matrix. The way the fission matrix is estimated and used differs from other recent works. Preliminary results are promising.     

Stability of the entry flow rate of pulsating liquid in a tapered leaking elastic tube

Summary The two gas-kinetical simulation methods, the molecular dynamics method and the direct simulation Monte Carlo method, are used to calculate the Oseen vortex decay. The simulation solutions are compared with the analytical solution of the Navier-Stokes equation, this allowing examination of the influence of method-specific parameters on the simulation solution, particularly in relation to the conservation of angular momentum.The results obtained with the molecular dynamics method near the limit of the continuum-mechanical regime correlate very well with the analytical solution, the angular momentum being conserved exactly, due to the nature of the method. The direct simulation Monte Carlo method can also be used to simulate the vortex decay, provided that suitable parameters are selected. In this case, however, the angular momentum is not conserved exactly, due to the statistical assumptions. One of the objectives of this work is to optimize the Monte Carlo simulation method in relation to the conservation of angular momentum.With 10 Figures     

Reliability of reinforced concrete beams in limit state of cracking— failure rate analysis approach

The reliability of a reinforced concrete beam with respect to the limit state of crack width under monotonically increasing short-time loading conditions is estimated using the Monte Carlo technique and failure rate analysis. Using both methods the reliabilities of 24 beams whose test results are available in the literature and three beams tested in the laboratory have been determined for a limiting crack width of 0.2 mm. A comparison of the reliabilities obtained using failure rate analysis with those obtained directly from Monte Carlo simulation shows good agreement. From failure rate analysis it is found that a two-parameter Weibull distribution can be used to describe the failure life of a reinforced concrete beam with respect to the limit state of crack width. The mean moment to failure of each beam determined from failure rate analysis is compared with the experimental moment to failure.     

Experimental and computational determination of neutron dose equivalent around radiotherapy accelerators

An experimental and computational investigation of the neutron dose equivalent in the treatment room and maze of various radiotherapy accelerators in Canada was completed. A bubble detector was selected for the bulk of the measurements due to its relative insensitivity to gamma radiation. The rooms and accelerator heads were modelled using the MCNP4B Monte Carlo radiation transport code, modified with a photoneutron patch. The results of the investigation showed generally good agreement between the experiments. Monte Carlo and analytical approximations to the neutron dose equivalent, and suggest that if the analytical approximations are used carefully they may be substituted for more costly experimental or Monte Carlo determinations.     

Pitfalls and modelling inconsistencies in computational radiation dosimetry: lessons learnt from the QUADOS intercomparison. Part I: Neutrons and uncertainties

The QUADOS EU cost shared action conducted an intercomparison on the usage of numerical methods in radiation protection and dosimetry. The eight problems proposed were intended to test the usage of Monte Carlo and deterministic methods by assessing the accuracy with which the codes are applied and also the methods used to evaluate uncertainty in the answer gained through these methods. The overall objective was to spread good practice through the community and give users information on how to assess the uncertainties associated with their calculated results.     

A modification of the Monte Carlo method for steady heat conduction problems

The unique capability of calculating single points by Monte Carlo Methods is used in an attempt to develop an efficient Monte Carlo method for whole field calculations of heat conduction problems. Rather than calculate the whole field by Monte Carlo, only select points around the boundaries of inscribed figures of standard shapes are are so calculated. Interior points of the inscribed figures are then calculated by analytical (non-numerical) techniques. The procedure is described and shown to be more efficient than previously published Monte Carlo modifications but not as efficient as the Gauss–Seidel method. This new procedure will most likely be competitive for subregion calculations rather than for whole field calculations (for which standard numerical procedures are most efficient) or for single point calculations (for which other Monte Carlo methods are most efficient).