Clinical considerations of Monte Carlo for electron radiotherapy treatment planning

International Nuclear Information System (INIS)

Faddegon, Bruce; Balogh, Judith; Mackenzie, Robert; Scora, Daryl

1998-01-01

Technical requirements for Monte Carlo based electron radiotherapy treatment planning are outlined. The targeted overall accuracy for estimate of the delivered dose is the least restrictive of 5% in dose, 5 mm in isodose position. A system based on EGS4 and capable of achieving this accuracy is described. Experience gained in system design and commissioning is summarized. The key obstacle to widespread clinical use of Monte Carlo is lack of clinically acceptable measurement based methodology for accurate commissioning

Monte Carlo simulation applied to alpha spectrometry

International Nuclear Information System (INIS)

Baccouche, S.; Gharbi, F.; Trabelsi, A.

2007-01-01

Alpha particle spectrometry is a widely-used analytical method, in particular when we deal with pure alpha emitting radionuclides. Monte Carlo simulation is an adequate tool to investigate the influence of various phenomena on this analytical method. We performed an investigation of those phenomena using the simulation code GEANT of CERN. The results concerning the geometrical detection efficiency in different measurement geometries agree with analytical calculations. This work confirms that Monte Carlo simulation of solid angle of detection is a very useful tool to determine with very good accuracy the detection efficiency.

Exploring Monte Carlo methods

CERN Document Server

Dunn, William L

2012-01-01

Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as "Monte Carlo." The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear engineering, which are illustrative of uses in other fields. Five appendices are included, which provide useful information on probability distributions, general-purpose Monte Carlo codes for radiation transport, and other matters. The famous "Buffon's needle proble

Specialized Monte Carlo codes versus general-purpose Monte Carlo codes

International Nuclear Information System (INIS)

Moskvin, Vadim; DesRosiers, Colleen; Papiez, Lech; Lu, Xiaoyi

2002-01-01

The possibilities of Monte Carlo modeling for dose calculations and optimization treatment are quite limited in radiation oncology applications. The main reason is that the Monte Carlo technique for dose calculations is time consuming while treatment planning may require hundreds of possible cases of dose simulations to be evaluated for dose optimization. The second reason is that general-purpose codes widely used in practice, require an experienced user to customize them for calculations. This paper discusses the concept of Monte Carlo code design that can avoid the main problems that are preventing wide spread use of this simulation technique in medical physics. (authors)

Monte Carlo principles and applications

Energy Technology Data Exchange (ETDEWEB)

Raeside, D E [Oklahoma Univ., Oklahoma City (USA). Health Sciences Center

1976-03-01

The principles underlying the use of Monte Carlo methods are explained, for readers who may not be familiar with the approach. The generation of random numbers is discussed, and the connection between Monte Carlo methods and random numbers is indicated. Outlines of two well established Monte Carlo sampling techniques are given, together with examples illustrating their use. The general techniques for improving the efficiency of Monte Carlo calculations are considered. The literature relevant to the applications of Monte Carlo calculations in medical physics is reviewed.

11th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing

CERN Document Server

Nuyens, Dirk

2016-01-01

This book presents the refereed proceedings of the Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Leuven (Belgium) in April 2014. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in finance, statistics and computer graphics.

Evaluation of tomographic-image based geometries with PENELOPE Monte Carlo

International Nuclear Information System (INIS)

Kakoi, A.A.Y.; Galina, A.C.; Nicolucci, P.

2009-01-01

The Monte Carlo method can be used to evaluate treatment planning systems or for the determination of dose distributions in radiotherapy planning due to its accuracy and precision. In Monte Carlo simulation packages typically used in radiotherapy, however, a realistic representation of the geometry of the patient can not be used, which compromises the accuracy of the results. In this work, an algorithm for the description of geometries based on CT images of patients, developed to be used with Monte Carlo simulation package PENELOPE, is tested by simulating the dose distribution produced by a photon beam of 10 MV. The geometry simulated was based on CT images of a planning of prostate cancer. The volumes of interest in the treatment were adequately represented in the simulation geometry, allowing the algorithm to be used in verification of doses in radiotherapy treatments. (author)

Bayesian Monte Carlo method

International Nuclear Information System (INIS)

Rajabalinejad, M.

2010-01-01

To reduce cost of Monte Carlo (MC) simulations for time-consuming processes, Bayesian Monte Carlo (BMC) is introduced in this paper. The BMC method reduces number of realizations in MC according to the desired accuracy level. BMC also provides a possibility of considering more priors. In other words, different priors can be integrated into one model by using BMC to further reduce cost of simulations. This study suggests speeding up the simulation process by considering the logical dependence of neighboring points as prior information. This information is used in the BMC method to produce a predictive tool through the simulation process. The general methodology and algorithm of BMC method are presented in this paper. The BMC method is applied to the simplified break water model as well as the finite element model of 17th Street Canal in New Orleans, and the results are compared with the MC and Dynamic Bounds methods.

Improving system modeling accuracy with Monte Carlo codes

International Nuclear Information System (INIS)

Johnson, A.S.

1996-01-01

The use of computer codes based on Monte Carlo methods to perform criticality calculations has become common-place. Although results frequently published in the literature report calculated k eff values to four decimal places, people who use the codes in their everyday work say that they only believe the first two decimal places of any result. The lack of confidence in the computed k eff values may be due to the tendency of the reported standard deviation to underestimate errors associated with the Monte Carlo process. The standard deviation as reported by the codes is the standard deviation of the mean of the k eff values for individual generations in the computer simulation, not the standard deviation of the computed k eff value compared with the physical system. A more subtle problem with the standard deviation of the mean as reported by the codes is that all the k eff values from the separate generations are not statistically independent since the k eff of a given generation is a function of k eff of the previous generation, which is ultimately based on the starting source. To produce a standard deviation that is more representative of the physical system, statistically independent values of k eff are needed

Weighted-delta-tracking for Monte Carlo particle transport

International Nuclear Information System (INIS)

Morgan, L.W.G.; Kotlyar, D.

2015-01-01

Highlights: • This paper presents an alteration to the Monte Carlo Woodcock tracking technique. • The alteration improves computational efficiency within regions of high absorbers. • The rejection technique is replaced by a statistical weighting mechanism. • The modified Woodcock method is shown to be faster than standard Woodcock tracking. • The modified Woodcock method achieves a lower variance, given a specified accuracy. - Abstract: Monte Carlo particle transport (MCPT) codes are incredibly powerful and versatile tools to simulate particle behavior in a multitude of scenarios, such as core/criticality studies, radiation protection, shielding, medicine and fusion research to name just a small subset applications. However, MCPT codes can be very computationally expensive to run when the model geometry contains large attenuation depths and/or contains many components. This paper proposes a simple modification to the Woodcock tracking method used by some Monte Carlo particle transport codes. The Woodcock method utilizes the rejection method for sampling virtual collisions as a method to remove collision distance sampling at material boundaries. However, it suffers from poor computational efficiency when the sample acceptance rate is low. The proposed method removes rejection sampling from the Woodcock method in favor of a statistical weighting scheme, which improves the computational efficiency of a Monte Carlo particle tracking code. It is shown that the modified Woodcock method is less computationally expensive than standard ray-tracing and rejection-based Woodcock tracking methods and achieves a lower variance, given a specified accuracy

Current and future applications of Monte Carlo

International Nuclear Information System (INIS)

Zaidi, H.

2003-01-01

Full text: The use of radionuclides in medicine has a long history and encompasses a large area of applications including diagnosis and radiation treatment of cancer patients using either external or radionuclide radiotherapy. The 'Monte Carlo method'describes a very broad area of science, in which many processes, physical systems, and phenomena are simulated by statistical methods employing random numbers. The general idea of Monte Carlo analysis is to create a model, which is as similar as possible to the real physical system of interest, and to create interactions within that system based on known probabilities of occurrence, with random sampling of the probability density functions (pdfs). As the number of individual events (called 'histories') is increased, the quality of the reported average behavior of the system improves, meaning that the statistical uncertainty decreases. The use of the Monte Carlo method to simulate radiation transport has become the most accurate means of predicting absorbed dose distributions and other quantities of interest in the radiation treatment of cancer patients using either external or radionuclide radiotherapy. The same trend has occurred for the estimation of the absorbed dose in diagnostic procedures using radionuclides as well as the assessment of image quality and quantitative accuracy of radionuclide imaging. As a consequence of this generalized use, many questions are being raised primarily about the need and potential of Monte Carlo techniques, but also about how accurate it really is, what would it take to apply it clinically and make it available widely to the nuclear medicine community at large. Many of these questions will be answered when Monte Carlo techniques are implemented and used for more routine calculations and for in-depth investigations. In this paper, the conceptual role of the Monte Carlo method is briefly introduced and followed by a survey of its different applications in diagnostic and therapeutic

Monte Carlo method in neutron activation analysis

International Nuclear Information System (INIS)

Majerle, M.; Krasa, A.; Svoboda, O.; Wagner, V.; Adam, J.; Peetermans, S.; Slama, O.; Stegajlov, V.I.; Tsupko-Sitnikov, V.M.

2009-01-01

Neutron activation detectors are a useful technique for the neutron flux measurements in spallation experiments. The study of the usefulness and the accuracy of this method at similar experiments was performed with the help of Monte Carlo codes MCNPX and FLUKA

Adaptive Multilevel Monte Carlo Simulation

KAUST Repository

Hoel, H

2011-08-23

This work generalizes a multilevel forward Euler Monte Carlo method introduced in Michael B. Giles. (Michael Giles. Oper. Res. 56(3):607–617, 2008.) for the approximation of expected values depending on the solution to an Itô stochastic differential equation. The work (Michael Giles. Oper. Res. 56(3):607– 617, 2008.) proposed and analyzed a forward Euler multilevelMonte Carlo method based on a hierarchy of uniform time discretizations and control variates to reduce the computational effort required by a standard, single level, Forward Euler Monte Carlo method. This work introduces an adaptive hierarchy of non uniform time discretizations, generated by an adaptive algorithmintroduced in (AnnaDzougoutov et al. Raùl Tempone. Adaptive Monte Carlo algorithms for stopped diffusion. In Multiscale methods in science and engineering, volume 44 of Lect. Notes Comput. Sci. Eng., pages 59–88. Springer, Berlin, 2005; Kyoung-Sook Moon et al. Stoch. Anal. Appl. 23(3):511–558, 2005; Kyoung-Sook Moon et al. An adaptive algorithm for ordinary, stochastic and partial differential equations. In Recent advances in adaptive computation, volume 383 of Contemp. Math., pages 325–343. Amer. Math. Soc., Providence, RI, 2005.). This form of the adaptive algorithm generates stochastic, path dependent, time steps and is based on a posteriori error expansions first developed in (Anders Szepessy et al. Comm. Pure Appl. Math. 54(10):1169– 1214, 2001). Our numerical results for a stopped diffusion problem, exhibit savings in the computational cost to achieve an accuracy of ϑ(TOL),from(TOL−3), from using a single level version of the adaptive algorithm to ϑ(((TOL−1)log(TOL))2).

Uncertainty analysis in Monte Carlo criticality computations

International Nuclear Information System (INIS)

Qi Ao

2011-01-01

Highlights: ► Two types of uncertainty methods for k eff Monte Carlo computations are examined. ► Sampling method has the least restrictions on perturbation but computing resources. ► Analytical method is limited to small perturbation on material properties. ► Practicality relies on efficiency, multiparameter applicability and data availability. - Abstract: Uncertainty analysis is imperative for nuclear criticality risk assessments when using Monte Carlo neutron transport methods to predict the effective neutron multiplication factor (k eff ) for fissionable material systems. For the validation of Monte Carlo codes for criticality computations against benchmark experiments, code accuracy and precision are measured by both the computational bias and uncertainty in the bias. The uncertainty in the bias accounts for known or quantified experimental, computational and model uncertainties. For the application of Monte Carlo codes for criticality analysis of fissionable material systems, an administrative margin of subcriticality must be imposed to provide additional assurance of subcriticality for any unknown or unquantified uncertainties. Because of a substantial impact of the administrative margin of subcriticality on economics and safety of nuclear fuel cycle operations, recently increasing interests in reducing the administrative margin of subcriticality make the uncertainty analysis in criticality safety computations more risk-significant. This paper provides an overview of two most popular k eff uncertainty analysis methods for Monte Carlo criticality computations: (1) sampling-based methods, and (2) analytical methods. Examples are given to demonstrate their usage in the k eff uncertainty analysis due to uncertainties in both neutronic and non-neutronic parameters of fissionable material systems.

On the use of stochastic approximation Monte Carlo for Monte Carlo integration

KAUST Repository

Liang, Faming

2009-03-01

The stochastic approximation Monte Carlo (SAMC) algorithm has recently been proposed as a dynamic optimization algorithm in the literature. In this paper, we show in theory that the samples generated by SAMC can be used for Monte Carlo integration via a dynamically weighted estimator by calling some results from the literature of nonhomogeneous Markov chains. Our numerical results indicate that SAMC can yield significant savings over conventional Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, for the problems for which the energy landscape is rugged. © 2008 Elsevier B.V. All rights reserved.

On the use of stochastic approximation Monte Carlo for Monte Carlo integration

KAUST Repository

Liang, Faming

2009-01-01

The stochastic approximation Monte Carlo (SAMC) algorithm has recently been proposed as a dynamic optimization algorithm in the literature. In this paper, we show in theory that the samples generated by SAMC can be used for Monte Carlo integration

Continuous energy Monte Carlo method based lattice homogeinzation

International Nuclear Information System (INIS)

Li Mancang; Yao Dong; Wang Kan

2014-01-01

Based on the Monte Carlo code MCNP, the continuous energy Monte Carlo multi-group constants generation code MCMC has been developed. The track length scheme has been used as the foundation of cross section generation. The scattering matrix and Legendre components require special techniques, and the scattering event method has been proposed to solve this problem. Three methods have been developed to calculate the diffusion coefficients for diffusion reactor core codes and the Legendre method has been applied in MCMC. To the satisfaction of the equivalence theory, the general equivalence theory (GET) and the superhomogenization method (SPH) have been applied to the Monte Carlo method based group constants. The super equivalence method (SPE) has been proposed to improve the equivalence. GET, SPH and SPE have been implemented into MCMC. The numerical results showed that generating the homogenization multi-group constants via Monte Carlo method overcomes the difficulties in geometry and treats energy in continuum, thus provides more accuracy parameters. Besides, the same code and data library can be used for a wide range of applications due to the versatility. The MCMC scheme can be seen as a potential alternative to the widely used deterministic lattice codes. (authors)

Time step length versus efficiency of Monte Carlo burnup calculations

International Nuclear Information System (INIS)

Dufek, Jan; Valtavirta, Ville

2014-01-01

Highlights: • Time step length largely affects efficiency of MC burnup calculations. • Efficiency of MC burnup calculations improves with decreasing time step length. • Results were obtained from SIE-based Monte Carlo burnup calculations. - Abstract: We demonstrate that efficiency of Monte Carlo burnup calculations can be largely affected by the selected time step length. This study employs the stochastic implicit Euler based coupling scheme for Monte Carlo burnup calculations that performs a number of inner iteration steps within each time step. In a series of calculations, we vary the time step length and the number of inner iteration steps; the results suggest that Monte Carlo burnup calculations get more efficient as the time step length is reduced. More time steps must be simulated as they get shorter; however, this is more than compensated by the decrease in computing cost per time step needed for achieving a certain accuracy

Monte Carlo and experimental evaluation of accuracy and noise properties of two scatter correction methods for SPECT

International Nuclear Information System (INIS)

Narita, Y.; Eberl, S.; Bautovich, G.; Iida, H.; Hutton, B.F.; Braun, M.; Nakamura, T.

1996-01-01

Scatter correction is a prerequisite for quantitative SPECT, but potentially increases noise. Monte Carlo simulations (EGS4) and physical phantom measurements were used to compare accuracy and noise properties of two scatter correction techniques: the triple-energy window (TEW), and the transmission dependent convolution subtraction (TDCS) techniques. Two scatter functions were investigated for TDCS: (i) the originally proposed mono-exponential function (TDCS mono ) and (ii) an exponential plus Gaussian scatter function (TDCS Gauss ) demonstrated to be superior from our Monte Carlo simulations. Signal to noise ratio (S/N) and accuracy were investigated in cylindrical phantoms and a chest phantom. Results from each method were compared to the true primary counts (simulations), or known activity concentrations (phantom studies). 99m Tc was used in all cases. The optimized TDCS Gauss method overall performed best, with an accuracy of better than 4% for all simulations and physical phantom studies. Maximum errors for TEW and TDCS mono of -30 and -22%, respectively, were observed in the heart chamber of the simulated chest phantom. TEW had the worst S/N ratio of the three techniques. The S/N ratios of the two TDCS methods were similar and only slightly lower than those of simulated true primary data. Thus, accurate quantitation can be obtained with TDCS Gauss , with a relatively small reduction in S/N ratio. (author)

Accuracy estimation for intermediate and low energy neutron transport calculation with Monte Carlo code MCNP

International Nuclear Information System (INIS)

Kotegawa, Hiroshi; Sasamoto, Nobuo; Tanaka, Shun-ichi

1987-02-01

Both ''measured radioactive inventory due to neutron activation in the shield concrete of JPDR'' and ''measured intermediate and low energy neutron spectra penetrating through a graphite sphere'' are analyzed using a continuous energy model Monte Carlo code MCNP so as to estimate calculational accuracy of the code for neutron transport in thermal and epithermal energy regions. Analyses reveal that MCNP calculates thermal neutron spectra fairly accurately, while it apparently over-estimates epithermal neutron spectra (of approximate 1/E distribution) as compared with the measurements. (author)

Vectorized Monte Carlo

International Nuclear Information System (INIS)

Brown, F.B.

1981-01-01

Examination of the global algorithms and local kernels of conventional general-purpose Monte Carlo codes shows that multigroup Monte Carlo methods have sufficient structure to permit efficient vectorization. A structured multigroup Monte Carlo algorithm for vector computers is developed in which many particle events are treated at once on a cell-by-cell basis. Vectorization of kernels for tracking and variance reduction is described, and a new method for discrete sampling is developed to facilitate the vectorization of collision analysis. To demonstrate the potential of the new method, a vectorized Monte Carlo code for multigroup radiation transport analysis was developed. This code incorporates many features of conventional general-purpose production codes, including general geometry, splitting and Russian roulette, survival biasing, variance estimation via batching, a number of cutoffs, and generalized tallies of collision, tracklength, and surface crossing estimators with response functions. Predictions of vectorized performance characteristics for the CYBER-205 were made using emulated coding and a dynamic model of vector instruction timing. Computation rates were examined for a variety of test problems to determine sensitivities to batch size and vector lengths. Significant speedups are predicted for even a few hundred particles per batch, and asymptotic speedups by about 40 over equivalent Amdahl 470V/8 scalar codes arepredicted for a few thousand particles per batch. The principal conclusion is that vectorization of a general-purpose multigroup Monte Carlo code is well worth the significant effort required for stylized coding and major algorithmic changes

MONK - a general purpose Monte Carlo neutronics program

International Nuclear Information System (INIS)

Sherriffs, V.S.W.

1978-01-01

MONK is a Monte Carlo neutronics code written principally for criticality calculations relevant to the transport, storage, and processing of fissile material. The code exploits the ability of the Monte Carlo method to represent complex shapes with very great accuracy. The nuclear data used is derived from the UK Nuclear Data File processed to the required format by a subsidiary program POND. A general description is given of the MONK code together with the subsidiary program SCAN which produces diagrams of the system specified. Details of the data input required by MONK and SCAN are also given. (author)

Adjoint electron Monte Carlo calculations

International Nuclear Information System (INIS)

Jordan, T.M.

1986-01-01

Adjoint Monte Carlo is the most efficient method for accurate analysis of space systems exposed to natural and artificially enhanced electron environments. Recent adjoint calculations for isotropic electron environments include: comparative data for experimental measurements on electronics boxes; benchmark problem solutions for comparing total dose prediction methodologies; preliminary assessment of sectoring methods used during space system design; and total dose predictions on an electronics package. Adjoint Monte Carlo, forward Monte Carlo, and experiment are in excellent agreement for electron sources that simulate space environments. For electron space environments, adjoint Monte Carlo is clearly superior to forward Monte Carlo, requiring one to two orders of magnitude less computer time for relatively simple geometries. The solid-angle sectoring approximations used for routine design calculations can err by more than a factor of 2 on dose in simple shield geometries. For critical space systems exposed to severe electron environments, these potential sectoring errors demand the establishment of large design margins and/or verification of shield design by adjoint Monte Carlo/experiment

Monte Carlo: Basics

OpenAIRE

Murthy, K. P. N.

2001-01-01

An introduction to the basics of Monte Carlo is given. The topics covered include, sample space, events, probabilities, random variables, mean, variance, covariance, characteristic function, chebyshev inequality, law of large numbers, central limit theorem (stable distribution, Levy distribution), random numbers (generation and testing), random sampling techniques (inversion, rejection, sampling from a Gaussian, Metropolis sampling), analogue Monte Carlo and Importance sampling (exponential b...

MORSE Monte Carlo code

International Nuclear Information System (INIS)

Cramer, S.N.

1984-01-01

The MORSE code is a large general-use multigroup Monte Carlo code system. Although no claims can be made regarding its superiority in either theoretical details or Monte Carlo techniques, MORSE has been, since its inception at ORNL in the late 1960s, the most widely used Monte Carlo radiation transport code. The principal reason for this popularity is that MORSE is relatively easy to use, independent of any installation or distribution center, and it can be easily customized to fit almost any specific need. Features of the MORSE code are described

Monte Carlo theory and practice

International Nuclear Information System (INIS)

James, F.

1987-01-01

Historically, the first large-scale calculations to make use of the Monte Carlo method were studies of neutron scattering and absorption, random processes for which it is quite natural to employ random numbers. Such calculations, a subset of Monte Carlo calculations, are known as direct simulation, since the 'hypothetical population' of the narrower definition above corresponds directly to the real population being studied. The Monte Carlo method may be applied wherever it is possible to establish equivalence between the desired result and the expected behaviour of a stochastic system. The problem to be solved may already be of a probabilistic or statistical nature, in which case its Monte Carlo formulation will usually be a straightforward simulation, or it may be of a deterministic or analytic nature, in which case an appropriate Monte Carlo formulation may require some imagination and may appear contrived or artificial. In any case, the suitability of the method chosen will depend on its mathematical properties and not on its superficial resemblance to the problem to be solved. The authors show how Monte Carlo techniques may be compared with other methods of solution of the same physical problem

Beyond chemical accuracy: The pseudopotential approximation in diffusion Monte Carlo calculations of the HCP to BCC phase transition in beryllium.

Energy Technology Data Exchange (ETDEWEB)

Shulenburger, Luke [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mattsson, Thomas Kjell Rene [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Desjarlais, Michael Paul [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-01-01

Motivated by the disagreement between recent diffusion Monte Carlo calculations of the phase transition pressure between the ambient and beta-Sn phases of silicon and experiments, we present a study of the HCP to BCC phase transition in beryllium. This lighter element provides an opportunity for directly testing many of the approximations required for calculations on silicon and may suggest a path towards increasing the practical accuracy of diffusion Monte Carlo calculations of solids in general. We demonstrate that the single largest approximation in these calculations is the pseudopotential approximation and after removing this we find excellent agreement with experiment for the ambient HCP phase and results similar to careful calculations using density functional theory for the phase transition pressure.

Monte Carlo evaluation of accuracy and noise properties of two scatter correction methods

International Nuclear Information System (INIS)

Narita, Y.; Eberl, S.; Nakamura, T.

1996-01-01

Two independent scatter correction techniques, transmission dependent convolution subtraction (TDCS) and triple-energy window (TEW) method, were evaluated in terms of quantitative accuracy and noise properties using Monte Carlo simulation (EGS4). Emission projections (primary, scatter and scatter plus primary) were simulated for 99m Tc and 201 Tl for numerical chest phantoms. Data were reconstructed with ordered-subset ML-EM algorithm including attenuation correction using the transmission data. In the chest phantom simulation, TDCS provided better S/N than TEW, and better accuracy, i.e., 1.0% vs -7.2% in myocardium, and -3.7% vs -30.1% in the ventricular chamber for 99m Tc with TDCS and TEW, respectively. For 201 Tl, TDCS provided good visual and quantitative agreement with simulated true primary image without noticeably increasing the noise after scatter correction. Overall TDCS proved to be more accurate and less noisy than TEW, facilitating quantitative assessment of physiological functions with SPECT

Monte Carlo Methods in Physics

International Nuclear Information System (INIS)

Santoso, B.

1997-01-01

Method of Monte Carlo integration is reviewed briefly and some of its applications in physics are explained. A numerical experiment on random generators used in the monte Carlo techniques is carried out to show the behavior of the randomness of various methods in generating them. To account for the weight function involved in the Monte Carlo, the metropolis method is used. From the results of the experiment, one can see that there is no regular patterns of the numbers generated, showing that the program generators are reasonably good, while the experimental results, shows a statistical distribution obeying statistical distribution law. Further some applications of the Monte Carlo methods in physics are given. The choice of physical problems are such that the models have available solutions either in exact or approximate values, in which comparisons can be mode, with the calculations using the Monte Carlo method. Comparison show that for the models to be considered, good agreement have been obtained

SCALE Continuous-Energy Monte Carlo Depletion with Parallel KENO in TRITON

International Nuclear Information System (INIS)

Goluoglu, Sedat; Bekar, Kursat B.; Wiarda, Dorothea

2012-01-01

The TRITON sequence of the SCALE code system is a powerful and robust tool for performing multigroup (MG) reactor physics analysis using either the 2-D deterministic solver NEWT or the 3-D Monte Carlo transport code KENO. However, as with all MG codes, the accuracy of the results depends on the accuracy of the MG cross sections that are generated and/or used. While SCALE resonance self-shielding modules provide rigorous resonance self-shielding, they are based on 1-D models and therefore 2-D or 3-D effects such as heterogeneity of the lattice structures may render final MG cross sections inaccurate. Another potential drawback to MG Monte Carlo depletion is the need to perform resonance self-shielding calculations at each depletion step for each fuel segment that is being depleted. The CPU time and memory required for self-shielding calculations can often eclipse the resources needed for the Monte Carlo transport. This summary presents the results of the new continuous-energy (CE) calculation mode in TRITON. With the new capability, accurate reactor physics analyses can be performed for all types of systems using the SCALE Monte Carlo code KENO as the CE transport solver. In addition, transport calculations can be performed in parallel mode on multiple processors.

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Directory of Open Access Journals (Sweden)

Joko Siswantoro

2014-01-01

Full Text Available Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method.

Monte Carlo method with heuristic adjustment for irregularly shaped food product volume measurement.

Science.gov (United States)

Siswantoro, Joko; Prabuwono, Anton Satria; Abdullah, Azizi; Idrus, Bahari

2014-01-01

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method.

Monte Carlo techniques in radiation therapy

CERN Document Server

Verhaegen, Frank

2013-01-01

Modern cancer treatment relies on Monte Carlo simulations to help radiotherapists and clinical physicists better understand and compute radiation dose from imaging devices as well as exploit four-dimensional imaging data. With Monte Carlo-based treatment planning tools now available from commercial vendors, a complete transition to Monte Carlo-based dose calculation methods in radiotherapy could likely take place in the next decade. Monte Carlo Techniques in Radiation Therapy explores the use of Monte Carlo methods for modeling various features of internal and external radiation sources, including light ion beams. The book-the first of its kind-addresses applications of the Monte Carlo particle transport simulation technique in radiation therapy, mainly focusing on external beam radiotherapy and brachytherapy. It presents the mathematical and technical aspects of the methods in particle transport simulations. The book also discusses the modeling of medical linacs and other irradiation devices; issues specific...

Statistical implications in Monte Carlo depletions - 051

International Nuclear Information System (INIS)

Zhiwen, Xu; Rhodes, J.; Smith, K.

2010-01-01

As a result of steady advances of computer power, continuous-energy Monte Carlo depletion analysis is attracting considerable attention for reactor burnup calculations. The typical Monte Carlo analysis is set up as a combination of a Monte Carlo neutron transport solver and a fuel burnup solver. Note that the burnup solver is a deterministic module. The statistical errors in Monte Carlo solutions are introduced into nuclide number densities and propagated along fuel burnup. This paper is towards the understanding of the statistical implications in Monte Carlo depletions, including both statistical bias and statistical variations in depleted fuel number densities. The deterministic Studsvik lattice physics code, CASMO-5, is modified to model the Monte Carlo depletion. The statistical bias in depleted number densities is found to be negligible compared to its statistical variations, which, in turn, demonstrates the correctness of the Monte Carlo depletion method. Meanwhile, the statistical variation in number densities generally increases with burnup. Several possible ways of reducing the statistical errors are discussed: 1) to increase the number of individual Monte Carlo histories; 2) to increase the number of time steps; 3) to run additional independent Monte Carlo depletion cases. Finally, a new Monte Carlo depletion methodology, called the batch depletion method, is proposed, which consists of performing a set of independent Monte Carlo depletions and is thus capable of estimating the overall statistical errors including both the local statistical error and the propagated statistical error. (authors)

Monte Carlo simulation for IRRMA

International Nuclear Information System (INIS)

Gardner, R.P.; Liu Lianyan

2000-01-01

Monte Carlo simulation is fast becoming a standard approach for many radiation applications that were previously treated almost entirely by experimental techniques. This is certainly true for Industrial Radiation and Radioisotope Measurement Applications - IRRMA. The reasons for this include: (1) the increased cost and inadequacy of experimentation for design and interpretation purposes; (2) the availability of low cost, large memory, and fast personal computers; and (3) the general availability of general purpose Monte Carlo codes that are increasingly user-friendly, efficient, and accurate. This paper discusses the history and present status of Monte Carlo simulation for IRRMA including the general purpose (GP) and specific purpose (SP) Monte Carlo codes and future needs - primarily from the experience of the authors

Comparative evaluation of photon cross section libraries for materials of interest in PET Monte Carlo simulations

CERN Document Server

Zaidi, H

1999-01-01

the many applications of Monte Carlo modelling in nuclear medicine imaging make it desirable to increase the accuracy and computational speed of Monte Carlo codes. The accuracy of Monte Carlo simulations strongly depends on the accuracy in the probability functions and thus on the cross section libraries used for photon transport calculations. A comparison between different photon cross section libraries and parametrizations implemented in Monte Carlo simulation packages developed for positron emission tomography and the most recent Evaluated Photon Data Library (EPDL97) developed by the Lawrence Livermore National Laboratory was performed for several human tissues and common detector materials for energies from 1 keV to 1 MeV. Different photon cross section libraries and parametrizations show quite large variations as compared to the EPDL97 coefficients. This latter library is more accurate and was carefully designed in the form of look-up tables providing efficient data storage, access, and management. Toge...

Monte Carlo Simulation of stepping source in afterloading intracavitary brachytherapy for GZP6 unit

International Nuclear Information System (INIS)

Toossi, M.T.B.; Abdollahi, M.; Ghorbani, M.

2010-01-01

Full text: Stepping source in brachytherapy systems is used to treat a target lesion longer than the effective treatment length of the source. Dose calculation accuracy plays a vital role in the outcome of brachytherapy treatment. In this study, the stepping source (channel 6) of GZP6 brachytherapy unit was simulated by Monte Carlo simulation and matrix shift method. The stepping source of GZP6 was simulated by Monte Carlo MCNPX code. The Mesh tally (type I) was employed for absorbed dose calculation in a cylindrical water phantom. 5 x 108 photon histories were scored and a 0.2% statistical uncertainty was obtained by Monte Carlo calculations. Dose distributions were obtained by our matrix shift method for esophageal cancer tumor lengths of 8 and 10 cm. Isodose curves produced by simulation and TPS were superimposed to estimate the differences. Results Comparison of Monte Carlo and TPS dose distributions show that in longitudinal direction (source movement direction) Monte Carlo and TPS dose distributions are comparable. [n transverse direction, the dose differences of 7 and 5% were observed for esophageal tumor lengths of 8 and 10 cm respectively. Conclusions Although, the results show that the maximum difference between Monte Carlo and TPS calculations is about 7%, but considering that the certified activity is given with ± I 0%, uncertainty, then an error of the order of 20% for Monte Carlo calculation would be reasonable. It can be suggested that accuracy of the dose distribution produced by TPS is acceptable for clinical applications. (author)

Reactor perturbation calculations by Monte Carlo methods

International Nuclear Information System (INIS)

Gubbins, M.E.

1965-09-01

Whilst Monte Carlo methods are useful for reactor calculations involving complicated geometry, it is difficult to apply them to the calculation of perturbation worths because of the large amount of computing time needed to obtain good accuracy. Various ways of overcoming these difficulties are investigated in this report, with the problem of estimating absorbing control rod worths particularly in mind. As a basis for discussion a method of carrying out multigroup reactor calculations by Monte Carlo methods is described. Two methods of estimating a perturbation worth directly, without differencing two quantities of like magnitude, are examined closely but are passed over in favour of a third method based on a correlation technique. This correlation method is described, and demonstrated by a limited range of calculations for absorbing control rods in a fast reactor. In these calculations control rod worths of between 1% and 7% in reactivity are estimated to an accuracy better than 10% (3 standard errors) in about one hour's computing time on the English Electric KDF.9 digital computer. (author)

(U) Introduction to Monte Carlo Methods

Energy Technology Data Exchange (ETDEWEB)

Hungerford, Aimee L. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-20

Monte Carlo methods are very valuable for representing solutions to particle transport problems. Here we describe a “cook book” approach to handling the terms in a transport equation using Monte Carlo methods. Focus is on the mechanics of a numerical Monte Carlo code, rather than the mathematical foundations of the method.

Hybrid SN/Monte Carlo research and results

International Nuclear Information System (INIS)

Baker, R.S.

1993-01-01

The neutral particle transport equation is solved by a hybrid method that iteratively couples regions where deterministic (S N ) and stochastic (Monte Carlo) methods are applied. The Monte Carlo and S N regions are fully coupled in the sense that no assumption is made about geometrical separation or decoupling. The hybrid Monte Carlo/S N method provides a new means of solving problems involving both optically thick and optically thin regions that neither Monte Carlo nor S N is well suited for by themselves. The hybrid method has been successfully applied to realistic shielding problems. The vectorized Monte Carlo algorithm in the hybrid method has been ported to the massively parallel architecture of the Connection Machine. Comparisons of performance on a vector machine (Cray Y-MP) and the Connection Machine (CM-2) show that significant speedups are obtainable for vectorized Monte Carlo algorithms on massively parallel machines, even when realistic problems requiring variance reduction are considered. However, the architecture of the Connection Machine does place some limitations on the regime in which the Monte Carlo algorithm may be expected to perform well

Benchmarking time-dependent neutron problems with Monte Carlo codes

International Nuclear Information System (INIS)

Couet, B.; Loomis, W.A.

1990-01-01

Many nuclear logging tools measure the time dependence of a neutron flux in a geological formation to infer important properties of the formation. The complex geometry of the tool and the borehole within the formation does not permit an exact deterministic modelling of the neutron flux behaviour. While this exact simulation is possible with Monte Carlo methods the computation time does not facilitate quick turnaround of results useful for design and diagnostic purposes. Nonetheless a simple model based on the diffusion-decay equation for the flux of neutrons of a single energy group can be useful in this situation. A combination approach where a Monte Carlo calculation benchmarks a deterministic model in terms of the diffusion constants of the neutrons propagating in the media and their flux depletion rates thus offers the possibility of quick calculation with assurance as to accuracy. We exemplify this approach with the Monte Carlo benchmarking of a logging tool problem, showing standoff and bedding response. (author)

Application of OMEGA Monte Carlo codes for radiation therapy treatment planning

International Nuclear Information System (INIS)

Ayyangar, Komanduri M.; Jiang, Steve B.

1998-01-01

The accuracy of conventional dose algorithms for radiosurgery treatment planning is limited, due to the inadequate consideration of the lateral radiation transport and the difficulty of acquiring accurate dosimetric data for very small beams. In the present paper, some initial work on the application of Monte Carlo method in radiation treatment planning in general, and in radiosurgery treatment planning in particular, has been presented. Two OMEGA Monte Carlo codes, BEAM and DOSXYZ, are used. The BEAM code is used to simulate the transport of particles in the linac treatment head and radiosurgery collimator. A phase space file is obtained from the BEAM simulation for each collimator size. The DOSXYZ code is used to calculate the dose distribution in the patient's body reconstructed from CT slices using the phase space file as input. The accuracy of OMEGA Monte Carlo simulation for radiosurgery dose calculation is verified by comparing the calculated and measured basic dosimetric data for several radiosurgery beams and a 4 x 4 cm 2 conventional beam. The dose distributions for three clinical cases are calculated using OMEGA codes as the dose engine for an in-house developed radiosurgery treatment planning system. The verification using basic dosimetric data and the dose calculation for clinical cases demonstrate the feasibility of applying OMEGA Monte Carlo code system to radiosurgery treatment planning. (author)

Electron transport in radiotherapy using local-to-global Monte Carlo

International Nuclear Information System (INIS)

Svatos, M.M.; Chandler, W.P.; Siantar, C.L.H.; Rathkopf, J.A.; Ballinger, C.T.

1994-09-01

Local-to-Global (L-G) Monte Carlo methods are a way to make three-dimensional electron transport both fast and accurate relative to other Monte Carlo methods. This is achieved by breaking the simulation into two stages: a local calculation done over small geometries having the size and shape of the ''steps'' to be taken through the mesh; and a global calculation which relies on a stepping code that samples the stored results of the local calculation. The increase in speed results from taking fewer steps in the global calculation than required by ordinary Monte Carlo codes and by speeding up the calculation per step. The potential for accuracy comes from the ability to use long runs of detailed codes to compile probability distribution functions (PDFs) in the local calculation. Specific examples of successful Local-to-Global algorithms are given

A hybrid transport-diffusion Monte Carlo method for frequency-dependent radiative-transfer simulations

International Nuclear Information System (INIS)

Densmore, Jeffery D.; Thompson, Kelly G.; Urbatsch, Todd J.

2012-01-01

Discrete Diffusion Monte Carlo (DDMC) is a technique for increasing the efficiency of Implicit Monte Carlo radiative-transfer simulations in optically thick media. In DDMC, particles take discrete steps between spatial cells according to a discretized diffusion equation. Each discrete step replaces many smaller Monte Carlo steps, thus improving the efficiency of the simulation. In this paper, we present an extension of DDMC for frequency-dependent radiative transfer. We base our new DDMC method on a frequency-integrated diffusion equation for frequencies below a specified threshold, as optical thickness is typically a decreasing function of frequency. Above this threshold we employ standard Monte Carlo, which results in a hybrid transport-diffusion scheme. With a set of frequency-dependent test problems, we confirm the accuracy and increased efficiency of our new DDMC method.

Molecular Monte Carlo Simulations Using Graphics Processing Units: To Waste Recycle or Not?

Science.gov (United States)

Kim, Jihan; Rodgers, Jocelyn M; Athènes, Manuel; Smit, Berend

2011-10-11

In the waste recycling Monte Carlo (WRMC) algorithm, (1) multiple trial states may be simultaneously generated and utilized during Monte Carlo moves to improve the statistical accuracy of the simulations, suggesting that such an algorithm may be well posed for implementation in parallel on graphics processing units (GPUs). In this paper, we implement two waste recycling Monte Carlo algorithms in CUDA (Compute Unified Device Architecture) using uniformly distributed random trial states and trial states based on displacement random-walk steps, and we test the methods on a methane-zeolite MFI framework system to evaluate their utility. We discuss the specific implementation details of the waste recycling GPU algorithm and compare the methods to other parallel algorithms optimized for the framework system. We analyze the relationship between the statistical accuracy of our simulations and the CUDA block size to determine the efficient allocation of the GPU hardware resources. We make comparisons between the GPU and the serial CPU Monte Carlo implementations to assess speedup over conventional microprocessors. Finally, we apply our optimized GPU algorithms to the important problem of determining free energy landscapes, in this case for molecular motion through the zeolite LTA.

Lectures on Monte Carlo methods

CERN Document Server

Madras, Neal

2001-01-01

Monte Carlo methods form an experimental branch of mathematics that employs simulations driven by random number generators. These methods are often used when others fail, since they are much less sensitive to the "curse of dimensionality", which plagues deterministic methods in problems with a large number of variables. Monte Carlo methods are used in many fields: mathematics, statistics, physics, chemistry, finance, computer science, and biology, for instance. This book is an introduction to Monte Carlo methods for anyone who would like to use these methods to study various kinds of mathemati

Monte Carlo simulation in nuclear medicine

International Nuclear Information System (INIS)

Morel, Ch.

2007-01-01

The Monte Carlo method allows for simulating random processes by using series of pseudo-random numbers. It became an important tool in nuclear medicine to assist in the design of new medical imaging devices, optimise their use and analyse their data. Presently, the sophistication of the simulation tools allows the introduction of Monte Carlo predictions in data correction and image reconstruction processes. The availability to simulate time dependent processes opens up new horizons for Monte Carlo simulation in nuclear medicine. In a near future, these developments will allow to tackle simultaneously imaging and dosimetry issues and soon, case system Monte Carlo simulations may become part of the nuclear medicine diagnostic process. This paper describes some Monte Carlo method basics and the sampling methods that were developed for it. It gives a referenced list of different simulation software used in nuclear medicine and enumerates some of their present and prospective applications. (author)

Continuum variational and diffusion quantum Monte Carlo calculations

International Nuclear Information System (INIS)

Needs, R J; Towler, M D; Drummond, N D; Lopez RIos, P

2010-01-01

This topical review describes the methodology of continuum variational and diffusion quantum Monte Carlo calculations. These stochastic methods are based on many-body wavefunctions and are capable of achieving very high accuracy. The algorithms are intrinsically parallel and well suited to implementation on petascale computers, and the computational cost scales as a polynomial in the number of particles. A guide to the systems and topics which have been investigated using these methods is given. The bulk of the article is devoted to an overview of the basic quantum Monte Carlo methods, the forms and optimization of wavefunctions, performing calculations under periodic boundary conditions, using pseudopotentials, excited-state calculations, sources of calculational inaccuracy, and calculating energy differences and forces. (topical review)

Multiple-time-stepping generalized hybrid Monte Carlo methods

Energy Technology Data Exchange (ETDEWEB)

Escribano, Bruno, E-mail: bescribano@bcamath.org [BCAM—Basque Center for Applied Mathematics, E-48009 Bilbao (Spain); Akhmatskaya, Elena [BCAM—Basque Center for Applied Mathematics, E-48009 Bilbao (Spain); IKERBASQUE, Basque Foundation for Science, E-48013 Bilbao (Spain); Reich, Sebastian [Universität Potsdam, Institut für Mathematik, D-14469 Potsdam (Germany); Azpiroz, Jon M. [Kimika Fakultatea, Euskal Herriko Unibertsitatea (UPV/EHU) and Donostia International Physics Center (DIPC), P.K. 1072, Donostia (Spain)

2015-01-01

Performance of the generalized shadow hybrid Monte Carlo (GSHMC) method [1], which proved to be superior in sampling efficiency over its predecessors [2–4], molecular dynamics and hybrid Monte Carlo, can be further improved by combining it with multi-time-stepping (MTS) and mollification of slow forces. We demonstrate that the comparatively simple modifications of the method not only lead to better performance of GSHMC itself but also allow for beating the best performed methods, which use the similar force splitting schemes. In addition we show that the same ideas can be successfully applied to the conventional generalized hybrid Monte Carlo method (GHMC). The resulting methods, MTS-GHMC and MTS-GSHMC, provide accurate reproduction of thermodynamic and dynamical properties, exact temperature control during simulation and computational robustness and efficiency. MTS-GHMC uses a generalized momentum update to achieve weak stochastic stabilization to the molecular dynamics (MD) integrator. MTS-GSHMC adds the use of a shadow (modified) Hamiltonian to filter the MD trajectories in the HMC scheme. We introduce a new shadow Hamiltonian formulation adapted to force-splitting methods. The use of such Hamiltonians improves the acceptance rate of trajectories and has a strong impact on the sampling efficiency of the method. Both methods were implemented in the open-source MD package ProtoMol and were tested on a water and a protein systems. Results were compared to those obtained using a Langevin Molly (LM) method [5] on the same systems. The test results demonstrate the superiority of the new methods over LM in terms of stability, accuracy and sampling efficiency. This suggests that putting the MTS approach in the framework of hybrid Monte Carlo and using the natural stochasticity offered by the generalized hybrid Monte Carlo lead to improving stability of MTS and allow for achieving larger step sizes in the simulation of complex systems.

Failure Probability Estimation of Wind Turbines by Enhanced Monte Carlo

DEFF Research Database (Denmark)

Sichani, Mahdi Teimouri; Nielsen, Søren R.K.; Naess, Arvid

2012-01-01

This paper discusses the estimation of the failure probability of wind turbines required by codes of practice for designing them. The Standard Monte Carlo (SMC) simulations may be used for this reason conceptually as an alternative to the popular Peaks-Over-Threshold (POT) method. However......, estimation of very low failure probabilities with SMC simulations leads to unacceptably high computational costs. In this study, an Enhanced Monte Carlo (EMC) method is proposed that overcomes this obstacle. The method has advantages over both POT and SMC in terms of its low computational cost and accuracy...... is controlled by the pitch controller. This provides a fair framework for comparison of the behavior and failure event of the wind turbine with emphasis on the effect of the pitch controller. The Enhanced Monte Carlo method is then applied to the model and the failure probabilities of the model are estimated...

Comparative evaluations of the Monte Carlo-based light propagation simulation packages for optical imaging

Directory of Open Access Journals (Sweden)

Lin Wang

2018-01-01

Full Text Available Monte Carlo simulation of light propagation in turbid medium has been studied for years. A number of software packages have been developed to handle with such issue. However, it is hard to compare these simulation packages, especially for tissues with complex heterogeneous structures. Here, we first designed a group of mesh datasets generated by Iso2Mesh software, and used them to cross-validate the accuracy and to evaluate the performance of four Monte Carlo-based simulation packages, including Monte Carlo model of steady-state light transport in multi-layered tissues (MCML, tetrahedron-based inhomogeneous Monte Carlo optical simulator (TIMOS, Molecular Optical Simulation Environment (MOSE, and Mesh-based Monte Carlo (MMC. The performance of each package was evaluated based on the designed mesh datasets. The merits and demerits of each package were also discussed. Comparative results showed that the TIMOS package provided the best performance, which proved to be a reliable, efficient, and stable MC simulation package for users.

Advanced Multilevel Monte Carlo Methods

KAUST Repository

Jasra, Ajay

2017-04-24

This article reviews the application of advanced Monte Carlo techniques in the context of Multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations which can be biased in some sense, for instance, by using the discretization of a associated probability law. The MLMC approach works with a hierarchy of biased approximations which become progressively more accurate and more expensive. Using a telescoping representation of the most accurate approximation, the method is able to reduce the computational cost for a given level of error versus i.i.d. sampling from this latter approximation. All of these ideas originated for cases where exact sampling from couples in the hierarchy is possible. This article considers the case where such exact sampling is not currently possible. We consider Markov chain Monte Carlo and sequential Monte Carlo methods which have been introduced in the literature and we describe different strategies which facilitate the application of MLMC within these methods.

Advanced Multilevel Monte Carlo Methods

KAUST Repository

Jasra, Ajay; Law, Kody; Suciu, Carina

2017-01-01

This article reviews the application of advanced Monte Carlo techniques in the context of Multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations which can be biased in some sense, for instance, by using the discretization of a associated probability law. The MLMC approach works with a hierarchy of biased approximations which become progressively more accurate and more expensive. Using a telescoping representation of the most accurate approximation, the method is able to reduce the computational cost for a given level of error versus i.i.d. sampling from this latter approximation. All of these ideas originated for cases where exact sampling from couples in the hierarchy is possible. This article considers the case where such exact sampling is not currently possible. We consider Markov chain Monte Carlo and sequential Monte Carlo methods which have been introduced in the literature and we describe different strategies which facilitate the application of MLMC within these methods.

Evaluating the calculation accuracy of AAA algorithm for the situation with small fields in bone by monte carlo

International Nuclear Information System (INIS)

Zhang Yanqiu; Qiu Xiaoping; Yang Zhen; Lu Zhiping

2011-01-01

In order to evaluate the calculation accuracy of Anisotropic Analytical Algorithm (AAA) for the situation with small fields in a water-bone phantom using Monte Carlo simulation as benchmarks. A water phantom with a bone slab was built,in which the depth dose (DD) and off-axis ratio (OAR) for field 2 cm x 2 cm to field 8 cm x 8 cm were calculated by AAA algorithms, PBC algorithms (as comparison), and Monte Carlo (MC) simulation. The evaluation of algorithms by MC simulation was achieved by the comparisons of DD and the 1 dimension gamma analysis of OAR. It was shown that both of AAA algorithm and PBC algorithm overestimated the DD in bone region, and the dose differences ranged from 2.16%-2.7%, 1.4%-2.03%, respectively. AAA algorithm and PBC algorithm underestimated the DD in back of bone region, and the dose differences ranged from -0.39% - -1.19%, -0.13% - -0.4%, respectively. AAA algorithm and PBC algorithm overestimated the dose of field inner edge and field outer edge,respectively. One dimension gamma analysis indicated that AAA algorithm and PBC algorithm gamma pass rate was 100%, 100%, 100%, 86%, 100%, 100%, 72%, 64%, respectively. In bone medium,the dose calculated by AAA was slightly higher than MC simulation, the calculation accuracy was not evidently higher than PBC. (authors)

Enhancement of precision and accuracy by Monte-Carlo simulation of a well-type pressurized ionization chamber used in radionuclide metrology

International Nuclear Information System (INIS)

Kryeziu, D.

2006-09-01

The aim of this work was to test and validate the Monte-Carlo (MC) ionization chamber simulation method in calculating the activity of radioactive solutions. This is required when no or not sufficient experimental calibration figures are available as well as to improve the accuracy of activity measurements for other radionuclides. Well-type or 4π γ ISOCAL IV ionization chambers (IC) are widely used in many national standard laboratories around the world. As secondary standard measuring systems these radionuclide calibrators serve to maintain measurement consistency checks and to ensure the quality of standards disseminated to users for a wide range of radionuclide where many of them are with special interest in nuclear medicine as well as in different applications on radionuclide metrology. For the studied radionuclides the calibration figures (efficiencies) and their respective volume correction factors are determined by using the PENELOPE MC computer code system. The ISOCAL IV IC filled with nitrogen gas at approximately 1 MPa is simulated. The simulated models of the chamber are designed by means of reduced quadric equation and applying the appropriate mathematical transformations. The simulations are done for various container geometries of the standard solution which take forms of: i) sealed Jena glass 5 ml PTB standard ampoule, ii) 10 ml (P6) vial and iii) 10 R Schott Type 1+ vial. Simulation of the ISOCAL IV IC is explained. The effect of density variation of the nitrogen filling gas on the sensitivity of the chamber is investigated. The code is also used to examine the effects of using lead and copper shields as well as to evaluate the sensitivity of the chamber to electrons and positrons. Validation of the Monte-Carlo simulation method has been proved by comparing the Monte-Carlo simulation calculated and experimental calibration figures available from the National Physical Laboratory (NPL) England which are deduced from the absolute activity

Monte Carlo - Advances and Challenges

International Nuclear Information System (INIS)

Brown, Forrest B.; Mosteller, Russell D.; Martin, William R.

2008-01-01

Abstract only, full text follows: With ever-faster computers and mature Monte Carlo production codes, there has been tremendous growth in the application of Monte Carlo methods to the analysis of reactor physics and reactor systems. In the past, Monte Carlo methods were used primarily for calculating k eff of a critical system. More recently, Monte Carlo methods have been increasingly used for determining reactor power distributions and many design parameters, such as β eff , l eff , τ, reactivity coefficients, Doppler defect, dominance ratio, etc. These advanced applications of Monte Carlo methods are now becoming common, not just feasible, but bring new challenges to both developers and users: Convergence of 3D power distributions must be assured; confidence interval bias must be eliminated; iterated fission probabilities are required, rather than single-generation probabilities; temperature effects including Doppler and feedback must be represented; isotopic depletion and fission product buildup must be modeled. This workshop focuses on recent advances in Monte Carlo methods and their application to reactor physics problems, and on the resulting challenges faced by code developers and users. The workshop is partly tutorial, partly a review of the current state-of-the-art, and partly a discussion of future work that is needed. It should benefit both novice and expert Monte Carlo developers and users. In each of the topic areas, we provide an overview of needs, perspective on past and current methods, a review of recent work, and discussion of further research and capabilities that are required. Electronic copies of all workshop presentations and material will be available. The workshop is structured as 2 morning and 2 afternoon segments: - Criticality Calculations I - convergence diagnostics, acceleration methods, confidence intervals, and the iterated fission probability, - Criticality Calculations II - reactor kinetics parameters, dominance ratio, temperature

Advanced Monte Carlo methods for thermal radiation transport

Science.gov (United States)

Wollaber, Allan B.

During the past 35 years, the Implicit Monte Carlo (IMC) method proposed by Fleck and Cummings has been the standard Monte Carlo approach to solving the thermal radiative transfer (TRT) equations. However, the IMC equations are known to have accuracy limitations that can produce unphysical solutions. In this thesis, we explicitly provide the IMC equations with a Monte Carlo interpretation by including particle weight as one of its arguments. We also develop and test a stability theory for the 1-D, gray IMC equations applied to a nonlinear problem. We demonstrate that the worst case occurs for 0-D problems, and we extend the results to a stability algorithm that may be used for general linearizations of the TRT equations. We derive gray, Quasidiffusion equations that may be deterministically solved in conjunction with IMC to obtain an inexpensive, accurate estimate of the temperature at the end of the time step. We then define an average temperature T* to evaluate the temperature-dependent problem data in IMC, and we demonstrate that using T* is more accurate than using the (traditional) beginning-of-time-step temperature. We also propose an accuracy enhancement to the IMC equations: the use of a time-dependent "Fleck factor". This Fleck factor can be considered an automatic tuning of the traditionally defined user parameter alpha, which generally provides more accurate solutions at an increased cost relative to traditional IMC. We also introduce a global weight window that is proportional to the forward scalar intensity calculated by the Quasidiffusion method. This weight window improves the efficiency of the IMC calculation while conserving energy. All of the proposed enhancements are tested in 1-D gray and frequency-dependent problems. These enhancements do not unconditionally eliminate the unphysical behavior that can be seen in the IMC calculations. However, for fixed spatial and temporal grids, they suppress them and clearly work to make the solution more

Fast sequential Monte Carlo methods for counting and optimization

CERN Document Server

Rubinstein, Reuven Y; Vaisman, Radislav

2013-01-01

A comprehensive account of the theory and application of Monte Carlo methods Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. Written by authorities in the

The MC21 Monte Carlo Transport Code

International Nuclear Information System (INIS)

Sutton TM; Donovan TJ; Trumbull TH; Dobreff PS; Caro E; Griesheimer DP; Tyburski LJ; Carpenter DC; Joo H

2007-01-01

MC21 is a new Monte Carlo neutron and photon transport code currently under joint development at the Knolls Atomic Power Laboratory and the Bettis Atomic Power Laboratory. MC21 is the Monte Carlo transport kernel of the broader Common Monte Carlo Design Tool (CMCDT), which is also currently under development. The vision for CMCDT is to provide an automated, computer-aided modeling and post-processing environment integrated with a Monte Carlo solver that is optimized for reactor analysis. CMCDT represents a strategy to push the Monte Carlo method beyond its traditional role as a benchmarking tool or ''tool of last resort'' and into a dominant design role. This paper describes various aspects of the code, including the neutron physics and nuclear data treatments, the geometry representation, and the tally and depletion capabilities

Monte Carlo Treatment Planning for Advanced Radiotherapy

DEFF Research Database (Denmark)

Cronholm, Rickard

This Ph.d. project describes the development of a workflow for Monte Carlo Treatment Planning for clinical radiotherapy plans. The workflow may be utilized to perform an independent dose verification of treatment plans. Modern radiotherapy treatment delivery is often conducted by dynamically...... modulating the intensity of the field during the irradiation. The workflow described has the potential to fully model the dynamic delivery, including gantry rotation during irradiation, of modern radiotherapy. Three corner stones of Monte Carlo Treatment Planning are identified: Building, commissioning...... and validation of a Monte Carlo model of a medical linear accelerator (i), converting a CT scan of a patient to a Monte Carlo compliant phantom (ii) and translating the treatment plan parameters (including beam energy, angles of incidence, collimator settings etc) to a Monte Carlo input file (iii). A protocol...

Range uncertainties in proton therapy and the role of Monte Carlo simulations

International Nuclear Information System (INIS)

Paganetti, Harald

2012-01-01

The main advantages of proton therapy are the reduced total energy deposited in the patient as compared to photon techniques and the finite range of the proton beam. The latter adds an additional degree of freedom to treatment planning. The range in tissue is associated with considerable uncertainties caused by imaging, patient setup, beam delivery and dose calculation. Reducing the uncertainties would allow a reduction of the treatment volume and thus allow a better utilization of the advantages of protons. This paper summarizes the role of Monte Carlo simulations when aiming at a reduction of range uncertainties in proton therapy. Differences in dose calculation when comparing Monte Carlo with analytical algorithms are analyzed as well as range uncertainties due to material constants and CT conversion. Range uncertainties due to biological effects and the role of Monte Carlo for in vivo range verification are discussed. Furthermore, the current range uncertainty recipes used at several proton therapy facilities are revisited. We conclude that a significant impact of Monte Carlo dose calculation can be expected in complex geometries where local range uncertainties due to multiple Coulomb scattering will reduce the accuracy of analytical algorithms. In these cases Monte Carlo techniques might reduce the range uncertainty by several mm. (topical review)

Evaluation of cobalt-60 energy deposit in mouse and monkey using Monte Carlo simulation

Energy Technology Data Exchange (ETDEWEB)

Woo, Sang Keun; Kim, Wook; Park, Yong Sung; Kang, Joo Hyun; Lee, Yong Jin [Korea Institute of Radiological and Medical Sciences, KIRAMS, Seoul (Korea, Republic of); Cho, Doo Wan; Lee, Hong Soo; Han, Su Cheol [Jeonbuk Department of Inhalation Research, Korea Institute of toxicology, KRICT, Jeongeup (Korea, Republic of)

2016-12-15

These absorbed dose can calculated using the Monte Carlo transport code MCNP (Monte Carlo N-particle transport code). Internal radiotherapy absorbed dose was calculated using conventional software, such as OLINDA/EXM or Monte Carlo simulation. However, the OLINDA/EXM does not calculate individual absorbed dose and non-standard organ, such as tumor. While the Monte Carlo simulation can calculated non-standard organ and specific absorbed dose using individual CT image. External radiotherapy, absorbed dose can calculated by specific absorbed energy in specific organs using Monte Carlo simulation. The specific absorbed energy in each organ was difference between species or even if the same species. Since they have difference organ sizes, position, and density of organs. The aim of this study was to individually evaluated cobalt-60 energy deposit in mouse and monkey using Monte Carlo simulation. We evaluation of cobalt-60 energy deposit in mouse and monkey using Monte Carlo simulation. The absorbed energy in each organ compared with mouse heart was 54.6 fold higher than monkey absorbed energy in heart. Likewise lung was 88.4, liver was 16.0, urinary bladder was 29.4 fold higher than monkey. It means that the distance of each organs and organ mass was effects of the absorbed energy. This result may help to can calculated absorbed dose and more accuracy plan for external radiation beam therapy and internal radiotherapy.

Evaluation of cobalt-60 energy deposit in mouse and monkey using Monte Carlo simulation

International Nuclear Information System (INIS)

Woo, Sang Keun; Kim, Wook; Park, Yong Sung; Kang, Joo Hyun; Lee, Yong Jin; Cho, Doo Wan; Lee, Hong Soo; Han, Su Cheol

2016-01-01

These absorbed dose can calculated using the Monte Carlo transport code MCNP (Monte Carlo N-particle transport code). Internal radiotherapy absorbed dose was calculated using conventional software, such as OLINDA/EXM or Monte Carlo simulation. However, the OLINDA/EXM does not calculate individual absorbed dose and non-standard organ, such as tumor. While the Monte Carlo simulation can calculated non-standard organ and specific absorbed dose using individual CT image. External radiotherapy, absorbed dose can calculated by specific absorbed energy in specific organs using Monte Carlo simulation. The specific absorbed energy in each organ was difference between species or even if the same species. Since they have difference organ sizes, position, and density of organs. The aim of this study was to individually evaluated cobalt-60 energy deposit in mouse and monkey using Monte Carlo simulation. We evaluation of cobalt-60 energy deposit in mouse and monkey using Monte Carlo simulation. The absorbed energy in each organ compared with mouse heart was 54.6 fold higher than monkey absorbed energy in heart. Likewise lung was 88.4, liver was 16.0, urinary bladder was 29.4 fold higher than monkey. It means that the distance of each organs and organ mass was effects of the absorbed energy. This result may help to can calculated absorbed dose and more accuracy plan for external radiation beam therapy and internal radiotherapy.

A clinical study of lung cancer dose calculation accuracy with Monte Carlo simulation.

Science.gov (United States)

Zhao, Yanqun; Qi, Guohai; Yin, Gang; Wang, Xianliang; Wang, Pei; Li, Jian; Xiao, Mingyong; Li, Jie; Kang, Shengwei; Liao, Xiongfei

2014-12-16

The accuracy of dose calculation is crucial to the quality of treatment planning and, consequently, to the dose delivered to patients undergoing radiation therapy. Current general calculation algorithms such as Pencil Beam Convolution (PBC) and Collapsed Cone Convolution (CCC) have shortcomings in regard to severe inhomogeneities, particularly in those regions where charged particle equilibrium does not hold. The aim of this study was to evaluate the accuracy of the PBC and CCC algorithms in lung cancer radiotherapy using Monte Carlo (MC) technology. Four treatment plans were designed using Oncentra Masterplan TPS for each patient. Two intensity-modulated radiation therapy (IMRT) plans were developed using the PBC and CCC algorithms, and two three-dimensional conformal therapy (3DCRT) plans were developed using the PBC and CCC algorithms. The DICOM-RT files of the treatment plans were exported to the Monte Carlo system to recalculate. The dose distributions of GTV, PTV and ipsilateral lung calculated by the TPS and MC were compared. For 3DCRT and IMRT plans, the mean dose differences for GTV between the CCC and MC increased with decreasing of the GTV volume. For IMRT, the mean dose differences were found to be higher than that of 3DCRT. The CCC algorithm overestimated the GTV mean dose by approximately 3% for IMRT. For 3DCRT plans, when the volume of the GTV was greater than 100 cm(3), the mean doses calculated by CCC and MC almost have no difference. PBC shows large deviations from the MC algorithm. For the dose to the ipsilateral lung, the CCC algorithm overestimated the dose to the entire lung, and the PBC algorithm overestimated V20 but underestimated V5; the difference in V10 was not statistically significant. PBC substantially overestimates the dose to the tumour, but the CCC is similar to the MC simulation. It is recommended that the treatment plans for lung cancer be developed using an advanced dose calculation algorithm other than PBC. MC can accurately

Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2013-01-01

The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. We perform the HMC simulations of the SV model for two liquid stock returns traded on the Tokyo Stock Exchange and measure the volatilities of those stock returns. Then we calculate the accuracy of the volatility measurement using the realized volatility as a proxy of the true volatility and compare the SV model with the GARCH model which is one of other volatility models. Using the accuracy calculated with the realized volatility we find that empirically the SV model performs better than the GARCH model.

Comparison of ONETRAN calculations of electron beam dose profiles with Monte Carlo and experiment

International Nuclear Information System (INIS)

Garth, J.C.; Woolf, S.

1987-01-01

Electron beam dose profiles have been calculated using a multigroup, discrete ordinates solution of the Spencer-Lewis electron transport equation. This was accomplished by introducing electron transport cross-sections into the ONETRAN code in a simple manner. The authors' purpose is to ''benchmark'' this electron transport model and to demonstrate its accuracy and capabilities over the energy range from 30 keV to 20 MeV. Many of their results are compared with the extensive measurements and TIGER Monte Carlo data. In general the ONETRAN results are smoother, agree with TIGER within the statistical error of the Monte Carlo histograms and require about one tenth the running time of Monte Carlo

Monte carlo simulation for soot dynamics

KAUST Repository

Zhou, Kun

2012-01-01

A new Monte Carlo method termed Comb-like frame Monte Carlo is developed to simulate the soot dynamics. Detailed stochastic error analysis is provided. Comb-like frame Monte Carlo is coupled with the gas phase solver Chemkin II to simulate soot formation in a 1-D premixed burner stabilized flame. The simulated soot number density, volume fraction, and particle size distribution all agree well with the measurement available in literature. The origin of the bimodal distribution of particle size distribution is revealed with quantitative proof.

Statistical estimation Monte Carlo for unreliability evaluation of highly reliable system

International Nuclear Information System (INIS)

Xiao Gang; Su Guanghui; Jia Dounan; Li Tianduo

2000-01-01

Based on analog Monte Carlo simulation, statistical Monte Carlo methods for unreliable evaluation of highly reliable system are constructed, including direct statistical estimation Monte Carlo method and weighted statistical estimation Monte Carlo method. The basal element is given, and the statistical estimation Monte Carlo estimators are derived. Direct Monte Carlo simulation method, bounding-sampling method, forced transitions Monte Carlo method, direct statistical estimation Monte Carlo and weighted statistical estimation Monte Carlo are used to evaluate unreliability of a same system. By comparing, weighted statistical estimation Monte Carlo estimator has smallest variance, and has highest calculating efficiency

Multilevel sequential Monte Carlo samplers

KAUST Repository

Beskos, Alexandros; Jasra, Ajay; Law, Kody; Tempone, Raul; Zhou, Yan

2016-01-01

In this article we consider the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods which depend on the step-size level . hL. In addition, the expectation cannot be computed analytically and one often resorts to Monte Carlo methods. In the context of this problem, it is known that the introduction of the multilevel Monte Carlo (MLMC) method can reduce the amount of computational effort to estimate expectations, for a given level of error. This is achieved via a telescoping identity associated to a Monte Carlo approximation of a sequence of probability distributions with discretization levels . ∞>h0>h1⋯>hL. In many practical problems of interest, one cannot achieve an i.i.d. sampling of the associated sequence and a sequential Monte Carlo (SMC) version of the MLMC method is introduced to deal with this problem. It is shown that under appropriate assumptions, the attractive property of a reduction of the amount of computational effort to estimate expectations, for a given level of error, can be maintained within the SMC context. That is, relative to exact sampling and Monte Carlo for the distribution at the finest level . hL. The approach is numerically illustrated on a Bayesian inverse problem. © 2016 Elsevier B.V.

Multilevel sequential Monte Carlo samplers

KAUST Repository

Beskos, Alexandros

2016-08-29

In this article we consider the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods which depend on the step-size level . hL. In addition, the expectation cannot be computed analytically and one often resorts to Monte Carlo methods. In the context of this problem, it is known that the introduction of the multilevel Monte Carlo (MLMC) method can reduce the amount of computational effort to estimate expectations, for a given level of error. This is achieved via a telescoping identity associated to a Monte Carlo approximation of a sequence of probability distributions with discretization levels . ∞>h0>h1⋯>hL. In many practical problems of interest, one cannot achieve an i.i.d. sampling of the associated sequence and a sequential Monte Carlo (SMC) version of the MLMC method is introduced to deal with this problem. It is shown that under appropriate assumptions, the attractive property of a reduction of the amount of computational effort to estimate expectations, for a given level of error, can be maintained within the SMC context. That is, relative to exact sampling and Monte Carlo for the distribution at the finest level . hL. The approach is numerically illustrated on a Bayesian inverse problem. © 2016 Elsevier B.V.

Applications of Monte Carlo method in Medical Physics

International Nuclear Information System (INIS)

Diez Rios, A.; Labajos, M.

1989-01-01

The basic ideas of Monte Carlo techniques are presented. Random numbers and their generation by congruential methods, which underlie Monte Carlo calculations are shown. Monte Carlo techniques to solve integrals are discussed. The evaluation of a simple monodimensional integral with a known answer, by means of two different Monte Carlo approaches are discussed. The basic principles to simualate on a computer photon histories reduce variance and the current applications in Medical Physics are commented. (Author)

Guideline of Monte Carlo calculation. Neutron/gamma ray transport simulation by Monte Carlo method

CERN Document Server

2002-01-01

This report condenses basic theories and advanced applications of neutron/gamma ray transport calculations in many fields of nuclear energy research. Chapters 1 through 5 treat historical progress of Monte Carlo methods, general issues of variance reduction technique, cross section libraries used in continuous energy Monte Carlo codes. In chapter 6, the following issues are discussed: fusion benchmark experiments, design of ITER, experiment analyses of fast critical assembly, core analyses of JMTR, simulation of pulsed neutron experiment, core analyses of HTTR, duct streaming calculations, bulk shielding calculations, neutron/gamma ray transport calculations of the Hiroshima atomic bomb. Chapters 8 and 9 treat function enhancements of MCNP and MVP codes, and a parallel processing of Monte Carlo calculation, respectively. An important references are attached at the end of this report.

Multilevel markov chain monte carlo method for high-contrast single-phase flow problems

KAUST Repository

Efendiev, Yalchin R.

2014-12-19

In this paper we propose a general framework for the uncertainty quantification of quantities of interest for high-contrast single-phase flow problems. It is based on the generalized multiscale finite element method (GMsFEM) and multilevel Monte Carlo (MLMC) methods. The former provides a hierarchy of approximations of different resolution, whereas the latter gives an efficient way to estimate quantities of interest using samples on different levels. The number of basis functions in the online GMsFEM stage can be varied to determine the solution resolution and the computational cost, and to efficiently generate samples at different levels. In particular, it is cheap to generate samples on coarse grids but with low resolution, and it is expensive to generate samples on fine grids with high accuracy. By suitably choosing the number of samples at different levels, one can leverage the expensive computation in larger fine-grid spaces toward smaller coarse-grid spaces, while retaining the accuracy of the final Monte Carlo estimate. Further, we describe a multilevel Markov chain Monte Carlo method, which sequentially screens the proposal with different levels of approximations and reduces the number of evaluations required on fine grids, while combining the samples at different levels to arrive at an accurate estimate. The framework seamlessly integrates the multiscale features of the GMsFEM with the multilevel feature of the MLMC methods following the work in [26], and our numerical experiments illustrate its efficiency and accuracy in comparison with standard Monte Carlo estimates. © Global Science Press Limited 2015.

Experience with the Monte Carlo Method

Energy Technology Data Exchange (ETDEWEB)

Hussein, E M.A. [Department of Mechanical Engineering University of New Brunswick, Fredericton, N.B., (Canada)

2007-06-15

Monte Carlo simulation of radiation transport provides a powerful research and design tool that resembles in many aspects laboratory experiments. Moreover, Monte Carlo simulations can provide an insight not attainable in the laboratory. However, the Monte Carlo method has its limitations, which if not taken into account can result in misleading conclusions. This paper will present the experience of this author, over almost three decades, in the use of the Monte Carlo method for a variety of applications. Examples will be shown on how the method was used to explore new ideas, as a parametric study and design optimization tool, and to analyze experimental data. The consequences of not accounting in detail for detector response and the scattering of radiation by surrounding structures are two of the examples that will be presented to demonstrate the pitfall of condensed.

Experience with the Monte Carlo Method

International Nuclear Information System (INIS)

Hussein, E.M.A.

2007-01-01

Monte Carlo simulation of radiation transport provides a powerful research and design tool that resembles in many aspects laboratory experiments. Moreover, Monte Carlo simulations can provide an insight not attainable in the laboratory. However, the Monte Carlo method has its limitations, which if not taken into account can result in misleading conclusions. This paper will present the experience of this author, over almost three decades, in the use of the Monte Carlo method for a variety of applications. Examples will be shown on how the method was used to explore new ideas, as a parametric study and design optimization tool, and to analyze experimental data. The consequences of not accounting in detail for detector response and the scattering of radiation by surrounding structures are two of the examples that will be presented to demonstrate the pitfall of condensed

Monte Carlo alpha calculation

Energy Technology Data Exchange (ETDEWEB)

Brockway, D.; Soran, P.; Whalen, P.

1985-01-01

A Monte Carlo algorithm to efficiently calculate static alpha eigenvalues, N = ne/sup ..cap alpha..t/, for supercritical systems has been developed and tested. A direct Monte Carlo approach to calculating a static alpha is to simply follow the buildup in time of neutrons in a supercritical system and evaluate the logarithmic derivative of the neutron population with respect to time. This procedure is expensive, and the solution is very noisy and almost useless for a system near critical. The modified approach is to convert the time-dependent problem to a static ..cap alpha../sup -/eigenvalue problem and regress ..cap alpha.. on solutions of a/sup -/ k/sup -/eigenvalue problem. In practice, this procedure is much more efficient than the direct calculation, and produces much more accurate results. Because the Monte Carlo codes are intrinsically three-dimensional and use elaborate continuous-energy cross sections, this technique is now used as a standard for evaluating other calculational techniques in odd geometries or with group cross sections.

Monte Carlo sensitivity analysis of an Eulerian large-scale air pollution model

International Nuclear Information System (INIS)

Dimov, I.; Georgieva, R.; Ostromsky, Tz.

2012-01-01

Variance-based approaches for global sensitivity analysis have been applied and analyzed to study the sensitivity of air pollutant concentrations according to variations of rates of chemical reactions. The Unified Danish Eulerian Model has been used as a mathematical model simulating a remote transport of air pollutants. Various Monte Carlo algorithms for numerical integration have been applied to compute Sobol's global sensitivity indices. A newly developed Monte Carlo algorithm based on Sobol's quasi-random points MCA-MSS has been applied for numerical integration. It has been compared with some existing approaches, namely Sobol's ΛΠ τ sequences, an adaptive Monte Carlo algorithm, the plain Monte Carlo algorithm, as well as, eFAST and Sobol's sensitivity approaches both implemented in SIMLAB software. The analysis and numerical results show advantages of MCA-MSS for relatively small sensitivity indices in terms of accuracy and efficiency. Practical guidelines on the estimation of Sobol's global sensitivity indices in the presence of computational difficulties have been provided. - Highlights: ► Variance-based global sensitivity analysis is performed for the air pollution model UNI-DEM. ► The main effect of input parameters dominates over higher-order interactions. ► Ozone concentrations are influenced mostly by variability of three chemical reactions rates. ► The newly developed MCA-MSS for multidimensional integration is compared with other approaches. ► More precise approaches like MCA-MSS should be applied when the needed accuracy has not been achieved.

Monte Carlo simulations of neutron scattering instruments

International Nuclear Information System (INIS)

Aestrand, Per-Olof; Copenhagen Univ.; Lefmann, K.; Nielsen, K.

2001-01-01

A Monte Carlo simulation is an important computational tool used in many areas of science and engineering. The use of Monte Carlo techniques for simulating neutron scattering instruments is discussed. The basic ideas, techniques and approximations are presented. Since the construction of a neutron scattering instrument is very expensive, Monte Carlo software used for design of instruments have to be validated and tested extensively. The McStas software was designed with these aspects in mind and some of the basic principles of the McStas software will be discussed. Finally, some future prospects are discussed for using Monte Carlo simulations in optimizing neutron scattering experiments. (R.P.)

Linear filtering applied to Monte Carlo criticality calculations

International Nuclear Information System (INIS)

Morrison, G.W.; Pike, D.H.; Petrie, L.M.

1975-01-01

A significant improvement in the acceleration of the convergence of the eigenvalue computed by Monte Carlo techniques has been developed by applying linear filtering theory to Monte Carlo calculations for multiplying systems. A Kalman filter was applied to a KENO Monte Carlo calculation of an experimental critical system consisting of eight interacting units of fissile material. A comparison of the filter estimate and the Monte Carlo realization was made. The Kalman filter converged in five iterations to 0.9977. After 95 iterations, the average k-eff from the Monte Carlo calculation was 0.9981. This demonstrates that the Kalman filter has the potential of reducing the calculational effort of multiplying systems. Other examples and results are discussed

Burnup calculations using Monte Carlo method

International Nuclear Information System (INIS)

Ghosh, Biplab; Degweker, S.B.

2009-01-01

In the recent years, interest in burnup calculations using Monte Carlo methods has gained momentum. Previous burn up codes have used multigroup transport theory based calculations followed by diffusion theory based core calculations for the neutronic portion of codes. The transport theory methods invariably make approximations with regard to treatment of the energy and angle variables involved in scattering, besides approximations related to geometry simplification. Cell homogenisation to produce diffusion, theory parameters adds to these approximations. Moreover, while diffusion theory works for most reactors, it does not produce accurate results in systems that have strong gradients, strong absorbers or large voids. Also, diffusion theory codes are geometry limited (rectangular, hexagonal, cylindrical, and spherical coordinates). Monte Carlo methods are ideal to solve very heterogeneous reactors and/or lattices/assemblies in which considerable burnable poisons are used. The key feature of this approach is that Monte Carlo methods permit essentially 'exact' modeling of all geometrical detail, without resort to ene and spatial homogenization of neutron cross sections. Monte Carlo method would also be better for in Accelerator Driven Systems (ADS) which could have strong gradients due to the external source and a sub-critical assembly. To meet the demand for an accurate burnup code, we have developed a Monte Carlo burnup calculation code system in which Monte Carlo neutron transport code is coupled with a versatile code (McBurn) for calculating the buildup and decay of nuclides in nuclear materials. McBurn is developed from scratch by the authors. In this article we will discuss our effort in developing the continuous energy Monte Carlo burn-up code, McBurn. McBurn is intended for entire reactor core as well as for unit cells and assemblies. Generally, McBurn can do burnup of any geometrical system which can be handled by the underlying Monte Carlo transport code

Monte Carlo simulations for plasma physics

International Nuclear Information System (INIS)

Okamoto, M.; Murakami, S.; Nakajima, N.; Wang, W.X.

2000-07-01

Plasma behaviours are very complicated and the analyses are generally difficult. However, when the collisional processes play an important role in the plasma behaviour, the Monte Carlo method is often employed as a useful tool. For examples, in neutral particle injection heating (NBI heating), electron or ion cyclotron heating, and alpha heating, Coulomb collisions slow down high energetic particles and pitch angle scatter them. These processes are often studied by the Monte Carlo technique and good agreements can be obtained with the experimental results. Recently, Monte Carlo Method has been developed to study fast particle transports associated with heating and generating the radial electric field. Further it is applied to investigating the neoclassical transport in the plasma with steep gradients of density and temperatures which is beyong the conventional neoclassical theory. In this report, we briefly summarize the researches done by the present authors utilizing the Monte Carlo method. (author)

A hybrid transport-diffusion method for Monte Carlo radiative-transfer simulations

International Nuclear Information System (INIS)

Densmore, Jeffery D.; Urbatsch, Todd J.; Evans, Thomas M.; Buksas, Michael W.

2007-01-01

Discrete Diffusion Monte Carlo (DDMC) is a technique for increasing the efficiency of Monte Carlo particle-transport simulations in diffusive media. If standard Monte Carlo is used in such media, particle histories will consist of many small steps, resulting in a computationally expensive calculation. In DDMC, particles take discrete steps between spatial cells according to a discretized diffusion equation. Each discrete step replaces many small Monte Carlo steps, thus increasing the efficiency of the simulation. In addition, given that DDMC is based on a diffusion equation, it should produce accurate solutions if used judiciously. In practice, DDMC is combined with standard Monte Carlo to form a hybrid transport-diffusion method that can accurately simulate problems with both diffusive and non-diffusive regions. In this paper, we extend previously developed DDMC techniques in several ways that improve the accuracy and utility of DDMC for nonlinear, time-dependent, radiative-transfer calculations. The use of DDMC in these types of problems is advantageous since, due to the underlying linearizations, optically thick regions appear to be diffusive. First, we employ a diffusion equation that is discretized in space but is continuous in time. Not only is this methodology theoretically more accurate than temporally discretized DDMC techniques, but it also has the benefit that a particle's time is always known. Thus, there is no ambiguity regarding what time to assign a particle that leaves an optically thick region (where DDMC is used) and begins transporting by standard Monte Carlo in an optically thin region. Also, we treat the interface between optically thick and optically thin regions with an improved method, based on the asymptotic diffusion-limit boundary condition, that can produce accurate results regardless of the angular distribution of the incident Monte Carlo particles. Finally, we develop a technique for estimating radiation momentum deposition during the

Performance of quantum Monte Carlo for calculating molecular bond lengths

Energy Technology Data Exchange (ETDEWEB)

Cleland, Deidre M., E-mail: deidre.cleland@csiro.au; Per, Manolo C., E-mail: manolo.per@csiro.au [CSIRO Virtual Nanoscience Laboratory, 343 Royal Parade, Parkville, Victoria 3052 (Australia)

2016-03-28

This work investigates the accuracy of real-space quantum Monte Carlo (QMC) methods for calculating molecular geometries. We present the equilibrium bond lengths of a test set of 30 diatomic molecules calculated using variational Monte Carlo (VMC) and diffusion Monte Carlo (DMC) methods. The effect of different trial wavefunctions is investigated using single determinants constructed from Hartree-Fock (HF) and Density Functional Theory (DFT) orbitals with LDA, PBE, and B3LYP functionals, as well as small multi-configurational self-consistent field (MCSCF) multi-determinant expansions. When compared to experimental geometries, all DMC methods exhibit smaller mean-absolute deviations (MADs) than those given by HF, DFT, and MCSCF. The most accurate MAD of 3 ± 2 × 10{sup −3} Å is achieved using DMC with a small multi-determinant expansion. However, the more computationally efficient multi-determinant VMC method has a similar MAD of only 4.0 ± 0.9 × 10{sup −3} Å, suggesting that QMC forces calculated from the relatively simple VMC algorithm may often be sufficient for accurate molecular geometries.

Monte Carlo methods and models in finance and insurance

CERN Document Server

Korn, Ralf; Kroisandt, Gerald

2010-01-01

Offering a unique balance between applications and calculations, Monte Carlo Methods and Models in Finance and Insurance incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods. It presents recent methods and algorithms, including the multilevel Monte Carlo method, the statistical Romberg method, and the Heath-Platen estimator, as well as recent financial and actuarial models, such as the Cheyette and dynamic mortality models. The authors separately discuss Monte Carlo techniques, stochastic process basics, and the theoretical background and intuition behind financial and actuarial mathematics, before bringing the topics together to apply the Monte Carlo methods to areas of finance and insurance. This allows for the easy identification of standard Monte Carlo tools and for a detailed focus on the main principles of financial and insurance mathematics. The book describes high-level Monte Carlo methods for standard simulation and the simulation of...

Monte Carlo approaches to light nuclei

International Nuclear Information System (INIS)

Carlson, J.

1990-01-01

Significant progress has been made recently in the application of Monte Carlo methods to the study of light nuclei. We review new Green's function Monte Carlo results for the alpha particle, Variational Monte Carlo studies of 16 O, and methods for low-energy scattering and transitions. Through these calculations, a coherent picture of the structure and electromagnetic properties of light nuclei has arisen. In particular, we examine the effect of the three-nucleon interaction and the importance of exchange currents in a variety of experimentally measured properties, including form factors and capture cross sections. 29 refs., 7 figs

Monte Carlo approaches to light nuclei

Energy Technology Data Exchange (ETDEWEB)

Carlson, J.

1990-01-01

Significant progress has been made recently in the application of Monte Carlo methods to the study of light nuclei. We review new Green's function Monte Carlo results for the alpha particle, Variational Monte Carlo studies of {sup 16}O, and methods for low-energy scattering and transitions. Through these calculations, a coherent picture of the structure and electromagnetic properties of light nuclei has arisen. In particular, we examine the effect of the three-nucleon interaction and the importance of exchange currents in a variety of experimentally measured properties, including form factors and capture cross sections. 29 refs., 7 figs.

Advanced Mesh-Enabled Monte carlo capability for Multi-Physics Reactor Analysis

Energy Technology Data Exchange (ETDEWEB)

Wilson, Paul; Evans, Thomas; Tautges, Tim

2012-12-24

This project will accumulate high-precision fluxes throughout reactor geometry on a non- orthogonal grid of cells to support multi-physics coupling, in order to more accurately calculate parameters such as reactivity coefficients and to generate multi-group cross sections. This work will be based upon recent developments to incorporate advanced geometry and mesh capability in a modular Monte Carlo toolkit with computational science technology that is in use in related reactor simulation software development. Coupling this capability with production-scale Monte Carlo radiation transport codes can provide advanced and extensible test-beds for these developments. Continuous energy Monte Carlo methods are generally considered to be the most accurate computational tool for simulating radiation transport in complex geometries, particularly neutron transport in reactors. Nevertheless, there are several limitations for their use in reactor analysis. Most significantly, there is a trade-off between the fidelity of results in phase space, statistical accuracy, and the amount of computer time required for simulation. Consequently, to achieve an acceptable level of statistical convergence in high-fidelity results required for modern coupled multi-physics analysis, the required computer time makes Monte Carlo methods prohibitive for design iterations and detailed whole-core analysis. More subtly, the statistical uncertainty is typically not uniform throughout the domain, and the simulation quality is limited by the regions with the largest statistical uncertainty. In addition, the formulation of neutron scattering laws in continuous energy Monte Carlo methods makes it difficult to calculate adjoint neutron fluxes required to properly determine important reactivity parameters. Finally, most Monte Carlo codes available for reactor analysis have relied on orthogonal hexahedral grids for tallies that do not conform to the geometric boundaries and are thus generally not well

Simulation and the Monte Carlo method

CERN Document Server

Rubinstein, Reuven Y

2016-01-01

Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo, variance reduction techniques such as the transform likelihood ratio...

Lecture 1. Monte Carlo basics. Lecture 2. Adjoint Monte Carlo. Lecture 3. Coupled Forward-Adjoint calculations

Energy Technology Data Exchange (ETDEWEB)

Hoogenboom, J.E. [Delft University of Technology, Interfaculty Reactor Institute, Delft (Netherlands)

2000-07-01

The Monte Carlo method is a statistical method to solve mathematical and physical problems using random numbers. The principle of the methods will be demonstrated for a simple mathematical problem and for neutron transport. Various types of estimators will be discussed, as well as generally applied variance reduction methods like splitting, Russian roulette and importance biasing. The theoretical formulation for solving eigenvalue problems for multiplying systems will be shown. Some reflections will be given about the applicability of the Monte Carlo method, its limitations and its future prospects for reactor physics calculations. Adjoint Monte Carlo is a Monte Carlo game to solve the adjoint neutron (or photon) transport equation. The adjoint transport equation can be interpreted in terms of simulating histories of artificial particles, which show properties of neutrons that move backwards in history. These particles will start their history at the detector from which the response must be estimated and give a contribution to the estimated quantity when they hit or pass through the neutron source. Application to multigroup transport formulation will be demonstrated Possible implementation for the continuous energy case will be outlined. The inherent advantages and disadvantages of the method will be discussed. The Midway Monte Carlo method will be presented for calculating a detector response due to a (neutron or photon) source. A derivation will be given of the basic formula for the Midway Monte Carlo method The black absorber technique, allowing for a cutoff of particle histories when reaching the midway surface in one of the calculations will be derived. An extension of the theory to coupled neutron-photon problems is given. The method will be demonstrated for an oil well logging problem, comprising a neutron source in a borehole and photon detectors to register the photons generated by inelastic neutron scattering. (author)

Lecture 1. Monte Carlo basics. Lecture 2. Adjoint Monte Carlo. Lecture 3. Coupled Forward-Adjoint calculations

International Nuclear Information System (INIS)

Hoogenboom, J.E.

2000-01-01

The Monte Carlo method is a statistical method to solve mathematical and physical problems using random numbers. The principle of the methods will be demonstrated for a simple mathematical problem and for neutron transport. Various types of estimators will be discussed, as well as generally applied variance reduction methods like splitting, Russian roulette and importance biasing. The theoretical formulation for solving eigenvalue problems for multiplying systems will be shown. Some reflections will be given about the applicability of the Monte Carlo method, its limitations and its future prospects for reactor physics calculations. Adjoint Monte Carlo is a Monte Carlo game to solve the adjoint neutron (or photon) transport equation. The adjoint transport equation can be interpreted in terms of simulating histories of artificial particles, which show properties of neutrons that move backwards in history. These particles will start their history at the detector from which the response must be estimated and give a contribution to the estimated quantity when they hit or pass through the neutron source. Application to multigroup transport formulation will be demonstrated Possible implementation for the continuous energy case will be outlined. The inherent advantages and disadvantages of the method will be discussed. The Midway Monte Carlo method will be presented for calculating a detector response due to a (neutron or photon) source. A derivation will be given of the basic formula for the Midway Monte Carlo method The black absorber technique, allowing for a cutoff of particle histories when reaching the midway surface in one of the calculations will be derived. An extension of the theory to coupled neutron-photon problems is given. The method will be demonstrated for an oil well logging problem, comprising a neutron source in a borehole and photon detectors to register the photons generated by inelastic neutron scattering. (author)

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media.

Science.gov (United States)

Crevillén-García, D; Power, H

2017-08-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

Science.gov (United States)

Crevillén-García, D.; Power, H.

2017-08-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

Adaptable three-dimensional Monte Carlo modeling of imaged blood vessels in skin

Science.gov (United States)

Pfefer, T. Joshua; Barton, Jennifer K.; Chan, Eric K.; Ducros, Mathieu G.; Sorg, Brian S.; Milner, Thomas E.; Nelson, J. Stuart; Welch, Ashley J.

1997-06-01

In order to reach a higher level of accuracy in simulation of port wine stain treatment, we propose to discard the typical layered geometry and cylindrical blood vessel assumptions made in optical models and use imaging techniques to define actual tissue geometry. Two main additions to the typical 3D, weighted photon, variable step size Monte Carlo routine were necessary to achieve this goal. First, optical low coherence reflectometry (OLCR) images of rat skin were used to specify a 3D material array, with each entry assigned a label to represent the type of tissue in that particular voxel. Second, the Monte Carlo algorithm was altered so that when a photon crosses into a new voxel, the remaining path length is recalculated using the new optical properties, as specified by the material array. The model has shown good agreement with data from the literature. Monte Carlo simulations using OLCR images of asymmetrically curved blood vessels show various effects such as shading, scattering-induced peaks at vessel surfaces, and directionality-induced gradients in energy deposition. In conclusion, this augmentation of the Monte Carlo method can accurately simulate light transport for a wide variety of nonhomogeneous tissue geometries.

Monte Carlo Techniques for Nuclear Systems - Theory Lectures

International Nuclear Information System (INIS)

Brown, Forrest B.; Univ. of New Mexico, Albuquerque, NM

2016-01-01

These are lecture notes for a Monte Carlo class given at the University of New Mexico. The following topics are covered: course information; nuclear eng. review & MC; random numbers and sampling; computational geometry; collision physics; tallies and statistics; eigenvalue calculations I; eigenvalue calculations II; eigenvalue calculations III; variance reduction; parallel Monte Carlo; parameter studies; fission matrix and higher eigenmodes; doppler broadening; Monte Carlo depletion; HTGR modeling; coupled MC and T/H calculations; fission energy deposition. Solving particle transport problems with the Monte Carlo method is simple - just simulate the particle behavior. The devil is in the details, however. These lectures provide a balanced approach to the theory and practice of Monte Carlo simulation codes. The first lectures provide an overview of Monte Carlo simulation methods, covering the transport equation, random sampling, computational geometry, collision physics, and statistics. The next lectures focus on the state-of-the-art in Monte Carlo criticality simulations, covering the theory of eigenvalue calculations, convergence analysis, dominance ratio calculations, bias in Keff and tallies, bias in uncertainties, a case study of a realistic calculation, and Wielandt acceleration techniques. The remaining lectures cover advanced topics, including HTGR modeling and stochastic geometry, temperature dependence, fission energy deposition, depletion calculations, parallel calculations, and parameter studies. This portion of the class focuses on using MCNP to perform criticality calculations for reactor physics and criticality safety applications. It is an intermediate level class, intended for those with at least some familiarity with MCNP. Class examples provide hands-on experience at running the code, plotting both geometry and results, and understanding the code output. The class includes lectures & hands-on computer use for a variety of Monte Carlo calculations

Monte Carlo Techniques for Nuclear Systems - Theory Lectures

Energy Technology Data Exchange (ETDEWEB)

Brown, Forrest B. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Monte Carlo Methods, Codes, and Applications Group; Univ. of New Mexico, Albuquerque, NM (United States). Nuclear Engineering Dept.

2016-11-29

These are lecture notes for a Monte Carlo class given at the University of New Mexico. The following topics are covered: course information; nuclear eng. review & MC; random numbers and sampling; computational geometry; collision physics; tallies and statistics; eigenvalue calculations I; eigenvalue calculations II; eigenvalue calculations III; variance reduction; parallel Monte Carlo; parameter studies; fission matrix and higher eigenmodes; doppler broadening; Monte Carlo depletion; HTGR modeling; coupled MC and T/H calculations; fission energy deposition. Solving particle transport problems with the Monte Carlo method is simple - just simulate the particle behavior. The devil is in the details, however. These lectures provide a balanced approach to the theory and practice of Monte Carlo simulation codes. The first lectures provide an overview of Monte Carlo simulation methods, covering the transport equation, random sampling, computational geometry, collision physics, and statistics. The next lectures focus on the state-of-the-art in Monte Carlo criticality simulations, covering the theory of eigenvalue calculations, convergence analysis, dominance ratio calculations, bias in Keff and tallies, bias in uncertainties, a case study of a realistic calculation, and Wielandt acceleration techniques. The remaining lectures cover advanced topics, including HTGR modeling and stochastic geometry, temperature dependence, fission energy deposition, depletion calculations, parallel calculations, and parameter studies. This portion of the class focuses on using MCNP to perform criticality calculations for reactor physics and criticality safety applications. It is an intermediate level class, intended for those with at least some familiarity with MCNP. Class examples provide hands-on experience at running the code, plotting both geometry and results, and understanding the code output. The class includes lectures & hands-on computer use for a variety of Monte Carlo calculations

Generalized hybrid Monte Carlo - CMFD methods for fission source convergence

International Nuclear Information System (INIS)

Wolters, Emily R.; Larsen, Edward W.; Martin, William R.

2011-01-01

In this paper, we generalize the recently published 'CMFD-Accelerated Monte Carlo' method and present two new methods that reduce the statistical error in CMFD-Accelerated Monte Carlo. The CMFD-Accelerated Monte Carlo method uses Monte Carlo to estimate nonlinear functionals used in low-order CMFD equations for the eigenfunction and eigenvalue. The Monte Carlo fission source is then modified to match the resulting CMFD fission source in a 'feedback' procedure. The two proposed methods differ from CMFD-Accelerated Monte Carlo in the definition of the required nonlinear functionals, but they have identical CMFD equations. The proposed methods are compared with CMFD-Accelerated Monte Carlo on a high dominance ratio test problem. All hybrid methods converge the Monte Carlo fission source almost immediately, leading to a large reduction in the number of inactive cycles required. The proposed methods stabilize the fission source more efficiently than CMFD-Accelerated Monte Carlo, leading to a reduction in the number of active cycles required. Finally, as in CMFD-Accelerated Monte Carlo, the apparent variance of the eigenfunction is approximately equal to the real variance, so the real error is well-estimated from a single calculation. This is an advantage over standard Monte Carlo, in which the real error can be underestimated due to inter-cycle correlation. (author)

Evaluation of accuracy of Monte Carlo code MVP with VHTRC experiments. Multiplication factor at criticality, burnable poison worth and void worth

International Nuclear Information System (INIS)

Nojiri, Naoki; Yamashita, Kiyonobu; Fiujimoto, Nozomu; Nakano, Masaaki , Yamane, Tsuyoshi; Akino, Fujiyoshi.

1997-11-01

Experimental data of VHTRC (Very High Temperature Reactor Critical Assembly) were analyzed using Monte Carlo code MVP (general purpose Monte Carlo code of neutron and photon transport calculations based on the continuous energy method). The calculation accuracy of the code was evaluated by the analysis for nuclear characteristics of a HTGR (high temperature gas-cooled reactor). The MVP code can analyze with a detailed three-dimensional core model with a few approximations. The HTGRs have following characteristics from view point of nuclear design : they have burnable poisons, many void holes, namely, the control insertion holes and so on. Taking account of these characteristics, multiplication factor at criticality, burnable poison worth, and void worth were evaluated. The maximum calculation errors were 0.8%Δk, 7%, and 25% respectively, From these results, it can be concluded that the MVP code is able to be applied to the nuclear characteristics analysis of the HTGR like the High Temperature Engineering Test Reactor (HTTR). (author)

Is Monte Carlo embarrassingly parallel?

Energy Technology Data Exchange (ETDEWEB)

Hoogenboom, J. E. [Delft Univ. of Technology, Mekelweg 15, 2629 JB Delft (Netherlands); Delft Nuclear Consultancy, IJsselzoom 2, 2902 LB Capelle aan den IJssel (Netherlands)

2012-07-01

Monte Carlo is often stated as being embarrassingly parallel. However, running a Monte Carlo calculation, especially a reactor criticality calculation, in parallel using tens of processors shows a serious limitation in speedup and the execution time may even increase beyond a certain number of processors. In this paper the main causes of the loss of efficiency when using many processors are analyzed using a simple Monte Carlo program for criticality. The basic mechanism for parallel execution is MPI. One of the bottlenecks turn out to be the rendez-vous points in the parallel calculation used for synchronization and exchange of data between processors. This happens at least at the end of each cycle for fission source generation in order to collect the full fission source distribution for the next cycle and to estimate the effective multiplication factor, which is not only part of the requested results, but also input to the next cycle for population control. Basic improvements to overcome this limitation are suggested and tested. Also other time losses in the parallel calculation are identified. Moreover, the threading mechanism, which allows the parallel execution of tasks based on shared memory using OpenMP, is analyzed in detail. Recommendations are given to get the maximum efficiency out of a parallel Monte Carlo calculation. (authors)

Is Monte Carlo embarrassingly parallel?

International Nuclear Information System (INIS)

Hoogenboom, J. E.

2012-01-01

Monte Carlo is often stated as being embarrassingly parallel. However, running a Monte Carlo calculation, especially a reactor criticality calculation, in parallel using tens of processors shows a serious limitation in speedup and the execution time may even increase beyond a certain number of processors. In this paper the main causes of the loss of efficiency when using many processors are analyzed using a simple Monte Carlo program for criticality. The basic mechanism for parallel execution is MPI. One of the bottlenecks turn out to be the rendez-vous points in the parallel calculation used for synchronization and exchange of data between processors. This happens at least at the end of each cycle for fission source generation in order to collect the full fission source distribution for the next cycle and to estimate the effective multiplication factor, which is not only part of the requested results, but also input to the next cycle for population control. Basic improvements to overcome this limitation are suggested and tested. Also other time losses in the parallel calculation are identified. Moreover, the threading mechanism, which allows the parallel execution of tasks based on shared memory using OpenMP, is analyzed in detail. Recommendations are given to get the maximum efficiency out of a parallel Monte Carlo calculation. (authors)

Application of MCAM in generating Monte Carlo model for ITER port limiter

International Nuclear Information System (INIS)

Lu Lei; Li Ying; Ding Aiping; Zeng Qin; Huang Chenyu; Wu Yican

2007-01-01

On the basis of the pre-processing and conversion functions supplied by MCAM (Monte-Carlo Particle Transport Calculated Automatic Modeling System), this paper performed the generation of ITER Port Limiter MC (Monte-Carlo) calculation model from the CAD engineering model. The result was validated by using reverse function of MCAM and MCNP PLOT 2D cross-section drawing program. the successful application of MCAM to ITER Port Limiter demonstrates that MCAM is capable of dramatically increasing the efficiency and accuracy to generate MC calculation models from CAD engineering models with complex geometry comparing with the traditional manual modeling method. (authors)

Mean field simulation for Monte Carlo integration

CERN Document Server

Del Moral, Pierre

2013-01-01

In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to parallel and distributed computation, these advanced particle algorithms include nonlinear interacting jump diffusions; quantum, diffusion, and resampled Monte Carlo methods; Feynman-Kac particle models; genetic and evolutionary algorithms; sequential Monte Carlo methods; adaptive and interacting Marko

Variational Variance Reduction for Monte Carlo Criticality Calculations

International Nuclear Information System (INIS)

Densmore, Jeffery D.; Larsen, Edward W.

2001-01-01

A new variational variance reduction (VVR) method for Monte Carlo criticality calculations was developed. This method employs (a) a variational functional that is more accurate than the standard direct functional, (b) a representation of the deterministically obtained adjoint flux that is especially accurate for optically thick problems with high scattering ratios, and (c) estimates of the forward flux obtained by Monte Carlo. The VVR method requires no nonanalog Monte Carlo biasing, but it may be used in conjunction with Monte Carlo biasing schemes. Some results are presented from a class of criticality calculations involving alternating arrays of fuel and moderator regions

Monte Carlo Solutions for Blind Phase Noise Estimation

Directory of Open Access Journals (Sweden)

Çırpan Hakan

2009-01-01

Full Text Available This paper investigates the use of Monte Carlo sampling methods for phase noise estimation on additive white Gaussian noise (AWGN channels. The main contributions of the paper are (i the development of a Monte Carlo framework for phase noise estimation, with special attention to sequential importance sampling and Rao-Blackwellization, (ii the interpretation of existing Monte Carlo solutions within this generic framework, and (iii the derivation of a novel phase noise estimator. Contrary to the ad hoc phase noise estimators that have been proposed in the past, the estimators considered in this paper are derived from solid probabilistic and performance-determining arguments. Computer simulations demonstrate that, on one hand, the Monte Carlo phase noise estimators outperform the existing estimators and, on the other hand, our newly proposed solution exhibits a lower complexity than the existing Monte Carlo solutions.

Accuracy assessment of a new Monte Carlo based burnup computer code

International Nuclear Information System (INIS)

El Bakkari, B.; ElBardouni, T.; Nacir, B.; ElYounoussi, C.; Boulaich, Y.; Meroun, O.; Zoubair, M.; Chakir, E.

2012-01-01

Highlights: ► A new burnup code called BUCAL1 was developed. ► BUCAL1 uses the MCNP tallies directly in the calculation of the isotopic inventories. ► Validation of BUCAL1 was done by code to code comparison using VVER-1000 LEU Benchmark Assembly. ► Differences from BM value were found to be ± 600 pcm for k ∞ and ±6% for the isotopic compositions. ► The effect on reactivity due to the burnup of Gd isotopes is well reproduced by BUCAL1. - Abstract: This study aims to test for the suitability and accuracy of a new home-made Monte Carlo burnup code, called BUCAL1, by investigating and predicting the neutronic behavior of a “VVER-1000 LEU Assembly Computational Benchmark”, at lattice level. BUCAL1 uses MCNP tally information directly in the computation; this approach allows performing straightforward and accurate calculation without having to use the calculated group fluxes to perform transmutation analysis in a separate code. ENDF/B-VII evaluated nuclear data library was used in these calculations. Processing of the data library is performed using recent updates of NJOY99 system. Code to code comparisons with the reported Nuclear OECD/NEA results are presented and analyzed.

Monte Carlo based diffusion coefficients for LMFBR analysis

International Nuclear Information System (INIS)

Van Rooijen, Willem F.G.; Takeda, Toshikazu; Hazama, Taira

2010-01-01

A method based on Monte Carlo calculations is developed to estimate the diffusion coefficient of unit cells. The method uses a geometrical model similar to that used in lattice theory, but does not use the assumption of a separable fundamental mode used in lattice theory. The method uses standard Monte Carlo flux and current tallies, and the continuous energy Monte Carlo code MVP was used without modifications. Four models are presented to derive the diffusion coefficient from tally results of flux and partial currents. In this paper the method is applied to the calculation of a plate cell of the fast-spectrum critical facility ZEBRA. Conventional calculations of the diffusion coefficient diverge in the presence of planar voids in the lattice, but our Monte Carlo method can treat this situation without any problem. The Monte Carlo method was used to investigate the influence of geometrical modeling as well as the directional dependence of the diffusion coefficient. The method can be used to estimate the diffusion coefficient of complicated unit cells, the limitation being the capabilities of the Monte Carlo code. The method will be used in the future to confirm results for the diffusion coefficient obtained of the Monte Carlo code. The method will be used in the future to confirm results for the diffusion coefficient obtained with deterministic codes. (author)

Direct aperture optimization for IMRT using Monte Carlo generated beamlets

International Nuclear Information System (INIS)

Bergman, Alanah M.; Bush, Karl; Milette, Marie-Pierre; Popescu, I. Antoniu; Otto, Karl; Duzenli, Cheryl

2006-01-01

This work introduces an EGSnrc-based Monte Carlo (MC) beamlet does distribution matrix into a direct aperture optimization (DAO) algorithm for IMRT inverse planning. The technique is referred to as Monte Carlo-direct aperture optimization (MC-DAO). The goal is to assess if the combination of accurate Monte Carlo tissue inhomogeneity modeling and DAO inverse planning will improve the dose accuracy and treatment efficiency for treatment planning. Several authors have shown that the presence of small fields and/or inhomogeneous materials in IMRT treatment fields can cause dose calculation errors for algorithms that are unable to accurately model electronic disequilibrium. This issue may also affect the IMRT optimization process because the dose calculation algorithm may not properly model difficult geometries such as targets close to low-density regions (lung, air etc.). A clinical linear accelerator head is simulated using BEAMnrc (NRC, Canada). A novel in-house algorithm subdivides the resulting phase space into 2.5x5.0 mm 2 beamlets. Each beamlet is projected onto a patient-specific phantom. The beamlet dose contribution to each voxel in a structure-of-interest is calculated using DOSXYZnrc. The multileaf collimator (MLC) leaf positions are linked to the location of the beamlet does distributions. The MLC shapes are optimized using direct aperture optimization (DAO). A final Monte Carlo calculation with MLC modeling is used to compute the final dose distribution. Monte Carlo simulation can generate accurate beamlet dose distributions for traditionally difficult-to-calculate geometries, particularly for small fields crossing regions of tissue inhomogeneity. The introduction of DAO results in an additional improvement by increasing the treatment delivery efficiency. For the examples presented in this paper the reduction in the total number of monitor units to deliver is ∼33% compared to fluence-based optimization methods

Patient dose in image guided radiotherapy: Monte Carlo study of the CBCT dose contribution

OpenAIRE

Leotta, Salvatore; Amato, Ernesto; Settineri, Nicola; Basile, Emilia; Italiano, Antonio; Auditore, Lucrezia; Santacaterina, Anna; Pergolizzi, Stefano

2018-01-01

Image Guided RadioTherapy (IGRT) is a technique whose diffusion is growing thanks to the well-recognized gain in accuracy of dose delivery. However, multiple Cone Beam Computed Tomography (CBCT) scans add dose to patients, and its contribution has to be assessed and minimized. Aim of our work was to evaluate, through Monte Carlo simulations, organ doses in IGRT due to CBCT and therapeutic MV irradiation in head-neck, thorax and pelvis districts. We developed a Monte Carlo simulation in GAMOS ...

Pore-scale uncertainty quantification with multilevel Monte Carlo

KAUST Repository

Icardi, Matteo

2014-01-06

Computational fluid dynamics (CFD) simulations of pore-scale transport processes in porous media have recently gained large popularity. However the geometrical details of the pore structures can be known only in a very low number of samples and the detailed flow computations can be carried out only on a limited number of cases. The explicit introduction of randomness in the geometry and in other setup parameters can be crucial for the optimization of pore-scale investigations for random homogenization. Since there are no generic ways to parametrize the randomness in the porescale structures, Monte Carlo techniques are the most accessible to compute statistics. We propose a multilevel Monte Carlo (MLMC) technique to reduce the computational cost of estimating quantities of interest within a prescribed accuracy constraint. Random samples of pore geometries with a hierarchy of geometrical complexities and grid refinements, are synthetically generated and used to propagate the uncertainties in the flow simulations and compute statistics of macro-scale effective parameters.

The OpenMC Monte Carlo particle transport code

International Nuclear Information System (INIS)

Romano, Paul K.; Forget, Benoit

2013-01-01

Highlights: ► An open source Monte Carlo particle transport code, OpenMC, has been developed. ► Solid geometry and continuous-energy physics allow high-fidelity simulations. ► Development has focused on high performance and modern I/O techniques. ► OpenMC is capable of scaling up to hundreds of thousands of processors. ► Results on a variety of benchmark problems agree with MCNP5. -- Abstract: A new Monte Carlo code called OpenMC is currently under development at the Massachusetts Institute of Technology as a tool for simulation on high-performance computing platforms. Given that many legacy codes do not scale well on existing and future parallel computer architectures, OpenMC has been developed from scratch with a focus on high performance scalable algorithms as well as modern software design practices. The present work describes the methods used in the OpenMC code and demonstrates the performance and accuracy of the code on a variety of problems.

Computer system for Monte Carlo experimentation

International Nuclear Information System (INIS)

Grier, D.A.

1986-01-01

A new computer system for Monte Carlo Experimentation is presented. The new system speeds and simplifies the process of coding and preparing a Monte Carlo Experiment; it also encourages the proper design of Monte Carlo Experiments, and the careful analysis of the experimental results. A new functional language is the core of this system. Monte Carlo Experiments, and their experimental designs, are programmed in this new language; those programs are compiled into Fortran output. The Fortran output is then compiled and executed. The experimental results are analyzed with a standard statistics package such as Si, Isp, or Minitab or with a user-supplied program. Both the experimental results and the experimental design may be directly loaded into the workspace of those packages. The new functional language frees programmers from many of the details of programming an experiment. Experimental designs such as factorial, fractional factorial, or latin square are easily described by the control structures and expressions of the language. Specific mathematical modes are generated by the routines of the language

Estimating the Partition Function Zeros by Using the Wang-Landau Monte Carlo Algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Seung-Yeon [Korea National University of Transportation, Chungju (Korea, Republic of)

2017-03-15

The concept of the partition function zeros is one of the most efficient methods for investigating the phase transitions and the critical phenomena in various physical systems. Estimating the partition function zeros requires information on the density of states Ω(E) as a function of the energy E. Currently, the Wang-Landau Monte Carlo algorithm is one of the best methods for calculating Ω(E). The partition function zeros in the complex temperature plane of the Ising model on an L × L square lattice (L = 10 ∼ 80) with a periodic boundary condition have been estimated by using the Wang-Landau Monte Carlo algorithm. The efficiency of the Wang-Landau Monte Carlo algorithm and the accuracies of the partition function zeros have been evaluated for three different, 5%, 10%, and 20%, flatness criteria for the histogram H(E).

Random Numbers and Monte Carlo Methods

Science.gov (United States)

Scherer, Philipp O. J.

Many-body problems often involve the calculation of integrals of very high dimension which cannot be treated by standard methods. For the calculation of thermodynamic averages Monte Carlo methods are very useful which sample the integration volume at randomly chosen points. After summarizing some basic statistics, we discuss algorithms for the generation of pseudo-random numbers with given probability distribution which are essential for all Monte Carlo methods. We show how the efficiency of Monte Carlo integration can be improved by sampling preferentially the important configurations. Finally the famous Metropolis algorithm is applied to classical many-particle systems. Computer experiments visualize the central limit theorem and apply the Metropolis method to the traveling salesman problem.

LCG Monte-Carlo Data Base

CERN Document Server

Bartalini, P.; Kryukov, A.; Selyuzhenkov, Ilya V.; Sherstnev, A.; Vologdin, A.

2004-01-01

We present the Monte-Carlo events Data Base (MCDB) project and its development plans. MCDB facilitates communication between authors of Monte-Carlo generators and experimental users. It also provides a convenient book-keeping and an easy access to generator level samples. The first release of MCDB is now operational for the CMS collaboration. In this paper we review the main ideas behind MCDB and discuss future plans to develop this Data Base further within the CERN LCG framework.

Alternative implementations of the Monte Carlo power method

International Nuclear Information System (INIS)

Blomquist, R.N.; Gelbard, E.M.

2002-01-01

We compare nominal efficiencies, i.e. variances in power shapes for equal running time, of different versions of the Monte Carlo eigenvalue computation, as applied to criticality safety analysis calculations. The two main methods considered here are ''conventional'' Monte Carlo and the superhistory method, and both are used in criticality safety codes. Within each of these major methods, different variants are available for the main steps of the basic Monte Carlo algorithm. Thus, for example, different treatments of the fission process may vary in the extent to which they follow, in analog fashion, the details of real-world fission, or may vary in details of the methods by which they choose next-generation source sites. In general the same options are available in both the superhistory method and conventional Monte Carlo, but there seems not to have been much examination of the special properties of the two major methods and their minor variants. We find, first, that the superhistory method is just as efficient as conventional Monte Carlo and, secondly, that use of different variants of the basic algorithms may, in special cases, have a surprisingly large effect on Monte Carlo computational efficiency

Igo - A Monte Carlo Code For Radiotherapy Planning

International Nuclear Information System (INIS)

Goldstein, M.; Regev, D.

1999-01-01

The goal of radiation therapy is to deliver a lethal dose to the tumor, while minimizing the dose to normal tissues and vital organs. To carry out this task, it is critical to calculate correctly the 3-D dose delivered. Monte Carlo transport methods (especially the Adjoint Monte Carlo have the potential to provide more accurate predictions of the 3-D dose the currently used methods. IG0 is a Monte Carlo code derived from the general Monte Carlo Program - MCNP, tailored specifically for calculating the effects of radiation therapy. This paper describes the IG0 transport code, the PIG0 interface and some preliminary results

Monte Carlo techniques for analyzing deep-penetration problems

International Nuclear Information System (INIS)

Cramer, S.N.; Gonnord, J.; Hendricks, J.S.

1986-01-01

Current methods and difficulties in Monte Carlo deep-penetration calculations are reviewed, including statistical uncertainty and recent adjoint optimization of splitting, Russian roulette, and exponential transformation biasing. Other aspects of the random walk and estimation processes are covered, including the relatively new DXANG angular biasing technique. Specific items summarized are albedo scattering, Monte Carlo coupling techniques with discrete ordinates and other methods, adjoint solutions, and multigroup Monte Carlo. The topic of code-generated biasing parameters is presented, including the creation of adjoint importance functions from forward calculations. Finally, current and future work in the area of computer learning and artificial intelligence is discussed in connection with Monte Carlo applications

Odd-flavor Simulations by the Hybrid Monte Carlo

CERN Document Server

Takaishi, Tetsuya; Takaishi, Tetsuya; De Forcrand, Philippe

2001-01-01

The standard hybrid Monte Carlo algorithm is known to simulate even flavors QCD only. Simulations of odd flavors QCD, however, can be also performed in the framework of the hybrid Monte Carlo algorithm where the inverse of the fermion matrix is approximated by a polynomial. In this exploratory study we perform three flavors QCD simulations. We make a comparison of the hybrid Monte Carlo algorithm and the R-algorithm which also simulates odd flavors systems but has step-size errors. We find that results from our hybrid Monte Carlo algorithm are in agreement with those from the R-algorithm obtained at very small step-size.

Quantum Monte Carlo approaches for correlated systems

CERN Document Server

Becca, Federico

2017-01-01

Over the past several decades, computational approaches to studying strongly-interacting systems have become increasingly varied and sophisticated. This book provides a comprehensive introduction to state-of-the-art quantum Monte Carlo techniques relevant for applications in correlated systems. Providing a clear overview of variational wave functions, and featuring a detailed presentation of stochastic samplings including Markov chains and Langevin dynamics, which are developed into a discussion of Monte Carlo methods. The variational technique is described, from foundations to a detailed description of its algorithms. Further topics discussed include optimisation techniques, real-time dynamics and projection methods, including Green's function, reptation and auxiliary-field Monte Carlo, from basic definitions to advanced algorithms for efficient codes, and the book concludes with recent developments on the continuum space. Quantum Monte Carlo Approaches for Correlated Systems provides an extensive reference ...

Track 4: basic nuclear science variance reduction for Monte Carlo criticality simulations. 6. Variational Variance Reduction for Monte Carlo Criticality Calculations

International Nuclear Information System (INIS)

Densmore, Jeffery D.; Larsen, Edward W.

2001-01-01

Recently, it has been shown that the figure of merit (FOM) of Monte Carlo source-detector problems can be enhanced by using a variational rather than a direct functional to estimate the detector response. The direct functional, which is traditionally employed in Monte Carlo simulations, requires an estimate of the solution of the forward problem within the detector region. The variational functional is theoretically more accurate than the direct functional, but it requires estimates of the solutions of the forward and adjoint source-detector problems over the entire phase-space of the problem. In recent work, we have performed Monte Carlo simulations using the variational functional by (a) approximating the adjoint solution deterministically and representing this solution as a function in phase-space and (b) estimating the forward solution using Monte Carlo. We have called this general procedure variational variance reduction (VVR). The VVR method is more computationally expensive per history than traditional Monte Carlo because extra information must be tallied and processed. However, the variational functional yields a more accurate estimate of the detector response. Our simulations have shown that the VVR reduction in variance usually outweighs the increase in cost, resulting in an increased FOM. In recent work on source-detector problems, we have calculated the adjoint solution deterministically and represented this solution as a linear-in-angle, histogram-in-space function. This procedure has several advantages over previous implementations: (a) it requires much less adjoint information to be stored and (b) it is highly efficient for diffusive problems, due to the accurate linear-in-angle representation of the adjoint solution. (Traditional variance-reduction methods perform poorly for diffusive problems.) Here, we extend this VVR method to Monte Carlo criticality calculations, which are often diffusive and difficult for traditional variance-reduction methods

Response matrix Monte Carlo based on a general geometry local calculation for electron transport

International Nuclear Information System (INIS)

Ballinger, C.T.; Rathkopf, J.A.; Martin, W.R.

1991-01-01

A Response Matrix Monte Carlo (RMMC) method has been developed for solving electron transport problems. This method was born of the need to have a reliable, computationally efficient transport method for low energy electrons (below a few hundred keV) in all materials. Today, condensed history methods are used which reduce the computation time by modeling the combined effect of many collisions but fail at low energy because of the assumptions required to characterize the electron scattering. Analog Monte Carlo simulations are prohibitively expensive since electrons undergo coulombic scattering with little state change after a collision. The RMMC method attempts to combine the accuracy of an analog Monte Carlo simulation with the speed of the condensed history methods. Like condensed history, the RMMC method uses probability distributions functions (PDFs) to describe the energy and direction of the electron after several collisions. However, unlike the condensed history method the PDFs are based on an analog Monte Carlo simulation over a small region. Condensed history theories require assumptions about the electron scattering to derive the PDFs for direction and energy. Thus the RMMC method samples from PDFs which more accurately represent the electron random walk. Results show good agreement between the RMMC method and analog Monte Carlo. 13 refs., 8 figs

Study on critical effect in lattice homogenization via Monte Carlo method

International Nuclear Information System (INIS)

Li Mancang; Wang Kan; Yao Dong

2012-01-01

In contrast to the traditional deterministic lattice codes, generating the homogenization multigroup constants via Monte Carlo method overcomes the difficulties in geometry and treats energy in continuum. thus provides more accuracy parameters. An infinite lattice of identical symmetric motives is usually assumed when performing the homogenization. However, the finite size of a reactor is reality and it should influence the lattice calculation. In practice of the homogenization with Monte Carlo method, B N theory is applied to take the leakage effect into account. The fundamental mode with the buckling B is used as a measure of the finite size. The critical spectrum in the solution of 0-dimensional fine-group B 1 equations is used to correct the weighted spectrum for homogenization. A PWR prototype core is examined to verify that the presented method indeed generates few group constants effectively. In addition, a zero power physical experiment verification is performed. The results show that B N theory is adequate for leakage correction in the multigroup constants generation via Monte Carlo method. (authors)

Monte Carlo techniques for analyzing deep penetration problems

International Nuclear Information System (INIS)

Cramer, S.N.; Gonnord, J.; Hendricks, J.S.

1985-01-01

A review of current methods and difficulties in Monte Carlo deep-penetration calculations is presented. Statistical uncertainty is discussed, and recent adjoint optimization of splitting, Russian roulette, and exponential transformation biasing is reviewed. Other aspects of the random walk and estimation processes are covered, including the relatively new DXANG angular biasing technique. Specific items summarized are albedo scattering, Monte Carlo coupling techniques with discrete ordinates and other methods, adjoint solutions, and multi-group Monte Carlo. The topic of code-generated biasing parameters is presented, including the creation of adjoint importance functions from forward calculations. Finally, current and future work in the area of computer learning and artificial intelligence is discussed in connection with Monte Carlo applications

Monte Carlo techniques for analyzing deep penetration problems

International Nuclear Information System (INIS)

Cramer, S.N.; Gonnord, J.; Hendricks, J.S.

1985-01-01

A review of current methods and difficulties in Monte Carlo deep-penetration calculations is presented. Statistical uncertainty is discussed, and recent adjoint optimization of splitting, Russian roulette, and exponential transformation biasing is reviewed. Other aspects of the random walk and estimation processes are covered, including the relatively new DXANG angular biasing technique. Specific items summarized are albedo scattering, Monte Carlo coupling techniques with discrete ordinates and other methods, adjoint solutions, and multi-group Monte Carlo. The topic of code-generated biasing parameters is presented, including the creation of adjoint importance functions from forward calculations. Finally, current and future work in the area of computer learning and artificial intelligence is discussed in connection with Monte Carlo applications. 29 refs

Extending the alias Monte Carlo sampling method to general distributions

International Nuclear Information System (INIS)

Edwards, A.L.; Rathkopf, J.A.; Smidt, R.K.

1991-01-01

The alias method is a Monte Carlo sampling technique that offers significant advantages over more traditional methods. It equals the accuracy of table lookup and the speed of equal probable bins. The original formulation of this method sampled from discrete distributions and was easily extended to histogram distributions. We have extended the method further to applications more germane to Monte Carlo particle transport codes: continuous distributions. This paper presents the alias method as originally derived and our extensions to simple continuous distributions represented by piecewise linear functions. We also present a method to interpolate accurately between distributions tabulated at points other than the point of interest. We present timing studies that demonstrate the method's increased efficiency over table lookup and show further speedup achieved through vectorization. 6 refs., 12 figs., 2 tabs

Methods for coupling radiation, ion, and electron energies in grey Implicit Monte Carlo

International Nuclear Information System (INIS)

Evans, T.M.; Densmore, J.D.

2007-01-01

We present three methods for extending the Implicit Monte Carlo (IMC) method to treat the time-evolution of coupled radiation, electron, and ion energies. The first method splits the ion and electron coupling and conduction from the standard IMC radiation-transport process. The second method recasts the IMC equations such that part of the coupling is treated during the Monte Carlo calculation. The third method treats all of the coupling and conduction in the Monte Carlo simulation. We apply modified equation analysis (MEA) to simplified forms of each method that neglects the errors in the conduction terms. Through MEA we show that the third method is theoretically the most accurate. We demonstrate the effectiveness of each method on a series of 0-dimensional, nonlinear benchmark problems where the accuracy of the third method is shown to be up to ten times greater than the other coupling methods for selected calculations

Biases in Monte Carlo eigenvalue calculations

Energy Technology Data Exchange (ETDEWEB)

Gelbard, E.M.

1992-12-01

The Monte Carlo method has been used for many years to analyze the neutronics of nuclear reactors. In fact, as the power of computers has increased the importance of Monte Carlo in neutronics has also increased, until today this method plays a central role in reactor analysis and design. Monte Carlo is used in neutronics for two somewhat different purposes, i.e., (a) to compute the distribution of neutrons in a given medium when the neutron source-density is specified, and (b) to compute the neutron distribution in a self-sustaining chain reaction, in which case the source is determined as the eigenvector of a certain linear operator. In (b), then, the source is not given, but must be computed. In the first case (the ``fixed-source`` case) the Monte Carlo calculation is unbiased. That is to say that, if the calculation is repeated (``replicated``) over and over, with independent random number sequences for each replica, then averages over all replicas will approach the correct neutron distribution as the number of replicas goes to infinity. Unfortunately, the computation is not unbiased in the second case, which we discuss here.

Biases in Monte Carlo eigenvalue calculations

Energy Technology Data Exchange (ETDEWEB)

Gelbard, E.M.

1992-01-01

The Monte Carlo method has been used for many years to analyze the neutronics of nuclear reactors. In fact, as the power of computers has increased the importance of Monte Carlo in neutronics has also increased, until today this method plays a central role in reactor analysis and design. Monte Carlo is used in neutronics for two somewhat different purposes, i.e., (a) to compute the distribution of neutrons in a given medium when the neutron source-density is specified, and (b) to compute the neutron distribution in a self-sustaining chain reaction, in which case the source is determined as the eigenvector of a certain linear operator. In (b), then, the source is not given, but must be computed. In the first case (the fixed-source'' case) the Monte Carlo calculation is unbiased. That is to say that, if the calculation is repeated ( replicated'') over and over, with independent random number sequences for each replica, then averages over all replicas will approach the correct neutron distribution as the number of replicas goes to infinity. Unfortunately, the computation is not unbiased in the second case, which we discuss here.

Biases in Monte Carlo eigenvalue calculations

International Nuclear Information System (INIS)

Gelbard, E.M.

1992-01-01

The Monte Carlo method has been used for many years to analyze the neutronics of nuclear reactors. In fact, as the power of computers has increased the importance of Monte Carlo in neutronics has also increased, until today this method plays a central role in reactor analysis and design. Monte Carlo is used in neutronics for two somewhat different purposes, i.e., (a) to compute the distribution of neutrons in a given medium when the neutron source-density is specified, and (b) to compute the neutron distribution in a self-sustaining chain reaction, in which case the source is determined as the eigenvector of a certain linear operator. In (b), then, the source is not given, but must be computed. In the first case (the ''fixed-source'' case) the Monte Carlo calculation is unbiased. That is to say that, if the calculation is repeated (''replicated'') over and over, with independent random number sequences for each replica, then averages over all replicas will approach the correct neutron distribution as the number of replicas goes to infinity. Unfortunately, the computation is not unbiased in the second case, which we discuss here

Importance iteration in MORSE Monte Carlo calculations

International Nuclear Information System (INIS)

Kloosterman, J.L.; Hoogenboom, J.E.

1994-01-01

An expression to calculate point values (the expected detector response of a particle emerging from a collision or the source) is derived and implemented in the MORSE-SGC/S Monte Carlo code. It is outlined how these point values can be smoothed as a function of energy and as a function of the optical thickness between the detector and the source. The smoothed point values are subsequently used to calculate the biasing parameters of the Monte Carlo runs to follow. The method is illustrated by an example that shows that the obtained biasing parameters lead to a more efficient Monte Carlo calculation

Importance iteration in MORSE Monte Carlo calculations

International Nuclear Information System (INIS)

Kloosterman, J.L.; Hoogenboom, J.E.

1994-02-01

An expression to calculate point values (the expected detector response of a particle emerging from a collision or the source) is derived and implemented in the MORSE-SGC/S Monte Carlo code. It is outlined how these point values can be smoothed as a function of energy and as a function of the optical thickness between the detector and the source. The smoothed point values are subsequently used to calculate the biasing parameters of the Monte Carlo runs to follow. The method is illustrated by an example, which shows that the obtained biasing parameters lead to a more efficient Monte Carlo calculation. (orig.)

Present Status and Extensions of the Monte Carlo Performance Benchmark

Science.gov (United States)

Hoogenboom, J. Eduard; Petrovic, Bojan; Martin, William R.

2014-06-01

The NEA Monte Carlo Performance benchmark started in 2011 aiming to monitor over the years the abilities to perform a full-size Monte Carlo reactor core calculation with a detailed power production for each fuel pin with axial distribution. This paper gives an overview of the contributed results thus far. It shows that reaching a statistical accuracy of 1 % for most of the small fuel zones requires about 100 billion neutron histories. The efficiency of parallel execution of Monte Carlo codes on a large number of processor cores shows clear limitations for computer clusters with common type computer nodes. However, using true supercomputers the speedup of parallel calculations is increasing up to large numbers of processor cores. More experience is needed from calculations on true supercomputers using large numbers of processors in order to predict if the requested calculations can be done in a short time. As the specifications of the reactor geometry for this benchmark test are well suited for further investigations of full-core Monte Carlo calculations and a need is felt for testing other issues than its computational performance, proposals are presented for extending the benchmark to a suite of benchmark problems for evaluating fission source convergence for a system with a high dominance ratio, for coupling with thermal-hydraulics calculations to evaluate the use of different temperatures and coolant densities and to study the correctness and effectiveness of burnup calculations. Moreover, other contemporary proposals for a full-core calculation with realistic geometry and material composition will be discussed.

Present status and extensions of the Monte Carlo performance benchmark

International Nuclear Information System (INIS)

Hoogenboom, J.E.; Petrovic, B.; Martin, W.R.

2013-01-01

The NEA Monte Carlo Performance benchmark started in 2011 aiming to monitor over the years the abilities to perform a full-size Monte Carlo reactor core calculation with a detailed power production for each fuel pin with axial distribution. This paper gives an overview of the contributed results thus far. It shows that reaching a statistical accuracy of 1 % for most of the small fuel zones requires about 100 billion neutron histories. The efficiency of parallel execution of Monte Carlo codes on a large number of processor cores shows clear limitations for computer clusters with common type computer nodes. However, using true supercomputers the speedup of parallel calculations is increasing up to large numbers of processor cores. More experience is needed from calculations on true supercomputers using large numbers of processors in order to predict if the requested calculations can be done in a short time. As the specifications of the reactor geometry for this benchmark test are well suited for further investigations of full-core Monte Carlo calculations and a need is felt for testing other issues than its computational performance, proposals are presented for extending the benchmark to a suite of benchmark problems for evaluating fission source convergence for a system with a high dominance ratio, for coupling with thermal-hydraulics calculations to evaluate the use of different temperatures and coolant densities and to study the correctness and effectiveness of burnup calculations. Moreover, other contemporary proposals for a full-core calculation with realistic geometry and material composition will be discussed. (authors)

Advanced Computational Methods for Monte Carlo Calculations

Energy Technology Data Exchange (ETDEWEB)

Brown, Forrest B. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2018-01-12

This course is intended for graduate students who already have a basic understanding of Monte Carlo methods. It focuses on advanced topics that may be needed for thesis research, for developing new state-of-the-art methods, or for working with modern production Monte Carlo codes.

A computationally efficient moment-preserving Monte Carlo electron transport method with implementation in Geant4

Energy Technology Data Exchange (ETDEWEB)

Dixon, D.A., E-mail: ddixon@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663, MS P365, Los Alamos, NM 87545 (United States); Prinja, A.K., E-mail: prinja@unm.edu [Department of Nuclear Engineering, MSC01 1120, 1 University of New Mexico, Albuquerque, NM 87131-0001 (United States); Franke, B.C., E-mail: bcfrank@sandia.gov [Sandia National Laboratories, Albuquerque, NM 87123 (United States)

2015-09-15

This paper presents the theoretical development and numerical demonstration of a moment-preserving Monte Carlo electron transport method. Foremost, a full implementation of the moment-preserving (MP) method within the Geant4 particle simulation toolkit is demonstrated. Beyond implementation details, it is shown that the MP method is a viable alternative to the condensed history (CH) method for inclusion in current and future generation transport codes through demonstration of the key features of the method including: systematically controllable accuracy, computational efficiency, mathematical robustness, and versatility. A wide variety of results common to electron transport are presented illustrating the key features of the MP method. In particular, it is possible to achieve accuracy that is statistically indistinguishable from analog Monte Carlo, while remaining up to three orders of magnitude more efficient than analog Monte Carlo simulations. Finally, it is shown that the MP method can be generalized to any applicable analog scattering DCS model by extending previous work on the MP method beyond analytical DCSs to the partial-wave (PW) elastic tabulated DCS data.

Golden Ratio Versus Pi as Random Sequence Sources for Monte Carlo Integration

Science.gov (United States)

Sen, S. K.; Agarwal, Ravi P.; Shaykhian, Gholam Ali

2007-01-01

We discuss here the relative merits of these numbers as possible random sequence sources. The quality of these sequences is not judged directly based on the outcome of all known tests for the randomness of a sequence. Instead, it is determined implicitly by the accuracy of the Monte Carlo integration in a statistical sense. Since our main motive of using a random sequence is to solve real world problems, it is more desirable if we compare the quality of the sequences based on their performances for these problems in terms of quality/accuracy of the output. We also compare these sources against those generated by a popular pseudo-random generator, viz., the Matlab rand and the quasi-random generator ha/ton both in terms of error and time complexity. Our study demonstrates that consecutive blocks of digits of each of these numbers produce a good random sequence source. It is observed that randomly chosen blocks of digits do not have any remarkable advantage over consecutive blocks for the accuracy of the Monte Carlo integration. Also, it reveals that pi is a better source of a random sequence than theta when the accuracy of the integration is concerned.

Prospect on general software of Monte Carlo method

International Nuclear Information System (INIS)

Pei Lucheng

1992-01-01

This is a short paper on the prospect of Monte Carlo general software. The content consists of cluster sampling method, zero variance technique, self-improved method, and vectorized Monte Carlo method

Strategije drevesnega preiskovanja Monte Carlo

OpenAIRE

VODOPIVEC, TOM

2018-01-01

Po preboju pri igri go so metode drevesnega preiskovanja Monte Carlo (ang. Monte Carlo tree search – MCTS) sprožile bliskovit napredek agentov za igranje iger: raziskovalna skupnost je od takrat razvila veliko variant in izboljšav algoritma MCTS ter s tem zagotovila napredek umetne inteligence ne samo pri igrah, ampak tudi v številnih drugih domenah. Čeprav metode MCTS združujejo splošnost naključnega vzorčenja z natančnostjo drevesnega preiskovanja, imajo lahko v praksi težave s počasno konv...

Monte Carlo electron/photon transport

International Nuclear Information System (INIS)

Mack, J.M.; Morel, J.E.; Hughes, H.G.

1985-01-01

A review of nonplasma coupled electron/photon transport using Monte Carlo method is presented. Remarks are mainly restricted to linerarized formalisms at electron energies from 1 keV to 1000 MeV. Applications involving pulse-height estimation, transport in external magnetic fields, and optical Cerenkov production are discussed to underscore the importance of this branch of computational physics. Advances in electron multigroup cross-section generation is reported, and its impact on future code development assessed. Progress toward the transformation of MCNP into a generalized neutral/charged-particle Monte Carlo code is described. 48 refs

Experimental validation of a Monte Carlo proton therapy nozzle model incorporating magnetically steered protons

International Nuclear Information System (INIS)

Peterson, S W; Polf, J; Archambault, L; Beddar, S; Bues, M; Ciangaru, G; Smith, A

2009-01-01

The purpose of this study is to validate the accuracy of a Monte Carlo calculation model of a proton magnetic beam scanning delivery nozzle developed using the Geant4 toolkit. The Monte Carlo model was used to produce depth dose and lateral profiles, which were compared to data measured in the clinical scanning treatment nozzle at several energies. Comparisons were also made between measured and simulated off-axis profiles to test the accuracy of the model's magnetic steering. Comparison of the 80% distal dose fall-off values for the measured and simulated depth dose profiles agreed to within 1 mm for the beam energies evaluated. Agreement of the full width at half maximum values for the measured and simulated lateral fluence profiles was within 1.3 mm for all energies. The position of measured and simulated spot positions for the magnetically steered beams agreed to within 0.7 mm of each other. Based on these results, we found that the Geant4 Monte Carlo model of the beam scanning nozzle has the ability to accurately predict depth dose profiles, lateral profiles perpendicular to the beam axis and magnetic steering of a proton beam during beam scanning proton therapy.

Monte Carlo method for array criticality calculations

International Nuclear Information System (INIS)

Dickinson, D.; Whitesides, G.E.

1976-01-01

The Monte Carlo method for solving neutron transport problems consists of mathematically tracing paths of individual neutrons collision by collision until they are lost by absorption or leakage. The fate of the neutron after each collision is determined by the probability distribution functions that are formed from the neutron cross-section data. These distributions are sampled statistically to establish the successive steps in the neutron's path. The resulting data, accumulated from following a large number of batches, are analyzed to give estimates of k/sub eff/ and other collision-related quantities. The use of electronic computers to produce the simulated neutron histories, initiated at Los Alamos Scientific Laboratory, made the use of the Monte Carlo method practical for many applications. In analog Monte Carlo simulation, the calculation follows the physical events of neutron scattering, absorption, and leakage. To increase calculational efficiency, modifications such as the use of statistical weights are introduced. The Monte Carlo method permits the use of a three-dimensional geometry description and a detailed cross-section representation. Some of the problems in using the method are the selection of the spatial distribution for the initial batch, the preparation of the geometry description for complex units, and the calculation of error estimates for region-dependent quantities such as fluxes. The Monte Carlo method is especially appropriate for criticality safety calculations since it permits an accurate representation of interacting units of fissile material. Dissimilar units, units of complex shape, moderators between units, and reflected arrays may be calculated. Monte Carlo results must be correlated with relevant experimental data, and caution must be used to ensure that a representative set of neutron histories is produced

Quantum Monte Carlo for atoms and molecules

International Nuclear Information System (INIS)

Barnett, R.N.

1989-11-01

The diffusion quantum Monte Carlo with fixed nodes (QMC) approach has been employed in studying energy-eigenstates for 1--4 electron systems. Previous work employing the diffusion QMC technique yielded energies of high quality for H 2 , LiH, Li 2 , and H 2 O. Here, the range of calculations with this new approach has been extended to include additional first-row atoms and molecules. In addition, improvements in the previously computed fixed-node energies of LiH, Li 2 , and H 2 O have been obtained using more accurate trial functions. All computations were performed within, but are not limited to, the Born-Oppenheimer approximation. In our computations, the effects of variation of Monte Carlo parameters on the QMC solution of the Schroedinger equation were studied extensively. These parameters include the time step, renormalization time and nodal structure. These studies have been very useful in determining which choices of such parameters will yield accurate QMC energies most efficiently. Generally, very accurate energies (90--100% of the correlation energy is obtained) have been computed with single-determinant trail functions multiplied by simple correlation functions. Improvements in accuracy should be readily obtained using more complex trial functions

SU-F-T-575: Verification of a Monte-Carlo Small Field SRS/SBRT Dose Calculation System

International Nuclear Information System (INIS)

Sudhyadhom, A; McGuinness, C; Descovich, M

2016-01-01

Purpose: To develop a methodology for validation of a Monte-Carlo dose calculation model for robotic small field SRS/SBRT deliveries. Methods: In a robotic treatment planning system, a Monte-Carlo model was iteratively optimized to match with beam data. A two-part analysis was developed to verify this model. 1) The Monte-Carlo model was validated in a simulated water phantom versus a Ray-Tracing calculation on a single beam collimator-by-collimator calculation. 2) The Monte-Carlo model was validated to be accurate in the most challenging situation, lung, by acquiring in-phantom measurements. A plan was created and delivered in a CIRS lung phantom with film insert. Separately, plans were delivered in an in-house created lung phantom with a PinPoint chamber insert within a lung simulating material. For medium to large collimator sizes, a single beam was delivered to the phantom. For small size collimators (10, 12.5, and 15mm), a robotically delivered plan was created to generate a uniform dose field of irradiation over a 2×2cm 2 area. Results: Dose differences in simulated water between Ray-Tracing and Monte-Carlo were all within 1% at dmax and deeper. Maximum dose differences occurred prior to dmax but were all within 3%. Film measurements in a lung phantom show high correspondence of over 95% gamma at the 2%/2mm level for Monte-Carlo. Ion chamber measurements for collimator sizes of 12.5mm and above were within 3% of Monte-Carlo calculated values. Uniform irradiation involving the 10mm collimator resulted in a dose difference of ∼8% for both Monte-Carlo and Ray-Tracing indicating that there may be limitations with the dose calculation. Conclusion: We have developed a methodology to validate a Monte-Carlo model by verifying that it matches in water and, separately, that it corresponds well in lung simulating materials. The Monte-Carlo model and algorithm tested may have more limited accuracy for 10mm fields and smaller.

Clinical treatment planning for stereotactic radiotherapy, evaluation by Monte Carlo simulation

International Nuclear Information System (INIS)

Kairn, T.; Aland, T.; Kenny, J.; Knight, R.T.; Crowe, S.B.; Langton, C.M.; Franich, R.D.; Johnston, P.N.

2010-01-01

Full text: This study uses re-evaluates the doses delivered by a series of clinical stereotactic radiotherapy treatments, to test the accuracy of treatment planning predictions for very small radiation fields. Stereotactic radiotherapy treatment plans for meningiomas near the petrous temporal bone and the foramen magnum (incorp rating fields smaller than I c m2) were examined using Monte Carlo simulations. Important differences between treatment planning predictions and Monte Carlo calculations of doses delivered to stereotactic radiotherapy patients are apparent. For example, in one case the Monte Carlo calculation shows that the delivery a planned meningioma treatment would spare the patient's critical structures (eyes, brainstem) more effectively than the treatment plan predicted, and therefore suggests that this patient could safely receive an increased dose to their tumour. Monte Carlo simulations can be used to test the dose predictions made by a conventional treatment planning system, for dosimetrically challenging small fields, and can thereby suggest valuable modifications to clinical treatment plans. This research was funded by the Wesley Research Institute, Australia. The authors wish to thank Andrew Fielding and David Schlect for valuable discussions of aspects of this work. The authors are also grateful to Muhammad Kakakhel, for assisting with the design and calibration of our linear accelerator model, and to the stereotactic radiation therapy team at Premion, who designed the treatment plans. Computational resources and services used in this work were provided by the HPC and Research Support Unit, QUT, Brisbane, Australia. (author)

Quantum Monte Carlo studies in Hamiltonian lattice gauge theory

International Nuclear Information System (INIS)

Hamer, C.J.; Samaras, M.; Bursill, R.J.

2000-01-01

Full text: The application of Monte Carlo methods to the 'Hamiltonian' formulation of lattice gauge theory has been somewhat neglected, and lags at least ten years behind the classical Monte Carlo simulations of Euclidean lattice gauge theory. We have applied a Green's Function Monte Carlo algorithm to lattice Yang-Mills theories in the Hamiltonian formulation, combined with a 'forward-walking' technique to estimate expectation values and correlation functions. In this approach, one represents the wave function in configuration space by a discrete ensemble of random walkers, and application of the time development operator is simulated by a diffusion and branching process. The approach has been used to estimate the ground-state energy and Wilson loop values in the U(1) theory in (2+1)D, and the SU(3) Yang-Mills theory in (3+1)D. The finite-size scaling behaviour has been explored, and agrees with the predictions of effective Lagrangian theory, and weak-coupling expansions. Crude estimates of the string tension are derived, which agree with previous results at intermediate couplings; but more accurate results for larger loops will be required to establish scaling behaviour at weak couplings. A drawback to this method is that it is necessary to introduce a 'trial' or 'guiding wave function' to guide the walkers towards the most probable regions of configuration space, in order to achieve convergence and accuracy. The 'forward-walking' estimates should be independent of this guidance, but in fact for the SU(3) case they turn out to be sensitive to the choice of trial wave function. It would be preferable to use some sort of Metropolis algorithm instead to produce a correct distribution of walkers: this may point in the direction of a Path Integral Monte Carlo approach

Bayesian Optimal Experimental Design Using Multilevel Monte Carlo

KAUST Repository

Ben Issaid, Chaouki; Long, Quan; Scavino, Marco; Tempone, Raul

2015-01-01

Experimental design is very important since experiments are often resource-exhaustive and time-consuming. We carry out experimental design in the Bayesian framework. To measure the amount of information, which can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions. Optimizing this utility function enables us to design experiments that yield the most informative data for our purpose. One of the major difficulties in evaluating the expected information gain is that the integral is nested and can be high dimensional. We propose using Multilevel Monte Carlo techniques to accelerate the computation of the nested high dimensional integral. The advantages are twofold. First, the Multilevel Monte Carlo can significantly reduce the cost of the nested integral for a given tolerance, by using an optimal sample distribution among different sample averages of the inner integrals. Second, the Multilevel Monte Carlo method imposes less assumptions, such as the concentration of measures, required by Laplace method. We test our Multilevel Monte Carlo technique using a numerical example on the design of sensor deployment for a Darcy flow problem governed by one dimensional Laplace equation. We also compare the performance of the Multilevel Monte Carlo, Laplace approximation and direct double loop Monte Carlo.

Bayesian Optimal Experimental Design Using Multilevel Monte Carlo

KAUST Repository

Ben Issaid, Chaouki

2015-01-07

Experimental design is very important since experiments are often resource-exhaustive and time-consuming. We carry out experimental design in the Bayesian framework. To measure the amount of information, which can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions. Optimizing this utility function enables us to design experiments that yield the most informative data for our purpose. One of the major difficulties in evaluating the expected information gain is that the integral is nested and can be high dimensional. We propose using Multilevel Monte Carlo techniques to accelerate the computation of the nested high dimensional integral. The advantages are twofold. First, the Multilevel Monte Carlo can significantly reduce the cost of the nested integral for a given tolerance, by using an optimal sample distribution among different sample averages of the inner integrals. Second, the Multilevel Monte Carlo method imposes less assumptions, such as the concentration of measures, required by Laplace method. We test our Multilevel Monte Carlo technique using a numerical example on the design of sensor deployment for a Darcy flow problem governed by one dimensional Laplace equation. We also compare the performance of the Multilevel Monte Carlo, Laplace approximation and direct double loop Monte Carlo.

How Monte Carlo heuristics aid to identify the physical processes of drug release kinetics.

Science.gov (United States)

Lecca, Paola

2018-01-01

We implement a Monte Carlo heuristic algorithm to model drug release from a solid dosage form. We show that with Monte Carlo simulations it is possible to identify and explain the causes of the unsatisfactory predictive power of current drug release models. It is well known that the power-law, the exponential models, as well as those derived from or inspired by them accurately reproduce only the first 60% of the release curve of a drug from a dosage form. In this study, by using Monte Carlo simulation approaches, we show that these models fit quite accurately almost the entire release profile when the release kinetics is not governed by the coexistence of different physico-chemical mechanisms. We show that the accuracy of the traditional models are comparable with those of Monte Carlo heuristics when these heuristics approximate and oversimply the phenomenology of drug release. This observation suggests to develop and use novel Monte Carlo simulation heuristics able to describe the complexity of the release kinetics, and consequently to generate data more similar to those observed in real experiments. Implementing Monte Carlo simulation heuristics of the drug release phenomenology may be much straightforward and efficient than hypothesizing and implementing from scratch complex mathematical models of the physical processes involved in drug release. Identifying and understanding through simulation heuristics what processes of this phenomenology reproduce the observed data and then formalize them in mathematics may allow avoiding time-consuming, trial-error based regression procedures. Three bullet points, highlighting the customization of the procedure. •An efficient heuristics based on Monte Carlo methods for simulating drug release from solid dosage form encodes is presented. It specifies the model of the physical process in a simple but accurate way in the formula of the Monte Carlo Micro Step (MCS) time interval.•Given the experimentally observed curve of

Present status of transport code development based on Monte Carlo method

International Nuclear Information System (INIS)

Nakagawa, Masayuki

1985-01-01

The present status of development in Monte Carlo code is briefly reviewed. The main items are the followings; Application fields, Methods used in Monte Carlo code (geometry spectification, nuclear data, estimator and variance reduction technique) and unfinished works, Typical Monte Carlo codes and Merits of continuous energy Monte Carlo code. (author)

A recursive Monte Carlo method for estimating importance functions in deep penetration problems

International Nuclear Information System (INIS)

Goldstein, M.

1980-04-01

A pratical recursive Monte Carlo method for estimating the importance function distribution, aimed at importance sampling for the solution of deep penetration problems in three-dimensional systems, was developed. The efficiency of the recursive method was investigated for sample problems including one- and two-dimensional, monoenergetic and and multigroup problems, as well as for a practical deep-penetration problem with streaming. The results of the recursive Monte Carlo calculations agree fairly well with Ssub(n) results. It is concluded that the recursive Monte Carlo method promises to become a universal method for estimating the importance function distribution for the solution of deep-penetration problems, in all kinds of systems: for many systems the recursive method is likely to be more efficient than previously existing methods; for three-dimensional systems it is the first method that can estimate the importance function with the accuracy required for an efficient solution based on importance sampling of neutron deep-penetration problems in those systems

A positive-weight next-to-leading-order Monte Carlo for Z pair hadroproduction

International Nuclear Information System (INIS)

Nason, Paolo; Ridolfi, Giovanni

2006-01-01

We present a first application of a previously published method for the computation of QCD processes that is accurate at next-to-leading order, and that can be interfaced consistently to standard shower Monte Carlo programs. We have considered Z pair production in hadron-hadron collisions, a process whose complexity is sufficient to test the general applicability of the method. We have interfaced our result to the HERWIG and PYTHIA shower Monte Carlo programs. Previous work on next-to-leading order corrections in a shower Monte Carlo (the MC-NLO program) may involve the generation of events with negative weights, that are avoided with the present method. We have compared our results with those obtained with MC-NLO, and found remarkable consistency. Our method can also be used as a standalone, alternative implementation of QCD corrections, with the advantage of positivity, improved convergence, and next-to-leading logarithmic accuracy in the region of small transverse momentum of the radiated parton

Successful vectorization - reactor physics Monte Carlo code

International Nuclear Information System (INIS)

Martin, W.R.

1989-01-01

Most particle transport Monte Carlo codes in use today are based on the ''history-based'' algorithm, wherein one particle history at a time is simulated. Unfortunately, the ''history-based'' approach (present in all Monte Carlo codes until recent years) is inherently scalar and cannot be vectorized. In particular, the history-based algorithm cannot take advantage of vector architectures, which characterize the largest and fastest computers at the current time, vector supercomputers such as the Cray X/MP or IBM 3090/600. However, substantial progress has been made in recent years in developing and implementing a vectorized Monte Carlo algorithm. This algorithm follows portions of many particle histories at the same time and forms the basis for all successful vectorized Monte Carlo codes that are in use today. This paper describes the basic vectorized algorithm along with descriptions of several variations that have been developed by different researchers for specific applications. These applications have been mainly in the areas of neutron transport in nuclear reactor and shielding analysis and photon transport in fusion plasmas. The relative merits of the various approach schemes will be discussed and the present status of known vectorization efforts will be summarized along with available timing results, including results from the successful vectorization of 3-D general geometry, continuous energy Monte Carlo. (orig.)

Quantum Mechanical Single Molecule Partition Function from PathIntegral Monte Carlo Simulations

Energy Technology Data Exchange (ETDEWEB)

Chempath, Shaji; Bell, Alexis T.; Predescu, Cristian

2006-10-01

An algorithm for calculating the partition function of a molecule with the path integral Monte Carlo method is presented. Staged thermodynamic perturbation with respect to a reference harmonic potential is utilized to evaluate the ratio of partition functions. Parallel tempering and a new Monte Carlo estimator for the ratio of partition functions are implemented here to achieve well converged simulations that give an accuracy of 0.04 kcal/mol in the reported free energies. The method is applied to various test systems, including a catalytic system composed of 18 atoms. Absolute free energies calculated by this method lead to corrections as large as 2.6 kcal/mol at 300 K for some of the examples presented.

Bayesian phylogeny analysis via stochastic approximation Monte Carlo

KAUST Repository

Cheon, Sooyoung; Liang, Faming

2009-01-01

in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method

Reflections on early Monte Carlo calculations

International Nuclear Information System (INIS)

Spanier, J.

1992-01-01

Monte Carlo methods for solving various particle transport problems developed in parallel with the evolution of increasingly sophisticated computer programs implementing diffusion theory and low-order moments calculations. In these early years, Monte Carlo calculations and high-order approximations to the transport equation were seen as too expensive to use routinely for nuclear design but served as invaluable aids and supplements to design with less expensive tools. The earliest Monte Carlo programs were quite literal; i.e., neutron and other particle random walk histories were simulated by sampling from the probability laws inherent in the physical system without distoration. Use of such analogue sampling schemes resulted in a good deal of time being spent in examining the possibility of lowering the statistical uncertainties in the sample estimates by replacing simple, and intuitively obvious, random variables by those with identical means but lower variances

Reconstruction of Monte Carlo replicas from Hessian parton distributions

Energy Technology Data Exchange (ETDEWEB)

Hou, Tie-Jiun [Department of Physics, Southern Methodist University,Dallas, TX 75275-0181 (United States); Gao, Jun [INPAC, Shanghai Key Laboratory for Particle Physics and Cosmology,Department of Physics and Astronomy, Shanghai Jiao-Tong University, Shanghai 200240 (China); High Energy Physics Division, Argonne National Laboratory,Argonne, Illinois, 60439 (United States); Huston, Joey [Department of Physics and Astronomy, Michigan State University,East Lansing, MI 48824 (United States); Nadolsky, Pavel [Department of Physics, Southern Methodist University,Dallas, TX 75275-0181 (United States); Schmidt, Carl; Stump, Daniel [Department of Physics and Astronomy, Michigan State University,East Lansing, MI 48824 (United States); Wang, Bo-Ting; Xie, Ke Ping [Department of Physics, Southern Methodist University,Dallas, TX 75275-0181 (United States); Dulat, Sayipjamal [Department of Physics and Astronomy, Michigan State University,East Lansing, MI 48824 (United States); School of Physics Science and Technology, Xinjiang University,Urumqi, Xinjiang 830046 (China); Center for Theoretical Physics, Xinjiang University,Urumqi, Xinjiang 830046 (China); Pumplin, Jon; Yuan, C.P. [Department of Physics and Astronomy, Michigan State University,East Lansing, MI 48824 (United States)

2017-03-20

We explore connections between two common methods for quantifying the uncertainty in parton distribution functions (PDFs), based on the Hessian error matrix and Monte-Carlo sampling. CT14 parton distributions in the Hessian representation are converted into Monte-Carlo replicas by a numerical method that reproduces important properties of CT14 Hessian PDFs: the asymmetry of CT14 uncertainties and positivity of individual parton distributions. The ensembles of CT14 Monte-Carlo replicas constructed this way at NNLO and NLO are suitable for various collider applications, such as cross section reweighting. Master formulas for computation of asymmetric standard deviations in the Monte-Carlo representation are derived. A correction is proposed to address a bias in asymmetric uncertainties introduced by the Taylor series approximation. A numerical program is made available for conversion of Hessian PDFs into Monte-Carlo replicas according to normal, log-normal, and Watt-Thorne sampling procedures.

Sampling from a polytope and hard-disk Monte Carlo

International Nuclear Information System (INIS)

Kapfer, Sebastian C; Krauth, Werner

2013-01-01

The hard-disk problem, the statics and the dynamics of equal two-dimensional hard spheres in a periodic box, has had a profound influence on statistical and computational physics. Markov-chain Monte Carlo and molecular dynamics were first discussed for this model. Here we reformulate hard-disk Monte Carlo algorithms in terms of another classic problem, namely the sampling from a polytope. Local Markov-chain Monte Carlo, as proposed by Metropolis et al. in 1953, appears as a sequence of random walks in high-dimensional polytopes, while the moves of the more powerful event-chain algorithm correspond to molecular dynamics evolution. We determine the convergence properties of Monte Carlo methods in a special invariant polytope associated with hard-disk configurations, and the implications for convergence of hard-disk sampling. Finally, we discuss parallelization strategies for event-chain Monte Carlo and present results for a multicore implementation

Cluster monte carlo method for nuclear criticality safety calculation

International Nuclear Information System (INIS)

Pei Lucheng

1984-01-01

One of the most important applications of the Monte Carlo method is the calculation of the nuclear criticality safety. The fair source game problem was presented at almost the same time as the Monte Carlo method was applied to calculating the nuclear criticality safety. The source iteration cost may be reduced as much as possible or no need for any source iteration. This kind of problems all belongs to the fair source game prolems, among which, the optimal source game is without any source iteration. Although the single neutron Monte Carlo method solved the problem without the source iteration, there is still quite an apparent shortcoming in it, that is, it solves the problem without the source iteration only in the asymptotic sense. In this work, a new Monte Carlo method called the cluster Monte Carlo method is given to solve the problem further

Wielandt acceleration for MCNP5 Monte Carlo eigenvalue calculations

International Nuclear Information System (INIS)

Brown, F.

2007-01-01

Monte Carlo criticality calculations use the power iteration method to determine the eigenvalue (k eff ) and eigenfunction (fission source distribution) of the fundamental mode. A recently proposed method for accelerating convergence of the Monte Carlo power iteration using Wielandt's method has been implemented in a test version of MCNP5. The method is shown to provide dramatic improvements in convergence rates and to greatly reduce the possibility of false convergence assessment. The method is effective and efficient, improving the Monte Carlo figure-of-merit for many problems. In addition, the method should eliminate most of the underprediction bias in confidence intervals for Monte Carlo criticality calculations. (authors)

Monte Carlo shielding analyses using an automated biasing procedure

International Nuclear Information System (INIS)

Tang, J.S.; Hoffman, T.J.

1988-01-01

A systematic and automated approach for biasing Monte Carlo shielding calculations is described. In particular, adjoint fluxes from a one-dimensional discrete ordinates calculation are used to generate biasing parameters for a Monte Carlo calculation. The entire procedure of adjoint calculation, biasing parameters generation, and Monte Carlo calculation has been automated. The automated biasing procedure has been applied to several realistic deep-penetration shipping cask problems. The results obtained for neutron and gamma-ray transport indicate that with the automated biasing procedure Monte Carlo shielding calculations of spent-fuel casks can be easily performed with minimum effort and that accurate results can be obtained at reasonable computing cost

Applications of the Monte Carlo method in radiation protection

International Nuclear Information System (INIS)

Kulkarni, R.N.; Prasad, M.A.

1999-01-01

This paper gives a brief introduction to the application of the Monte Carlo method in radiation protection. It may be noted that an exhaustive review has not been attempted. The special advantage of the Monte Carlo method has been first brought out. The fundamentals of the Monte Carlo method have next been explained in brief, with special reference to two applications in radiation protection. Some sample current applications have been reported in the end in brief as examples. They are, medical radiation physics, microdosimetry, calculations of thermoluminescence intensity and probabilistic safety analysis. The limitations of the Monte Carlo method have also been mentioned in passing. (author)

Pore-scale uncertainty quantification with multilevel Monte Carlo

KAUST Repository

Icardi, Matteo; Hoel, Haakon; Long, Quan; Tempone, Raul

2014-01-01

. Since there are no generic ways to parametrize the randomness in the porescale structures, Monte Carlo techniques are the most accessible to compute statistics. We propose a multilevel Monte Carlo (MLMC) technique to reduce the computational cost

Quantum statistical Monte Carlo methods and applications to spin systems

International Nuclear Information System (INIS)

Suzuki, M.

1986-01-01

A short review is given concerning the quantum statistical Monte Carlo method based on the equivalence theorem that d-dimensional quantum systems are mapped onto (d+1)-dimensional classical systems. The convergence property of this approximate tansformation is discussed in detail. Some applications of this general appoach to quantum spin systems are reviewed. A new Monte Carlo method, ''thermo field Monte Carlo method,'' is presented, which is an extension of the projection Monte Carlo method at zero temperature to that at finite temperatures

SPQR: a Monte Carlo reactor kinetics code

International Nuclear Information System (INIS)

Cramer, S.N.; Dodds, H.L.

1980-02-01

The SPQR Monte Carlo code has been developed to analyze fast reactor core accident problems where conventional methods are considered inadequate. The code is based on the adiabatic approximation of the quasi-static method. This initial version contains no automatic material motion or feedback. An existing Monte Carlo code is used to calculate the shape functions and the integral quantities needed in the kinetics module. Several sample problems have been devised and analyzed. Due to the large statistical uncertainty associated with the calculation of reactivity in accident simulations, the results, especially at later times, differ greatly from deterministic methods. It was also found that in large uncoupled systems, the Monte Carlo method has difficulty in handling asymmetric perturbations

Bayesian phylogeny analysis via stochastic approximation Monte Carlo

KAUST Repository

Cheon, Sooyoung

2009-11-01

Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time. © 2009 Elsevier Inc. All rights reserved.

Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity

International Nuclear Information System (INIS)

Stern, R.E.; Song, J.; Work, D.B.

2017-01-01

The two-terminal reliability problem in system reliability analysis is known to be computationally intractable for large infrastructure graphs. Monte Carlo techniques can estimate the probability of a disconnection between two points in a network by selecting a representative sample of network component failure realizations and determining the source-terminal connectivity of each realization. To reduce the runtime required for the Monte Carlo approximation, this article proposes an approximate framework in which the connectivity check of each sample is estimated using a machine-learning-based classifier. The framework is implemented using both a support vector machine (SVM) and a logistic regression based surrogate model. Numerical experiments are performed on the California gas distribution network using the epicenter and magnitude of the 1989 Loma Prieta earthquake as well as randomly-generated earthquakes. It is shown that the SVM and logistic regression surrogate models are able to predict network connectivity with accuracies of 99% for both methods, and are 1–2 orders of magnitude faster than using a Monte Carlo method with an exact connectivity check. - Highlights: • Surrogate models of network connectivity are developed by machine-learning algorithms. • Developed surrogate models can reduce the runtime required for Monte Carlo simulations. • Support vector machine and logistic regressions are employed to develop surrogate models. • Numerical example of California gas distribution network demonstrate the proposed approach. • The developed models have accuracies 99%, and are 1–2 orders of magnitude faster than MCS.

Present status and future prospects of neutronics Monte Carlo

International Nuclear Information System (INIS)

Gelbard, E.M.

1990-01-01

It is fair to say that the Monte Carlo method, over the last decade, has grown steadily more important as a neutronics computational tool. Apparently this has happened for assorted reasons. Thus, for example, as the power of computers has increased, the cost of the method has dropped, steadily becoming less and less of an obstacle to its use. In addition, more and more sophisticated input processors have now made it feasible to model extremely complicated systems routinely with really remarkable fidelity. Finally, as we demand greater and greater precision in reactor calculations, Monte Carlo is often found to be the only method accurate enough for use in benchmarking. Cross section uncertainties are now almost the only inherent limitations in our Monte Carlo capabilities. For this reason Monte Carlo has come to occupy a special position, interposed between experiment and other computational techniques. More and more often deterministic methods are tested by comparison with Monte Carlo, and cross sections are tested by comparing Monte Carlo with experiment. In this way one can distinguish very clearly between errors due to flaws in our numerical methods, and those due to deficiencies in cross section files. The special role of Monte Carlo as a benchmarking tool, often the only available benchmarking tool, makes it crucially important that this method should be polished to perfection. Problems relating to Eigenvalue calculations, variance reduction and the use of advanced computers are reviewed in this paper. (author)

Diffusion Monte Carlo approach versus adiabatic computation for local Hamiltonians

Science.gov (United States)

Bringewatt, Jacob; Dorland, William; Jordan, Stephen P.; Mink, Alan

2018-02-01

Most research regarding quantum adiabatic optimization has focused on stoquastic Hamiltonians, whose ground states can be expressed with only real non-negative amplitudes and thus for whom destructive interference is not manifest. This raises the question of whether classical Monte Carlo algorithms can efficiently simulate quantum adiabatic optimization with stoquastic Hamiltonians. Recent results have given counterexamples in which path-integral and diffusion Monte Carlo fail to do so. However, most adiabatic optimization algorithms, such as for solving MAX-k -SAT problems, use k -local Hamiltonians, whereas our previous counterexample for diffusion Monte Carlo involved n -body interactions. Here we present a 6-local counterexample which demonstrates that even for these local Hamiltonians there are cases where diffusion Monte Carlo cannot efficiently simulate quantum adiabatic optimization. Furthermore, we perform empirical testing of diffusion Monte Carlo on a standard well-studied class of permutation-symmetric tunneling problems and similarly find large advantages for quantum optimization over diffusion Monte Carlo.

An intercomparison of Monte Carlo codes used for in-situ gamma-ray spectrometry

International Nuclear Information System (INIS)

Hurtado, S.; Villa, M.

2010-01-01

In-situ gamma-ray spectrometry is widely used for monitoring of natural as well as man-made radionuclides and corresponding gamma fields in the environment or working places. It finds effective application in the operational and accidental monitoring of nuclear facilities and their vicinity, waste depositories, radioactive contamination measurements and environmental mapping or geological prospecting. In order to determine accurate radionuclide concentrations in these research fields, Monte Carlo codes have recently been used to obtain the efficiency calibration of in-situ gamma-ray detectors. This work presents an inter-comparison between two Monte Carlo codes applied to in-situ gamma-ray spectrometry. On the commercial market, Canberra has its LABSOCS/ISOCS software which is relatively inexpensive. The ISOCS mathematical efficiency calibration software uses a combination of Monte Carlo calculations and discrete ordinate attenuation computations. Efficiencies can be generated in a few minutes in the field and can be modified easily if needed. However, it has been reported in the literature that ISOCS computation method is accurate on average only within 5%, and additionally in order to use LABSOCS/ISOCS it is necessary a previous characterization of the detector by Canberra, which is an expensive process. On the other hand, the multipurpose and open source GEANT4 takes significant computer time and presents a non-friendly but powerful toolkit, independent of the manufacturer of the detector. Different experimental measurements of calibrated sources were performed with a Canberra portable HPGe detector and compared to the results obtained using both Monte Carlo codes. Furthermore, a variety of efficiency calibrations for different radioactive source distributions were calculated and tested, like plane shapes or containers filled with different materials such as soil, water, etc. LabSOCS simulated efficiencies for medium and high energies were given within an

Neutron point-flux calculation by Monte Carlo

International Nuclear Information System (INIS)

Eichhorn, M.

1986-04-01

A survey of the usual methods for estimating flux at a point is given. The associated variance-reducing techniques in direct Monte Carlo games are explained. The multigroup Monte Carlo codes MC for critical systems and PUNKT for point source-point detector-systems are represented, and problems in applying the codes to practical tasks are discussed. (author)

Frequency domain Monte Carlo simulation method for cross power spectral density driven by periodically pulsed spallation neutron source using complex-valued weight Monte Carlo

International Nuclear Information System (INIS)

Yamamoto, Toshihiro

2014-01-01

Highlights: • The cross power spectral density in ADS has correlated and uncorrelated components. • A frequency domain Monte Carlo method to calculate the uncorrelated one is developed. • The method solves the Fourier transformed transport equation. • The method uses complex-valued weights to solve the equation. • The new method reproduces well the CPSDs calculated with time domain MC method. - Abstract: In an accelerator driven system (ADS), pulsed spallation neutrons are injected at a constant frequency. The cross power spectral density (CPSD), which can be used for monitoring the subcriticality of the ADS, is composed of the correlated and uncorrelated components. The uncorrelated component is described by a series of the Dirac delta functions that occur at the integer multiples of the pulse repetition frequency. In the present paper, a Monte Carlo method to solve the Fourier transformed neutron transport equation with a periodically pulsed neutron source term has been developed to obtain the CPSD in ADSs. Since the Fourier transformed flux is a complex-valued quantity, the Monte Carlo method introduces complex-valued weights to solve the Fourier transformed equation. The Monte Carlo algorithm used in this paper is similar to the one that was developed by the author of this paper to calculate the neutron noise caused by cross section perturbations. The newly-developed Monte Carlo algorithm is benchmarked to the conventional time domain Monte Carlo simulation technique. The CPSDs are obtained both with the newly-developed frequency domain Monte Carlo method and the conventional time domain Monte Carlo method for a one-dimensional infinite slab. The CPSDs obtained with the frequency domain Monte Carlo method agree well with those with the time domain method. The higher order mode effects on the CPSD in an ADS with a periodically pulsed neutron source are discussed

Stabilizing canonical-ensemble calculations in the auxiliary-field Monte Carlo method

Science.gov (United States)

Gilbreth, C. N.; Alhassid, Y.

2015-03-01

Quantum Monte Carlo methods are powerful techniques for studying strongly interacting Fermi systems. However, implementing these methods on computers with finite-precision arithmetic requires careful attention to numerical stability. In the auxiliary-field Monte Carlo (AFMC) method, low-temperature or large-model-space calculations require numerically stabilized matrix multiplication. When adapting methods used in the grand-canonical ensemble to the canonical ensemble of fixed particle number, the numerical stabilization increases the number of required floating-point operations for computing observables by a factor of the size of the single-particle model space, and thus can greatly limit the systems that can be studied. We describe an improved method for stabilizing canonical-ensemble calculations in AFMC that exhibits better scaling, and present numerical tests that demonstrate the accuracy and improved performance of the method.

Shell model the Monte Carlo way

International Nuclear Information System (INIS)

Ormand, W.E.

1995-01-01

The formalism for the auxiliary-field Monte Carlo approach to the nuclear shell model is presented. The method is based on a linearization of the two-body part of the Hamiltonian in an imaginary-time propagator using the Hubbard-Stratonovich transformation. The foundation of the method, as applied to the nuclear many-body problem, is discussed. Topics presented in detail include: (1) the density-density formulation of the method, (2) computation of the overlaps, (3) the sign of the Monte Carlo weight function, (4) techniques for performing Monte Carlo sampling, and (5) the reconstruction of response functions from an imaginary-time auto-correlation function using MaxEnt techniques. Results obtained using schematic interactions, which have no sign problem, are presented to demonstrate the feasibility of the method, while an extrapolation method for realistic Hamiltonians is presented. In addition, applications at finite temperature are outlined

Shell model the Monte Carlo way

Energy Technology Data Exchange (ETDEWEB)

Ormand, W.E.

1995-03-01

The formalism for the auxiliary-field Monte Carlo approach to the nuclear shell model is presented. The method is based on a linearization of the two-body part of the Hamiltonian in an imaginary-time propagator using the Hubbard-Stratonovich transformation. The foundation of the method, as applied to the nuclear many-body problem, is discussed. Topics presented in detail include: (1) the density-density formulation of the method, (2) computation of the overlaps, (3) the sign of the Monte Carlo weight function, (4) techniques for performing Monte Carlo sampling, and (5) the reconstruction of response functions from an imaginary-time auto-correlation function using MaxEnt techniques. Results obtained using schematic interactions, which have no sign problem, are presented to demonstrate the feasibility of the method, while an extrapolation method for realistic Hamiltonians is presented. In addition, applications at finite temperature are outlined.

Monte Carlo learning/biasing experiment with intelligent random numbers

International Nuclear Information System (INIS)

Booth, T.E.

1985-01-01

A Monte Carlo learning and biasing technique is described that does its learning and biasing in the random number space rather than the physical phase-space. The technique is probably applicable to all linear Monte Carlo problems, but no proof is provided here. Instead, the technique is illustrated with a simple Monte Carlo transport problem. Problems encountered, problems solved, and speculations about future progress are discussed. 12 refs

Temperature variance study in Monte-Carlo photon transport theory

International Nuclear Information System (INIS)

Giorla, J.

1985-10-01

We study different Monte-Carlo methods for solving radiative transfer problems, and particularly Fleck's Monte-Carlo method. We first give the different time-discretization schemes and the corresponding stability criteria. Then we write the temperature variance as a function of the variances of temperature and absorbed energy at the previous time step. Finally we obtain some stability criteria for the Monte-Carlo method in the stationary case [fr

Monte Carlo applications to radiation shielding problems

International Nuclear Information System (INIS)

Subbaiah, K.V.

2009-01-01

Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling of physical and mathematical systems to compute their results. However, basic concepts of MC are both simple and straightforward and can be learned by using a personal computer. Uses of Monte Carlo methods require large amounts of random numbers, and it was their use that spurred the development of pseudorandom number generators, which were far quicker to use than the tables of random numbers which had been previously used for statistical sampling. In Monte Carlo simulation of radiation transport, the history (track) of a particle is viewed as a random sequence of free flights that end with an interaction event where the particle changes its direction of movement, loses energy and, occasionally, produces secondary particles. The Monte Carlo simulation of a given experimental arrangement (e.g., an electron beam, coming from an accelerator and impinging on a water phantom) consists of the numerical generation of random histories. To simulate these histories we need an interaction model, i.e., a set of differential cross sections (DCS) for the relevant interaction mechanisms. The DCSs determine the probability distribution functions (pdf) of the random variables that characterize a track; 1) free path between successive interaction events, 2) type of interaction taking place and 3) energy loss and angular deflection in a particular event (and initial state of emitted secondary particles, if any). Once these pdfs are known, random histories can be generated by using appropriate sampling methods. If the number of generated histories is large enough, quantitative information on the transport process may be obtained by simply averaging over the simulated histories. The Monte Carlo method yields the same information as the solution of the Boltzmann transport equation, with the same interaction model, but is easier to implement. In particular, the simulation of radiation

Randomized quasi-Monte Carlo simulation of fast-ion thermalization

Science.gov (United States)

Höök, L. J.; Johnson, T.; Hellsten, T.

2012-01-01

This work investigates the applicability of the randomized quasi-Monte Carlo method for simulation of fast-ion thermalization processes in fusion plasmas, e.g. for simulation of neutral beam injection and radio frequency heating. In contrast to the standard Monte Carlo method, the quasi-Monte Carlo method uses deterministic numbers instead of pseudo-random numbers and has a statistical weak convergence close to {O}(N^{-1}) , where N is the number of markers. We have compared different quasi-Monte Carlo methods for a neutral beam injection scenario, which is solved by many realizations of the associated stochastic differential equation, discretized with the Euler-Maruyama scheme. The statistical convergence of the methods is measured for time steps up to 214.

On-the-fly doppler broadening for Monte Carlo codes

International Nuclear Information System (INIS)

Yesilyurt, G.; Martin, W. R.; Brown, F. B.

2009-01-01

A methodology to allow on-the-fly Doppler broadening of neutron cross sections for use in Monte Carlo codes has been developed. The Monte Carlo code only needs to store 0 K cross sections for each isotope and the method will broaden the 0 K cross sections for any isotope in the library to any temperature in the range 77 K-3200 K. The methodology is based on a combination of Taylor series expansions and asymptotic series expansions. The type of series representation was determined by investigating the temperature dependence of U3o8 resonance cross sections in three regions: near the resonance peaks, mid-resonance, and the resonance wings. The coefficients for these series expansions were determined by a regression over the energy and temperature range of interest. Since the resonance parameters are a function of the neutron energy and target nuclide, the ψ and χ functions in the Adler-Adler multi-level resonance model can be represented by series expansions in temperature only, allowing the least number of terms to approximate the temperature dependent cross sections within a given accuracy. The comparison of the broadened cross sections using this methodology with the NJOY cross sections was excellent over the entire temperature range (77 K-3200 K) and energy range. A Monte Carlo code was implemented to apply the combined regression model and used to estimate the additional computing cost which was found to be less than <1%. (authors)

Nested Sampling with Constrained Hamiltonian Monte Carlo

OpenAIRE

Betancourt, M. J.

2010-01-01

Nested sampling is a powerful approach to Bayesian inference ultimately limited by the computationally demanding task of sampling from a heavily constrained probability distribution. An effective algorithm in its own right, Hamiltonian Monte Carlo is readily adapted to efficiently sample from any smooth, constrained distribution. Utilizing this constrained Hamiltonian Monte Carlo, I introduce a general implementation of the nested sampling algorithm.

Monte Carlo computation in the applied research of nuclear technology

International Nuclear Information System (INIS)

Xu Shuyan; Liu Baojie; Li Qin

2007-01-01

This article briefly introduces Monte Carlo Methods and their properties. It narrates the Monte Carlo methods with emphasis in their applications to several domains of nuclear technology. Monte Carlo simulation methods and several commonly used computer software to implement them are also introduced. The proposed methods are demonstrated by a real example. (authors)

First Passage Probability Estimation of Wind Turbines by Markov Chain Monte Carlo

DEFF Research Database (Denmark)

Sichani, Mahdi Teimouri; Nielsen, Søren R.K.

2013-01-01

Markov Chain Monte Carlo simulation has received considerable attention within the past decade as reportedly one of the most powerful techniques for the first passage probability estimation of dynamic systems. A very popular method in this direction capable of estimating probability of rare events...... of the method by modifying the conditional sampler. In this paper, applicability of the original SS is compared to the recently introduced modifications of the method on a wind turbine model. The model incorporates a PID pitch controller which aims at keeping the rotational speed of the wind turbine rotor equal...... to its nominal value. Finally Monte Carlo simulations are performed which allow assessment of the accuracy of the first passage probability estimation by the SS methods....

Auxiliary-Field Quantum Monte Carlo Simulations of Strongly-Correlated Molecules and Solids

Energy Technology Data Exchange (ETDEWEB)

Chang, C. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Morales, M. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-11-10

We propose a method of implementing projected wave functions for second-quantized auxiliary-field quantum Monte Carlo (AFQMC) techniques. The method is based on expressing the two-body projector as one-body terms coupled to binary Ising fields. To benchmark the method, we choose to study the two-dimensional (2D) one-band Hubbard model with repulsive interactions using the constrained-path MC (CPMC). The CPMC uses a trial wave function to guide the random walks so that the so-called fermion sign problem can be eliminated. The trial wave function also serves as the importance function in Monte Carlo sampling. As such, the quality of the trial wave function has a direct impact to the efficiency and accuracy of the simulations.

Auxiliary-Field Quantum Monte Carlo Simulations of Strongly-Correlated Molecules and Solids

International Nuclear Information System (INIS)

Chang, C.; Morales, M. A.

2016-01-01

We propose a method of implementing projected wave functions for second-quantized auxiliary-field quantum Monte Carlo (AFQMC) techniques. The method is based on expressing the two-body projector as one-body terms coupled to binary Ising fields. To benchmark the method, we choose to study the two-dimensional (2D) one-band Hubbard model with repulsive interactions using the constrained-path MC (CPMC). The CPMC uses a trial wave function to guide the random walks so that the so-called fermion sign problem can be eliminated. The trial wave function also serves as the importance function in Monte Carlo sampling. As such, the quality of the trial wave function has a direct impact to the efficiency and accuracy of the simulations.

Monte Carlo Depletion with Critical Spectrum for Assembly Group Constant Generation

International Nuclear Information System (INIS)

Park, Ho Jin; Joo, Han Gyu; Shim, Hyung Jin; Kim, Chang Hyo

2010-01-01

The conventional two-step procedure has been used in practical nuclear reactor analysis. In this procedure, a deterministic assembly transport code such as HELIOS and CASMO is normally to generate multigroup flux distribution to be used in few-group cross section generation. Recently there are accuracy issues related with the resonance treatment or the double heterogeneity (DH) treatment for VHTR fuel blocks. In order to mitigate the accuracy issues, Monte Carlo (MC) methods can be used as an alternative way to generate few-group cross sections because the accuracy of the MC calculations benefits from its ability to use continuous energy nuclear data and detailed geometric information. In an earlier work, the conventional methods of obtaining multigroup cross sections and the critical spectrum are implemented into the McCARD Monte Carlo code. However, it was not complete in that the critical spectrum is not reflected in the depletion calculation. The purpose of this study is to develop a method to apply the critical spectrum to MC depletion calculations to correct for the leakage effect in the depletion calculation and then to examine the MC based group constants within the two-step procedure by comparing the two-step solution with the direct whole core MC depletion result

Statistics of Monte Carlo methods used in radiation transport calculation

International Nuclear Information System (INIS)

Datta, D.

2009-01-01

Radiation transport calculation can be carried out by using either deterministic or statistical methods. Radiation transport calculation based on statistical methods is basic theme of the Monte Carlo methods. The aim of this lecture is to describe the fundamental statistics required to build the foundations of Monte Carlo technique for radiation transport calculation. Lecture note is organized in the following way. Section (1) will describe the introduction of Basic Monte Carlo and its classification towards the respective field. Section (2) will describe the random sampling methods, a key component of Monte Carlo radiation transport calculation, Section (3) will provide the statistical uncertainty of Monte Carlo estimates, Section (4) will describe in brief the importance of variance reduction techniques while sampling particles such as photon, or neutron in the process of radiation transport

Shell model Monte Carlo methods

International Nuclear Information System (INIS)

Koonin, S.E.

1996-01-01

We review quantum Monte Carlo methods for dealing with large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in fluctuating one-body fields; resultant path integral is evaluated stochastically. We first discuss the motivation, formalism, and implementation of such Shell Model Monte Carlo methods. There then follows a sampler of results and insights obtained from a number of applications. These include the ground state and thermal properties of pf-shell nuclei, thermal behavior of γ-soft nuclei, and calculation of double beta-decay matrix elements. Finally, prospects for further progress in such calculations are discussed. 87 refs

Multiple histogram method and static Monte Carlo sampling

NARCIS (Netherlands)

Inda, M.A.; Frenkel, D.

2004-01-01

We describe an approach to use multiple-histogram methods in combination with static, biased Monte Carlo simulations. To illustrate this, we computed the force-extension curve of an athermal polymer from multiple histograms constructed in a series of static Rosenbluth Monte Carlo simulations. From

Forest canopy BRDF simulation using Monte Carlo method

NARCIS (Netherlands)

Huang, J.; Wu, B.; Zeng, Y.; Tian, Y.

2006-01-01

Monte Carlo method is a random statistic method, which has been widely used to simulate the Bidirectional Reflectance Distribution Function (BRDF) of vegetation canopy in the field of visible remote sensing. The random process between photons and forest canopy was designed using Monte Carlo method.

Monte Carlo verification of polymer gel dosimetry applied to radionuclide therapy: a phantom study

International Nuclear Information System (INIS)

Gear, J I; Partridge, M; Flux, G D; Charles-Edwards, E

2011-01-01

This study evaluates the dosimetric performance of the polymer gel dosimeter 'Methacrylic and Ascorbic acid in Gelatin, initiated by Copper' and its suitability for quality assurance and analysis of I-131-targeted radionuclide therapy dosimetry. Four batches of gel were manufactured in-house and sets of calibration vials and phantoms were created containing different concentrations of I-131-doped gel. Multiple dose measurements were made up to 700 h post preparation and compared to equivalent Monte Carlo simulations. In addition to uniformly filled phantoms the cross-dose distribution from a hot insert to a surrounding phantom was measured. In this example comparisons were made with both Monte Carlo and a clinical scintigraphic dosimetry method. Dose-response curves generated from the calibration data followed a sigmoid function. The gels appeared to be stable over many weeks of internal irradiation with a delay in gel response observed at 29 h post preparation. This was attributed to chemical inhibitors and slow reaction rates of long-chain radical species. For this reason, phantom measurements were only made after 190 h of irradiation. For uniformly filled phantoms of I-131 the accuracy of dose measurements agreed to within 10% when compared to Monte Carlo simulations. A radial cross-dose distribution measured using the gel dosimeter compared well to that calculated with Monte Carlo. Small inhomogeneities were observed in the dosimeter attributed to non-uniform mixing of monomer during preparation. However, they were not detrimental to this study where the quantitative accuracy and spatial resolution of polymer gel dosimetry were far superior to that calculated using scintigraphy. The difference between Monte Carlo and gel measurements was of the order of a few cGy, whilst with the scintigraphic method differences of up to 8 Gy were observed. A manipulation technique is also presented which allows 3D scintigraphic dosimetry measurements to be compared to polymer

Monte Carlo techniques in diagnostic and therapeutic nuclear medicine

International Nuclear Information System (INIS)

Zaidi, H.

2002-01-01

Monte Carlo techniques have become one of the most popular tools in different areas of medical radiation physics following the development and subsequent implementation of powerful computing systems for clinical use. In particular, they have been extensively applied to simulate processes involving random behaviour and to quantify physical parameters that are difficult or even impossible to calculate analytically or to determine by experimental measurements. The use of the Monte Carlo method to simulate radiation transport turned out to be the most accurate means of predicting absorbed dose distributions and other quantities of interest in the radiation treatment of cancer patients using either external or radionuclide radiotherapy. The same trend has occurred for the estimation of the absorbed dose in diagnostic procedures using radionuclides. There is broad consensus in accepting that the earliest Monte Carlo calculations in medical radiation physics were made in the area of nuclear medicine, where the technique was used for dosimetry modelling and computations. Formalism and data based on Monte Carlo calculations, developed by the Medical Internal Radiation Dose (MIRD) committee of the Society of Nuclear Medicine, were published in a series of supplements to the Journal of Nuclear Medicine, the first one being released in 1968. Some of these pamphlets made extensive use of Monte Carlo calculations to derive specific absorbed fractions for electron and photon sources uniformly distributed in organs of mathematical phantoms. Interest in Monte Carlo-based dose calculations with β-emitters has been revived with the application of radiolabelled monoclonal antibodies to radioimmunotherapy. As a consequence of this generalized use, many questions are being raised primarily about the need and potential of Monte Carlo techniques, but also about how accurate it really is, what would it take to apply it clinically and make it available widely to the medical physics

Monte Carlo strategies in scientific computing

CERN Document Server

Liu, Jun S

2008-01-01

This paperback edition is a reprint of the 2001 Springer edition This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians It can also be used as the textbook for a graduate-level course on Monte Carlo methods Many problems discussed in the alter chapters can be potential thesis topics for masters’ or PhD students in statistics or computer science departments Jun Liu is Professor of Statistics at Harvard University, with a courtesy Professor appointment at Harvard Biostatistics Department Professor Liu was the recipient of the 2002 COPSS Presidents' Award, the most prestigious one for sta...

Off-diagonal expansion quantum Monte Carlo.

Science.gov (United States)

Albash, Tameem; Wagenbreth, Gene; Hen, Itay

2017-12-01

We propose a Monte Carlo algorithm designed to simulate quantum as well as classical systems at equilibrium, bridging the algorithmic gap between quantum and classical thermal simulation algorithms. The method is based on a decomposition of the quantum partition function that can be viewed as a series expansion about its classical part. We argue that the algorithm not only provides a theoretical advancement in the field of quantum Monte Carlo simulations, but is optimally suited to tackle quantum many-body systems that exhibit a range of behaviors from "fully quantum" to "fully classical," in contrast to many existing methods. We demonstrate the advantages, sometimes by orders of magnitude, of the technique by comparing it against existing state-of-the-art schemes such as path integral quantum Monte Carlo and stochastic series expansion. We also illustrate how our method allows for the unification of quantum and classical thermal parallel tempering techniques into a single algorithm and discuss its practical significance.

Developing and investigating a pure Monte-Carlo module for transient neutron transport analysis

International Nuclear Information System (INIS)

Mylonakis, Antonios G.; Varvayanni, M.; Grigoriadis, D.G.E.; Catsaros, N.

2017-01-01

quite challenging field. More specifically, in this work, a capability for transient neutronic analysis has been introduced in the open-source Monte Carlo code OpenMC. The selected methodology that has been proposed recently by other researchers is inserted in OpenMC following its own features, trying to minimize the necessary modifications and to maximize the advantage by its existing capabilities. The key points of the module which is under development, as well as the results of the analysis of preliminary numerical experiments are presented and discussed. The obtained results are encouraging and very promising in terms of accuracy, giving motivation for further investigation and development.

Coded aperture optimization using Monte Carlo simulations

International Nuclear Information System (INIS)

Martineau, A.; Rocchisani, J.M.; Moretti, J.L.

2010-01-01

Coded apertures using Uniformly Redundant Arrays (URA) have been unsuccessfully evaluated for two-dimensional and three-dimensional imaging in Nuclear Medicine. The images reconstructed from coded projections contain artifacts and suffer from poor spatial resolution in the longitudinal direction. We introduce a Maximum-Likelihood Expectation-Maximization (MLEM) algorithm for three-dimensional coded aperture imaging which uses a projection matrix calculated by Monte Carlo simulations. The aim of the algorithm is to reduce artifacts and improve the three-dimensional spatial resolution in the reconstructed images. Firstly, we present the validation of GATE (Geant4 Application for Emission Tomography) for Monte Carlo simulations of a coded mask installed on a clinical gamma camera. The coded mask modelling was validated by comparison between experimental and simulated data in terms of energy spectra, sensitivity and spatial resolution. In the second part of the study, we use the validated model to calculate the projection matrix with Monte Carlo simulations. A three-dimensional thyroid phantom study was performed to compare the performance of the three-dimensional MLEM reconstruction with conventional correlation method. The results indicate that the artifacts are reduced and three-dimensional spatial resolution is improved with the Monte Carlo-based MLEM reconstruction.

Variational Monte Carlo Technique

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 19; Issue 8. Variational Monte Carlo Technique: Ground State Energies of Quantum Mechanical Systems. Sukanta Deb. General Article Volume 19 Issue 8 August 2014 pp 713-739 ...

Monte Carlo calculation of dose rate conversion factors for external exposure to photon emitters in soil

CERN Document Server

Clouvas, A; Antonopoulos-Domis, M; Silva, J

2000-01-01

The dose rate conversion factors D/sub CF/ (absorbed dose rate in air per unit activity per unit of soil mass, nGy h/sup -1/ per Bq kg/sup -1/) are calculated 1 m above ground for photon emitters of natural radionuclides uniformly distributed in the soil. Three Monte Carlo codes are used: 1) The MCNP code of Los Alamos; 2) The GEANT code of CERN; and 3) a Monte Carlo code developed in the Nuclear Technology Laboratory of the Aristotle University of Thessaloniki. The accuracy of the Monte Carlo results is tested by the comparison of the unscattered flux obtained by the three Monte Carlo codes with an independent straightforward calculation. All codes and particularly the MCNP calculate accurately the absorbed dose rate in air due to the unscattered radiation. For the total radiation (unscattered plus scattered) the D/sub CF/ values calculated from the three codes are in very good agreement between them. The comparison between these results and the results deduced previously by other authors indicates a good ag...

Randomized quasi-Monte Carlo simulation of fast-ion thermalization

International Nuclear Information System (INIS)

Höök, L J; Johnson, T; Hellsten, T

2012-01-01

This work investigates the applicability of the randomized quasi-Monte Carlo method for simulation of fast-ion thermalization processes in fusion plasmas, e.g. for simulation of neutral beam injection and radio frequency heating. In contrast to the standard Monte Carlo method, the quasi-Monte Carlo method uses deterministic numbers instead of pseudo-random numbers and has a statistical weak convergence close to O(N -1 ), where N is the number of markers. We have compared different quasi-Monte Carlo methods for a neutral beam injection scenario, which is solved by many realizations of the associated stochastic differential equation, discretized with the Euler-Maruyama scheme. The statistical convergence of the methods is measured for time steps up to 2 14 . (paper)

Usefulness of the Monte Carlo method in reliability calculations

International Nuclear Information System (INIS)

Lanore, J.M.; Kalli, H.

1977-01-01

Three examples of reliability Monte Carlo programs developed in the LEP (Laboratory for Radiation Shielding Studies in the Nuclear Research Center at Saclay) are presented. First, an uncertainty analysis is given for a simplified spray system; a Monte Carlo program PATREC-MC has been written to solve the problem with the system components given in the fault tree representation. The second program MONARC 2 has been written to solve the problem of complex systems reliability by the Monte Carlo simulation, here again the system (a residual heat removal system) is in the fault tree representation. Third, the Monte Carlo program MONARC was used instead of the Markov diagram to solve the simulation problem of an electric power supply including two nets and two stand-by diesels

Combinatorial nuclear level density by a Monte Carlo method

International Nuclear Information System (INIS)

Cerf, N.

1994-01-01

We present a new combinatorial method for the calculation of the nuclear level density. It is based on a Monte Carlo technique, in order to avoid a direct counting procedure which is generally impracticable for high-A nuclei. The Monte Carlo simulation, making use of the Metropolis sampling scheme, allows a computationally fast estimate of the level density for many fermion systems in large shell model spaces. We emphasize the advantages of this Monte Carlo approach, particularly concerning the prediction of the spin and parity distributions of the excited states,and compare our results with those derived from a traditional combinatorial or a statistical method. Such a Monte Carlo technique seems very promising to determine accurate level densities in a large energy range for nuclear reaction calculations

Monte Carlo variance reduction approaches for non-Boltzmann tallies

International Nuclear Information System (INIS)

Booth, T.E.

1992-12-01

Quantities that depend on the collective effects of groups of particles cannot be obtained from the standard Boltzmann transport equation. Monte Carlo estimates of these quantities are called non-Boltzmann tallies and have become increasingly important recently. Standard Monte Carlo variance reduction techniques were designed for tallies based on individual particles rather than groups of particles. Experience with non-Boltzmann tallies and analog Monte Carlo has demonstrated the severe limitations of analog Monte Carlo for many non-Boltzmann tallies. In fact, many calculations absolutely require variance reduction methods to achieve practical computation times. Three different approaches to variance reduction for non-Boltzmann tallies are described and shown to be unbiased. The advantages and disadvantages of each of the approaches are discussed

The vector and parallel processing of MORSE code on Monte Carlo Machine

International Nuclear Information System (INIS)

Hasegawa, Yukihiro; Higuchi, Kenji.

1995-11-01

Multi-group Monte Carlo Code for particle transport, MORSE is modified for high performance computing on Monte Carlo Machine Monte-4. The method and the results are described. Monte-4 was specially developed to realize high performance computing of Monte Carlo codes for particle transport, which have been difficult to obtain high performance in vector processing on conventional vector processors. Monte-4 has four vector processor units with the special hardware called Monte Carlo pipelines. The vectorization and parallelization of MORSE code and the performance evaluation on Monte-4 are described. (author)

Discrete diffusion Monte Carlo for frequency-dependent radiative transfer

International Nuclear Information System (INIS)

Densmore, Jeffery D.; Thompson, Kelly G.; Urbatsch, Todd J.

2011-01-01

Discrete Diffusion Monte Carlo (DDMC) is a technique for increasing the efficiency of Implicit Monte Carlo radiative-transfer simulations. In this paper, we develop an extension of DDMC for frequency-dependent radiative transfer. We base our new DDMC method on a frequency integrated diffusion equation for frequencies below a specified threshold. Above this threshold we employ standard Monte Carlo. With a frequency-dependent test problem, we confirm the increased efficiency of our new DDMC technique. (author)

Modified Monte Carlo procedure for particle transport problems

International Nuclear Information System (INIS)

Matthes, W.

1978-01-01

The simulation of photon transport in the atmosphere with the Monte Carlo method forms part of the EURASEP-programme. The specifications for the problems posed for a solution were such, that the direct application of the analogue Monte Carlo method was not feasible. For this reason the standard Monte Carlo procedure was modified in the sense that additional properly weighted branchings at each collision and transport process in a photon history were introduced. This modified Monte Carlo procedure leads to a clear and logical separation of the essential parts of a problem and offers a large flexibility for variance reducing techniques. More complex problems, as foreseen in the EURASEP-programme (e.g. clouds in the atmosphere, rough ocean-surface and chlorophyl-distribution in the ocean) can be handled by recoding some subroutines. This collision- and transport-splitting procedure can of course be performed differently in different space- and energy regions. It is applied here only for a homogeneous problem

An Overview of the Monte Carlo Application ToolKit (MCATK)

Energy Technology Data Exchange (ETDEWEB)

Trahan, Travis John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-01-07

MCATK is a C++ component-based Monte Carlo neutron-gamma transport software library designed to build specialized applications and designed to provide new functionality in existing general-purpose Monte Carlo codes like MCNP; it was developed with Agile software engineering methodologies under the motivation to reduce costs. The characteristics of MCATK can be summarized as follows: MCATK physics – continuous energy neutron-gamma transport with multi-temperature treatment, static eigenvalue (k and α) algorithms, time-dependent algorithm, fission chain algorithms; MCATK geometry – mesh geometries, solid body geometries. MCATK provides verified, unit-tested Monte Carlo components, flexibility in Monte Carlo applications development, and numerous tools such as geometry and cross section plotters. Recent work has involved deterministic and Monte Carlo analysis of stochastic systems. Static and dynamic analysis is discussed, and the results of a dynamic test problem are given.

ANALISIS PERBANDINGAN PENENTUAN HARGA CALL OPTION DENGAN MENGGUNAKAN METODE BLACK-SCHOLES DAN METODE SIMULASI MONTE CARLO

Directory of Open Access Journals (Sweden)

Krishna Kusumahadi

2016-03-01

Full Text Available Abstract - This study was conducted to determine the accuracy of the Black-Scholes method compared with the Monte Carlo simulation method to predict the price of a call option on KOMPAS 100 Index at maturity in 1 month, 2 months, and 3 months. The method used in this research is descriptive analysis by using historical data and perform price comparisons with absolute error value to determine whether the Black-Scholes method is more accurate than the method of Monte Carlo simulation in maturities. Result from this research; found that the price value at maturity absolute error for 1 month is 3.76 and the Black-Scholes method for Monte Carlo simulation method is 0:03. Value price absolute error at maturity for 2 months is 3.76 and the Black-Scholes method for Monte Carlo simulation method is 0.03. Value price absolute error on the maturity using Black-Scholes method for 3 months is 3.48 and 2.99 for the Monte Carlo method. Judging from the data obtained that the Monte Carlo method is more accurate than the Black-Scholes method to predict the price of the call option KOMPAS 100 Stock Index in the period of 1 month, 2 months, and 3 months. Implications for investors and capital market participants is when investors want to invest in stocks included in the KOMPAS 100 Index, Monte Carlo simulation method could be use to predict the price of the call option.  It is also advisable to compare with other methods such as GARCH, Neural Network, etc.   Keywords: Black-Scholes, Monte Carlo, Garch, and Artificial Neural Networks.   Abstrak - Penelitian ini dilakukan untuk mengetahui keakuratan Metode Black Scholes dibandingkan dengan Metode Simulasi Monte Carlo dalam memprediksi harga call option Indeks KOMPAS 100 pada saat jatuh tempo 1 bulan, 2 bulan, dan 3 bulan. Metode penelitian yang digunakan dalam penelitian ini adalah deskriptif analitis dengan menggunakan data-data historis, dan melakukan perbandingan nilai price absolute error untuk mengetahui

Suppression of the initial transient in Monte Carlo criticality simulations; Suppression du regime transitoire initial des simulations Monte-Carlo de criticite

Energy Technology Data Exchange (ETDEWEB)

Richet, Y

2006-12-15

Criticality Monte Carlo calculations aim at estimating the effective multiplication factor (k-effective) for a fissile system through iterations simulating neutrons propagation (making a Markov chain). Arbitrary initialization of the neutron population can deeply bias the k-effective estimation, defined as the mean of the k-effective computed at each iteration. A simplified model of this cycle k-effective sequence is built, based on characteristics of industrial criticality Monte Carlo calculations. Statistical tests, inspired by Brownian bridge properties, are designed to discriminate stationarity of the cycle k-effective sequence. The initial detected transient is, then, suppressed in order to improve the estimation of the system k-effective. The different versions of this methodology are detailed and compared, firstly on a plan of numerical tests fitted on criticality Monte Carlo calculations, and, secondly on real criticality calculations. Eventually, the best methodologies observed in these tests are selected and allow to improve industrial Monte Carlo criticality calculations. (author)

Monte Carlo criticality analysis for dissolvers with neutron poison

International Nuclear Information System (INIS)

Yu, Deshun; Dong, Xiufang; Pu, Fuxiang.

1987-01-01

Criticality analysis for dissolvers with neutron poison is given on the basis of Monte Carlo method. In Monte Carlo calculations of thermal neutron group parameters for fuel pieces, neutron transport length is determined in terms of maximum cross section approach. A set of related effective multiplication factors (K eff ) are calculated by Monte Carlo method for the three cases. Related numerical results are quite useful for the design and operation of this kind of dissolver in the criticality safety analysis. (author)

Monte Carlo criticality calculations accelerated by a growing neutron population

International Nuclear Information System (INIS)

Dufek, Jan; Tuttelberg, Kaur

2016-01-01

Highlights: • Efficiency is significantly improved when population size grows over cycles. • The bias in the fission source is balanced to other errors in the source. • The bias in the fission source decays over the cycle as the population grows. - Abstract: We propose a fission source convergence acceleration method for Monte Carlo criticality simulation. As the efficiency of Monte Carlo criticality simulations is sensitive to the selected neutron population size, the method attempts to achieve the acceleration via on-the-fly control of the neutron population size. The neutron population size is gradually increased over successive criticality cycles so that the fission source bias amounts to a specific fraction of the total error in the cumulative fission source. An optimal setting then gives a reasonably small neutron population size, allowing for an efficient source iteration; at the same time the neutron population size is chosen large enough to ensure a sufficiently small source bias, such that does not limit accuracy of the simulation.

Monte Carlo dose distributions for radiosurgery

International Nuclear Information System (INIS)

Perucha, M.; Leal, A.; Rincon, M.; Carrasco, E.

2001-01-01

The precision of Radiosurgery Treatment planning systems is limited by the approximations of their algorithms and by their dosimetrical input data. This fact is especially important in small fields. However, the Monte Carlo methods is an accurate alternative as it considers every aspect of particle transport. In this work an acoustic neurinoma is studied by comparing the dose distribution of both a planning system and Monte Carlo. Relative shifts have been measured and furthermore, Dose-Volume Histograms have been calculated for target and adjacent organs at risk. (orig.)

Shell model Monte Carlo methods

International Nuclear Information System (INIS)

Koonin, S.E.; Dean, D.J.; Langanke, K.

1997-01-01

We review quantum Monte Carlo methods for dealing with large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in fluctuating one-body fields; the resultant path integral is evaluated stochastically. We first discuss the motivation, formalism, and implementation of such Shell Model Monte Carlo (SMMC) methods. There then follows a sampler of results and insights obtained from a number of applications. These include the ground state and thermal properties of pf-shell nuclei, the thermal and rotational behavior of rare-earth and γ-soft nuclei, and the calculation of double beta-decay matrix elements. Finally, prospects for further progress in such calculations are discussed. (orig.)

Monte Carlo Methods in ICF

Science.gov (United States)

Zimmerman, George B.

Monte Carlo methods appropriate to simulate the transport of x-rays, neutrons, ions and electrons in Inertial Confinement Fusion targets are described and analyzed. The Implicit Monte Carlo method of x-ray transport handles symmetry within indirect drive ICF hohlraums well, but can be improved 50X in efficiency by angular biasing the x-rays towards the fuel capsule. Accurate simulation of thermonuclear burn and burn diagnostics involves detailed particle source spectra, charged particle ranges, inflight reaction kinematics, corrections for bulk and thermal Doppler effects and variance reduction to obtain adequate statistics for rare events. It is found that the effects of angular Coulomb scattering must be included in models of charged particle transport through heterogeneous materials.

Monte Carlo methods in ICF

International Nuclear Information System (INIS)

Zimmerman, George B.

1997-01-01

Monte Carlo methods appropriate to simulate the transport of x-rays, neutrons, ions and electrons in Inertial Confinement Fusion targets are described and analyzed. The Implicit Monte Carlo method of x-ray transport handles symmetry within indirect drive ICF hohlraums well, but can be improved 50X in efficiency by angular biasing the x-rays towards the fuel capsule. Accurate simulation of thermonuclear burn and burn diagnostics involves detailed particle source spectra, charged particle ranges, inflight reaction kinematics, corrections for bulk and thermal Doppler effects and variance reduction to obtain adequate statistics for rare events. It is found that the effects of angular Coulomb scattering must be included in models of charged particle transport through heterogeneous materials

Quasi Monte Carlo methods for optimization models of the energy industry with pricing and load processes; Quasi-Monte Carlo Methoden fuer Optimierungsmodelle der Energiewirtschaft mit Preis- und Last-Prozessen

Energy Technology Data Exchange (ETDEWEB)

Leoevey, H.; Roemisch, W. [Humboldt-Univ., Berlin (Germany)

2015-07-01

We discuss progress in quasi Monte Carlo methods for numerical calculation integrals or expected values and justify why these methods are more efficient than the classic Monte Carlo methods. Quasi Monte Carlo methods are found to be particularly efficient if the integrands have a low effective dimension. That's why We also discuss the concept of effective dimension and prove on the example of a stochastic Optimization model of the energy industry that such models can posses a low effective dimension. Modern quasi Monte Carlo methods are therefore for such models very promising. [German] Wir diskutieren Fortschritte bei Quasi-Monte Carlo Methoden zur numerischen Berechnung von Integralen bzw. Erwartungswerten und begruenden warum diese Methoden effizienter sind als die klassischen Monte Carlo Methoden. Quasi-Monte Carlo Methoden erweisen sich als besonders effizient, falls die Integranden eine geringe effektive Dimension besitzen. Deshalb diskutieren wir auch den Begriff effektive Dimension und weisen am Beispiel eines stochastischen Optimierungsmodell aus der Energiewirtschaft nach, dass solche Modelle eine niedrige effektive Dimension besitzen koennen. Moderne Quasi-Monte Carlo Methoden sind deshalb fuer solche Modelle sehr erfolgversprechend.

BREM5 electroweak Monte Carlo

International Nuclear Information System (INIS)

Kennedy, D.C. II.

1987-01-01

This is an update on the progress of the BREMMUS Monte Carlo simulator, particularly in its current incarnation, BREM5. The present report is intended only as a follow-up to the Mark II/Granlibakken proceedings, and those proceedings should be consulted for a complete description of the capabilities and goals of the BREMMUS program. The new BREM5 program improves on the previous version of BREMMUS, BREM2, in a number of important ways. In BREM2, the internal loop (oblique) corrections were not treated in consistent fashion, a deficiency that led to renormalization scheme-dependence; i.e., physical results, such as cross sections, were dependent on the method used to eliminate infinities from the theory. Of course, this problem cannot be tolerated in a Monte Carlo designed for experimental use. BREM5 incorporates a new way of treating the oblique corrections, as explained in the Granlibakken proceedings, that guarantees renormalization scheme-independence and dramatically simplifies the organization and calculation of radiative corrections. This technique is to be presented in full detail in a forthcoming paper. BREM5 is, at this point, the only Monte Carlo to contain the entire set of one-loop corrections to electroweak four-fermion processes and renormalization scheme-independence. 3 figures

PEPSI: a Monte Carlo generator for polarized leptoproduction

International Nuclear Information System (INIS)

Mankiewicz, L.

1992-01-01

We describe PEPSI (Polarized Electron Proton Scattering Interactions) a Monte Carlo program for the polarized deep inelastic leptoproduction mediated by electromagnetic interaction. The code is a modification of the LEPTO 4.3 Lund Monte Carlo for unpolarized scattering and requires the standard polarization-independent JETSET routines to perform fragmentation into final hadrons. (orig.)

Importance estimation in Monte Carlo modelling of neutron and photon transport

International Nuclear Information System (INIS)

Mickael, M.W.

1992-01-01

The estimation of neutron and photon importance in a three-dimensional geometry is achieved using a coupled Monte Carlo and diffusion theory calculation. The parameters required for the solution of the multigroup adjoint diffusion equation are estimated from an analog Monte Carlo simulation of the system under investigation. The solution of the adjoint diffusion equation is then used as an estimate of the particle importance in the actual simulation. This approach provides an automated and efficient variance reduction method for Monte Carlo simulations. The technique has been successfully applied to Monte Carlo simulation of neutron and coupled neutron-photon transport in the nuclear well-logging field. The results show that the importance maps obtained in a few minutes of computer time using this technique are in good agreement with Monte Carlo generated importance maps that require prohibitive computing times. The application of this method to Monte Carlo modelling of the response of neutron porosity and pulsed neutron instruments has resulted in major reductions in computation time. (Author)

Iterative acceleration methods for Monte Carlo and deterministic criticality calculations

Energy Technology Data Exchange (ETDEWEB)

Urbatsch, T.J.

1995-11-01

If you have ever given up on a nuclear criticality calculation and terminated it because it took so long to converge, you might find this thesis of interest. The author develops three methods for improving the fission source convergence in nuclear criticality calculations for physical systems with high dominance ratios for which convergence is slow. The Fission Matrix Acceleration Method and the Fission Diffusion Synthetic Acceleration (FDSA) Method are acceleration methods that speed fission source convergence for both Monte Carlo and deterministic methods. The third method is a hybrid Monte Carlo method that also converges for difficult problems where the unaccelerated Monte Carlo method fails. The author tested the feasibility of all three methods in a test bed consisting of idealized problems. He has successfully accelerated fission source convergence in both deterministic and Monte Carlo criticality calculations. By filtering statistical noise, he has incorporated deterministic attributes into the Monte Carlo calculations in order to speed their source convergence. He has used both the fission matrix and a diffusion approximation to perform unbiased accelerations. The Fission Matrix Acceleration method has been implemented in the production code MCNP and successfully applied to a real problem. When the unaccelerated calculations are unable to converge to the correct solution, they cannot be accelerated in an unbiased fashion. A Hybrid Monte Carlo method weds Monte Carlo and a modified diffusion calculation to overcome these deficiencies. The Hybrid method additionally possesses reduced statistical errors.

Iterative acceleration methods for Monte Carlo and deterministic criticality calculations

International Nuclear Information System (INIS)

Urbatsch, T.J.

1995-11-01

If you have ever given up on a nuclear criticality calculation and terminated it because it took so long to converge, you might find this thesis of interest. The author develops three methods for improving the fission source convergence in nuclear criticality calculations for physical systems with high dominance ratios for which convergence is slow. The Fission Matrix Acceleration Method and the Fission Diffusion Synthetic Acceleration (FDSA) Method are acceleration methods that speed fission source convergence for both Monte Carlo and deterministic methods. The third method is a hybrid Monte Carlo method that also converges for difficult problems where the unaccelerated Monte Carlo method fails. The author tested the feasibility of all three methods in a test bed consisting of idealized problems. He has successfully accelerated fission source convergence in both deterministic and Monte Carlo criticality calculations. By filtering statistical noise, he has incorporated deterministic attributes into the Monte Carlo calculations in order to speed their source convergence. He has used both the fission matrix and a diffusion approximation to perform unbiased accelerations. The Fission Matrix Acceleration method has been implemented in the production code MCNP and successfully applied to a real problem. When the unaccelerated calculations are unable to converge to the correct solution, they cannot be accelerated in an unbiased fashion. A Hybrid Monte Carlo method weds Monte Carlo and a modified diffusion calculation to overcome these deficiencies. The Hybrid method additionally possesses reduced statistical errors

Study on random number generator in Monte Carlo code

International Nuclear Information System (INIS)

Oya, Kentaro; Kitada, Takanori; Tanaka, Shinichi

2011-01-01

The Monte Carlo code uses a sequence of pseudo-random numbers with a random number generator (RNG) to simulate particle histories. A pseudo-random number has its own period depending on its generation method and the period is desired to be long enough not to exceed the period during one Monte Carlo calculation to ensure the correctness especially for a standard deviation of results. The linear congruential generator (LCG) is widely used as Monte Carlo RNG and the period of LCG is not so long by considering the increasing rate of simulation histories in a Monte Carlo calculation according to the remarkable enhancement of computer performance. Recently, many kinds of RNG have been developed and some of their features are better than those of LCG. In this study, we investigate the appropriate RNG in a Monte Carlo code as an alternative to LCG especially for the case of enormous histories. It is found that xorshift has desirable features compared with LCG, and xorshift has a larger period, a comparable speed to generate random numbers, a better randomness, and good applicability to parallel calculation. (author)

Combinatorial geometry domain decomposition strategies for Monte Carlo simulations

Energy Technology Data Exchange (ETDEWEB)

Li, G.; Zhang, B.; Deng, L.; Mo, Z.; Liu, Z.; Shangguan, D.; Ma, Y.; Li, S.; Hu, Z. [Institute of Applied Physics and Computational Mathematics, Beijing, 100094 (China)

2013-07-01

Analysis and modeling of nuclear reactors can lead to memory overload for a single core processor when it comes to refined modeling. A method to solve this problem is called 'domain decomposition'. In the current work, domain decomposition algorithms for a combinatorial geometry Monte Carlo transport code are developed on the JCOGIN (J Combinatorial Geometry Monte Carlo transport INfrastructure). Tree-based decomposition and asynchronous communication of particle information between domains are described in the paper. Combination of domain decomposition and domain replication (particle parallelism) is demonstrated and compared with that of MERCURY code. A full-core reactor model is simulated to verify the domain decomposition algorithms using the Monte Carlo particle transport code JMCT (J Monte Carlo Transport Code), which has being developed on the JCOGIN infrastructure. Besides, influences of the domain decomposition algorithms to tally variances are discussed. (authors)

Combinatorial geometry domain decomposition strategies for Monte Carlo simulations

International Nuclear Information System (INIS)

Li, G.; Zhang, B.; Deng, L.; Mo, Z.; Liu, Z.; Shangguan, D.; Ma, Y.; Li, S.; Hu, Z.

2013-01-01

Analysis and modeling of nuclear reactors can lead to memory overload for a single core processor when it comes to refined modeling. A method to solve this problem is called 'domain decomposition'. In the current work, domain decomposition algorithms for a combinatorial geometry Monte Carlo transport code are developed on the JCOGIN (J Combinatorial Geometry Monte Carlo transport INfrastructure). Tree-based decomposition and asynchronous communication of particle information between domains are described in the paper. Combination of domain decomposition and domain replication (particle parallelism) is demonstrated and compared with that of MERCURY code. A full-core reactor model is simulated to verify the domain decomposition algorithms using the Monte Carlo particle transport code JMCT (J Monte Carlo Transport Code), which has being developed on the JCOGIN infrastructure. Besides, influences of the domain decomposition algorithms to tally variances are discussed. (authors)

Monte Carlo method applied to medical physics

International Nuclear Information System (INIS)

Oliveira, C.; Goncalves, I.F.; Chaves, A.; Lopes, M.C.; Teixeira, N.; Matos, B.; Goncalves, I.C.; Ramalho, A.; Salgado, J.

2000-01-01

The main application of the Monte Carlo method to medical physics is dose calculation. This paper shows some results of two dose calculation studies and two other different applications: optimisation of neutron field for Boron Neutron Capture Therapy and optimization of a filter for a beam tube for several purposes. The time necessary for Monte Carlo calculations - the highest boundary for its intensive utilisation - is being over-passed with faster and cheaper computers. (author)

A radiating shock evaluated using Implicit Monte Carlo Diffusion

International Nuclear Information System (INIS)

Cleveland, M.; Gentile, N.

2013-01-01

Implicit Monte Carlo [1] (IMC) has been shown to be very expensive when used to evaluate a radiation field in opaque media. Implicit Monte Carlo Diffusion (IMD) [2], which evaluates a spatial discretized diffusion equation using a Monte Carlo algorithm, can be used to reduce the cost of evaluating the radiation field in opaque media [2]. This work couples IMD to the hydrodynamics equations to evaluate opaque diffusive radiating shocks. The Lowrie semi-analytic diffusive radiating shock benchmark[a] is used to verify our implementation of the coupled system of equations. (authors)

The Monte Carlo method the method of statistical trials

CERN Document Server

Shreider, YuA

1966-01-01

The Monte Carlo Method: The Method of Statistical Trials is a systematic account of the fundamental concepts and techniques of the Monte Carlo method, together with its range of applications. Some of these applications include the computation of definite integrals, neutron physics, and in the investigation of servicing processes. This volume is comprised of seven chapters and begins with an overview of the basic features of the Monte Carlo method and typical examples of its application to simple problems in computational mathematics. The next chapter examines the computation of multi-dimensio

Applicability of quasi-Monte Carlo for lattice systems

International Nuclear Information System (INIS)

Ammon, Andreas; Deutsches Elektronen-Synchrotron; Hartung, Tobias; Jansen, Karl; Leovey, Hernan; Griewank, Andreas; Mueller-Preussker, Michael

2013-11-01

This project investigates the applicability of quasi-Monte Carlo methods to Euclidean lattice systems in order to improve the asymptotic error scaling of observables for such theories. The error of an observable calculated by averaging over random observations generated from ordinary Monte Carlo simulations scales like N -1/2 , where N is the number of observations. By means of quasi-Monte Carlo methods it is possible to improve this scaling for certain problems to N -1 , or even further if the problems are regular enough. We adapted and applied this approach to simple systems like the quantum harmonic and anharmonic oscillator and verified an improved error scaling of all investigated observables in both cases.

Applicability of quasi-Monte Carlo for lattice systems

Energy Technology Data Exchange (ETDEWEB)

Ammon, Andreas [Berlin Humboldt-Univ. (Germany). Dept. of Physics; Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Hartung, Tobias [King' s College London (United Kingdom). Dept. of Mathematics; Jansen, Karl [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Leovey, Hernan; Griewank, Andreas [Berlin Humboldt-Univ. (Germany). Dept. of Mathematics; Mueller-Preussker, Michael [Berlin Humboldt-Univ. (Germany). Dept. of Physics

2013-11-15

This project investigates the applicability of quasi-Monte Carlo methods to Euclidean lattice systems in order to improve the asymptotic error scaling of observables for such theories. The error of an observable calculated by averaging over random observations generated from ordinary Monte Carlo simulations scales like N{sup -1/2}, where N is the number of observations. By means of quasi-Monte Carlo methods it is possible to improve this scaling for certain problems to N{sup -1}, or even further if the problems are regular enough. We adapted and applied this approach to simple systems like the quantum harmonic and anharmonic oscillator and verified an improved error scaling of all investigated observables in both cases.

Automated Monte Carlo biasing for photon-generated electrons near surfaces.

Energy Technology Data Exchange (ETDEWEB)

Franke, Brian Claude; Crawford, Martin James; Kensek, Ronald Patrick

2009-09-01

This report describes efforts to automate the biasing of coupled electron-photon Monte Carlo particle transport calculations. The approach was based on weight-windows biasing. Weight-window settings were determined using adjoint-flux Monte Carlo calculations. A variety of algorithms were investigated for adaptivity of the Monte Carlo tallies. Tree data structures were used to investigate spatial partitioning. Functional-expansion tallies were used to investigate higher-order spatial representations.

SU-F-T-619: Dose Evaluation of Specific Patient Plans Based On Monte Carlo Algorithm for a CyberKnife Stereotactic Radiosurgery System

Energy Technology Data Exchange (ETDEWEB)

Piao, J [PLA General Hospital, Beijing (China); PLA 302 Hospital, Beijing (China); Xu, S [PLA General Hospital, Beijing (China); Tsinghua University, Beijing (China); Wu, Z; Liu, Y [Tsinghua University, Beijing (China); Li, Y [Beihang University, Beijing (China); Qu, B [PLA General Hospital, Beijing (China); Duan, X [PLA 302 Hospital, Beijing (China)

2016-06-15

Purpose: This study will use Monte Carlo to simulate the Cyberknife system, and intend to develop the third-party tool to evaluate the dose verification of specific patient plans in TPS. Methods: By simulating the treatment head using the BEAMnrc and DOSXYZnrc software, the comparison between the calculated and measured data will be done to determine the beam parameters. The dose distribution calculated in the Raytracing, Monte Carlo algorithms of TPS (Multiplan Ver4.0.2) and in-house Monte Carlo simulation method for 30 patient plans, which included 10 head, lung and liver cases in each, were analyzed. The γ analysis with the combined 3mm/3% criteria would be introduced to quantitatively evaluate the difference of the accuracy between three algorithms. Results: More than 90% of the global error points were less than 2% for the comparison of the PDD and OAR curves after determining the mean energy and FWHM.The relative ideal Monte Carlo beam model had been established. Based on the quantitative evaluation of dose accuracy for three algorithms, the results of γ analysis shows that the passing rates (84.88±9.67% for head,98.83±1.05% for liver,98.26±1.87% for lung) of PTV in 30 plans between Monte Carlo simulation and TPS Monte Carlo algorithms were good. And the passing rates (95.93±3.12%,99.84±0.33% in each) of PTV in head and liver plans between Monte Carlo simulation and TPS Ray-tracing algorithms were also good. But the difference of DVHs in lung plans between Monte Carlo simulation and Ray-tracing algorithms was obvious, and the passing rate (51.263±38.964%) of γ criteria was not good. It is feasible that Monte Carlo simulation was used for verifying the dose distribution of patient plans. Conclusion: Monte Carlo simulation algorithm developed in the CyberKnife system of this study can be used as a reference tool for the third-party tool, which plays an important role in dose verification of patient plans. This work was supported in part by the grant

Uniform distribution and quasi-Monte Carlo methods discrepancy, integration and applications

CERN Document Server

Kritzer, Peter; Pillichshammer, Friedrich; Winterhof, Arne

2014-01-01

The survey articles in this book focus on number theoretic point constructions, uniform distribution theory, and quasi-Monte Carlo methods. As deterministic versions of the Monte Carlo method, quasi-Monte Carlo rules enjoy increasing popularity, with many fruitful applications in mathematical practice, as for example in finance, computer graphics, and biology.

Clinical implementation of full Monte Carlo dose calculation in proton beam therapy

International Nuclear Information System (INIS)

Paganetti, Harald; Jiang, Hongyu; Parodi, Katia; Slopsema, Roelf; Engelsman, Martijn

2008-01-01

The goal of this work was to facilitate the clinical use of Monte Carlo proton dose calculation to support routine treatment planning and delivery. The Monte Carlo code Geant4 was used to simulate the treatment head setup, including a time-dependent simulation of modulator wheels (for broad beam modulation) and magnetic field settings (for beam scanning). Any patient-field-specific setup can be modeled according to the treatment control system of the facility. The code was benchmarked against phantom measurements. Using a simulation of the ionization chamber reading in the treatment head allows the Monte Carlo dose to be specified in absolute units (Gy per ionization chamber reading). Next, the capability of reading CT data information was implemented into the Monte Carlo code to model patient anatomy. To allow time-efficient dose calculation, the standard Geant4 tracking algorithm was modified. Finally, a software link of the Monte Carlo dose engine to the patient database and the commercial planning system was established to allow data exchange, thus completing the implementation of the proton Monte Carlo dose calculation engine ('DoC++'). Monte Carlo re-calculated plans are a valuable tool to revisit decisions in the planning process. Identification of clinically significant differences between Monte Carlo and pencil-beam-based dose calculations may also drive improvements of current pencil-beam methods. As an example, four patients (29 fields in total) with tumors in the head and neck regions were analyzed. Differences between the pencil-beam algorithm and Monte Carlo were identified in particular near the end of range, both due to dose degradation and overall differences in range prediction due to bony anatomy in the beam path. Further, the Monte Carlo reports dose-to-tissue as compared to dose-to-water by the planning system. Our implementation is tailored to a specific Monte Carlo code and the treatment planning system XiO (Computerized Medical Systems Inc

Exponential convergence on a continuous Monte Carlo transport problem

International Nuclear Information System (INIS)

Booth, T.E.

1997-01-01

For more than a decade, it has been known that exponential convergence on discrete transport problems was possible using adaptive Monte Carlo techniques. An adaptive Monte Carlo method that empirically produces exponential convergence on a simple continuous transport problem is described

Quantum Monte Carlo algorithms for electronic structure at the petascale; the endstation project.

Energy Technology Data Exchange (ETDEWEB)

Kim, J; Ceperley, D M; Purwanto, W; Walter, E J; Krakauer, H; Zhang, S W; Kent, P.R. C; Hennig, R G; Umrigar, C; Bajdich, M; Kolorenc, J; Mitas, L

2008-10-01

Over the past two decades, continuum quantum Monte Carlo (QMC) has proved to be an invaluable tool for predicting of the properties of matter from fundamental principles. By solving the Schrodinger equation through a stochastic projection, it achieves the greatest accuracy and reliability of methods available for physical systems containing more than a few quantum particles. QMC enjoys scaling favorable to quantum chemical methods, with a computational effort which grows with the second or third power of system size. This accuracy and scalability has enabled scientific discovery across a broad spectrum of disciplines. The current methods perform very efficiently at the terascale. The quantum Monte Carlo Endstation project is a collaborative effort among researchers in the field to develop a new generation of algorithms, and their efficient implementations, which will take advantage of the upcoming petaflop architectures. Some aspects of these developments are discussed here. These tools will expand the accuracy, efficiency and range of QMC applicability and enable us to tackle challenges which are currently out of reach. The methods will be applied to several important problems including electronic and structural properties of water, transition metal oxides, nanosystems and ultracold atoms.

Isotopic depletion with Monte Carlo

International Nuclear Information System (INIS)

Martin, W.R.; Rathkopf, J.A.

1996-06-01

This work considers a method to deplete isotopes during a time- dependent Monte Carlo simulation of an evolving system. The method is based on explicitly combining a conventional estimator for the scalar flux with the analytical solutions to the isotopic depletion equations. There are no auxiliary calculations; the method is an integral part of the Monte Carlo calculation. The method eliminates negative densities and reduces the variance in the estimates for the isotope densities, compared to existing methods. Moreover, existing methods are shown to be special cases of the general method described in this work, as they can be derived by combining a high variance estimator for the scalar flux with a low-order approximation to the analytical solution to the depletion equation

Multilevel sequential Monte-Carlo samplers

KAUST Repository

Jasra, Ajay

2016-01-01

Multilevel Monte-Carlo methods provide a powerful computational technique for reducing the computational cost of estimating expectations for a given computational effort. They are particularly relevant for computational problems when approximate distributions are determined via a resolution parameter h, with h=0 giving the theoretical exact distribution (e.g. SDEs or inverse problems with PDEs). The method provides a benefit by coupling samples from successive resolutions, and estimating differences of successive expectations. We develop a methodology that brings Sequential Monte-Carlo (SMC) algorithms within the framework of the Multilevel idea, as SMC provides a natural set-up for coupling samples over different resolutions. We prove that the new algorithm indeed preserves the benefits of the multilevel principle, even if samples at all resolutions are now correlated.

Monte Carlo methods in ICF

International Nuclear Information System (INIS)

Zimmerman, G.B.

1997-01-01

Monte Carlo methods appropriate to simulate the transport of x-rays, neutrons, ions and electrons in Inertial Confinement Fusion targets are described and analyzed. The Implicit Monte Carlo method of x-ray transport handles symmetry within indirect drive ICF hohlraums well, but can be improved 50X in efficiency by angular biasing the x-rays towards the fuel capsule. Accurate simulation of thermonuclear burn and burn diagnostics involves detailed particle source spectra, charged particle ranges, inflight reaction kinematics, corrections for bulk and thermal Doppler effects and variance reduction to obtain adequate statistics for rare events. It is found that the effects of angular Coulomb scattering must be included in models of charged particle transport through heterogeneous materials. copyright 1997 American Institute of Physics

Multilevel sequential Monte-Carlo samplers

KAUST Repository

Jasra, Ajay

2016-01-05

Multilevel Monte-Carlo methods provide a powerful computational technique for reducing the computational cost of estimating expectations for a given computational effort. They are particularly relevant for computational problems when approximate distributions are determined via a resolution parameter h, with h=0 giving the theoretical exact distribution (e.g. SDEs or inverse problems with PDEs). The method provides a benefit by coupling samples from successive resolutions, and estimating differences of successive expectations. We develop a methodology that brings Sequential Monte-Carlo (SMC) algorithms within the framework of the Multilevel idea, as SMC provides a natural set-up for coupling samples over different resolutions. We prove that the new algorithm indeed preserves the benefits of the multilevel principle, even if samples at all resolutions are now correlated.

A flexible coupling scheme for Monte Carlo and thermal-hydraulics codes

Energy Technology Data Exchange (ETDEWEB)

Hoogenboom, J. Eduard, E-mail: J.E.Hoogenboom@tudelft.nl [Delft University of Technology (Netherlands); Ivanov, Aleksandar; Sanchez, Victor, E-mail: Aleksandar.Ivanov@kit.edu, E-mail: Victor.Sanchez@kit.edu [Karlsruhe Institute of Technology, Institute of Neutron Physics and Reactor Technology, Eggenstein-Leopoldshafen (Germany); Diop, Cheikh, E-mail: Cheikh.Diop@cea.fr [CEA/DEN/DANS/DM2S/SERMA, Commissariat a l' Energie Atomique, Gif-sur-Yvette (France)

2011-07-01

A coupling scheme between a Monte Carlo code and a thermal-hydraulics code is being developed within the European NURISP project for comprehensive and validated reactor analysis. The scheme is flexible as it allows different Monte Carlo codes and different thermal-hydraulics codes to be used. At present the MCNP and TRIPOLI4 Monte Carlo codes can be used and the FLICA4 and SubChanFlow thermal-hydraulics codes. For all these codes only an original executable is necessary. A Python script drives the iterations between Monte Carlo and thermal-hydraulics calculations. It also calls a conversion program to merge a master input file for the Monte Carlo code with the appropriate temperature and coolant density data from the thermal-hydraulics calculation. Likewise it calls another conversion program to merge a master input file for the thermal-hydraulics code with the power distribution data from the Monte Carlo calculation. Special attention is given to the neutron cross section data for the various required temperatures in the Monte Carlo calculation. Results are shown for an infinite lattice of PWR fuel pin cells and a 3 x 3 fuel BWR pin cell cluster. Various possibilities for further improvement and optimization of the coupling system are discussed. (author)

A flexible coupling scheme for Monte Carlo and thermal-hydraulics codes

International Nuclear Information System (INIS)

Hoogenboom, J. Eduard; Ivanov, Aleksandar; Sanchez, Victor; Diop, Cheikh

2011-01-01

A coupling scheme between a Monte Carlo code and a thermal-hydraulics code is being developed within the European NURISP project for comprehensive and validated reactor analysis. The scheme is flexible as it allows different Monte Carlo codes and different thermal-hydraulics codes to be used. At present the MCNP and TRIPOLI4 Monte Carlo codes can be used and the FLICA4 and SubChanFlow thermal-hydraulics codes. For all these codes only an original executable is necessary. A Python script drives the iterations between Monte Carlo and thermal-hydraulics calculations. It also calls a conversion program to merge a master input file for the Monte Carlo code with the appropriate temperature and coolant density data from the thermal-hydraulics calculation. Likewise it calls another conversion program to merge a master input file for the thermal-hydraulics code with the power distribution data from the Monte Carlo calculation. Special attention is given to the neutron cross section data for the various required temperatures in the Monte Carlo calculation. Results are shown for an infinite lattice of PWR fuel pin cells and a 3 x 3 fuel BWR pin cell cluster. Various possibilities for further improvement and optimization of the coupling system are discussed. (author)

Parallel MCNP Monte Carlo transport calculations with MPI

International Nuclear Information System (INIS)

Wagner, J.C.; Haghighat, A.

1996-01-01

The steady increase in computational performance has made Monte Carlo calculations for large/complex systems possible. However, in order to make these calculations practical, order of magnitude increases in performance are necessary. The Monte Carlo method is inherently parallel (particles are simulated independently) and thus has the potential for near-linear speedup with respect to the number of processors. Further, the ever-increasing accessibility of parallel computers, such as workstation clusters, facilitates the practical use of parallel Monte Carlo. Recognizing the nature of the Monte Carlo method and the trends in available computing, the code developers at Los Alamos National Laboratory implemented the message-passing general-purpose Monte Carlo radiation transport code MCNP (version 4A). The PVM package was chosen by the MCNP code developers because it supports a variety of communication networks, several UNIX platforms, and heterogeneous computer systems. This PVM version of MCNP has been shown to produce speedups that approach the number of processors and thus, is a very useful tool for transport analysis. Due to software incompatibilities on the local IBM SP2, PVM has not been available, and thus it is not possible to take advantage of this useful tool. Hence, it became necessary to implement an alternative message-passing library package into MCNP. Because the message-passing interface (MPI) is supported on the local system, takes advantage of the high-speed communication switches in the SP2, and is considered to be the emerging standard, it was selected

Monte Carlo systems used for treatment planning and dose verification

Energy Technology Data Exchange (ETDEWEB)

Brualla, Lorenzo [Universitaetsklinikum Essen, NCTeam, Strahlenklinik, Essen (Germany); Rodriguez, Miguel [Centro Medico Paitilla, Balboa (Panama); Lallena, Antonio M. [Universidad de Granada, Departamento de Fisica Atomica, Molecular y Nuclear, Granada (Spain)

2017-04-15

General-purpose radiation transport Monte Carlo codes have been used for estimation of the absorbed dose distribution in external photon and electron beam radiotherapy patients since several decades. Results obtained with these codes are usually more accurate than those provided by treatment planning systems based on non-stochastic methods. Traditionally, absorbed dose computations based on general-purpose Monte Carlo codes have been used only for research, owing to the difficulties associated with setting up a simulation and the long computation time required. To take advantage of radiation transport Monte Carlo codes applied to routine clinical practice, researchers and private companies have developed treatment planning and dose verification systems that are partly or fully based on fast Monte Carlo algorithms. This review presents a comprehensive list of the currently existing Monte Carlo systems that can be used to calculate or verify an external photon and electron beam radiotherapy treatment plan. Particular attention is given to those systems that are distributed, either freely or commercially, and that do not require programming tasks from the end user. These systems are compared in terms of features and the simulation time required to compute a set of benchmark calculations. (orig.) [German] Seit mehreren Jahrzehnten werden allgemein anwendbare Monte-Carlo-Codes zur Simulation des Strahlungstransports benutzt, um die Verteilung der absorbierten Dosis in der perkutanen Strahlentherapie mit Photonen und Elektronen zu evaluieren. Die damit erzielten Ergebnisse sind meist akkurater als solche, die mit nichtstochastischen Methoden herkoemmlicher Bestrahlungsplanungssysteme erzielt werden koennen. Wegen des damit verbundenen Arbeitsaufwands und der langen Dauer der Berechnungen wurden Monte-Carlo-Simulationen von Dosisverteilungen in der konventionellen Strahlentherapie in der Vergangenheit im Wesentlichen in der Forschung eingesetzt. Im Bemuehen, Monte-Carlo

Multilevel Monte Carlo in Approximate Bayesian Computation

KAUST Repository

Jasra, Ajay

2017-02-13

In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given.

Monte Carlo simulation of Markov unreliability models

International Nuclear Information System (INIS)

Lewis, E.E.; Boehm, F.

1984-01-01

A Monte Carlo method is formulated for the evaluation of the unrealibility of complex systems with known component failure and repair rates. The formulation is in terms of a Markov process allowing dependences between components to be modeled and computational efficiencies to be achieved in the Monte Carlo simulation. Two variance reduction techniques, forced transition and failure biasing, are employed to increase computational efficiency of the random walk procedure. For an example problem these result in improved computational efficiency by more than three orders of magnitudes over analog Monte Carlo. The method is generalized to treat problems with distributed failure and repair rate data, and a batching technique is introduced and shown to result in substantial increases in computational efficiency for an example problem. A method for separating the variance due to the data uncertainty from that due to the finite number of random walks is presented. (orig.)

A residual Monte Carlo method for discrete thermal radiative diffusion

International Nuclear Information System (INIS)

Evans, T.M.; Urbatsch, T.J.; Lichtenstein, H.; Morel, J.E.

2003-01-01

Residual Monte Carlo methods reduce statistical error at a rate of exp(-bN), where b is a positive constant and N is the number of particle histories. Contrast this convergence rate with 1/√N, which is the rate of statistical error reduction for conventional Monte Carlo methods. Thus, residual Monte Carlo methods hold great promise for increased efficiency relative to conventional Monte Carlo methods. Previous research has shown that the application of residual Monte Carlo methods to the solution of continuum equations, such as the radiation transport equation, is problematic for all but the simplest of cases. However, the residual method readily applies to discrete systems as long as those systems are monotone, i.e., they produce positive solutions given positive sources. We develop a residual Monte Carlo method for solving a discrete 1D non-linear thermal radiative equilibrium diffusion equation, and we compare its performance with that of the discrete conventional Monte Carlo method upon which it is based. We find that the residual method provides efficiency gains of many orders of magnitude. Part of the residual gain is due to the fact that we begin each timestep with an initial guess equal to the solution from the previous timestep. Moreover, fully consistent non-linear solutions can be obtained in a reasonable amount of time because of the effective lack of statistical noise. We conclude that the residual approach has great potential and that further research into such methods should be pursued for more general discrete and continuum systems

Use of Monte Carlo method in low-energy gamma radiation applications

International Nuclear Information System (INIS)

Sulc, J.

1982-01-01

Modelling based on the Monte Carlo method is described in detail of the interaction of low-energy gamma radiation resulting in characteristic radiation of the K series of a pure element. The modelled system corresponds to the usual configuration of the measuring part of a radionuclide X-ray fluorescence analyzer. The accuracy of determination of the mean probability of impingement of characteristic radiation on the detector increases with the number of events. The number of events was selected with regard to the required accuracy, the demand on computer time and the accuracy of input parameters. The results of a comparison of computation and experiment are yet to be published. (M.D.)

Contributon Monte Carlo

International Nuclear Information System (INIS)

Dubi, A.; Gerstl, S.A.W.

1979-05-01

The contributon Monte Carlo method is based on a new recipe to calculate target responses by means of volume integral of the contributon current in a region between the source and the detector. A comprehensive description of the method, its implementation in the general-purpose MCNP code, and results of the method for realistic nonhomogeneous, energy-dependent problems are presented. 23 figures, 10 tables

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities

KAUST Repository

Cheon, Sooyoung

2013-02-16

Importance sampling and Markov chain Monte Carlo methods have been used in exact inference for contingency tables for a long time, however, their performances are not always very satisfactory. In this paper, we propose a stochastic approximation Monte Carlo importance sampling (SAMCIS) method for tackling this problem. SAMCIS is a combination of adaptive Markov chain Monte Carlo and importance sampling, which employs the stochastic approximation Monte Carlo algorithm (Liang et al., J. Am. Stat. Assoc., 102(477):305-320, 2007) to draw samples from an enlarged reference set with a known Markov basis. Compared to the existing importance sampling and Markov chain Monte Carlo methods, SAMCIS has a few advantages, such as fast convergence, ergodicity, and the ability to achieve a desired proportion of valid tables. The numerical results indicate that SAMCIS can outperform the existing importance sampling and Markov chain Monte Carlo methods: It can produce much more accurate estimates in much shorter CPU time than the existing methods, especially for the tables with high degrees of freedom. © 2013 Springer Science+Business Media New York.

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities

KAUST Repository

Cheon, Sooyoung; Liang, Faming; Chen, Yuguo; Yu, Kai

2013-01-01

Importance sampling and Markov chain Monte Carlo methods have been used in exact inference for contingency tables for a long time, however, their performances are not always very satisfactory. In this paper, we propose a stochastic approximation Monte Carlo importance sampling (SAMCIS) method for tackling this problem. SAMCIS is a combination of adaptive Markov chain Monte Carlo and importance sampling, which employs the stochastic approximation Monte Carlo algorithm (Liang et al., J. Am. Stat. Assoc., 102(477):305-320, 2007) to draw samples from an enlarged reference set with a known Markov basis. Compared to the existing importance sampling and Markov chain Monte Carlo methods, SAMCIS has a few advantages, such as fast convergence, ergodicity, and the ability to achieve a desired proportion of valid tables. The numerical results indicate that SAMCIS can outperform the existing importance sampling and Markov chain Monte Carlo methods: It can produce much more accurate estimates in much shorter CPU time than the existing methods, especially for the tables with high degrees of freedom. © 2013 Springer Science+Business Media New York.

Clinical dosimetry in photon radiotherapy. A Monte Carlo based investigation

International Nuclear Information System (INIS)

Wulff, Joerg

2010-01-01

Practical clinical dosimetry is a fundamental step within the radiation therapy process and aims at quantifying the absorbed radiation dose within a 1-2% uncertainty. To achieve this level of accuracy, corrections are needed for calibrated and air-filled ionization chambers, which are used for dose measurement. The procedures of correction are based on cavity theory of Spencer-Attix and are defined in current dosimetry protocols. Energy dependent corrections for deviations from calibration beams account for changed ionization chamber response in the treatment beam. The corrections applied are usually based on semi-analytical models or measurements and are generally hard to determine due to their magnitude of only a few percents or even less. Furthermore the corrections are defined for fixed geometrical reference-conditions and do not apply to non-reference conditions in modern radiotherapy applications. The stochastic Monte Carlo method for the simulation of radiation transport is becoming a valuable tool in the field of Medical Physics. As a suitable tool for calculation of these corrections with high accuracy the simulations enable the investigation of ionization chambers under various conditions. The aim of this work is the consistent investigation of ionization chamber dosimetry in photon radiation therapy with the use of Monte Carlo methods. Nowadays Monte Carlo systems exist, which enable the accurate calculation of ionization chamber response in principle. Still, their bare use for studies of this type is limited due to the long calculation times needed for a meaningful result with a small statistical uncertainty, inherent to every result of a Monte Carlo simulation. Besides heavy use of computer hardware, techniques methods of variance reduction to reduce the needed calculation time can be applied. Methods for increasing the efficiency in the results of simulation were developed and incorporated in a modern and established Monte Carlo simulation environment

Reducing uncertainty of Monte Carlo estimated fatigue damage in offshore wind turbines using FORM

DEFF Research Database (Denmark)

H. Horn, Jan-Tore; Jensen, Jørgen Juncher

2016-01-01

Uncertainties related to fatigue damage estimation of non-linear systems are highly dependent on the tail behaviour and extreme values of the stress range distribution. By using a combination of the First Order Reliability Method (FORM) and Monte Carlo simulations (MCS), the accuracy of the fatigue...

Closed-shell variational quantum Monte Carlo simulation for the ...

African Journals Online (AJOL)

Closed-shell variational quantum Monte Carlo simulation for the electric dipole moment calculation of hydrazine molecule using casino-code. ... Nigeria Journal of Pure and Applied Physics ... The variational quantum Monte Carlo (VQMC) technique used in this work employed the restricted Hartree-Fock (RHF) scheme.

New Approaches and Applications for Monte Carlo Perturbation Theory

Energy Technology Data Exchange (ETDEWEB)

Aufiero, Manuele; Bidaud, Adrien; Kotlyar, Dan; Leppänen, Jaakko; Palmiotti, Giuseppe; Salvatores, Massimo; Sen, Sonat; Shwageraus, Eugene; Fratoni, Massimiliano

2017-02-01

This paper presents some of the recent and new advancements in the extension of Monte Carlo Perturbation Theory methodologies and application. In particular, the discussed problems involve Brunup calculation, perturbation calculation based on continuous energy functions, and Monte Carlo Perturbation Theory in loosely coupled systems.

Rapid Monte Carlo Simulation of Gravitational Wave Galaxies

Science.gov (United States)

Breivik, Katelyn; Larson, Shane L.

2015-01-01

With the detection of gravitational waves on the horizon, astrophysical catalogs produced by gravitational wave observatories can be used to characterize the populations of sources and validate different galactic population models. Efforts to simulate gravitational wave catalogs and source populations generally focus on population synthesis models that require extensive time and computational power to produce a single simulated galaxy. Monte Carlo simulations of gravitational wave source populations can also be used to generate observation catalogs from the gravitational wave source population. Monte Carlo simulations have the advantes of flexibility and speed, enabling rapid galactic realizations as a function of galactic binary parameters with less time and compuational resources required. We present a Monte Carlo method for rapid galactic simulations of gravitational wave binary populations.

Acceleration of monte Carlo solution by conjugate gradient method

International Nuclear Information System (INIS)

Toshihisa, Yamamoto

2005-01-01

The conjugate gradient method (CG) was applied to accelerate Monte Carlo solutions in fixed source problems. The equilibrium model based formulation enables to use CG scheme as well as initial guess to maximize computational performance. This method is available to arbitrary geometry provided that the neutron source distribution in each subregion can be regarded as flat. Even if it is not the case, the method can still be used as a powerful tool to provide an initial guess very close to the converged solution. The major difference of Monte Carlo CG to deterministic CG is that residual error is estimated using Monte Carlo sampling, thus statistical error exists in the residual. This leads to a flow diagram specific to Monte Carlo-CG. Three pre-conditioners were proposed for CG scheme and the performance was compared with a simple 1-D slab heterogeneous test problem. One of them, Sparse-M option, showed an excellent performance in convergence. The performance per unit cost was improved by four times in the test problem. Although direct estimation of efficiency of the method is impossible mainly because of the strong problem-dependence of the optimized pre-conditioner in CG, the method seems to have efficient potential as a fast solution algorithm for Monte Carlo calculations. (author)

New sampling method in continuous energy Monte Carlo calculation for pebble bed reactors

International Nuclear Information System (INIS)

Murata, Isao; Takahashi, Akito; Mori, Takamasa; Nakagawa, Masayuki.

1997-01-01

A pebble bed reactor generally has double heterogeneity consisting of two kinds of spherical fuel element. In the core, there exist many fuel balls piled up randomly in a high packing fraction. And each fuel ball contains a lot of small fuel particles which are also distributed randomly. In this study, to realize precise neutron transport calculation of such reactors with the continuous energy Monte Carlo method, a new sampling method has been developed. The new method has been implemented in the general purpose Monte Carlo code MCNP to develop a modified version MCNP-BALL. This method was validated by calculating inventory of spherical fuel elements arranged successively by sampling during transport calculation and also by performing criticality calculations in ordered packing models. From the results, it was confirmed that the inventory of spherical fuel elements could be reproduced using MCNP-BALL within a sufficient accuracy of 0.2%. And the comparison of criticality calculations in ordered packing models between MCNP-BALL and the reference method shows excellent agreement in neutron spectrum as well as multiplication factor. MCNP-BALL enables us to analyze pebble bed type cores such as PROTEUS precisely with the continuous energy Monte Carlo method. (author)

Validation of cross sections for Monte Carlo simulation of the photoelectric effect

CERN Document Server

Han, Min Cheol; Pia, Maria Grazia; Basaglia, Tullio; Batic, Matej; Hoff, Gabriela; Kim, Chan Hyeong; Saracco, Paolo

2016-01-01

Several total and partial photoionization cross section calculations, based on both theoretical and empirical approaches, are quantitatively evaluated with statistical analyses using a large collection of experimental data retrieved from the literature to identify the state of the art for modeling the photoelectric effect in Monte Carlo particle transport. Some of the examined cross section models are available in general purpose Monte Carlo systems, while others have been implemented and subjected to validation tests for the first time to estimate whether they could improve the accuracy of particle transport codes. The validation process identifies Scofield's 1973 non-relativistic calculations, tabulated in the Evaluated Photon Data Library(EPDL), as the one best reproducing experimental measurements of total cross sections. Specialized total cross section models, some of which derive from more recent calculations, do not provide significant improvements. Scofield's non-relativistic calculations are not surp...

Automatic modeling for the Monte Carlo transport code Geant4

International Nuclear Information System (INIS)

Nie Fanzhi; Hu Liqin; Wang Guozhong; Wang Dianxi; Wu Yican; Wang Dong; Long Pengcheng; FDS Team

2015-01-01

Geant4 is a widely used Monte Carlo transport simulation package. Its geometry models could be described in Geometry Description Markup Language (GDML), but it is time-consuming and error-prone to describe the geometry models manually. This study implemented the conversion between computer-aided design (CAD) geometry models and GDML models. This method has been Studied based on Multi-Physics Coupling Analysis Modeling Program (MCAM). The tests, including FDS-Ⅱ model, demonstrated its accuracy and feasibility. (authors)

PERHITUNGAN VaR PORTOFOLIO SAHAM MENGGUNAKAN DATA HISTORIS DAN DATA SIMULASI MONTE CARLO

Directory of Open Access Journals (Sweden)

WAYAN ARTHINI

2012-09-01

Full Text Available Value at Risk (VaR is the maximum potential loss on a portfolio based on the probability at a certain time.  In this research, portfolio VaR values calculated from historical data and Monte Carlo simulation data. Historical data is processed so as to obtain stock returns, variance, correlation coefficient, and variance-covariance matrix, then the method of Markowitz sought proportion of each stock fund, and portfolio risk and return portfolio. The data was then simulated by Monte Carlo simulation, Exact Monte Carlo Simulation and Expected Monte Carlo Simulation. Exact Monte Carlo simulation have same returns and standard deviation  with historical data, while the Expected Monte Carlo Simulation satistic calculation similar to historical data. The results of this research is the portfolio VaR  with time horizon T=1, T=10, T=22 and the confidence level of 95 %, values obtained VaR between historical data and Monte Carlo simulation data with the method exact and expected. Value of VaR from both Monte Carlo simulation is greater than VaR historical data.

Monte Carlo methods for the reliability analysis of Markov systems

International Nuclear Information System (INIS)

Buslik, A.J.

1985-01-01

This paper presents Monte Carlo methods for the reliability analysis of Markov systems. Markov models are useful in treating dependencies between components. The present paper shows how the adjoint Monte Carlo method for the continuous time Markov process can be derived from the method for the discrete-time Markov process by a limiting process. The straightforward extensions to the treatment of mean unavailability (over a time interval) are given. System unavailabilities can also be estimated; this is done by making the system failed states absorbing, and not permitting repair from them. A forward Monte Carlo method is presented in which the weighting functions are related to the adjoint function. In particular, if the exact adjoint function is known then weighting factors can be constructed such that the exact answer can be obtained with a single Monte Carlo trial. Of course, if the exact adjoint function is known, there is no need to perform the Monte Carlo calculation. However, the formulation is useful since it gives insight into choices of the weight factors which will reduce the variance of the estimator

A general transform for variance reduction in Monte Carlo simulations

International Nuclear Information System (INIS)

Becker, T.L.; Larsen, E.W.

2011-01-01

This paper describes a general transform to reduce the variance of the Monte Carlo estimate of some desired solution, such as flux or biological dose. This transform implicitly includes many standard variance reduction techniques, including source biasing, collision biasing, the exponential transform for path-length stretching, and weight windows. Rather than optimizing each of these techniques separately or choosing semi-empirical biasing parameters based on the experience of a seasoned Monte Carlo practitioner, this General Transform unites all these variance techniques to achieve one objective: a distribution of Monte Carlo particles that attempts to optimize the desired solution. Specifically, this transform allows Monte Carlo particles to be distributed according to the user's specification by using information obtained from a computationally inexpensive deterministic simulation of the problem. For this reason, we consider the General Transform to be a hybrid Monte Carlo/Deterministic method. The numerical results con rm that the General Transform distributes particles according to the user-specified distribution and generally provide reasonable results for shielding applications. (author)

Monte Carlo Simulation of Electron Beams for Radiotherapy - EGS4, MCNP4b and GEANT3 Intercomparison

CERN Document Server

Trindade, A; Alves, C M; Chaves, A; Lopes, C; Oliveira, C; Peralta, L

2000-01-01

In medical radiation physics, an increasing number of Monte Carlo codes are being used, which requires intercomparison between them to evaluated the accuracy of the simulated results against benchmark experiments. The Monte Carlo code EGS4, commonly used to simulate electron beams from medical linear accelerators, was compared with GEANT3 and MCNP4b. Intercomparison of electron energy spectra, angular and spatial distribution were carried out for the Siemens KD2 linear accelerator, at beam energies of 10 and 15 MeV for a field size of 10x10 cm2. Indirect validation was performed against electron depth doses curves and beam profiles measured in a MP3-PTW water phantom using a Markus planar chamber. Monte Carlo isodose lines were reconstructed and compared to those from commercial treatment planning systems (TPS's) and with experimental data.

A Monte Carlo approach to combating delayed completion of ...

African Journals Online (AJOL)

The objective of this paper is to unveil the relevance of Monte Carlo critical path analysis in resolving problem of delays in scheduled completion of development projects. Commencing with deterministic network scheduling, Monte Carlo critical path analysis was advanced by assigning probability distributions to task times.

Monte Carlo numerical study of lattice field theories

International Nuclear Information System (INIS)

Gan Cheekwan; Kim Seyong; Ohta, Shigemi

1997-01-01

The authors are interested in the exact first-principle calculations of quantum field theories which are indeed exact ones. For quantum chromodynamics (QCD) at low energy scale, a nonperturbation method is needed, and the only known such method is the lattice method. The path integral can be evaluated by putting a system on a finite 4-dimensional volume and discretizing space time continuum into finite points, lattice. The continuum limit is taken by making the lattice infinitely fine. For evaluating such a finite-dimensional integral, the Monte Carlo numerical estimation of the path integral can be obtained. The calculation of light hadron mass in quenched lattice QCD with staggered quarks, 3-dimensional Thirring model calculation and the development of self-test Monte Carlo method have been carried out by using the RIKEN supercomputer. The motivation of this study, lattice QCD formulation, continuum limit, Monte Carlo update, hadron propagator, light hadron mass, auto-correlation and source size dependence are described on lattice QCD. The phase structure of the 3-dimensional Thirring model for a small 8 3 lattice has been mapped. The discussion on self-test Monte Carlo method is described again. (K.I.)

PENENTUAN HARGA OPSI BELI TIPE ASIA DENGAN METODE MONTE CARLO-CONTROL VARIATE

Directory of Open Access Journals (Sweden)

NI NYOMAN AYU ARTANADI

2017-01-01

Full Text Available Option is a contract between the writer and the holder which entitles the holder to buy or sell an underlying asset at the maturity date for a specified price known as an exercise price. Asian option is a type of financial derivatives which the payoff taking the average value over the time series of the asset price. The aim of the study is to present the Monte Carlo-Control Variate as an extension of Standard Monte Carlo applied on the calculation of the Asian option price. Standard Monte Carlo simulations 10.000.000 generate standard error 0.06 and the option price convergent at Rp.160.00 while Monte Carlo-Control Variate simulations 100.000 generate standard error 0.01 and the option price convergent at Rp.152.00. This shows the Monte Carlo-Control Variate achieve faster option price toward convergent of the Monte Carlo Standar.

Implications of Monte Carlo Statistical Errors in Criticality Safety Assessments

International Nuclear Information System (INIS)

Pevey, Ronald E.

2005-01-01

Most criticality safety calculations are performed using Monte Carlo techniques because of Monte Carlo's ability to handle complex three-dimensional geometries. For Monte Carlo calculations, the more histories sampled, the lower the standard deviation of the resulting estimates. The common intuition is, therefore, that the more histories, the better; as a result, analysts tend to run Monte Carlo analyses as long as possible (or at least to a minimum acceptable uncertainty). For Monte Carlo criticality safety analyses, however, the optimization situation is complicated by the fact that procedures usually require that an extra margin of safety be added because of the statistical uncertainty of the Monte Carlo calculations. This additional safety margin affects the impact of the choice of the calculational standard deviation, both on production and on safety. This paper shows that, under the assumptions of normally distributed benchmarking calculational errors and exact compliance with the upper subcritical limit (USL), the standard deviation that optimizes production is zero, but there is a non-zero value of the calculational standard deviation that minimizes the risk of inadvertently labeling a supercritical configuration as subcritical. Furthermore, this value is shown to be a simple function of the typical benchmarking step outcomes--the bias, the standard deviation of the bias, the upper subcritical limit, and the number of standard deviations added to calculated k-effectives before comparison to the USL

Biased Monte Carlo optimization: the basic approach

International Nuclear Information System (INIS)

Campioni, Luca; Scardovelli, Ruben; Vestrucci, Paolo

2005-01-01

It is well-known that the Monte Carlo method is very successful in tackling several kinds of system simulations. It often happens that one has to deal with rare events, and the use of a variance reduction technique is almost mandatory, in order to have Monte Carlo efficient applications. The main issue associated with variance reduction techniques is related to the choice of the value of the biasing parameter. Actually, this task is typically left to the experience of the Monte Carlo user, who has to make many attempts before achieving an advantageous biasing. A valuable result is provided: a methodology and a practical rule addressed to establish an a priori guidance for the choice of the optimal value of the biasing parameter. This result, which has been obtained for a single component system, has the notable property of being valid for any multicomponent system. In particular, in this paper, the exponential and the uniform biases of exponentially distributed phenomena are investigated thoroughly

Self-learning Monte Carlo (dynamical biasing)

International Nuclear Information System (INIS)

Matthes, W.

1981-01-01

In many applications the histories of a normal Monte Carlo game rarely reach the target region. An approximate knowledge of the importance (with respect to the target) may be used to guide the particles more frequently into the target region. A Monte Carlo method is presented in which each history contributes to update the importance field such that eventually most target histories are sampled. It is a self-learning method in the sense that the procedure itself: (a) learns which histories are important (reach the target) and increases their probability; (b) reduces the probabilities of unimportant histories; (c) concentrates gradually on the more important target histories. (U.K.)

CARMEN: a system Monte Carlo based on linear programming from direct openings; CARMEN: Un sistema de planficiacion Monte Carlo basado en programacion lineal a partir de aberturas directas

Energy Technology Data Exchange (ETDEWEB)

Ureba, A.; Pereira-Barbeiro, A. R.; Jimenez-Ortega, E.; Baeza, J. A.; Salguero, F. J.; Leal, A.

2013-07-01

The use of Monte Carlo (MC) has shown an improvement in the accuracy of the calculation of the dose compared to other analytics algorithms installed on the systems of business planning, especially in the case of non-standard situations typical of complex techniques such as IMRT and VMAT. Our treatment planning system called CARMEN, is based on the complete simulation, both the beam transport in the head of the accelerator and the patient, and simulation designed for efficient operation in terms of the accuracy of the estimate and the required computation times. (Author)

RNA folding kinetics using Monte Carlo and Gillespie algorithms.

Science.gov (United States)

Clote, Peter; Bayegan, Amir H

2018-04-01

RNA secondary structure folding kinetics is known to be important for the biological function of certain processes, such as the hok/sok system in E. coli. Although linear algebra provides an exact computational solution of secondary structure folding kinetics with respect to the Turner energy model for tiny ([Formula: see text]20 nt) RNA sequences, the folding kinetics for larger sequences can only be approximated by binning structures into macrostates in a coarse-grained model, or by repeatedly simulating secondary structure folding with either the Monte Carlo algorithm or the Gillespie algorithm. Here we investigate the relation between the Monte Carlo algorithm and the Gillespie algorithm. We prove that asymptotically, the expected time for a K-step trajectory of the Monte Carlo algorithm is equal to [Formula: see text] times that of the Gillespie algorithm, where [Formula: see text] denotes the Boltzmann expected network degree. If the network is regular (i.e. every node has the same degree), then the mean first passage time (MFPT) computed by the Monte Carlo algorithm is equal to MFPT computed by the Gillespie algorithm multiplied by [Formula: see text]; however, this is not true for non-regular networks. In particular, RNA secondary structure folding kinetics, as computed by the Monte Carlo algorithm, is not equal to the folding kinetics, as computed by the Gillespie algorithm, although the mean first passage times are roughly correlated. Simulation software for RNA secondary structure folding according to the Monte Carlo and Gillespie algorithms is publicly available, as is our software to compute the expected degree of the network of secondary structures of a given RNA sequence-see http://bioinformatics.bc.edu/clote/RNAexpNumNbors .

Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine

International Nuclear Information System (INIS)

Coulot, J

2003-01-01

Monte Carlo techniques are involved in many applications in medical physics, and the field of nuclear medicine has seen a great development in the past ten years due to their wider use. Thus, it is of great interest to look at the state of the art in this domain, when improving computer performances allow one to obtain improved results in a dramatically reduced time. The goal of this book is to make, in 15 chapters, an exhaustive review of the use of Monte Carlo techniques in nuclear medicine, also giving key features which are not necessary directly related to the Monte Carlo method, but mandatory for its practical application. As the book deals with therapeutic' nuclear medicine, it focuses on internal dosimetry. After a general introduction on Monte Carlo techniques and their applications in nuclear medicine (dosimetry, imaging and radiation protection), the authors give an overview of internal dosimetry methods (formalism, mathematical phantoms, quantities of interest). Then, some of the more widely used Monte Carlo codes are described, as well as some treatment planning softwares. Some original techniques are also mentioned, such as dosimetry for boron neutron capture synovectomy. It is generally well written, clearly presented, and very well documented. Each chapter gives an overview of each subject, and it is up to the reader to investigate it further using the extensive bibliography provided. Each topic is discussed from a practical point of view, which is of great help for non-experienced readers. For instance, the chapter about mathematical aspects of Monte Carlo particle transport is very clear and helps one to apprehend the philosophy of the method, which is often a difficulty with a more theoretical approach. Each chapter is put in the general (clinical) context, and this allows the reader to keep in mind the intrinsic limitation of each technique involved in dosimetry (for instance activity quantitation). Nevertheless, there are some minor remarks to

Grain-boundary melting: A Monte Carlo study

DEFF Research Database (Denmark)

Besold, Gerhard; Mouritsen, Ole G.

1994-01-01

Grain-boundary melting in a lattice-gas model of a bicrystal is studied by Monte Carlo simulation using the grand canonical ensemble. Well below the bulk melting temperature T(m), a disordered liquidlike layer gradually emerges at the grain boundary. Complete interfacial wetting can be observed...... when the temperature approaches T(m) from below. Monte Carlo data over an extended temperature range indicate a logarithmic divergence w(T) approximately - ln(T(m)-T) of the width of the disordered layer w, in agreement with mean-field theory....

Monte Carlo study of quantum number retention in hadron jets

International Nuclear Information System (INIS)

Hayward, S.K.; Weiss, N.

1992-01-01

We present a Monte Carlo study in which we used weighted quantum numbers of hadron jets in an attempt to identify the parent parton of these jets. Two-jet events produced by e + e- annihilation were studied using the Lund Monte Carlo program. It was found that the sign of the charge of the leading parton could be determined in a majority of events and that the quark jet could be distinguished from the antiquark jet in a majority of events containing baryons. A careful selection of a subset of the events by making cuts on the value of these weighted quantum numbers increased significantly the accuracy with which both the charge and the baryon number of the leading parton could be determined. Some success was also made in differentiating light-quark from heavy-quark events and in determining the leading quark flavor in the light-quark events. Unfortunately quantum number retention does not differentiate gluon jets from quark jets. The consequences of this for three-jet events and for jet identification in other reactions is discussed

Analysis of error in Monte Carlo transport calculations

International Nuclear Information System (INIS)

Booth, T.E.

1979-01-01

The Monte Carlo method for neutron transport calculations suffers, in part, because of the inherent statistical errors associated with the method. Without an estimate of these errors in advance of the calculation, it is difficult to decide what estimator and biasing scheme to use. Recently, integral equations have been derived that, when solved, predicted errors in Monte Carlo calculations in nonmultiplying media. The present work allows error prediction in nonanalog Monte Carlo calculations of multiplying systems, even when supercritical. Nonanalog techniques such as biased kernels, particle splitting, and Russian Roulette are incorporated. Equations derived here allow prediction of how much a specific variance reduction technique reduces the number of histories required, to be weighed against the change in time required for calculation of each history. 1 figure, 1 table

Neutron flux calculation by means of Monte Carlo methods

International Nuclear Information System (INIS)

Barz, H.U.; Eichhorn, M.

1988-01-01

In this report a survey of modern neutron flux calculation procedures by means of Monte Carlo methods is given. Due to the progress in the development of variance reduction techniques and the improvements of computational techniques this method is of increasing importance. The basic ideas in application of Monte Carlo methods are briefly outlined. In more detail various possibilities of non-analog games and estimation procedures are presented, problems in the field of optimizing the variance reduction techniques are discussed. In the last part some important international Monte Carlo codes and own codes of the authors are listed and special applications are described. (author)

PyMercury: Interactive Python for the Mercury Monte Carlo Particle Transport Code

International Nuclear Information System (INIS)

Iandola, F.N.; O'Brien, M.J.; Procassini, R.J.

2010-01-01

Monte Carlo particle transport applications are often written in low-level languages (C/C++) for optimal performance on clusters and supercomputers. However, this development approach often sacrifices straightforward usability and testing in the interest of fast application performance. To improve usability, some high-performance computing applications employ mixed-language programming with high-level and low-level languages. In this study, we consider the benefits of incorporating an interactive Python interface into a Monte Carlo application. With PyMercury, a new Python extension to the Mercury general-purpose Monte Carlo particle transport code, we improve application usability without diminishing performance. In two case studies, we illustrate how PyMercury improves usability and simplifies testing and validation in a Monte Carlo application. In short, PyMercury demonstrates the value of interactive Python for Monte Carlo particle transport applications. In the future, we expect interactive Python to play an increasingly significant role in Monte Carlo usage and testing.

Transport methods: general. 1. The Analytical Monte Carlo Method for Radiation Transport Calculations

International Nuclear Information System (INIS)

Martin, William R.; Brown, Forrest B.

2001-01-01

We present an alternative Monte Carlo method for solving the coupled equations of radiation transport and material energy. This method is based on incorporating the analytical solution to the material energy equation directly into the Monte Carlo simulation for the radiation intensity. This method, which we call the Analytical Monte Carlo (AMC) method, differs from the well known Implicit Monte Carlo (IMC) method of Fleck and Cummings because there is no discretization of the material energy equation since it is solved as a by-product of the Monte Carlo simulation of the transport equation. Our method also differs from the method recently proposed by Ahrens and Larsen since they use Monte Carlo to solve both equations, while we are solving only the radiation transport equation with Monte Carlo, albeit with effective sources and cross sections to represent the emission sources. Our method bears some similarity to a method developed and implemented by Carter and Forest nearly three decades ago, but there are substantive differences. We have implemented our method in a simple zero-dimensional Monte Carlo code to test the feasibility of the method, and the preliminary results are very promising, justifying further extension to more realistic geometries. (authors)

Markov Chain Monte Carlo

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 7; Issue 3. Markov Chain Monte Carlo - Examples. Arnab Chakraborty. General Article Volume 7 Issue 3 March 2002 pp 25-34. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/007/03/0025-0034. Keywords.

Monte Carlo simulation of the Leksell Gamma Knife: I. Source modelling and calculations in homogeneous media

Energy Technology Data Exchange (ETDEWEB)

Moskvin, Vadim [Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN (United States)]. E-mail: vmoskvin@iupui.edu; DesRosiers, Colleen; Papiez, Lech; Timmerman, Robert; Randall, Marcus; DesRosiers, Paul [Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN (United States)

2002-06-21

The Monte Carlo code PENELOPE has been used to simulate photon flux from the Leksell Gamma Knife, a precision method for treating intracranial lesions. Radiation from a single {sup 60}Co assembly traversing the collimator system was simulated, and phase space distributions at the output surface of the helmet for photons and electrons were calculated. The characteristics describing the emitted final beam were used to build a two-stage Monte Carlo simulation of irradiation of a target. A dose field inside a standard spherical polystyrene phantom, usually used for Gamma Knife dosimetry, has been computed and compared with experimental results, with calculations performed by other authors with the use of the EGS4 Monte Carlo code, and data provided by the treatment planning system Gamma Plan. Good agreement was found between these data and results of simulations in homogeneous media. Owing to this established accuracy, PENELOPE is suitable for simulating problems relevant to stereotactic radiosurgery. (author)

Monte Carlo studies of high-transverse-energy hadronic interactions

International Nuclear Information System (INIS)

Corcoran, M.D.

1985-01-01

A four-jet Monte Carlo calculation has been used to simulate hadron-hadron interactions which deposit high transverse energy into a large-solid-angle calorimeter and limited solid-angle regions of the calorimeter. The calculation uses first-order QCD cross sections to generate two scattered jets and also produces beam and target jets. Field-Feynman fragmentation has been used in the hadronization. The sensitivity of the results to a few features of the Monte Carlo program has been studied. The results are found to be very sensitive to the method used to ensure overall energy conservation after the fragmentation of the four jets is complete. Results are also sensitive to the minimum momentum transfer in the QCD subprocesses and to the distribution of p/sub T/ to the jet axis and the multiplicities in the fragmentation. With reasonable choices of these features of the Monte Carlo program, good agreement with data at Fermilab/CERN SPS energies is obtained, comparable to the agreement achieved with more sophisticated parton-shower models. With other choices, however, the calculation gives qualitatively different results which are in strong disagreement with the data. These results have important implications for extracting physics conclusions from Monte Carlo calculations. It is not possible to test the validity of a particular model or distinguish between different models unless the Monte Carlo results are unambiguous and different models exhibit clearly different behavior

Development of M3C code for Monte Carlo reactor physics criticality calculations

International Nuclear Information System (INIS)

Kumar, Anek; Kannan, Umasankari; Krishanani, P.D.

2015-06-01

The development of Monte Carlo code (M3C) for reactor design entails use of continuous energy nuclear data and Monte Carlo simulations for each of the neutron interaction processes. BARC has started a concentrated effort for developing a new general geometry continuous energy Monte Carlo code for reactor physics calculation indigenously. The code development required a comprehensive understanding of the basic continuous energy cross section sets. The important features of this code are treatment of heterogeneous lattices by general geometry, use of point cross sections along with unionized energy grid approach, thermal scattering model for low energy treatment, capability of handling the microscopic fuel particles dispersed randomly. The capability of handling the randomly dispersed microscopic fuel particles which is very useful for the modeling of High-Temperature Gas-Cooled reactor fuels which are composed of thousands of microscopic fuel particle (TRISO fuel particle), randomly dispersed in a graphite matrix. The Monte Carlo code for criticality calculation is a pioneering effort and has been used to study several types of lattices including cluster geometries. The code has been verified for its accuracy against more than 60 sample problems covering a wide range from simple (like spherical) to complex geometry (like PHWR lattice). Benchmark results show that the code performs quite well for the criticality calculation of the system. In this report, the current status of the code, features of the code, some of the benchmark results for the testing of the code and input preparation etc. are discussed. (author)

Determination of axial diffusion coefficients by the Monte-Carlo method

International Nuclear Information System (INIS)

Milgram, M.

1994-01-01

A simple method to calculate the homogenized diffusion coefficient for a lattice cell using Monte-Carlo techniques is demonstrated. The method relies on modelling a finite reactor volume to induce a curvature in the flux distribution, and then follows a large number of histories to obtain sufficient statistics for a meaningful result. The goal is to determine the diffusion coefficient with sufficient accuracy to test approximate methods built into deterministic lattice codes. Numerical results are given. (author). 4 refs., 8 figs

Canonical Least-Squares Monte Carlo Valuation of American Options: Convergence and Empirical Pricing Analysis

Directory of Open Access Journals (Sweden)

Xisheng Yu

2014-01-01

Full Text Available The paper by Liu (2010 introduces a method termed the canonical least-squares Monte Carlo (CLM which combines a martingale-constrained entropy model and a least-squares Monte Carlo algorithm to price American options. In this paper, we first provide the convergence results of CLM and numerically examine the convergence properties. Then, the comparative analysis is empirically conducted using a large sample of the S&P 100 Index (OEX puts and IBM puts. The results on the convergence show that choosing the shifted Legendre polynomials with four regressors is more appropriate considering the pricing accuracy and the computational cost. With this choice, CLM method is empirically demonstrated to be superior to the benchmark methods of binominal tree and finite difference with historical volatilities.

Monte Carlo methods and applications in nuclear physics

International Nuclear Information System (INIS)

Carlson, J.

1990-01-01

Monte Carlo methods for studying few- and many-body quantum systems are introduced, with special emphasis given to their applications in nuclear physics. Variational and Green's function Monte Carlo methods are presented in some detail. The status of calculations of light nuclei is reviewed, including discussions of the three-nucleon-interaction, charge and magnetic form factors, the coulomb sum rule, and studies of low-energy radiative transitions. 58 refs., 12 figs

Monte Carlo and analytic simulations in nanoparticle-enhanced radiation therapy

Directory of Open Access Journals (Sweden)

Paro AD

2016-09-01

Full Text Available Autumn D Paro,1 Mainul Hossain,2 Thomas J Webster,1,3,4 Ming Su1,4 1Department of Chemical Engineering, Northeastern University, Boston, MA, USA; 2NanoScience Technology Center and School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida, USA; 3Excellence for Advanced Materials Research, King Abdulaziz University, Jeddah, Saudi Arabia; 4Wenzhou Institute of Biomaterials and Engineering, Chinese Academy of Science, Wenzhou Medical University, Zhejiang, People’s Republic of China Abstract: Analytical and Monte Carlo simulations have been used to predict dose enhancement factors in nanoparticle-enhanced X-ray radiation therapy. Both simulations predict an increase in dose enhancement in the presence of nanoparticles, but the two methods predict different levels of enhancement over the studied energy, nanoparticle materials, and concentration regime for several reasons. The Monte Carlo simulation calculates energy deposited by electrons and photons, while the analytical one only calculates energy deposited by source photons and photoelectrons; the Monte Carlo simulation accounts for electron–hole recombination, while the analytical one does not; and the Monte Carlo simulation randomly samples photon or electron path and accounts for particle interactions, while the analytical simulation assumes a linear trajectory. This study demonstrates that the Monte Carlo simulation will be a better choice to evaluate dose enhancement with nanoparticles in radiation therapy. Keywords: nanoparticle, dose enhancement, Monte Carlo simulation, analytical simulation, radiation therapy, tumor cell, X-ray 

Microcanonical Monte Carlo

International Nuclear Information System (INIS)

Creutz, M.

1986-01-01

The author discusses a recently developed algorithm for simulating statistical systems. The procedure interpolates between molecular dynamics methods and canonical Monte Carlo. The primary advantages are extremely fast simulations of discrete systems such as the Ising model and a relative insensitivity to random number quality. A variation of the algorithm gives rise to a deterministic dynamics for Ising spins. This model may be useful for high speed simulation of non-equilibrium phenomena

Monte Carlo simulation of neutron scattering instruments

International Nuclear Information System (INIS)

Seeger, P.A.

1995-01-01

A library of Monte Carlo subroutines has been developed for the purpose of design of neutron scattering instruments. Using small-angle scattering as an example, the philosophy and structure of the library are described and the programs are used to compare instruments at continuous wave (CW) and long-pulse spallation source (LPSS) neutron facilities. The Monte Carlo results give a count-rate gain of a factor between 2 and 4 using time-of-flight analysis. This is comparable to scaling arguments based on the ratio of wavelength bandwidth to resolution width

Simulation of transport equations with Monte Carlo

International Nuclear Information System (INIS)

Matthes, W.

1975-09-01

The main purpose of the report is to explain the relation between the transport equation and the Monte Carlo game used for its solution. The introduction of artificial particles carrying a weight provides one with high flexibility in constructing many different games for the solution of the same equation. This flexibility opens a way to construct a Monte Carlo game for the solution of the adjoint transport equation. Emphasis is laid mostly on giving a clear understanding of what to do and not on the details of how to do a specific game

High-efficiency wavefunction updates for large scale Quantum Monte Carlo

Science.gov (United States)

Kent, Paul; McDaniel, Tyler; Li, Ying Wai; D'Azevedo, Ed

Within ab intio Quantum Monte Carlo (QMC) simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunctions. The evaluation of each Monte Carlo move requires finding the determinant of a dense matrix, which is traditionally iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. For calculations with thousands of electrons, this operation dominates the execution profile. We propose a novel rank- k delayed update scheme. This strategy enables probability evaluation for multiple successive Monte Carlo moves, with application of accepted moves to the matrices delayed until after a predetermined number of moves, k. Accepted events grouped in this manner are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency. This procedure does not change the underlying Monte Carlo sampling or the sampling efficiency. For large systems and algorithms such as diffusion Monte Carlo where the acceptance ratio is high, order of magnitude speedups can be obtained on both multi-core CPU and on GPUs, making this algorithm highly advantageous for current petascale and future exascale computations.

CARMEN: a system Monte Carlo based on linear programming from direct openings

International Nuclear Information System (INIS)

Ureba, A.; Pereira-Barbeiro, A. R.; Jimenez-Ortega, E.; Baeza, J. A.; Salguero, F. J.; Leal, A.

2013-01-01

The use of Monte Carlo (MC) has shown an improvement in the accuracy of the calculation of the dose compared to other analytics algorithms installed on the systems of business planning, especially in the case of non-standard situations typical of complex techniques such as IMRT and VMAT. Our treatment planning system called CARMEN, is based on the complete simulation, both the beam transport in the head of the accelerator and the patient, and simulation designed for efficient operation in terms of the accuracy of the estimate and the required computation times. (Author)

The Monte Carlo Simulation Method for System Reliability and Risk Analysis

CERN Document Server

Zio, Enrico

2013-01-01

Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. The Monte Carlo Simulation Method for System Reliability and Risk Analysis comprehensively illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Readers are given a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling.   Whilst many of the topics rely on a high-level understanding of calculus, probability and statistics, simple academic examples will be provided in support to the explanation of the theoretical foundations to facilitate comprehension of the subject matter. Case studies will be introduced to provide the practical value of the most advanced techniques.   This detailed approach makes The Monte Carlo Simulation Method for System Reliability and Risk Analysis a key reference for senior undergra...

A contribution Monte Carlo method

International Nuclear Information System (INIS)

Aboughantous, C.H.

1994-01-01

A Contribution Monte Carlo method is developed and successfully applied to a sample deep-penetration shielding problem. The random walk is simulated in most of its parts as in conventional Monte Carlo methods. The probability density functions (pdf's) are expressed in terms of spherical harmonics and are continuous functions in direction cosine and azimuthal angle variables as well as in position coordinates; the energy is discretized in the multigroup approximation. The transport pdf is an unusual exponential kernel strongly dependent on the incident and emergent directions and energies and on the position of the collision site. The method produces the same results obtained with the deterministic method with a very small standard deviation, with as little as 1,000 Contribution particles in both analog and nonabsorption biasing modes and with only a few minutes CPU time

Exact Monte Carlo for molecules

International Nuclear Information System (INIS)

Lester, W.A. Jr.; Reynolds, P.J.

1985-03-01

A brief summary of the fixed-node quantum Monte Carlo method is presented. Results obtained for binding energies, the classical barrier height for H + H 2 , and the singlet-triplet splitting in methylene are presented and discussed. 17 refs

Dose perturbation in the presence of metallic implants: treatment planning system versus Monte Carlo simulations

International Nuclear Information System (INIS)

Wieslander, Elinore; Knoeoes, Tommy

2003-01-01

An increasing number of patients receiving radiation therapy have metallic implants such as hip prostheses. Therefore, beams are normally set up to avoid irradiation through the implant; however, this cannot always be accomplished. In such situations, knowledge of the accuracy of the used treatment planning system (TPS) is required. Two algorithms, the pencil beam (PB) and the collapsed cone (CC), are implemented in the studied TPS. Comparisons are made with Monte Carlo simulations for 6 and 18 MV. The studied materials are steel, CoCrMo, Orthinox(a stainless steel alloy and registered trademark of Stryker Corporation), TiAlV and Ti. Monte Carlo simulated depth dose curves and dose profiles are compared to CC and PB calculated data. The CC algorithm shows overall a better agreement with Monte Carlo than the PB algorithm. Thus, it is recommended to use the CC algorithm to get the most accurate dose calculation both for the planning target volume and for tissues adjacent to the implants when beams are set up to pass through implants

The impact of Monte Carlo simulation: a scientometric analysis of scholarly literature

CERN Document Server

Pia, Maria Grazia; Bell, Zane W; Dressendorfer, Paul V

2010-01-01

A scientometric analysis of Monte Carlo simulation and Monte Carlo codes has been performed over a set of representative scholarly journals related to radiation physics. The results of this study are reported and discussed. They document and quantitatively appraise the role of Monte Carlo methods and codes in scientific research and engineering applications.

No-compromise reptation quantum Monte Carlo

International Nuclear Information System (INIS)

Yuen, W K; Farrar, Thomas J; Rothstein, Stuart M

2007-01-01

Since its publication, the reptation quantum Monte Carlo algorithm of Baroni and Moroni (1999 Phys. Rev. Lett. 82 4745) has been applied to several important problems in physics, but its mathematical foundations are not well understood. We show that their algorithm is not of typical Metropolis-Hastings type, and we specify conditions required for the generated Markov chain to be stationary and to converge to the intended distribution. The time-step bias may add up, and in many applications it is only the middle of a reptile that is the most important. Therefore, we propose an alternative, 'no-compromise reptation quantum Monte Carlo' to stabilize the middle of the reptile. (fast track communication)

Exploring cluster Monte Carlo updates with Boltzmann machines.

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Exploring cluster Monte Carlo updates with Boltzmann machines

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Monte Carlo simulation of continuous-space crystal growth

International Nuclear Information System (INIS)

Dodson, B.W.; Taylor, P.A.

1986-01-01

We describe a method, based on Monte Carlo techniques, of simulating the atomic growth of crystals without the discrete lattice space assumed by conventional Monte Carlo growth simulations. Since no lattice space is assumed, problems involving epitaxial growth, heteroepitaxy, phonon-driven mechanisms, surface reconstruction, and many other phenomena incompatible with the lattice-space approximation can be studied. Also, use of the Monte Carlo method circumvents to some extent the extreme limitations on simulated timescale inherent in crystal-growth techniques which might be proposed using molecular dynamics. The implementation of the new method is illustrated by studying the growth of strained-layer superlattice (SLS) interfaces in two-dimensional Lennard-Jones atomic systems. Despite the extreme simplicity of such systems, the qualitative features of SLS growth seen here are similar to those observed experimentally in real semiconductor systems

TU-AB-BRC-02: Accuracy Evaluation of GPU-Based OpenCL Carbon Monte Carlo Package (goCMC) in Biological Dose and Microdosimetry in Comparison to FLUKA Simulations

Energy Technology Data Exchange (ETDEWEB)

Taleei, R; Peeler, C; Qin, N; Jiang, S; Jia, X [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: One of the most accurate methods for radiation transport is Monte Carlo (MC) simulation. Long computation time prevents its wide applications in clinic. We have recently developed a fast MC code for carbon ion therapy called GPU-based OpenCL Carbon Monte Carlo (goCMC) and its accuracy in physical dose has been established. Since radiobiology is an indispensible aspect of carbon ion therapy, this study evaluates accuracy of goCMC in biological dose and microdosimetry by benchmarking it with FLUKA. Methods: We performed simulations of a carbon pencil beam with 150, 300 and 450 MeV/u in a homogeneous water phantom using goCMC and FLUKA. Dose and energy spectra for primary and secondary ions on the central beam axis were recorded. Repair-misrepair-fixation model was employed to calculate Relative Biological Effectiveness (RBE). Monte Carlo Damage Simulation (MCDS) tool was used to calculate microdosimetry parameters. Results: Physical dose differences on the central axis were <1.6% of the maximum value. Before the Bragg peak, differences in RBE and RBE-weighted dose were <2% and <1%. At the Bragg peak, the differences were 12.5% caused by small range discrepancy and sensitivity of RBE to beam spectra. Consequently, RBE-weighted dose difference was 11%. Beyond the peak, RBE differences were <20% and primarily caused by differences in the Helium-4 spectrum. However, the RBE-weighted dose agreed within 1% due to the low physical dose. Differences in microdosimetric quantities were small except at the Bragg peak. The simulation time per source particle with FLUKA was 0.08 sec, while goCMC was approximately 1000 times faster. Conclusion: Physical doses computed by FLUKA and goCMC were in good agreement. Although relatively large RBE differences were observed at and beyond the Bragg peak, the RBE-weighted dose differences were considered to be acceptable.

Effect of error propagation of nuclide number densities on Monte Carlo burn-up calculations

International Nuclear Information System (INIS)

Tohjoh, Masayuki; Endo, Tomohiro; Watanabe, Masato; Yamamoto, Akio

2006-01-01

As a result of improvements in computer technology, the continuous energy Monte Carlo burn-up calculation has received attention as a good candidate for an assembly calculation method. However, the results of Monte Carlo calculations contain the statistical errors. The results of Monte Carlo burn-up calculations, in particular, include propagated statistical errors through the variance of the nuclide number densities. Therefore, if statistical error alone is evaluated, the errors in Monte Carlo burn-up calculations may be underestimated. To make clear this effect of error propagation on Monte Carlo burn-up calculations, we here proposed an equation that can predict the variance of nuclide number densities after burn-up calculations, and we verified this equation using enormous numbers of the Monte Carlo burn-up calculations by changing only the initial random numbers. We also verified the effect of the number of burn-up calculation points on Monte Carlo burn-up calculations. From these verifications, we estimated the errors in Monte Carlo burn-up calculations including both statistical and propagated errors. Finally, we made clear the effects of error propagation on Monte Carlo burn-up calculations by comparing statistical errors alone versus both statistical and propagated errors. The results revealed that the effects of error propagation on the Monte Carlo burn-up calculations of 8 x 8 BWR fuel assembly are low up to 60 GWd/t

Monte Carlo simulation of experiments

International Nuclear Information System (INIS)

Opat, G.I.

1977-07-01

An outline of the technique of computer simulation of particle physics experiments by the Monte Carlo method is presented. Useful special purpose subprograms are listed and described. At each stage the discussion is made concrete by direct reference to the programs SIMUL8 and its variant MONTE-PION, written to assist in the analysis of the radiative decay experiments μ + → e + ν sub(e) antiνγ and π + → e + ν sub(e)γ, respectively. These experiments were based on the use of two large sodium iodide crystals, TINA and MINA, as e and γ detectors. Instructions for the use of SIMUL8 and MONTE-PION are given. (author)

Monte Carlo simulation of neutron counters for safeguards applications

International Nuclear Information System (INIS)

Looman, Marc; Peerani, Paolo; Tagziria, Hamid

2009-01-01

MCNP-PTA is a new Monte Carlo code for the simulation of neutron counters for nuclear safeguards applications developed at the Joint Research Centre (JRC) in Ispra (Italy). After some preliminary considerations outlining the general aspects involved in the computational modelling of neutron counters, this paper describes the specific details and approximations which make up the basis of the model implemented in the code. One of the major improvements allowed by the use of Monte Carlo simulation is a considerable reduction in both the experimental work and in the reference materials required for the calibration of the instruments. This new approach to the calibration of counters using Monte Carlo simulation techniques is also discussed.

Monte Carlo methods and applications in nuclear physics

Energy Technology Data Exchange (ETDEWEB)

Carlson, J.

1990-01-01

Monte Carlo methods for studying few- and many-body quantum systems are introduced, with special emphasis given to their applications in nuclear physics. Variational and Green's function Monte Carlo methods are presented in some detail. The status of calculations of light nuclei is reviewed, including discussions of the three-nucleon-interaction, charge and magnetic form factors, the coulomb sum rule, and studies of low-energy radiative transitions. 58 refs., 12 figs.

Research on Monte Carlo improved quasi-static method for reactor space-time dynamics

International Nuclear Information System (INIS)

Xu Qi; Wang Kan; Li Shirui; Yu Ganglin

2013-01-01

With large time steps, improved quasi-static (IQS) method can improve the calculation speed for reactor dynamic simulations. The Monte Carlo IQS method was proposed in this paper, combining the advantages of both the IQS method and MC method. Thus, the Monte Carlo IQS method is beneficial for solving space-time dynamics problems of new concept reactors. Based on the theory of IQS, Monte Carlo algorithms for calculating adjoint neutron flux, reactor kinetic parameters and shape function were designed and realized. A simple Monte Carlo IQS code and a corresponding diffusion IQS code were developed, which were used for verification of the Monte Carlo IQS method. (authors)

Lattice gauge theories and Monte Carlo simulations

International Nuclear Information System (INIS)

Rebbi, C.

1981-11-01

After some preliminary considerations, the discussion of quantum gauge theories on a Euclidean lattice takes up the definition of Euclidean quantum theory and treatment of the continuum limit; analogy is made with statistical mechanics. Perturbative methods can produce useful results for strong or weak coupling. In the attempts to investigate the properties of the systems for intermediate coupling, numerical methods known as Monte Carlo simulations have proved valuable. The bulk of this paper illustrates the basic ideas underlying the Monte Carlo numerical techniques and the major results achieved with them according to the following program: Monte Carlo simulations (general theory, practical considerations), phase structure of Abelian and non-Abelian models, the observables (coefficient of the linear term in the potential between two static sources at large separation, mass of the lowest excited state with the quantum numbers of the vacuum (the so-called glueball), the potential between two static sources at very small distance, the critical temperature at which sources become deconfined), gauge fields coupled to basonic matter (Higgs) fields, and systems with fermions

Final Report: 06-LW-013, Nuclear Physics the Monte Carlo Way

International Nuclear Information System (INIS)

Ormand, W.E.

2009-01-01

This is document reports the progress and accomplishments achieved in 2006-2007 with LDRD funding under the proposal 06-LW-013, 'Nuclear Physics the Monte Carlo Way'. The project was a theoretical study to explore a novel approach to dealing with a persistent problem in Monte Carlo approaches to quantum many-body systems. The goal was to implement a solution to the notorious 'sign-problem', which if successful, would permit, for the first time, exact solutions to quantum many-body systems that cannot be addressed with other methods. In this document, we outline the progress and accomplishments achieved during FY2006-2007 with LDRD funding in the proposal 06-LW-013, 'Nuclear Physics the Monte Carlo Way'. This project was funded under the Lab Wide LDRD competition at Lawrence Livermore National Laboratory. The primary objective of this project was to test the feasibility of implementing a novel approach to solving the generic quantum many-body problem, which is one of the most important problems being addressed in theoretical physics today. Instead of traditional methods based matrix diagonalization, this proposal focused a Monte Carlo method. The principal difficulty with Monte Carlo methods, is the so-called 'sign problem'. The sign problem, which will discussed in some detail later, is endemic to Monte Carlo approaches to the quantum many-body problem, and is the principal reason that they have not been completely successful in the past. Here, we outline our research in the 'shifted-contour method' applied the Auxiliary Field Monte Carlo (AFMC) method

Hybrid Adaptive Multilevel Monte Carlo Algorithm for Non-Smooth Observables of Itô Stochastic Differential Equations

KAUST Repository

Rached, Nadhir B.

2013-01-01

The Monte Carlo forward Euler method with uniform time stepping is the standard technique to compute an approximation of the expected payoff of a solution of an Itô SDE. For a given accuracy requirement TOL, the complexity of this technique for well

Investigation of Compton scattering correction methods in cardiac SPECT by Monte Carlo simulations

International Nuclear Information System (INIS)

Silva, A.M. Marques da; Furlan, A.M.; Robilotta, C.C.

2001-01-01

The goal of this work was the use of Monte Carlo simulations to investigate the effects of two scattering correction methods: dual energy window (DEW) and dual photopeak window (DPW), in quantitative cardiac SPECT reconstruction. MCAT torso-cardiac phantom, with 99m Tc and non-uniform attenuation map was simulated. Two different photopeak windows were evaluated in DEW method: 15% and 20%. Two 10% wide subwindows centered symmetrically within the photopeak were used in DPW method. Iterative ML-EM reconstruction with modified projector-backprojector for attenuation correction was applied. Results indicated that the choice of the scattering and photopeak windows determines the correction accuracy. For the 15% window, fitted scatter fraction gives better results than k = 0.5. For the 20% window, DPW is the best method, but it requires parameters estimation using Monte Carlo simulations. (author)

Interface methods for hybrid Monte Carlo-diffusion radiation-transport simulations

International Nuclear Information System (INIS)

Densmore, Jeffery D.

2006-01-01

Discrete diffusion Monte Carlo (DDMC) is a technique for increasing the efficiency of Monte Carlo simulations in diffusive media. An important aspect of DDMC is the treatment of interfaces between diffusive regions, where DDMC is used, and transport regions, where standard Monte Carlo is employed. Three previously developed methods exist for treating transport-diffusion interfaces: the Marshak interface method, based on the Marshak boundary condition, the asymptotic interface method, based on the asymptotic diffusion-limit boundary condition, and the Nth-collided source technique, a scheme that allows Monte Carlo particles to undergo several collisions in a diffusive region before DDMC is used. Numerical calculations have shown that each of these interface methods gives reasonable results as part of larger radiation-transport simulations. In this paper, we use both analytic and numerical examples to compare the ability of these three interface techniques to treat simpler, transport-diffusion interface problems outside of a more complex radiation-transport calculation. We find that the asymptotic interface method is accurate regardless of the angular distribution of Monte Carlo particles incident on the interface surface. In contrast, the Marshak boundary condition only produces correct solutions if the incident particles are isotropic. We also show that the Nth-collided source technique has the capacity to yield accurate results if spatial cells are optically small and Monte Carlo particles are allowed to undergo many collisions within a diffusive region before DDMC is employed. These requirements make the Nth-collided source technique impractical for realistic radiation-transport calculations

Artificial neural networks, a new alternative to Monte Carlo calculations for radiotherapy

International Nuclear Information System (INIS)

Martin, E.; Gschwind, R.; Henriet, J.; Sauget, M.; Makovicka, L.

2010-01-01

In order to reduce the computing time needed by Monte Carlo codes in the field of irradiation physics, notably in dosimetry, the authors report the use of artificial neural networks in combination with preliminary Monte Carlo calculations. During the learning phase, Monte Carlo calculations are performed in homogeneous media to allow the building up of the neural network. Then, dosimetric calculations (in heterogeneous media, unknown by the network) can be performed by the so-learned network. Results with an equivalent precision can be obtained within less than one minute on a simple PC whereas several days are needed with a Monte Carlo calculation

Pseudopotentials for quantum-Monte-Carlo-calculations; Pseudopotentiale fuer Quanten-Monte-Carlo-Rechnungen

Energy Technology Data Exchange (ETDEWEB)

Burkatzki, Mark Thomas

2008-07-01

The author presents scalar-relativistic energy-consistent Hartree-Fock pseudopotentials for the main-group and 3d-transition-metal elements. The pseudopotentials do not exhibit a singularity at the nucleus and are therefore suitable for quantum Monte Carlo (QMC) calculations. The author demonstrates their transferability through extensive benchmark calculations of atomic excitation spectra as well as molecular properties. In particular, the author computes the vibrational frequencies and binding energies of 26 first- and second-row diatomic molecules using post Hartree-Fock methods, finding excellent agreement with the corresponding all-electron values. The author shows that the presented pseudopotentials give superior accuracy than other existing pseudopotentials constructed specifically for QMC. The localization error and the efficiency in QMC are discussed. The author also presents QMC calculations for selected atomic and diatomic 3d-transitionmetal systems. Finally, valence basis sets of different sizes (VnZ with n=D,T,Q,5 for 1st and 2nd row; with n=D,T for 3rd to 5th row; with n=D,T,Q for the 3d transition metals) optimized for the pseudopotentials are presented. (orig.)

The study of importance sampling in Monte-carlo calculation of blocking dips

International Nuclear Information System (INIS)

Pan Zhengying; Zhou Peng

1988-01-01

Angular blocking dips around the axis in Al single crystal of α-particles of about 2 Mev produced at a depth of 0.2 μm are calculated by a Monte-carlo simulation. The influence of the small solid angle emission of particles and the importance sampling in the solid angle emission have been investigated. By means of importance sampling, a more reasonable results with high accuracy are obtained

Superficial dose distribution in breast for tangential radiation treatment, Monte Carlo evaluation of Eclipse algorithms in case of phantom and patient geometries

International Nuclear Information System (INIS)

Chakarova, Roumiana; Gustafsson, Magnus; Bäck, Anna; Drugge, Ninni; Palm, Åsa; Lindberg, Andreas; Berglund, Mattias

2012-01-01

Purpose: The aim of this study is to examine experimentally and by the Monte Carlo method the accuracy of the Eclipse Pencil Beam Convolution (PBC) and Analytical Anisotropic Algorithm (AAA) algorithms in the superficial region (0–2 cm) of the breast for tangential photon beams in a phantom case as well as in a number of patient geometries. The aim is also to identify differences in how the patient computer tomography data are handled by the treatment planning system and in the Monte Carlo simulations in order to reduce influences of these effects on the evaluation. Materials and methods: Measurements by thermoluminescent dosimeters and gafchromic film are performed for six MV tangential irradiation of the cylindrical solid water phantom. Tangential treatment of seven patients is investigated considering open beams. Dose distributions are obtained by the Eclipse PBC and AAA algorithms. Monte Carlo calculations are carried out by BEAMnrc/DOSXYZnrc code package. Calculations are performed with a calculation grid of 1.25 × 1.25 × 5 mm 3 for PBC and 2 × 2 × 5 mm 3 for AAA and Monte Carlo, respectively. Dose comparison is performed in both dose and spatial domains by the normalized dose difference method. Results: Experimental profiles from the surface toward the geometrical center of the cylindrical phantom are obtained at the beam entrance and exit as well as laterally. Full dose is received beyond 2 mm in the lateral superficial region and beyond 7 mm at the beam entrance. Good agreement between experimental, Monte Carlo and AAA data is obtained, whereas PBC is seen to underestimate the entrance dose the first 3–4 mm and the lateral dose by more than 5% up to 8 mm depth. In the patient cases considered, AAA and Monte Carlo show agreement within 3% dose and 4 mm spatial tolerance. PBC systematically underestimates the dose at the breast apex. The dimensions of region out of tolerance vary with the local breast shape. Different interpretations of patient

Herwig: The Evolution of a Monte Carlo Simulation

CERN Multimedia

CERN. Geneva

2015-01-01

Monte Carlo event generation has seen significant developments in the last 10 years starting with preparation for the LHC and then during the first LHC run. I will discuss the basic ideas behind Monte Carlo event generators and then go on to discuss these developments, focussing on the developments in Herwig(++) event generator. I will conclude by presenting the current status of event generation together with some results of the forthcoming new version of Herwig, Herwig 7.

Monte Carlo tests of the ELIPGRID-PC algorithm

International Nuclear Information System (INIS)

Davidson, J.R.

1995-04-01

The standard tool for calculating the probability of detecting pockets of contamination called hot spots has been the ELIPGRID computer code of Singer and Wickman. The ELIPGRID-PC program has recently made this algorithm available for an IBM reg-sign PC. However, no known independent validation of the ELIPGRID algorithm exists. This document describes a Monte Carlo simulation-based validation of a modified version of the ELIPGRID-PC code. The modified ELIPGRID-PC code is shown to match Monte Carlo-calculated hot-spot detection probabilities to within ±0.5% for 319 out of 320 test cases. The one exception, a very thin elliptical hot spot located within a rectangular sampling grid, differed from the Monte Carlo-calculated probability by about 1%. These results provide confidence in the ability of the modified ELIPGRID-PC code to accurately predict hot-spot detection probabilities within an acceptable range of error

Improved Monte Carlo Method for PSA Uncertainty Analysis

International Nuclear Information System (INIS)

Choi, Jongsoo

2016-01-01

The treatment of uncertainty is an important issue for regulatory decisions. Uncertainties exist from knowledge limitations. A probabilistic approach has exposed some of these limitations and provided a framework to assess their significance and assist in developing a strategy to accommodate them in the regulatory process. The uncertainty analysis (UA) is usually based on the Monte Carlo method. This paper proposes a Monte Carlo UA approach to calculate the mean risk metrics accounting for the SOKC between basic events (including CCFs) using efficient random number generators and to meet Capability Category III of the ASME/ANS PRA standard. Audit calculation is needed in PSA regulatory reviews of uncertainty analysis results submitted for licensing. The proposed Monte Carlo UA approach provides a high degree of confidence in PSA reviews. All PSA needs accounting for the SOKC between event probabilities to meet the ASME/ANS PRA standard

Two proposed convergence criteria for Monte Carlo solutions

International Nuclear Information System (INIS)

Forster, R.A.; Pederson, S.P.; Booth, T.E.

1992-01-01

The central limit theorem (CLT) can be applied to a Monte Carlo solution if two requirements are satisfied: (1) The random variable has a finite mean and a finite variance; and (2) the number N of independent observations grows large. When these two conditions are satisfied, a confidence interval (CI) based on the normal distribution with a specified coverage probability can be formed. The first requirement is generally satisfied by the knowledge of the Monte Carlo tally being used. The Monte Carlo practitioner has a limited number of marginal methods to assess the fulfillment of the second requirement, such as statistical error reduction proportional to 1/√N with error magnitude guidelines. Two proposed methods are discussed in this paper to assist in deciding if N is large enough: estimating the relative variance of the variance (VOV) and examining the empirical history score probability density function (pdf)

Improved Monte Carlo Method for PSA Uncertainty Analysis

Energy Technology Data Exchange (ETDEWEB)

Choi, Jongsoo [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)

2016-10-15

The treatment of uncertainty is an important issue for regulatory decisions. Uncertainties exist from knowledge limitations. A probabilistic approach has exposed some of these limitations and provided a framework to assess their significance and assist in developing a strategy to accommodate them in the regulatory process. The uncertainty analysis (UA) is usually based on the Monte Carlo method. This paper proposes a Monte Carlo UA approach to calculate the mean risk metrics accounting for the SOKC between basic events (including CCFs) using efficient random number generators and to meet Capability Category III of the ASME/ANS PRA standard. Audit calculation is needed in PSA regulatory reviews of uncertainty analysis results submitted for licensing. The proposed Monte Carlo UA approach provides a high degree of confidence in PSA reviews. All PSA needs accounting for the SOKC between event probabilities to meet the ASME/ANS PRA standard.

A keff calculation method by Monte Carlo

International Nuclear Information System (INIS)

Shen, H; Wang, K.

2008-01-01

The effective multiplication factor (k eff ) is defined as the ratio between the number of neutrons in successive generations, which definition is adopted by most Monte Carlo codes (e.g. MCNP). Also, it can be thought of as the ratio of the generation rate of neutrons by the sum of the leakage rate and the absorption rate, which should exclude the effect of the neutron reaction such as (n, 2n) and (n, 3n). This article discusses the Monte Carlo method for k eff calculation based on the second definition. A new code has been developed and the results are presented. (author)

Enhanced Monte-Carlo-Linked Depletion Capabilities in MCNPX

International Nuclear Information System (INIS)

Fensin, Michael L.; Hendricks, John S.; Anghaie, Samim

2006-01-01

As advanced reactor concepts challenge the accuracy of current modeling technologies, a higher-fidelity depletion calculation is necessary to model time-dependent core reactivity properly for accurate cycle length and safety margin determinations. The recent integration of CINDER90 into the MCNPX Monte Carlo radiation transport code provides a completely self-contained Monte-Carlo-linked depletion capability. Two advances have been made in the latest MCNPX capability based on problems observed in pre-released versions: continuous energy collision density tracking and proper fission yield selection. Pre-released versions of the MCNPX depletion code calculated the reaction rates for (n,2n), (n,3n), (n,p), (n,a), and (n,?) by matching the MCNPX steady-state 63-group flux with 63-group cross sections inherent in the CINDER90 library and then collapsing to one-group collision densities for the depletion calculation. This procedure led to inaccuracies due to the miscalculation of the reaction rates resulting from the collapsed multi-group approach. The current version of MCNPX eliminates this problem by using collapsed one-group collision densities generated from continuous energy reaction rates determined during the MCNPX steady-state calculation. MCNPX also now explicitly determines the proper fission yield to be used by the CINDER90 code for the depletion calculation. The CINDER90 code offers a thermal, fast, and high-energy fission yield for each fissile isotope contained in the CINDER90 data file. MCNPX determines which fission yield to use for a specified problem by calculating the integral fission rate for the defined energy boundaries (thermal, fast, and high energy), determining which energy range contains the majority of fissions, and then selecting the appropriate fission yield for the energy range containing the majority of fissions. The MCNPX depletion capability enables complete, relatively easy-to-use depletion calculations in a single Monte Carlo code

NOTE: Monte Carlo evaluation of kerma in an HDR brachytherapy bunker

Science.gov (United States)

Pérez-Calatayud, J.; Granero, D.; Ballester, F.; Casal, E.; Crispin, V.; Puchades, V.; León, A.; Verdú, G.

2004-12-01

In recent years, the use of high dose rate (HDR) after-loader machines has greatly increased due to the shift from traditional Cs-137/Ir-192 low dose rate (LDR) to HDR brachytherapy. The method used to calculate the required concrete and, where appropriate, lead shielding in the door is based on analytical methods provided by documents published by the ICRP, the IAEA and the NCRP. The purpose of this study is to perform a more realistic kerma evaluation at the entrance maze door of an HDR bunker using the Monte Carlo code GEANT4. The Monte Carlo results were validated experimentally. The spectrum at the maze entrance door, obtained with Monte Carlo, has an average energy of about 110 keV, maintaining a similar value along the length of the maze. The comparison of results from the aforementioned values with the Monte Carlo ones shows that results obtained using the albedo coefficient from the ICRP document more closely match those given by the Monte Carlo method, although the maximum value given by MC calculations is 30% greater.

Modelling of an industrial environment, part 1.: Monte Carlo simulations of photon transport

International Nuclear Information System (INIS)

Kis, Z.; Eged, K.; Meckbach, R.; Voigt, G.

2002-01-01

After a nuclear accident releasing radioactive material into the environment the external exposures may contribute significantly to the radiation exposure of the population (UNSCEAR 1988, 2000). For urban populations the external gamma exposure from radionuclides deposited on the surfaces of the urban-industrial environments yields the dominant contributions to the total dose to the public (Kelly 1987; Jacob and Meckbach 1990). The radiation field is naturally influenced by the environment around the sources. For calculations of the shielding effect of the structures in complex and realistic urban environments Monte Carlo methods turned out to be useful tools (Jacob and Meckbach 1987; Meckbach et al. 1988). Using these methods a complex environment can be set up in which the photon transport can be solved on a reliable way. The accuracy of the methods is in principle limited only by the knowledge of the atomic cross sections and the computational time. Several papers using Monte Carlo results for calculating doses from the external gamma exposures were published (Jacob and Meckbach 1987, 1990; Meckbach et al. 1988; Rochedo et al. 1996). In these papers the Monte Carlo simulations were run in urban environments and for different photon energies. The industrial environment can be defined as such an area where productive and/or commercial activity is carried out. A good example can be a factory or a supermarket. An industrial environment can rather be different from the urban ones as for the types and structures of the buildings and their dimensions. These variations will affect the radiation field of this environment. Hence there is a need to run new Monte Carlo simulations designed specially for the industrial environments

Crop canopy BRDF simulation and analysis using Monte Carlo method

NARCIS (Netherlands)

Huang, J.; Wu, B.; Tian, Y.; Zeng, Y.

2006-01-01

This author designs the random process between photons and crop canopy. A Monte Carlo model has been developed to simulate the Bi-directional Reflectance Distribution Function (BRDF) of crop canopy. Comparing Monte Carlo model to MCRM model, this paper analyzes the variations of different LAD and

Development of the criticality capability for the SAM-CE Monte Carlo System

International Nuclear Information System (INIS)

Lichtenstein, H.; Troubetzkoy, E.; Steinberg, H.; Cohen, M.O.

1979-04-01

A criticality capabilty has been developed and implemented in the SAM-CE Monte Carlo system. The data processing component, SAM-X, preserves, to any required accuracy, the data quality inherent in the ENDF/B library. The generated data is Doppler-broadened and includes (where applicable) probability tables for the unresolved resonance range, and thermal-scattering law data. Curves of several total and partial cross sections are generated and displayed. The Monte Carlo component, SAM-F, includes several eigenvalue estimators and variance reduction schemes. Stratification was found to effect significant improvement in calculational efficiency, but the usefulness of importance sampling is marginal in criticality problems. The entire system has been installed at BNL, for the analysis of TRX benchmarks. The TRX-1 and TRX-2 cell calculations have been performed, with estimated eigenvalues of 1.1751 +- 0.0016 and 1.1605 +- .0015, respectively. These results are shown to be statistically consistent with other sources

Estimation of magnetocaloric properties by using Monte Carlo method for AMRR cycle

International Nuclear Information System (INIS)

Arai, R; Fukuda, H; Numazawa, T; Tamura, R; Li, J; Saito, A T; Nakagome, H; Kaji, S

2015-01-01

In order to achieve a wide refrigerating temperature range in magnetic refrigeration, it is effective to layer multiple materials with different Curie temperatures. It is crucial to have a detailed understanding of physical properties of materials to optimize the material selection and the layered structure. In the present study, we discuss methods for estimating a change in physical properties, particularly the Curie temperature when some of the Gd atoms are substituted for non-magnetic elements for material design, based on Gd as a ferromagnetic material which is a typical magnetocaloric material. For this purpose, whilst making calculations using the S=7/2 Ising model and the Monte Carlo method, we made a specific heat measurement and a magnetization measurement of Gd-R alloy (R = Y, Zr) to compare experimental values and calculated ones. The results showed that the magnetic entropy change, specific heat, and Curie temperature can be estimated with good accuracy using the Monte Carlo method. (paper)

Monte Carlo radiation transport: A revolution in science

International Nuclear Information System (INIS)

Hendricks, J.

1993-01-01

When Enrico Fermi, Stan Ulam, Nicholas Metropolis, John von Neuman, and Robert Richtmyer invented the Monte Carlo method fifty years ago, little could they imagine the far-flung consequences, the international applications, and the revolution in science epitomized by their abstract mathematical method. The Monte Carlo method is used in a wide variety of fields to solve exact computational models approximately by statistical sampling. It is an alternative to traditional physics modeling methods which solve approximate computational models exactly by deterministic methods. Modern computers and improved methods, such as variance reduction, have enhanced the method to the point of enabling a true predictive capability in areas such as radiation or particle transport. This predictive capability has contributed to a radical change in the way science is done: design and understanding come from computations built upon experiments rather than being limited to experiments, and the computer codes doing the computations have become the repository for physics knowledge. The MCNP Monte Carlo computer code effort at Los Alamos is an example of this revolution. Physicians unfamiliar with physics details can design cancer treatments using physics buried in the MCNP computer code. Hazardous environments and hypothetical accidents can be explored. Many other fields, from underground oil well exploration to aerospace, from physics research to energy production, from safety to bulk materials processing, benefit from MCNP, the Monte Carlo method, and the revolution in science

Suppression of the initial transient in Monte Carlo criticality simulations

International Nuclear Information System (INIS)

Richet, Y.

2006-12-01

Criticality Monte Carlo calculations aim at estimating the effective multiplication factor (k-effective) for a fissile system through iterations simulating neutrons propagation (making a Markov chain). Arbitrary initialization of the neutron population can deeply bias the k-effective estimation, defined as the mean of the k-effective computed at each iteration. A simplified model of this cycle k-effective sequence is built, based on characteristics of industrial criticality Monte Carlo calculations. Statistical tests, inspired by Brownian bridge properties, are designed to discriminate stationarity of the cycle k-effective sequence. The initial detected transient is, then, suppressed in order to improve the estimation of the system k-effective. The different versions of this methodology are detailed and compared, firstly on a plan of numerical tests fitted on criticality Monte Carlo calculations, and, secondly on real criticality calculations. Eventually, the best methodologies observed in these tests are selected and allow to improve industrial Monte Carlo criticality calculations. (author)

TH-E-18A-01: Developments in Monte Carlo Methods for Medical Imaging

Energy Technology Data Exchange (ETDEWEB)

Badal, A [U.S. Food and Drug Administration (CDRH/OSEL), Silver Spring, MD (United States); Zbijewski, W [Johns Hopkins University, Baltimore, MD (United States); Bolch, W [University of Florida, Gainesville, FL (United States); Sechopoulos, I [Emory University, Atlanta, GA (United States)

2014-06-15

Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Traditionally, practical application of Monte Carlo simulation codes in diagnostic imaging was limited by the need for large computational resources or long execution times. However, recent advancements in high-performance computing hardware, combined with a new generation of Monte Carlo simulation algorithms and novel postprocessing methods, are allowing for the computation of relevant imaging parameters of interest such as patient organ doses and scatter-to-primaryratios in radiographic projections in just a few seconds using affordable computational resources. Programmable Graphics Processing Units (GPUs), for example, provide a convenient, affordable platform for parallelized Monte Carlo executions that yield simulation times on the order of 10{sup 7} xray/ s. Even with GPU acceleration, however, Monte Carlo simulation times can be prohibitive for routine clinical practice. To reduce simulation times further, variance reduction techniques can be used to alter the probabilistic models underlying the x-ray tracking process, resulting in lower variance in the results without biasing the estimates. Other complementary strategies for further reductions in computation time are denoising of the Monte Carlo estimates and estimating (scoring) the quantity of interest at a sparse set of sampling locations (e.g. at a small number of detector pixels in a scatter simulation) followed by interpolation. Beyond reduction of the computational resources required for performing Monte Carlo simulations in medical imaging, the use of accurate representations of patient anatomy is crucial to the

TH-E-18A-01: Developments in Monte Carlo Methods for Medical Imaging

International Nuclear Information System (INIS)

Badal, A; Zbijewski, W; Bolch, W; Sechopoulos, I

2014-01-01

Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Traditionally, practical application of Monte Carlo simulation codes in diagnostic imaging was limited by the need for large computational resources or long execution times. However, recent advancements in high-performance computing hardware, combined with a new generation of Monte Carlo simulation algorithms and novel postprocessing methods, are allowing for the computation of relevant imaging parameters of interest such as patient organ doses and scatter-to-primaryratios in radiographic projections in just a few seconds using affordable computational resources. Programmable Graphics Processing Units (GPUs), for example, provide a convenient, affordable platform for parallelized Monte Carlo executions that yield simulation times on the order of 10 7 xray/ s. Even with GPU acceleration, however, Monte Carlo simulation times can be prohibitive for routine clinical practice. To reduce simulation times further, variance reduction techniques can be used to alter the probabilistic models underlying the x-ray tracking process, resulting in lower variance in the results without biasing the estimates. Other complementary strategies for further reductions in computation time are denoising of the Monte Carlo estimates and estimating (scoring) the quantity of interest at a sparse set of sampling locations (e.g. at a small number of detector pixels in a scatter simulation) followed by interpolation. Beyond reduction of the computational resources required for performing Monte Carlo simulations in medical imaging, the use of accurate representations of patient anatomy is crucial to the virtual

The neutrons flux density calculations by Monte Carlo code for the double heterogeneity fuel

International Nuclear Information System (INIS)

Gurevich, M.I.; Brizgalov, V.I.

1994-01-01

This document provides the calculation technique for the fuel elements which consists of the one substance as a matrix and the other substance as the corn embedded in it. This technique can be used in the neutron flux density calculation by the universal Monte Carlo code. The estimation of accuracy is presented too. (authors). 6 refs., 1 fig

Condensed history Monte Carlo methods for photon transport problems

International Nuclear Information System (INIS)

Bhan, Katherine; Spanier, Jerome

2007-01-01

We study methods for accelerating Monte Carlo simulations that retain most of the accuracy of conventional Monte Carlo algorithms. These methods - called Condensed History (CH) methods - have been very successfully used to model the transport of ionizing radiation in turbid systems. Our primary objective is to determine whether or not such methods might apply equally well to the transport of photons in biological tissue. In an attempt to unify the derivations, we invoke results obtained first by Lewis, Goudsmit and Saunderson and later improved by Larsen and Tolar. We outline how two of the most promising of the CH models - one based on satisfying certain similarity relations and the second making use of a scattering phase function that permits only discrete directional changes - can be developed using these approaches. The main idea is to exploit the connection between the space-angle moments of the radiance and the angular moments of the scattering phase function. We compare the results obtained when the two CH models studied are used to simulate an idealized tissue transport problem. The numerical results support our findings based on the theoretical derivations and suggest that CH models should play a useful role in modeling light-tissue interactions

PEPSI - a Monte Carlo generator for polarized leptoproduction

International Nuclear Information System (INIS)

Mankiewicz, L.

1992-01-01

We describe PEPSI (Polarized Electron Proton Scattering Interactions) a Monte Carlo program for polarized deep inelastic leptoproduction mediated by electromagnetic interaction, and explain how to use it. The code is a modification of the Lepto 4.3 Lund Monte Carlo for unpolarized scattering. The hard virtual gamma-parton scattering is generated according to the polarization-dependent QCD cross-section of the first order in α S . PEPSI requires the standard polarization-independent JETSET routines to simulate the fragmentation into final hadrons. (orig.)

Monte Carlo method for solving a parabolic problem

Directory of Open Access Journals (Sweden)

Tian Yi

2016-01-01

Full Text Available In this paper, we present a numerical method based on random sampling for a parabolic problem. This method combines use of the Crank-Nicolson method and Monte Carlo method. In the numerical algorithm, we first discretize governing equations by Crank-Nicolson method, and obtain a large sparse system of linear algebraic equations, then use Monte Carlo method to solve the linear algebraic equations. To illustrate the usefulness of this technique, we apply it to some test problems.

NUEN-618 Class Project: Actually Implicit Monte Carlo

Energy Technology Data Exchange (ETDEWEB)

Vega, R. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Brunner, T. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-12-14

This research describes a new method for the solution of the thermal radiative transfer (TRT) equations that is implicit in time which will be called Actually Implicit Monte Carlo (AIMC). This section aims to introduce the TRT equations, as well as the current workhorse method which is known as Implicit Monte Carlo (IMC). As the name of the method proposed here indicates, IMC is a misnomer in that it is only semi-implicit, which will be shown in this section as well.

TITAN: a computer program for accident occurrence frequency analyses by component Monte Carlo simulation

Energy Technology Data Exchange (ETDEWEB)

Nomura, Yasushi [Department of Fuel Cycle Safety Research, Nuclear Safety Research Center, Tokai Research Establishment, Japan Atomic Energy Research Institute, Tokai, Ibaraki (Japan); Tamaki, Hitoshi [Department of Safety Research Technical Support, Tokai Research Establishment, Japan Atomic Energy Research Institute, Tokai, Ibaraki (Japan); Kanai, Shigeru [Fuji Research Institute Corporation, Tokyo (Japan)

2000-04-01

In a plant system consisting of complex equipments and components for a reprocessing facility, there might be grace time between an initiating event and a resultant serious accident, allowing operating personnel to take remedial actions, thus, terminating the ongoing accident sequence. A component Monte Carlo simulation computer program TITAN has been developed to analyze such a complex reliability model including the grace time without any difficulty to obtain an accident occurrence frequency. Firstly, basic methods for the component Monte Carlo simulation is introduced to obtain an accident occurrence frequency, and then, the basic performance such as precision, convergence, and parallelization of calculation, is shown through calculation of a prototype accident sequence model. As an example to illustrate applicability to a real scale plant model, a red oil explosion in a German reprocessing plant model is simulated to show that TITAN can give an accident occurrence frequency with relatively good accuracy. Moreover, results of uncertainty analyses by TITAN are rendered to show another performance, and a proposal is made for introducing of a new input-data format to adapt the component Monte Carlo simulation. The present paper describes the calculational method, performance, applicability to a real scale, and new proposal for the TITAN code. In the Appendixes, a conventional analytical method is shown to avoid complex and laborious calculation to obtain a strict solution of accident occurrence frequency, compared with Monte Carlo method. The user's manual and the list/structure of program are also contained in the Appendixes to facilitate TITAN computer program usage. (author)

TITAN: a computer program for accident occurrence frequency analyses by component Monte Carlo simulation

International Nuclear Information System (INIS)

Nomura, Yasushi; Tamaki, Hitoshi; Kanai, Shigeru

2000-04-01

In a plant system consisting of complex equipments and components for a reprocessing facility, there might be grace time between an initiating event and a resultant serious accident, allowing operating personnel to take remedial actions, thus, terminating the ongoing accident sequence. A component Monte Carlo simulation computer program TITAN has been developed to analyze such a complex reliability model including the grace time without any difficulty to obtain an accident occurrence frequency. Firstly, basic methods for the component Monte Carlo simulation is introduced to obtain an accident occurrence frequency, and then, the basic performance such as precision, convergence, and parallelization of calculation, is shown through calculation of a prototype accident sequence model. As an example to illustrate applicability to a real scale plant model, a red oil explosion in a German reprocessing plant model is simulated to show that TITAN can give an accident occurrence frequency with relatively good accuracy. Moreover, results of uncertainty analyses by TITAN are rendered to show another performance, and a proposal is made for introducing of a new input-data format to adapt the component Monte Carlo simulation. The present paper describes the calculational method, performance, applicability to a real scale, and new proposal for the TITAN code. In the Appendixes, a conventional analytical method is shown to avoid complex and laborious calculation to obtain a strict solution of accident occurrence frequency, compared with Monte Carlo method. The user's manual and the list/structure of program are also contained in the Appendixes to facilitate TITAN computer program usage. (author)

Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures

Energy Technology Data Exchange (ETDEWEB)

Souris, Kevin, E-mail: kevin.souris@uclouvain.be; Lee, John Aldo [Center for Molecular Imaging and Experimental Radiotherapy, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Avenue Hippocrate 54, 1200 Brussels, Belgium and ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve 1348 (Belgium); Sterpin, Edmond [Center for Molecular Imaging and Experimental Radiotherapy, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Avenue Hippocrate 54, 1200 Brussels, Belgium and Department of Oncology, Katholieke Universiteit Leuven, O& N I Herestraat 49, 3000 Leuven (Belgium)

2016-04-15

Purpose: Accuracy in proton therapy treatment planning can be improved using Monte Carlo (MC) simulations. However the long computation time of such methods hinders their use in clinical routine. This work aims to develop a fast multipurpose Monte Carlo simulation tool for proton therapy using massively parallel central processing unit (CPU) architectures. Methods: A new Monte Carlo, called MCsquare (many-core Monte Carlo), has been designed and optimized for the last generation of Intel Xeon processors and Intel Xeon Phi coprocessors. These massively parallel architectures offer the flexibility and the computational power suitable to MC methods. The class-II condensed history algorithm of MCsquare provides a fast and yet accurate method of simulating heavy charged particles such as protons, deuterons, and alphas inside voxelized geometries. Hard ionizations, with energy losses above a user-specified threshold, are simulated individually while soft events are regrouped in a multiple scattering theory. Elastic and inelastic nuclear interactions are sampled from ICRU 63 differential cross sections, thereby allowing for the computation of prompt gamma emission profiles. MCsquare has been benchmarked with the GATE/GEANT4 Monte Carlo application for homogeneous and heterogeneous geometries. Results: Comparisons with GATE/GEANT4 for various geometries show deviations within 2%–1 mm. In spite of the limited memory bandwidth of the coprocessor simulation time is below 25 s for 10{sup 7} primary 200 MeV protons in average soft tissues using all Xeon Phi and CPU resources embedded in a single desktop unit. Conclusions: MCsquare exploits the flexibility of CPU architectures to provide a multipurpose MC simulation tool. Optimized code enables the use of accurate MC calculation within a reasonable computation time, adequate for clinical practice. MCsquare also simulates prompt gamma emission and can thus be used also for in vivo range verification.

Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures

International Nuclear Information System (INIS)

Souris, Kevin; Lee, John Aldo; Sterpin, Edmond

2016-01-01

Purpose: Accuracy in proton therapy treatment planning can be improved using Monte Carlo (MC) simulations. However the long computation time of such methods hinders their use in clinical routine. This work aims to develop a fast multipurpose Monte Carlo simulation tool for proton therapy using massively parallel central processing unit (CPU) architectures. Methods: A new Monte Carlo, called MCsquare (many-core Monte Carlo), has been designed and optimized for the last generation of Intel Xeon processors and Intel Xeon Phi coprocessors. These massively parallel architectures offer the flexibility and the computational power suitable to MC methods. The class-II condensed history algorithm of MCsquare provides a fast and yet accurate method of simulating heavy charged particles such as protons, deuterons, and alphas inside voxelized geometries. Hard ionizations, with energy losses above a user-specified threshold, are simulated individually while soft events are regrouped in a multiple scattering theory. Elastic and inelastic nuclear interactions are sampled from ICRU 63 differential cross sections, thereby allowing for the computation of prompt gamma emission profiles. MCsquare has been benchmarked with the GATE/GEANT4 Monte Carlo application for homogeneous and heterogeneous geometries. Results: Comparisons with GATE/GEANT4 for various geometries show deviations within 2%–1 mm. In spite of the limited memory bandwidth of the coprocessor simulation time is below 25 s for 10"7 primary 200 MeV protons in average soft tissues using all Xeon Phi and CPU resources embedded in a single desktop unit. Conclusions: MCsquare exploits the flexibility of CPU architectures to provide a multipurpose MC simulation tool. Optimized code enables the use of accurate MC calculation within a reasonable computation time, adequate for clinical practice. MCsquare also simulates prompt gamma emission and can thus be used also for in vivo range verification.

Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures.

Science.gov (United States)

Souris, Kevin; Lee, John Aldo; Sterpin, Edmond

2016-04-01

Accuracy in proton therapy treatment planning can be improved using Monte Carlo (MC) simulations. However the long computation time of such methods hinders their use in clinical routine. This work aims to develop a fast multipurpose Monte Carlo simulation tool for proton therapy using massively parallel central processing unit (CPU) architectures. A new Monte Carlo, called MCsquare (many-core Monte Carlo), has been designed and optimized for the last generation of Intel Xeon processors and Intel Xeon Phi coprocessors. These massively parallel architectures offer the flexibility and the computational power suitable to MC methods. The class-II condensed history algorithm of MCsquare provides a fast and yet accurate method of simulating heavy charged particles such as protons, deuterons, and alphas inside voxelized geometries. Hard ionizations, with energy losses above a user-specified threshold, are simulated individually while soft events are regrouped in a multiple scattering theory. Elastic and inelastic nuclear interactions are sampled from ICRU 63 differential cross sections, thereby allowing for the computation of prompt gamma emission profiles. MCsquare has been benchmarked with the gate/geant4 Monte Carlo application for homogeneous and heterogeneous geometries. Comparisons with gate/geant4 for various geometries show deviations within 2%-1 mm. In spite of the limited memory bandwidth of the coprocessor simulation time is below 25 s for 10(7) primary 200 MeV protons in average soft tissues using all Xeon Phi and CPU resources embedded in a single desktop unit. MCsquare exploits the flexibility of CPU architectures to provide a multipurpose MC simulation tool. Optimized code enables the use of accurate MC calculation within a reasonable computation time, adequate for clinical practice. MCsquare also simulates prompt gamma emission and can thus be used also for in vivo range verification.

Monte Carlo burnup codes acceleration using the correlated sampling method

International Nuclear Information System (INIS)

Dieudonne, C.

2013-01-01

For several years, Monte Carlo burnup/depletion codes have appeared, which couple Monte Carlo codes to simulate the neutron transport to deterministic methods, which handle the medium depletion due to the neutron flux. Solving Boltzmann and Bateman equations in such a way allows to track fine 3-dimensional effects and to get rid of multi-group hypotheses done by deterministic solvers. The counterpart is the prohibitive calculation time due to the Monte Carlo solver called at each time step. In this document we present an original methodology to avoid the repetitive and time-expensive Monte Carlo simulations, and to replace them by perturbation calculations: indeed the different burnup steps may be seen as perturbations of the isotopic concentration of an initial Monte Carlo simulation. In a first time we will present this method, and provide details on the perturbative technique used, namely the correlated sampling. In a second time we develop a theoretical model to study the features of the correlated sampling method to understand its effects on depletion calculations. In a third time the implementation of this method in the TRIPOLI-4 code will be discussed, as well as the precise calculation scheme used to bring important speed-up of the depletion calculation. We will begin to validate and optimize the perturbed depletion scheme with the calculation of a REP-like fuel cell depletion. Then this technique will be used to calculate the depletion of a REP-like assembly, studied at beginning of its cycle. After having validated the method with a reference calculation we will show that it can speed-up by nearly an order of magnitude standard Monte-Carlo depletion codes. (author) [fr

Monte Carlo Simulation in Statistical Physics An Introduction

CERN Document Server

Binder, Kurt

2010-01-01

Monte Carlo Simulation in Statistical Physics deals with the computer simulation of many-body systems in condensed-matter physics and related fields of physics, chemistry and beyond, to traffic flows, stock market fluctuations, etc.). Using random numbers generated by a computer, probability distributions are calculated, allowing the estimation of the thermodynamic properties of various systems. This book describes the theoretical background to several variants of these Monte Carlo methods and gives a systematic presentation from which newcomers can learn to perform such simulations and to analyze their results. The fifth edition covers Classical as well as Quantum Monte Carlo methods. Furthermore a new chapter on the sampling of free-energy landscapes has been added. To help students in their work a special web server has been installed to host programs and discussion groups (http://wwwcp.tphys.uni-heidelberg.de). Prof. Binder was awarded the Berni J. Alder CECAM Award for Computational Physics 2001 as well ...

Monte Carlo simulation in statistical physics an introduction

CERN Document Server

Binder, Kurt

1992-01-01

The Monte Carlo method is a computer simulation method which uses random numbers to simulate statistical fluctuations The method is used to model complex systems with many degrees of freedom Probability distributions for these systems are generated numerically and the method then yields numerically exact information on the models Such simulations may be used tosee how well a model system approximates a real one or to see how valid the assumptions are in an analyical theory A short and systematic theoretical introduction to the method forms the first part of this book The second part is a practical guide with plenty of examples and exercises for the student Problems treated by simple sampling (random and self-avoiding walks, percolation clusters, etc) are included, along with such topics as finite-size effects and guidelines for the analysis of Monte Carlo simulations The two parts together provide an excellent introduction to the theory and practice of Monte Carlo simulations

Geometry and Dynamics for Markov Chain Monte Carlo

Science.gov (United States)

Barp, Alessandro; Briol, François-Xavier; Kennedy, Anthony D.; Girolami, Mark

2018-03-01

Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamiltonian dynamics have been proposed as an efficient way of building chains which can explore probability densities efficiently. The method emerges from physics and geometry and these links have been extensively studied by a series of authors through the last thirty years. However, there is currently a gap between the intuitions and knowledge of users of the methodology and our deep understanding of these theoretical foundations. The aim of this review is to provide a comprehensive introduction to the geometric tools used in Hamiltonian Monte Carlo at a level accessible to statisticians, machine learners and other users of the methodology with only a basic understanding of Monte Carlo methods. This will be complemented with some discussion of the most recent advances in the field which we believe will become increasingly relevant to applied scientists.

Vectorizing and macrotasking Monte Carlo neutral particle algorithms

International Nuclear Information System (INIS)

Heifetz, D.B.

1987-04-01

Monte Carlo algorithms for computing neutral particle transport in plasmas have been vectorized and macrotasked. The techniques used are directly applicable to Monte Carlo calculations of neutron and photon transport, and Monte Carlo integration schemes in general. A highly vectorized code was achieved by calculating test flight trajectories in loops over arrays of flight data, isolating the conditional branches to as few a number of loops as possible. A number of solutions are discussed to the problem of gaps appearing in the arrays due to completed flights, which impede vectorization. A simple and effective implementation of macrotasking is achieved by dividing the calculation of the test flight profile among several processors. A tree of random numbers is used to ensure reproducible results. The additional memory required for each task may preclude using a larger number of tasks. In future machines, the limit of macrotasking may be possible, with each test flight, and split test flight, being a separate task

A comparison study for dose calculation in radiation therapy: pencil beam Kernel based vs. Monte Carlo simulation vs. measurements

Energy Technology Data Exchange (ETDEWEB)

Cheong, Kwang-Ho; Suh, Tae-Suk; Lee, Hyoung-Koo; Choe, Bo-Young [The Catholic Univ. of Korea, Seoul (Korea, Republic of); Kim, Hoi-Nam; Yoon, Sei-Chul [Kangnam St. Mary' s Hospital, Seoul (Korea, Republic of)

2002-07-01

Accurate dose calculation in radiation treatment planning is most important for successful treatment. Since human body is composed of various materials and not an ideal shape, it is not easy to calculate the accurate effective dose in the patients. Many methods have been proposed to solve inhomogeneity and surface contour problems. Monte Carlo simulations are regarded as the most accurate method, but it is not appropriate for routine planning because it takes so much time. Pencil beam kernel based convolution/superposition methods were also proposed to correct those effects. Nowadays, many commercial treatment planning systems have adopted this algorithm as a dose calculation engine. The purpose of this study is to verify the accuracy of the dose calculated from pencil beam kernel based treatment planning system comparing to Monte Carlo simulations and measurements especially in inhomogeneous region. Home-made inhomogeneous phantom, Helax-TMS ver. 6.0 and Monte Carlo code BEAMnrc and DOSXYZnrc were used in this study. In homogeneous media, the accuracy was acceptable but in inhomogeneous media, the errors were more significant. However in general clinical situation, pencil beam kernel based convolution algorithm is thought to be a valuable tool to calculate the dose.

On the maximal use of Monte Carlo samples: re-weighting events at NLO accuracy

Energy Technology Data Exchange (ETDEWEB)

Mattelaer, Olivier [Durham University, Institute for Particle Physics Phenomenology (IPPP), Durham (United Kingdom)

2016-12-15

Accurate Monte Carlo simulations for high-energy events at CERN's Large Hadron Collider, are very expensive, both from the computing and storage points of view. We describe a method that allows to consistently re-use parton-level samples accurate up to NLO in QCD under different theoretical hypotheses. We implement it in MadGraph5{sub a}MC rate at NLO and show its validation by applying it to several cases of practical interest for the search of new physics at the LHC. (orig.)

Multi-Index Monte Carlo (MIMC)

KAUST Repository

Haji Ali, Abdul Lateef; Nobile, Fabio; Tempone, Raul

2015-01-01

We propose and analyze a novel Multi-Index Monte Carlo (MIMC) method for weak approximation of stochastic models that are described in terms of differential equations either driven by random measures or with random coefficients. The MIMC method is both a stochastic version of the combination technique introduced by Zenger, Griebel and collaborators and an extension of the Multilevel Monte Carlo (MLMC) method first described by Heinrich and Giles. Inspired by Giles’s seminal work, instead of using first-order differences as in MLMC, we use in MIMC high-order mixed differences to reduce the variance of the hierarchical differences dramatically. Under standard assumptions on the convergence rates of the weak error, variance and work per sample, the optimal index set turns out to be of Total Degree (TD) type. When using such sets, MIMC yields new and improved complexity results, which are natural generalizations of Giles’s MLMC analysis, and which increase the domain of problem parameters for which we achieve the optimal convergence.

Multi-Index Monte Carlo (MIMC)

KAUST Repository

Haji Ali, Abdul Lateef

2015-01-07

We propose and analyze a novel Multi-Index Monte Carlo (MIMC) method for weak approximation of stochastic models that are described in terms of differential equations either driven by random measures or with random coefficients. The MIMC method is both a stochastic version of the combination technique introduced by Zenger, Griebel and collaborators and an extension of the Multilevel Monte Carlo (MLMC) method first described by Heinrich and Giles. Inspired by Giles’s seminal work, instead of using first-order differences as in MLMC, we use in MIMC high-order mixed differences to reduce the variance of the hierarchical differences dramatically. Under standard assumptions on the convergence rates of the weak error, variance and work per sample, the optimal index set turns out to be of Total Degree (TD) type. When using such sets, MIMC yields new and improved complexity results, which are natural generalizations of Giles’s MLMC analysis, and which increase the domain of problem parameters for which we achieve the optimal convergence.

Simulation of Rossi-α method with analog Monte-Carlo method

International Nuclear Information System (INIS)

Lu Yuzhao; Xie Qilin; Song Lingli; Liu Hangang

2012-01-01

The analog Monte-Carlo code for simulating Rossi-α method based on Geant4 was developed. The prompt neutron decay constant α of six metal uranium configurations in Oak Ridge National Laboratory were calculated. α was also calculated by Burst-Neutron method and the result was consistent with the result of Rossi-α method. There is the difference between results of analog Monte-Carlo simulation and experiment, and the reasons for the difference is the gaps between uranium layers. The influence of gaps decrease as the sub-criticality deepens. The relative difference between results of analog Monte-Carlo simulation and experiment changes from 19% to 0.19%. (authors)

Quasi-Monte Carlo methods for lattice systems. A first look

International Nuclear Information System (INIS)

Jansen, K.; Cyprus Univ., Nicosia; Leovey, H.; Griewank, A.; Nube, A.; Humboldt-Universitaet, Berlin; Mueller-Preussker, M.

2013-02-01

We investigate the applicability of Quasi-Monte Carlo methods to Euclidean lattice systems for quantum mechanics in order to improve the asymptotic error behavior of observables for such theories. In most cases the error of an observable calculated by averaging over random observations generated from an ordinary Markov chain Monte Carlo simulation behaves like N -1/2 , where N is the number of observations. By means of Quasi-Monte Carlo methods it is possible to improve this behavior for certain problems up to N -1 . We adapted and applied this approach to simple systems like the quantum harmonic and anharmonic oscillator and verified an improved error scaling.

Quasi-Monte Carlo methods for lattice systems. A first look

Energy Technology Data Exchange (ETDEWEB)

Jansen, K. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Cyprus Univ., Nicosia (Cyprus). Dept. of Physics; Leovey, H.; Griewank, A. [Humboldt-Universitaet, Berlin (Germany). Inst. fuer Mathematik; Nube, A. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Humboldt-Universitaet, Berlin (Germany). Inst. fuer Physik; Mueller-Preussker, M. [Humboldt-Universitaet, Berlin (Germany). Inst. fuer Physik

2013-02-15

We investigate the applicability of Quasi-Monte Carlo methods to Euclidean lattice systems for quantum mechanics in order to improve the asymptotic error behavior of observables for such theories. In most cases the error of an observable calculated by averaging over random observations generated from an ordinary Markov chain Monte Carlo simulation behaves like N{sup -1/2}, where N is the number of observations. By means of Quasi-Monte Carlo methods it is possible to improve this behavior for certain problems up to N{sup -1}. We adapted and applied this approach to simple systems like the quantum harmonic and anharmonic oscillator and verified an improved error scaling.

Monte Carlo calculations of thermodynamic properties of deuterium under high pressures

International Nuclear Information System (INIS)

Levashov, P R; Filinov, V S; BoTan, A; Fortov, V E; Bonitz, M

2008-01-01

Two different numerical approaches have been applied for calculations of shock Hugoniots and compression isentrope of deuterium: direct path integral Monte Carlo and reactive Monte Carlo. The results show good agreement between two methods at intermediate pressure which is an indication of correct accounting of dissociation effects in the direct path integral Monte Carlo method. Experimental data on both shock and quasi-isentropic compression of deuterium are well described by calculations. Thus dissociation of deuterium molecules in these experiments together with interparticle interaction play significant role

Commissioning of a Monte Carlo treatment planning system for clinical use in radiation therapy; Evaluacion de un sistema de planificacion Monte Carlo de uso clinico para radioterapia

Energy Technology Data Exchange (ETDEWEB)

Zucca Aparcio, D.; Perez Moreno, J. M.; Fernandez Leton, P.; Garcia Ruiz-Zorrila, J.

2016-10-01

The commissioning procedures of a Monte Carlo treatment planning system (MC) for photon beams from a dedicated stereotactic body radiosurgery (SBRT) unit has been reported in this document. XVMC has been the MC Code available in the treatment planning system evaluated (BrainLAB iPlan RT Dose) which is based on Virtual Source Models that simulate the primary and scattered radiation, besides the electronic contamination, using gaussian components for whose modelling are required measurements of dose profiles, percentage depth dose and output factors, performed both in water and in air. The dosimetric accuracy of the particle transport simulation has been analyzed by validating the calculations in homogeneous and heterogeneous media versus measurements made under the same conditions as the dose calculation, and checking the stochastic behaviour of Monte Carlo calculations when using different statistical variances. Likewise, it has been verified how the planning system performs the conversion from dose to medium to dose to water, applying the stopping power ratio water to medium, in the presence of heterogeneities where this phenomenon is relevant, such as high density media (cortical bone). (Author)

Monte Carlo simulated dynamical magnetization of single-chain magnets

Energy Technology Data Exchange (ETDEWEB)

Li, Jun; Liu, Bang-Gui, E-mail: bgliu@iphy.ac.cn

2015-03-15

Here, a dynamical Monte-Carlo (DMC) method is used to study temperature-dependent dynamical magnetization of famous Mn{sub 2}Ni system as typical example of single-chain magnets with strong magnetic anisotropy. Simulated magnetization curves are in good agreement with experimental results under typical temperatures and sweeping rates, and simulated coercive fields as functions of temperature are also consistent with experimental curves. Further analysis indicates that the magnetization reversal is determined by both thermal-activated effects and quantum spin tunnelings. These can help explore basic properties and applications of such important magnetic systems. - Highlights: • Monte Carlo simulated magnetization curves are in good agreement with experimental results. • Simulated coercive fields as functions of temperature are consistent with experimental results. • The magnetization reversal is understood in terms of the Monte Carlo simulations.

LCG MCDB - a Knowledgebase of Monte Carlo Simulated Events

CERN Document Server

Belov, S; Galkin, E; Gusev, A; Pokorski, Witold; Sherstnev, A V

2008-01-01

In this paper we report on LCG Monte Carlo Data Base (MCDB) and software which has been developed to operate MCDB. The main purpose of the LCG MCDB project is to provide a storage and documentation system for sophisticated event samples simulated for the LHC collaborations by experts. In many cases, the modern Monte Carlo simulation of physical processes requires expert knowledge in Monte Carlo generators or significant amount of CPU time to produce the events. MCDB is a knowledgebase mainly to accumulate simulated events of this type. The main motivation behind LCG MCDB is to make the sophisticated MC event samples available for various physical groups. All the data from MCDB is accessible in several convenient ways. LCG MCDB is being developed within the CERN LCG Application Area Simulation project.

Exponentially-convergent Monte Carlo via finite-element trial spaces

International Nuclear Information System (INIS)

Morel, Jim E.; Tooley, Jared P.; Blamer, Brandon J.

2011-01-01

Exponentially-Convergent Monte Carlo (ECMC) methods, also known as adaptive Monte Carlo and residual Monte Carlo methods, were the subject of intense research over a decade ago, but they never became practical for solving the realistic problems. We believe that the failure of previous efforts may be related to the choice of trial spaces that were global and thus highly oscillatory. As an alternative, we consider finite-element trial spaces, which have the ability to treat fully realistic problems. As a first step towards more general methods, we apply piecewise-linear trial spaces to the spatially-continuous two-stream transport equation. Using this approach, we achieve exponential convergence and computationally demonstrate several fundamental properties of finite-element based ECMC methods. Finally, our results indicate that the finite-element approach clearly deserves further investigation. (author)

Monte Carlo Calculation of Sensitivities to Secondary Angular Distributions. Theory and Validation

International Nuclear Information System (INIS)

Perell, R. L.

2002-01-01

The basic methods for solution of the transport equation that are in practical use today are the discrete ordinates (SN) method, and the Monte Carlo (Monte Carlo) method. While the SN method is typically less computation time consuming, the Monte Carlo method is often preferred for detailed and general description of three-dimensional geometries, and for calculations using cross sections that are point-wise energy dependent. For analysis of experimental and calculated results, sensitivities are needed. Sensitivities to material parameters in general, and to the angular distribution of the secondary (scattered) neutrons in particular, can be calculated by well known SN methods, using the fluxes obtained from solution of the direct and the adjoint transport equations. Algorithms to calculate sensitivities to cross-sections with Monte Carlo methods have been known for quite a time. However, only just recently we have developed a general Monte Carlo algorithm for the calculation of sensitivities to the angular distribution of the secondary neutrons

Simplified monte carlo simulation for Beijing spectrometer

International Nuclear Information System (INIS)

Wang Taijie; Wang Shuqin; Yan Wuguang; Huang Yinzhi; Huang Deqiang; Lang Pengfei

1986-01-01

The Monte Carlo method based on the functionization of the performance of detectors and the transformation of values of kinematical variables into ''measured'' ones by means of smearing has been used to program the Monte Carlo simulation of the performance of the Beijing Spectrometer (BES) in FORTRAN language named BESMC. It can be used to investigate the multiplicity, the particle type, and the distribution of four-momentum of the final states of electron-positron collision, and also the response of the BES to these final states. Thus, it provides a measure to examine whether the overall design of the BES is reasonable and to decide the physical topics of the BES

Monte Carlo simulation of gas Cerenkov detectors

International Nuclear Information System (INIS)

Mack, J.M.; Jain, M.; Jordan, T.M.

1984-01-01

Theoretical study of selected gamma-ray and electron diagnostic necessitates coupling Cerenkov radiation to electron/photon cascades. A Cerenkov production model and its incorporation into a general geometry Monte Carlo coupled electron/photon transport code is discussed. A special optical photon ray-trace is implemented using bulk optical properties assigned to each Monte Carlo zone. Good agreement exists between experimental and calculated Cerenkov data in the case of a carbon-dioxide gas Cerenkov detector experiment. Cerenkov production and threshold data are presented for a typical carbon-dioxide gas detector that converts a 16.7 MeV photon source to Cerenkov light, which is collected by optics and detected by a photomultiplier

Proton therapy analysis using the Monte Carlo method

Energy Technology Data Exchange (ETDEWEB)

Noshad, Houshyar [Center for Theoretical Physics and Mathematics, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)]. E-mail: hnoshad@aeoi.org.ir; Givechi, Nasim [Islamic Azad University, Science and Research Branch, Tehran (Iran, Islamic Republic of)

2005-10-01

The range and straggling data obtained from the transport of ions in matter (TRIM) computer program were used to determine the trajectories of monoenergetic 60 MeV protons in muscle tissue by using the Monte Carlo technique. The appropriate profile for the shape of a proton pencil beam in proton therapy as well as the dose deposited in the tissue were computed. The good agreements between our results as compared with the corresponding experimental values are presented here to show the reliability of our Monte Carlo method.

Monte Carlo treatment planning with modulated electron radiotherapy: framework development and application

Science.gov (United States)

Alexander, Andrew William

Within the field of medical physics, Monte Carlo radiation transport simulations are considered to be the most accurate method for the determination of dose distributions in patients. The McGill Monte Carlo treatment planning system (MMCTP), provides a flexible software environment to integrate Monte Carlo simulations with current and new treatment modalities. A developing treatment modality called energy and intensity modulated electron radiotherapy (MERT) is a promising modality, which has the fundamental capabilities to enhance the dosimetry of superficial targets. An objective of this work is to advance the research and development of MERT with the end goal of clinical use. To this end, we present the MMCTP system with an integrated toolkit for MERT planning and delivery of MERT fields. Delivery is achieved using an automated "few leaf electron collimator" (FLEC) and a controller. Aside from the MERT planning toolkit, the MMCTP system required numerous add-ons to perform the complex task of large-scale autonomous Monte Carlo simulations. The first was a DICOM import filter, followed by the implementation of DOSXYZnrc as a dose calculation engine and by logic methods for submitting and updating the status of Monte Carlo simulations. Within this work we validated the MMCTP system with a head and neck Monte Carlo recalculation study performed by a medical dosimetrist. The impact of MMCTP lies in the fact that it allows for systematic and platform independent large-scale Monte Carlo dose calculations for different treatment sites and treatment modalities. In addition to the MERT planning tools, various optimization algorithms were created external to MMCTP. The algorithms produced MERT treatment plans based on dose volume constraints that employ Monte Carlo pre-generated patient-specific kernels. The Monte Carlo kernels are generated from patient-specific Monte Carlo dose distributions within MMCTP. The structure of the MERT planning toolkit software and