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Journal articles on the topic 'Monte Carlo calculation'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Carlson, J., and M. H. Kalos. "Variational Monte Carlo calculation ofO16." Physical Review C 32, no. 6 (1985): 2105–10. http://dx.doi.org/10.1103/physrevc.32.2105.

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2

Ohtani, Yoshihiko, Mamoru Ohkawa, Akira Uchida, and Tetsuo Yamaya. "Illuminance Calculation Using Monte Carlo Method." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 82, no. 2 (1998): 105–11. http://dx.doi.org/10.2150/jieij1980.82.2_105.

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3

Li, Deng, Xie Zhongsheng, and Li Shu. "Monte Carlo transport and burnup calculation." Annals of Nuclear Energy 30, no. 1 (2003): 127–32. http://dx.doi.org/10.1016/s0306-4549(02)00044-0.

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4

OHTANI, Yoshihiko, Mamoru OHKAWA, Akira UCHIDA, and Tetsuo YAMAYA. "Illuminance Calculation Using Monte Carlo Method." Journal of Light & Visual Environment 24, no. 1 (2000): 42–49. http://dx.doi.org/10.2150/jlve.24.1_42.

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5

Nemnyugin, S. A., and A. M. Petrov. "Monte Carlo calculation of muonic molecules." Computer Physics Communications 97, no. 1-2 (1996): 175–84. http://dx.doi.org/10.1016/0010-4655(96)00030-6.

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6

Li, Yuan Ying, and De Sheng Zhang. "Plane Truss Reliability Numerical Simulation Based on MATLAB." Applied Mechanics and Materials 256-259 (December 2012): 1091–96. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.1091.

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Based on the basic principles of structure reliability numerical analysis, the numerical simulation of the displacement and stress reliability of plane truss under vertical load was programmed with MATLAB. The failure probability of the most unfavorable structural vertical displacement and stress and reliable indicators were obtained through direct sampling Monte Carlo method, response surface method, response surface-Monte Carlo method and response surface-important sampling Monte Carlo method. It is found that calculation lasts longer since there are so many samples with Monte-Carlo method,

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7

Ivaschenko, Olena Valerivna. "Multiprocessor modeling technologies for the applied statistical tasks." System technologies 2, no. 127 (2020): 150–63. http://dx.doi.org/10.34185/1562-9945-2-127-2020-12.

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The work considers the multiprocessors technologies of modeling for Monte Carlo tasks. It is shown that only application of the modern super productive systems permitted the new way to realize the mechanism of corresponding partitioned computations. The calculating schemes that supply to provide the increase of productivity and calculations' speed effectiveness are shown. In this article the modified algorithm of parallel calculations is offered based on the Monte Carlo method. Here every calculator has its own random generator of numbers. Thus intermediate calculations come true independently

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8

Shvachych G. G., Sazonova M. S., Zaporozhchenko O. E., Karpova T. P., and Sushko L. F. "MULTIPROCESSOR MODELING TECHNOLOGIES FOR THE APPLIED STATISTICAL TASKS." International Academy Journal Web of Scholar, no. 3(33) (March 31, 2019): 3–9. http://dx.doi.org/10.31435/rsglobal_wos/31032019/6386.

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    The work considers the multiprocessors technologies of modeling for Monte Carlo tasks. It is shown that only application of the modern super productive systems permitted the new way to realize the mechanism of corresponding partitioned computations. The calculating schemes that supply to provide the increase of productivity and calculations' speed effectiveness are shown. In this article the modified algorithm of parallel calculations is offered based on the Monte Carlo method. Here every calculator has its own random generator of numbers. Thus intermediate calculat

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9

Nagasaki, Yoshihito, Yasuko Koga, Ken Anai, and Norio Igawa. "Daylighting calculation by the Monte Carlo method." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 83, Appendix (1999): 100–101. http://dx.doi.org/10.2150/jieij1980.83.appendix_100.

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10

Peterhans, Matthias, Daniel Frei, Peter Manser, Mauricio Reyes Aguirre, and Michael K. Fix. "Monte Carlo dose calculation on deforming anatomy." Zeitschrift für Medizinische Physik 21, no. 2 (2011): 113–23. http://dx.doi.org/10.1016/j.zemedi.2010.11.002.

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11

Travesset, Alex. "Monte Carlo renormalization group calculation in λΦ34". Nuclear Physics B - Proceedings Supplements 63, № 1-3 (1998): 640–42. http://dx.doi.org/10.1016/s0920-5632(97)00857-8.

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12

Read, F. H., and N. J. Bowring. "Monte-Carlo calculation of Boersch energy spreading." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 519, no. 1-2 (2004): 196–204. http://dx.doi.org/10.1016/j.nima.2003.11.156.

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13

Li, J. S., D. Findley, T. Pawlicki, et al. "Monte Carlo dose calculation for intracavitary brachytherapy." International Journal of Radiation Oncology*Biology*Physics 48, no. 3 (2000): 357–58. http://dx.doi.org/10.1016/s0360-3016(00)80521-0.

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14

Chuang, Keh-Shih, and Hong-Long Tzeng. "Source distribution in adjoint Monte Carlo calculation." Physics in Medicine and Biology 45, no. 2 (2000): L5—L7. http://dx.doi.org/10.1088/0031-9155/45/2/402.

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15

Nakamura, Tota, and Naomichi Hatano. "Quantum Monte Carlo Calculation of theJ1-J2Model." Journal of the Physical Society of Japan 62, no. 9 (1993): 3062–70. http://dx.doi.org/10.1143/jpsj.62.3062.

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16

OMORI, Toshiaki, Shinsuke KATO, Minsik KIM, and Shigehiro NUKATSUKA. "MONTE CARLO CALCULATION FOR RADIATION DOSE PREDICTION." Journal of Environmental Engineering (Transactions of AIJ) 81, no. 727 (2016): 835–43. http://dx.doi.org/10.3130/aije.81.835.

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17

Gläser, H., H. Bechtel, and F. Busse. "Monte Carlo calculation of CRT screen efficiency." Journal of the Society for Information Display 8, no. 3 (2000): 189. http://dx.doi.org/10.1889/1.1828748.

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18

Ueki, Taro. "Monte Carlo criticality calculation under extreme condition." Journal of Nuclear Science and Technology 49, no. 12 (2012): 1134–43. http://dx.doi.org/10.1080/00223131.2012.740356.

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19

Oleinik, D. S. "Monte Carlo Calculation of Weakly Coupled Systems." Atomic Energy 99, no. 4 (2005): 694–701. http://dx.doi.org/10.1007/s10512-006-0002-y.

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20

Merkuriev, S. P., and S. A. Nemnyugin. "Monte-Carlo calculation of bound-state properties." Few-Body Systems 14, no. 4 (1993): 191–205. http://dx.doi.org/10.1007/bf01080716.

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21

Davoudi, Mohammad, Ali Shabestani Monfared, and Mohammad Rahgoshay. "The comparison between 6 MV Primus LINAC simulation output using EGSnrc and commissioning data." Journal of Radiotherapy in Practice 17, no. 3 (2018): 302–8. http://dx.doi.org/10.1017/s1460396917000747.

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AbstractIntroductionMonte Carlo calculation method is considered to be the most accurate method for dose calculation in radiotherapy. The purpose of this research is comparison between 6 MV Primus LINAC simulation output with commissioning data using EGSnrc and build a Monte Carlo geometry of 6 MV Primus LINAC as realistically as possible. The BEAMnrc and DOSXYZnrc (EGSnrc package) Monte Carlo model of the LINAC head was used as a benchmark.MethodsIn the first part, the BEAMnrc was used for the designing of the LINAC treatment head. In the second part, dose calculation and for the design of 3D

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22

Parenica, Holly Marie, Christopher Kabat, Pamela Myers, et al. "4089 Clinical Implementation of Monte Carlo Dose Calculation for Patient-Specific Radiotherapy Quality Assurance." Journal of Clinical and Translational Science 4, s1 (2020): 106. http://dx.doi.org/10.1017/cts.2020.327.

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OBJECTIVES/GOALS: The Monte Carlo dose calculation method is often considered the “gold standard” for patient dose calculations and can be as radiation dose measurements. Our study aims to develop a true Monte Carlo model that can be implemented in our clinic as part of our routine patient-specific quality assurance. METHODS/STUDY POPULATION: We have configured and validated a model of one of our linear accelerators used for radiation therapy treatments using the EGSnrc Monte Carlo simulation software. Measured dosimetric data was obtained from the linear accelerator and was used as the standa

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23

He, Peng, Bin Wu, Lijuan Hao, Guangyao Sun, Bin Li, and Ulrich Fischer. "PERFORMANCE STUDY OF GLOBAL WEIGHT WINDOW GENERATOR BASED ON PARTICLE DENSITY UNIFORMITY." EPJ Web of Conferences 247 (2021): 18005. http://dx.doi.org/10.1051/epjconf/202124718005.

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The variance reduction techniques are necessary for Monte Carlo calculations in which obtaining a detailed calculation result for a large and complex model is required. The GVR method named as global weight window generator (GWWG) was proposed by the FDS team. In this paper, two typical calculation examples, ISPRA-Fe benchmark in SINBAD (Shielding Integral Benchmark Archive Database) and TF Coils (Toroidal Field coils) of European HCPB DEMO (Helium Cooled Pebble Bed demonstration fusion plant), are used to study the performance of GWWG method. It can be seen from the calculation results that t

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24

Belova, Irina V., M. J. Brown, and Graeme E. Murch. "Calculation of Phenomenological Coefficients by Monte Carlo Computer Simulation Methods." Defect and Diffusion Forum 249 (January 2006): 27–34. http://dx.doi.org/10.4028/www.scientific.net/ddf.249.27.

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In this paper we first review the principal indirect and direct Monte Carlo methods for calculating the Onsager phenomenological transport coefficients in solid state diffusion. We propose a new Monte Carlo method that makes use of a steady state calculation of a flux of atoms that is driven by a difference in chemical potential of the atoms between a source and a sink plane. The method is demonstrated for the simple cubic one component lattice gas with nearest neighbour interactions. The new method gives results in good agreement with a Monte Carlo method based on Einsteinian expressions for

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25

Kuznetsov, Oleg, Viktor Chepurnov, Albina Gurskaya, Mikhail Dolgopolov, and Sali Radzhapov. "C-beta energy converter efficiency modeling." EPJ Web of Conferences 222 (2019): 02012. http://dx.doi.org/10.1051/epjconf/201922202012.

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To construct beta converters with maximum efficiency it is necessary to carry out the theoretical calculation in order to determine their optimal parameters - the geometry of the structure, the thickness of the deposition of the radioisotope layer, the depth and the width of the p-n junction, and others. To date, many different theoretical models and calculations methods had been proposed. There are fairly simple theoretical models based on the Bethe-Bloch formula and the calculation of the rate of generation of electron-hole pairs, and on calculations by equivalent circuits. Also, the Monte-C

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26

Fiedler, K., and B. Grauert. "Monte Carlo Calculation: Thermodynamic Functions in Zeolites. I. Theoretical Fundamentals." Adsorption Science & Technology 3, no. 3 (1986): 181–87. http://dx.doi.org/10.1177/026361748600300308.

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A Monte Carlo method for calculating thermodynamic functions of zeolitic adsoption systems is presented, which is different from the method of Metropolis et al. (1949; 1953). The method is based on emphasizing sampling strategy for representing the canonical measure by means of a trajectory averaging. The method allows the calculation of free energy, energy and other derived thermodynamic functions directly from the histogram as well as the calculation of the empirical dispersion and the bias.

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27

D’Arienzo, Marco, Anna Sarnelli, Emilio Mezzenga, et al. "Dosimetric Issues Associated with Percutaneous Ablation of Small Liver Lesions with 90Y." Applied Sciences 10, no. 18 (2020): 6605. http://dx.doi.org/10.3390/app10186605.

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The aim of the present paper is twofold. Firstly, to assess the absorbed dose in small lesions using Monte Carlo calculations in a scenario of intratumoral injection of 90Y (e.g., percutaneous ablation). Secondly, to derive a practical analytical formula for the calculation of the absorbed dose that incorporates the absorbed fractions for 90Y. The absorbed dose per unit administered activity was assessed using Monte Carlo calculations in spheres of different size (diameter 0.5–20 cm). The spheres are representative of tumor regions and are assumed to be uniformly filled with 90Y. Monte Carlo r

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28

Deppman, A., E. Andrade-II, P. C. R. Rossi, F. Garcia, and J. R. Maiorino. "Monte Carlo Calculation of Fragment Distributions in Nuclear Reactions." Science and Technology of Nuclear Installations 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/480343.

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The fragments produced in nuclear reactions for accelarator driven systems (ADS) operation form elements that can have effects on the structure of the reactor. In this regard, the calculation of fragment distributions gives important information for the development of ADS. To obtain those distributions, the Monte Carlo (MC) method is an important tool, and in this work we describe calculations of fragment distributions through a MC code for reactions initiated by intermediate- and high-energy protons and photons on actinide and preactinide nuclei. We study the production of fragments through s

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29

Galchenko, Vitaliy, Ihor Shlapak, and Volodymyr Gulik. "Computational benchmark for fuel assembly of VVER-1000 using the Monte Carlo Serpent code." Nuclear Technology and Radiation Protection 33, no. 1 (2018): 24–30. http://dx.doi.org/10.2298/ntrp1801024g.

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The use of a new Monte Carlo Serpent code for the calculation of water-cooled reactors is presented and a calculation scheme of the fuel assembly for VVER-1000 reactors developed. The calculation of neutron-physical characteristics for the fuel assembly of VVER-1000 is carried out for different states and the results obtained by the Serpent model compared with the results of other reactor codes. The analyses of these results are presented in the paper submitted here. Based on this article, the Monte Carlo Serpent code could be used for neutron-physical calculations of VVER-1000 reactors.

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30

JOHN, GEORGE C., and VIJAY A. SINGH. "MONTE CARLO EVALUATION OF THE AHARONOV-BOHM EFFECT." International Journal of Modern Physics C 06, no. 01 (1995): 67–76. http://dx.doi.org/10.1142/s012918319500006x.

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The electron propagator in the Aharonov-Bohm effect is investigated using the Feynman path integral formalism. The calculation of the propagator is effected using a variation of the Metropolis Monte Carlo algorithm. Unlike “exact” calculations, our approach permits us to include a nonvanishing solenoid radius. We investigate the dependence of the resulting interference pattern on the magnetic field as well as the solenoid radius. Our results agree with the exact case in the limit of an infinitesimally small solenoid radius.

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31

Karg, Juergen, Stefan Speer, Manfred Schmidt, and Reinhold Mueller. "The Monte Carlo code MCPTV—Monte Carlo dose calculation in radiation therapy with carbon ions." Physics in Medicine and Biology 55, no. 13 (2010): 3917–36. http://dx.doi.org/10.1088/0031-9155/55/13/023.

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32

GEPNER, DORON. "MONTE–CARLO THERMODYNAMIC BETHE ANSATZ." International Journal of Modern Physics B 20, no. 14 (2006): 2049–64. http://dx.doi.org/10.1142/s0217979206034522.

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We introduce a Monte–Carlo simulation approach to thermodynamic Bethe ansatz (TBA). We exemplify the method on one-particle integrable models, which include a free boson and a free fermions systems along with the scaling Lee–Yang model (SLYM). It is confirmed that the central charges and energies are correct to a very good precision, typically 0.1% or so. The advantage of the method is that it allows the calculation of all the dimensions and even the particular partition function.

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33

Dettrick, S. A., H. J. Gardner, and S. L. Painter. "Monte Carlo Transport Simulation Techniques for Stellarator Fusion Experiments." Australian Journal of Physics 52, no. 4 (1999): 715. http://dx.doi.org/10.1071/ph98106.

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We describe an implementation of a particle orbit-following simulation approach to the Monte Carlo calculation of neoclassical transport coecients which has been developed for application to the H-1NF Heliac. We compare and contrast some Monte Carlo transport coecient estimators that can be used in such computer codes, from both physical and computational perspectives, and we make recommendations for their use. Transport coecient calculations are performed for the H-1NF in conditions that will be available after the full National Facility upgrade.

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34

Yang, Bo, He Xi Wu, Qiang Lin Wei, and Yi Bao Liu. "Thermal Neutron Utilization Factor Calculation by Monte Carlo." Applied Mechanics and Materials 539 (July 2014): 674–78. http://dx.doi.org/10.4028/www.scientific.net/amm.539.674.

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The Neutron Transport Theory is accurate in reactor engineering analysis, but the calculation process is tedious and complicated. The objective of the present study obtains the thermal utilization factor f by Monte Carlo method. The study establishes the pressurized water reactor model by MCNP program firstly, and calculates the dioxide pellets, zirconium alloy cladding and moderator’s neutron flux density distribution. The thermal neutron disadvantage factor ζ will be gained according to the definition formula. Based on the functional relationship between the above thermal neutron disadvantag

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35

UCHIDA, Akira, and Yoshihiko OHTANI. "Study on illuminance calculation using Monte Carlo Method." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 82, Appendix (1998): 104. http://dx.doi.org/10.2150/jieij1980.82.appendix_104.

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36

Iwai, Ikuo. "Improvement of Illuminance Calculation by Monte Carlo Method." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 84, no. 8 (2000): 534–36. http://dx.doi.org/10.2150/jieij1980.84.8_534.

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37

Aziz, MohdZahri Abdul, AL Yusoff, ND Osman, R. Abdullah, NA Rabaie, and MS Salikin. "Monte carlo dose calculation in dental amalgam phantom." Journal of Medical Physics 40, no. 3 (2015): 150. http://dx.doi.org/10.4103/0971-6203.165080.

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38

Arkani, M., H. Khalafi, and M. R. Eskandari. "Fast fission factor calculation using Monte Carlo method." Progress in Nuclear Energy 54, no. 1 (2012): 167–70. http://dx.doi.org/10.1016/j.pnucene.2011.06.007.

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39

Murata, Isao, Hiroyuki Yamamoto, Hiroyuki Miyamaru, Frank Goldenbaum, and Detlef Filges. "Scattering direction biasing for Monte Carlo transport calculation." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 562, no. 2 (2006): 845–48. http://dx.doi.org/10.1016/j.nima.2006.02.069.

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40

Djemil, T., R. Attallah, and J. N. Capdevielle. "Monte Carlo calculation of the atmospheric antinucleon flux." Nuclear Physics B - Proceedings Supplements 196 (December 2009): 375–78. http://dx.doi.org/10.1016/j.nuclphysbps.2009.09.070.

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41

Hartmann, Günther H., and Pedro Andreo. "Fluence calculation methods in Monte Carlo dosimetry simulations." Zeitschrift für Medizinische Physik 29, no. 3 (2019): 239–48. http://dx.doi.org/10.1016/j.zemedi.2018.08.003.

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42

Bánó, M., J. Marek, and M. Stupák. "Hydrodynamic parameters of hydrated macromolecules: Monte Carlo calculation." Phys. Chem. Chem. Phys. 6, no. 9 (2004): 2358–63. http://dx.doi.org/10.1039/b315620f.

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43

Keddy, R. J., and T. L. Nam. "A22 90Y synoviorthesis dosimetry: a Monte Carlo calculation." Nuclear Medicine Communications 25, no. 10 (2004): 1059–60. http://dx.doi.org/10.1097/00006231-200410000-00031.

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44

Ma, C.-M., R. A. Price, J. S. Li, et al. "Monitor unit calculation for Monte Carlo treatment planning." Physics in Medicine and Biology 49, no. 9 (2004): 1671–87. http://dx.doi.org/10.1088/0031-9155/49/9/006.

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45

Mitáš, Luboš. "Quantum Monte Carlo calculation of the Fe atom." Physical Review A 49, no. 6 (1994): 4411–14. http://dx.doi.org/10.1103/physreva.49.4411.

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46

Baer, Roi, and Daniel Neuhauser. "Communication: Monte Carlo calculation of the exchange energy." Journal of Chemical Physics 137, no. 5 (2012): 051103. http://dx.doi.org/10.1063/1.4743959.

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47

Zhao, Ying-Li, M. Mackenzie, C. Kirkby, and B. G. Fallone. "Monte Carlo calculation of helical tomotherapy dose delivery." Medical Physics 35, no. 8 (2008): 3491–500. http://dx.doi.org/10.1118/1.2948409.

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48

Zong, Fenghua, and D. M. Ceperley. "Path integral Monte Carlo calculation of electronic forces." Physical Review E 58, no. 4 (1998): 5123–30. http://dx.doi.org/10.1103/physreve.58.5123.

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49

Shigeta, Y., H. Nagao, and K. Yamaguchi. "Electronic structure calculation by monte carlo diagonalization method." International Journal of Quantum Chemistry 84, no. 5 (2001): 601–6. http://dx.doi.org/10.1002/qua.1414.

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50

Reggiani, Lino, Tilmann Kuhn, and Luca Varani. "Monte Carlo calculation of electronic noise in semiconductors." Computer Physics Communications 67, no. 1 (1991): 135–43. http://dx.doi.org/10.1016/0010-4655(91)90226-b.

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