Techniques for Efficient Monte Carlo Simulation. Volume I. Selecting Probability Distributions

McGrath, Elgie J.; Basin, S.L.; Burton, R W; Irving, D.C.; Jaquette, Stratton C.

doi:10.21236/ad0762721

Cited by 4 publications

(4 citation statements)

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“…Indeed, the Monte Carlo method is frequently formulated as a stochastic numerical model of physical phenomena, without attempting rigorous derivation of an appropriate 'transport equation' (Cashwell and Everett, 1957). There is, however, an extensive literature devoted to Monte Carlo which provides a sound theoretical basis (Carter and Cashwell, 1975;Hammersley and Handscomb, 1967;Kahn, 1956a;McGrath et al, 1975;Schreider, 1966;Spanier and Gelbard, 1969). Considering the complexity of current designs for fission reactors, fusion devices, and radiation shielding, a growing percentage of particle transport calculations requires detailed 3-dimensional analyses.…”

Section: The Monte Carlo Methods For Radiation Transport Analysismentioning

confidence: 99%

“…Numerous references detail techniques for random sampling from both discrete and continuous PDF's (McGrath et al, 1975;Kahn, 1956b).…”

Section: The Monte Carlo Methods For Radiation Transport Analysismentioning

confidence: 99%

“…Batchin 9 method for variance calculation. The variance of tallied scores is estimated in MCV and MCVMG via the batching method (McGrath et al, 1975;RSIC, 1977). That is, a problem is divided into independent batches of particles, and the variance is computed from the RMS deviation of batch scores from the overall mean.…”

Section: Tallying Considerationsmentioning

confidence: 99%

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Monte Carlo methods for radiation transport analysis on vector computers

Brown

Martin

1984

Progress in Nuclear Energy

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The development of advanced computers with special capabilities for vectorized or parallel calculations demands the development of new calculational methods. The very nature of the Monte Carlo process precludes direct conversion of old (scalar) codes to the new machines. Instead, major changes in global algorithms and careful selection of compatible physics treatments are required. Recent results for Monte Carlo in multigroup shielding applications and in continuous-energy reactor lattice analysis have demonstrated that Monte Carlo methods can be successfully vectorized. The significant effort required for stylized coding and major algorithmic changes is worthwhile, and significant gains in computational efficiency are realized. Speedups of at least twenty to forty times faster than CDC-7600 scalar calculations have been achieved on the CYBER-205 without sacrificing the accuracy of standard Monte Carlo methods. Speedups of this magnitude provide reductions in statistical uncertainties for a given amount of computing time, permit more detailed and realistic problems to be analyzed, and make the Monte Carlo method more accessible to nuclear analysts. Following overviews of the Monte Carlo method for particle transport analysis and of vector computer hardware and software characteristics, both general and specific aspects of the vectorization of Monte Carlo are discussed. Finally, numerical results obtained from vectorized Monte Carlo codes run on the CYBER-205 are presented.

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Section: The Monte Carlo Methods For Radiation Transport Analysismentioning

confidence: 99%

“…Numerous references detail techniques for random sampling from both discrete and continuous PDF's (McGrath et al, 1975;Kahn, 1956b).…”

Section: The Monte Carlo Methods For Radiation Transport Analysismentioning

confidence: 99%

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Monte Carlo methods for radiation transport analysis on vector computers

Brown

Martin

1984

Progress in Nuclear Energy

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“…A escolha de uma distribuição probabilística apropriada para uma amostra de variáveis em uma simulação Monte Carlo deve incluir a investigação do processo ou do evento, dos dados representativos da variabilidade, da sensibilidade do processo e dos programas de simulação considerados (MCGRATH et al, 1973).…”

Section: Métodos Para Avaliação De Incerteza De Cenáriounclassified

Incertezas de cenário no inventário de avaliação de ciclo de vida de edificações

Guimarães¹

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Aos meus pais, pelo apoio, suporte, torcida e empurrões amorosos ao longo do caminho. Sem vocês, não teria chegado até aqui.Ao Vinícius, pela parceria e amor, sempre me fazendo rir nos momentos que fiquei para baixo e me dando a mão quando precisava continuar.Ao meu irmão, Murilo, pelo carinho e tranquilidade de todos os momentosperto ou longe.Aos meus amigos e familiares, por compreenderem minha constante ausência e por me enviarem energias positivas de tantos lugares diferentes.A minha família de São Paulo, que acompanhou de perto essa jornada, sempre oferecendo carinho, suporte, moradia e pizzas.

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The effect of uncertainties in rate coefficients on densities ofEregion species

Myers¹,

Hamlin²,

Schoonover³

et al. 1974

J. Geophys. Res.

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A direct statistical method has been applied to determine the effect of uncertainties in rate coefficients on computed densities of E region species. Uncertainties in rate coefficients are described in terms of Gaussian error law distributions with a common fractional standard deviation. Mean densities are found to be relatively insensitive to rate coefficient uncertainties; fractional standard deviations of density distributions are, in general, less than twice those associated with rate coefficient distributions, provided the fractional standard deviations of the rate coefficient distributions are not greater than 0.45. Many reactionsare necessary to describe chemical proces•ses in the atmosphere. In such chemical systems it is difficult to evaluate inaccuracies in the computed species densities. These inaccuracies stem from uncertainties in the input data and from omissions or approximations of relevant physical and chemical processes. One of the uncertainties, that associated with reaction rate coefficients, is treated here by a direct statistical method for the case of an E region chemical reaction system operating over short time intervals. The calculations presented here are meant to be illustrative rather than exhaustive. Sufficient information about the errors and the distribution of errors associated with measurements of rate coefficients is not generally available. Accordingly, in the calculations described here, uncertainties in the rate coefficients were assumed to be describable in terms of the Gaussian error law with a mean value of the rate coefficient ko and a standard deviation s; the effects of variations in s on species densities and their distributions were examined. For simplicity a common fractional standard deviation was assigned to the rate coeffici6nt distributions. Consider a system consisting of n reactions and corres. ponding rate coefficients; let m(

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Techniques for Efficient Monte Carlo Simulation. Volume I. Selecting Probability Distributions

Abstract: I«. DEVOnT tICURITV CkAttiriCATION UNCLASSIFIED lb. OKOUP > mtpomr TITLI Techniques for Efficient Monte Carlo Simulation Volume I: Selecting Probability Distributions 4. 01 I (Trp* el ttpon end Inelintwe dele») Final Report s *uTHO"(tJ (Plfl naaw. middle Inlilel, leti nmrn»

Cited by 4 publications

References 10 publications

Monte Carlo methods for radiation transport analysis on vector computers

Monte Carlo methods for radiation transport analysis on vector computers

Incertezas de cenário no inventário de avaliação de ciclo de vida de edificações

The effect of uncertainties in rate coefficients on densities ofEregion species

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