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      저자 : J. M. MCWILLIAMS (검색결과 2 건)
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      • Effect of Supersonic Ion Beams on the Triple Probe Measurement

        Woo, H.-J.,Chung, K.-S.,Choi, Y.-S.,Lee, M.-J.,Zimmerman, D.,McWilliams, R. WILEY-VCH Verlag 2006 Contributions to plasma physics Vol.46 No.5

        <P>Although theory of the triple probe for Maxwellian plasma is well known, analysis with ion beams has not been established. Since plasma parameters, especially electron temperature, are distorted severely in the presence of supersonic ion beams, a new model for deducing electron temperature is introduced by solving the governing equations of the triple probe including the beam currents to three probe tips through simple assumptions. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)</P>

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        BAYESIAN KRIGING REGRESSION FOR THE ACCURACY IMPROVEMENT OF BEAM MODELED T-JUNCTIONS OF BUSES AND COACHES STRUCTURES WITH A METHODOLOGY BASED ON FEM BEHAVIORAL ANALYSIS

        F. B. ROMERO,J. M. MCWILLIAMS,E. A. FAZIO,F. A. IZQUIERDO 한국자동차공학회 2014 International journal of automotive technology Vol.15 No.6

        This paper has been developed in the framework of the alternative beam T-junction solution previouslypropoused by the authors (Alcalá et al., 2013), with the scope of optimizing the behavior of buses and coaches upper structuresmodeled with beam type elements. The alternative beam T-junction model proposed by the authors, had a total of six elasticelements at the junction level allowing to modify the localized rigidity of any modeled T-junctions, therefore improving theirbehavior and avoiding the well known rigidity issue these elements have. A fundamental aspect behind the use of thesealternative beam models is related to the necessity of correctly estimating the rigidity values of the elastic elements for eachmodeled T-junction. In this context, we propose applying a surrogate model for the reference calculations which is based onstatistical Bayesian kriging predictors. Statistical predictions have the added value, with respect to deterministic solutions, ofproviding a quantification of uncertainty. The development of the kriging predictors has required the application of a statisticalmethodology including computer experiment design, computation of moments of inertia, graphical and ANOVA typesensitivity analysis and Monte Carlo computation of Bayesian inference. The results of the application have been a verysatisfactory trade-off between accuracy of approximation (prediction) and computational cost. The surrogate kriging modelsalso provide an useful tool for a better understanding of the input-output relationships involved in the reference computations.

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