Seider, Warren D.

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2005 - 200932010 - 20121
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  • Publication
    Coarse-grained lattice kinetic Monte Carlo simulation of systems of strongly interacting particles
    (2008-05-15) Seider, Warren D.; Dai, Jianguo; Sinno, Talid
    A general approach is presented for spatially coarse-graining lattice kinetic Monte Carlo (LKMC) simulations of systems containing strongly interacting particles. While previous work has relied on approximations that are valid in the limit of weak interactions, here we show that it is possible to compute coarse-grained transition rates for strongly interacting systems without a large computational burden. A two-dimensional square lattice is employed on which a collection of (supersaturated) strongly interacting particles is allowed to reversibly evolve into clusters. A detailed analysis is presented of the various approximations applied in LKMC coarse graining, and a number of numerical closure rules are contrasted and compared. In each case, the overall cluster size distribution and individual cluster structures are used to assess the accuracy of the coarse-graining approach. The resulting closure approach is shown to provide an excellent coarse-grained representation of the systems considered in this study.
  • Publication
    Operational risk assessment of chemical industries by exploiting accident databases
    (2007-03-01) Meel, A.; O'Neill, L. M; Levin, J. H; Seider, Warren D.; Oktem, U.; Karen, N.
    Accident databases (NRC, RMP, and others) contain records of incidents (e.g., releases and spills) that have occurred in the USA chemical plants during recent years. For various chemical industries, [Kleindorfer, P. R., Belke, J. C., Elliott, M. R., Lee, K., Lowe, R. A., & Feldman, H. I. (2003). Accident epidemiology and the US chemical industry: Accident history and worst-case data from RMP*Info. Risk Analysis, 23(5), 865–881.] summarize the accident frequencies and severities in the RMP*Info database. Also, [Anand, S., Keren, N., Tretter, M. J., Wang, Y., O’Connor, T. M., & Mannan, M. S. (2006). Harnessing data mining to explore incident databases, the Journal of Hazardous Material, 130, 33–41.] use data mining to analyze the NRC database for Harris County, Texas. Classical statistical approaches are ineffective for low frequency, high consequence events because of their rarity. Given this information limitation, this paper uses Bayesian theory to forecast incident frequencies, their relevant causes, equipment involved, and their consequences, in specific chemical plants. Systematic analyses of the databases also help to avoid future accidents, thereby reducing the risk. More specifically, this paper presents dynamic analyses of incidents in the NRC database. The NRC database is exploited to model the rate of occurrence of incidents in various chemical and petrochemical companies using Bayesian theory. Probability density distributions are formulated for their causes (e.g., equipment failures, operator errors, etc.), and associated equipment items utilized within a particular industry. Bayesian techniques provide posterior estimates of the cause and equipment-failure probabilities. Cross-validation techniques are used for checking the modeling, validation, and prediction accuracies. Differences in the plant- and chemical-specific predictions with the overall predictions are demonstrated. Furthermore, extreme value theory is used for consequence modeling of rare events by formulating distributions for events over a threshold value. Finally, the fast-Fourier transform is used to estimate the capital at risk within an industry utilizing the frequency and loss-severity distributions.
  • Publication
    Coarse-Grained Lattice Monte Carlo Simulations with Continuous Interaction Potentials
    (2012-08-16) Seider, Warren D.; Liu, Xiao; Sinno, Talid
    A coarse-grained lattice Metropolis Monte Carlo (CG-MMC) method is presented for simulating fluid systems described by standard molecular force fields. First, a thermodynamically consistent coarse-grained interaction potential is obtained numerically and automatically from a continuous force field such as Lennard-Jones. The coarse-grained potential then is used to driveCG-MMC simulations of vapor-liquid equilibrium in Lennard-Jones, square-well, and simple point chargewater systems. The CG-MMC predicts vapor-liquid phase envelopes, as well as the particle density distributions in both the liquid and vapor phases, in excellent agreement with full-resolution Monte Carlo simulations, at a fraction of the computational cost.
  • Publication
    On-lattice kinetic Monte Carlo simulations of point defect aggregation in entropically influenced crystalline systems
    (2005-10-01) Dai, Jianguo; Kanter, Joshua M; Seider, Warren D.; Kapur, Sumeet S; Sinno, Talid
    An on-lattice kinetic Monte Carlo model of vacancy aggregation in crystalline silicon is parametrized using direct regression to evolution data from nonequilibrium molecular dynamics simulations. The approach bypasses the need to manually compute an energy barrier for each possible transition and leads to an excellent, robust representation of the molecular dynamics data. We show that the resulting lattice kinetic Monte Carlo model correctly captures the behavior of the real, continuous space system by properly accounting for continuous space entropic effects, which are often neglected in lattice-based models of atomistic processes. These contributions are particularly important at the high temperatures relevant to many steps in semiconductor materials processing.