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Izadparast A.H.,SOFEC Inc. | Niedzwecki J.M.,Texas A&M University
Probabilistic Engineering Mechanics | Year: 2013

The use of multi-parameter distribution functions that incorporate empirically derived parameters to more accurately capture the nature of data being studied is investigated. Improving the accuracy of these models is especially important for predicting the extreme values of the non-linear random variables. This study was motivated by problems commonly encountered in the design of offshore systems where the accurate modeling of the distribution tail is of significant importance. A four-parameter Weibull probability distribution model whose structural form is developed using a quadratic transformation of linear random variables is presented. The parameters of the distribution model are derived using the method of linear moments. For comparison, the model parameters are also derived using the more conventional method of moments. To illustrate the behavior of these models, laboratory data measuring the time series of wave run-up on a vertical column of a TLP structure and wave crests interacting in close proximity with an offshore platform are utilized. Comparisons of the extremal predictions using the four-parameter Weibull model and the three-parameter Rayleigh model verify the ability of the new formulation to better capture the tail of the sample distributions. © 2013 Elsevier Ltd. Source


Izadparast A.H.,SOFEC Inc. | Niedzwecki J.M.,Texas A&M University
International Journal of Offshore and Polar Engineering | Year: 2012

The Rayleigh-Stokes model has been widely applied to represent the probability distribution function of crests and troughs of weakly nonlinear random processes. In this study, the parameter estimates for the 3-parameter Rayleigh-Stokes probability distribution function model are obtained from application of 2 moment-based empirical parameter estimation methods, i.e. conventional method of moments and method of linear moments. Monte Carlo simulations are utilized to compare the performance of these parameter estimation approaches in estimating the parameters of the Rayleigh-Stokes distribution, and also to evaluate the uncertainty of the extreme statistics. Additionally, the effect of sample size on the uncertainty of the model statistics is evaluated. Finally, the Rayleigh-Stokes model is utilized to estimate the probability distribution function of disturbed wave crests beneath a mini-TLP, and the model performance is evaluated. © by The International Society of Offshore and Polar Engineers. Source


Bai B.,SOFEC Inc. | Sammes N.M.,Colorado School of Mines | Smirnova A.L.,University of Connecticut | Tompsett G.,University of Massachusetts Amherst
Journal of Fuel Cell Science and Technology | Year: 2010

Bi2O3 doped scandia stabilized zirconia systems have shown promise for use as electrolytes in intermediate temperature solid oxide fuel cells (IT-SOFC's). Sintering properties, crystal phase transformation, and electrical conductivity of the Bi2O3 doped Sc 2O3-ZrO2 systems were investigated. The effect of Bi2O3 doping from 0 mol % to 2.0 mol % and different sintering temperatures on the properties and performance of the electrolyte were examined. The presence of Bi2O3 aided the sintering process and better sintering for the doped system was achieved at lower temperatures. The cubic phase was successfully stabilized at room temperature with concentrations of 1 mol % and 2 mol % Bi2O3 sintered at 1100-1400°C. The achievement of a cubic structure depends on both the Bi2O3 concentration and the sintering temperature. Higher electrical conductivity was achieved with Bi2O3 doped Sc2O3-ZrO2 systems than 10ScSZ below 600 °C. A maximum conductivity of 1.68 X 10-2 SI cm at 700° C was obtained for 2 mol % Bi2O3 doped sample sintered at 1100°C. Copyright © 2010 by ASME. Source


Izadparast A.H.,SOFEC Inc. | Niedzwecki J.M.,Texas A&M University
Proceedings of the International Offshore and Polar Engineering Conference | Year: 2011

The Rayleigh-Stokes model has been widely applied to represent the probability distribution function of crests and troughs of weakly nonlinear random processes. In this study, the parameter estimates for the three-parameter Rayleigh-Stokes probability distribution function are obtained from application of two moment-based empirical parameter estimation methods, i.e. conventional method of moments and method of linear moments. Monte-Carlo simulations are utilized to compare the performance of these parameter estimation approaches in estimating the parameters of the Rayleigh-Stokes distribution and also to evaluate the uncertainty of the extreme statistics. Additionally, the effect of sample size on the uncertainty of the model statistics is evaluated. Finally, the Rayleigh-Stokes model is utilized to estimate the probability distribution function of disturbed wave crests beneath a mini-TLP and the model performance is evaluated. Copyright © 2011 by the International Society of Offshore and Polar Engineers (ISOPE). Source


Izadparast A.H.,SOFEC Inc. | Niedzwecki J.M.,Texas A&M University
American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM | Year: 2012

Ocean engineers are routinely faced with design problems for coastal and deepwater structures that must survive a wide range of environmental conditions. One of the most challenging problems in the field of ocean engineering is the accurate characterization and modeling of the interaction of ocean waves with these offshore structures. The random characteristic of ocean environment requires engineers to consider the effects of random variability of the pertinent variables in their predictive models and design processes. Thus, for ocean engineering purposes, one needs to have accurate estimates of the probability distribution of the key random variables that will be used in sensitivity studies, reliability analysis, and risk assessment in the design process. In this study, a family of semi-empirical probability distribution is developed based on the quadratic transformation of linear random variable assuming that the linear random variable follows a Rayleigh distribution law. The estimates of model parameters are obtained from two moment based parameter estimation methods, i.e. method of moments and method of linear moments. The studied semi-empirical distribution can be applied to estimate the probability distribution of a wide range of non-linear random variables in the fields of ocean wave mechanics and wave-structure interaction. As examples, the application of the semi-empirical model in estimation of probability distribution of: a) ocean wave power, b) ocean wave crests interacting with an offshore structure is illustrated. For this purpose, numerically generated timeseries and experimentally measured data sets are utilized. Copyright © 2012 by ASME. Source

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