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Probabilistic Methods (probabilistic + methods)

Distribution by Scientific Domains

Polymers and Materials Science	40%

Selected Abstracts

Statistical and Probabilistic Methods in Actuarial Science by Philip J. Boland

INTERNATIONAL STATISTICAL REVIEW, Issue 2 2007
N.H. Bingham
No abstract is available for this article. [source]

Dimensioning of secondary and tertiary control reserve by probabilistic methods

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 4 2009
Christoph Maurer
Abstract Given the rising share of intermittent generation out of renewable energy sources on the one hand and the increased regulatory efforts to lower transmission costs and tariffs on the other hand, the optimal dimensioning of necessary control reserve has gained additional importance during the last years. Grid codes like the UCTE Operation Handbook do not provide definitive and unambiguous methods for dimensioning of secondary and tertiary control reserves. This paper therefore presents a method which calculates the necessary control reserve considering all important drivers for power imbalances like power plant outages, load variations and forecast error. For dimensioning, a probabilistic criterion, the accepted probability of insufficient control reserve, is used. Probability density functions of control area imbalances are calculated using a convolution algorithm. This paper provides analyses for a stylised example system to demonstrate the capabilities of the method. In a sensitivity analysis the impact of drivers like plant failures and forecast errors of load and generation is shown. The presented method is used by transmission system operators and regulatory authorities to determine and substantiate the necessary amount of control reserve. Copyright © 2009 John Wiley & Sons, Ltd. [source]

PMAPS 2002 conference on probabilistic methods applied to power systems

EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 6 2003
Prof. Alfredo TestaArticle first published online: 22 MAR 200
No abstract is available for this article. [source]

Sensitivity analysis of creep crack growth prediction using the statistical distribution of uniaxial data

FATIGUE & FRACTURE OF ENGINEERING MATERIALS AND STRUCTURES, Issue 9 2010
M. YATOMI
ABSTRACT Due to the variables and unknowns in both material properties and predictive models in creep crack growth (CCG) rates, it is difficult to predict failure of a component precisely. A failure strain constraint based transient and steady state CCG model (called NSW) modified using probabilistic techniques, has been employed to predict CCG using uniaxial data as basic material property. In this paper the influence of scatter in the creep uniaxial properties, the parameter,C* and creep crack initiation and growth rate have been examined using probabilistic methods. Using uniaxial and CCG properties of C-Mn steel at 360 °C, a method is developed which takes into account the scatter of the data and its sensitivity to the correlating parameters employed. It is shown that for an improved prediction method in components containing cracks the NSW crack growth model employed would benefit from a probabilistic analysis. This should be performed by considering the experimental scatter in failure strain, the creep stress index and in estimating the,C* parameter. [source]

Theoretical Derivation of the Molecular Weight Distribution of End-Capped Linear Condensation Polymers

MACROMOLECULAR THEORY AND SIMULATIONS, Issue 1 2009
Henk Knoester
Abstract End-capped, low molecular weight polymers have found numerous practical applications. By providing the end-capper molecules with specific chemical functionality, the polymer material can be equipped with a desired chemical behavior for product application or polymer processing. Using probabilistic methods, formulas are derived for calculating the target molecular weight distribution and its averages for the case of linear condensation polymerization. The formulas are generally applicable, allowing for arbitrary amounts of monofunctional monomers or end-capper molecules affecting either one or both functional groups involved in the polymerization process. [source]