Estimation of Extreme Values by the Average Conditional Exceedance Rate Method

Næss, Arvid; Gaidai, Oleg; Karpa, Oleh

doi:10.1155/2013/797014

Cited by 29 publications

(8 citation statements)

References 42 publications

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“…The univariate concept of average conditional exceedance rate (ACER) is a method for estimation of extreme wind speed statistics proposed by [80] and by [81] that have given an extension for estimation of extreme wind speed statistics to the case of bivariate wind speed time series based on the previous research on the Monte Carlo methods for estimating the extreme response of dynamic systems [82,83]. Naess and Karpa, 2015 [81] calibrated and tested the bivariate method for simultaneous wind speed measurements from two separate locations.…”

Section: Analytical Models Based On the Acer Approachmentioning

confidence: 99%

Random Process Peaks Prediction: A Literature Overview

Giovanni¹,

D’Asdia²

2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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The prediction of the maximum or minimum peaks of a random process in the fields of civil and mechanical engineering is a hot topic for the scientific literature. It is known that the maximum of a random process depends on the process length. For this reason, a probabilistic approach is necessary to estimate a reliable value from the process. Researchers for specific case of studies proposed several analytical models to predict maxima of a random process. Mostly, they are grouped on two families, models for Gaussian processes and models for non-Gaussian processes. Each model was computed and calibrated based on a specific experimental campaign for a specific case of study. This makes difficult to select the best model for every application. In addition, codes and standards neglect this topic and one might happen to make the error to assume the maximum value as the statistical maximum of the random process. Commonly, in the field of the structural engineering, peaks of a wind induced, or wind-induced flow acceleration time histories are estimated following the probabilistic approach of Cook and Mayne. However, from 1950 to today hundreds different probabilistic approach was given by literature. The purpose of this paper is to discuss an overview of models grouped by theoretical underlying assumptions.

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Section: Analytical Models Based On the Acer Approachmentioning

confidence: 99%

Random Process Peaks Prediction: A Literature Overview

Giovanni¹,

D’Asdia²

2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…However, it has proven to be a suitable choice for the class of problems in this paper. In (Naess et al 2013) it is shown that the least squares optimization can be expressed as a weighted linear regression. Then the best choice of weight factor will be the inverse of the empirical variance for each value of (Montgomery et al 2001).…”

Section: Parametrizationmentioning

confidence: 99%

Reliability of Technical Systems Estimated by Enhanced Monte Carlo Simulation

Næss

2018

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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Computation of the reliability of large technical systems is usually a very di cult problem for realistic systems, and it is generally not possible to calculate the exact reliability. There are many techniques for approximate calculations, but they are often complicated and di cult to implement. In this paper the development of a new method based on Monte Carlo simulation for e cient calculation of system reliability is described. Standard Monte Carlo simulation forms a simple and robust alternative for calculating system reliability. If one can generate large samples, the law of large numbers ensures that the estimated reliability will be accurate as well. This may, however, be a very time consuming operation. The new method introduces a parametrized system that corresponds to the given system for a specific parameter value. By using regularity of the system reliability as a function of the introduced parameter, the system reliability for our original system can be predicted accurately from relatively small samples.

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“…Naess et al suggested regression based on a generalized extreme value distribution, that is,

ε_{k} ((), x) = q {[1 + a {((), x - b)}^{c}]}^{- γ} .

…”

Section: Extreme Reliability Analysismentioning

confidence: 99%

Reliability analysis of wind turbines under non‐Gaussian wind load

Shuang

Song

2017

Structural Design Tall Build

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Summary Based on translation models, both Gaussian and non‐Gaussian wind fields are generated using the harmony superposition method for examining the reliability of a typical wind turbine at operational and parked conditions. Using the blade aerodynamic model and multibody dynamics, wind turbine responses are calculated and then probability characteristics are analyzed in details. The short‐term extreme response distribution is estimated by the average conditional exceedance rate method at each mean wind speed bin, and the long‐term extreme response distribution is then determined by further integrating the short‐term extreme response distribution conditional on wind speed with the distribution of mean wind speed. Additionally, crack initiation life and crack propagation life are evaluated using the linear cumulative damage theory and linear crack propagation theory, respectively. The results indicate that non‐Gaussian characteristics of wind inflows have a noticeably greater influence on both extreme response and fatigue damage, and the Gaussian assumption cannot suit wind turbine in complex terrain.

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Estimation of Extreme Values by the Average Conditional Exceedance Rate Method

Cited by 29 publications

References 42 publications

Random Process Peaks Prediction: A Literature Overview

Random Process Peaks Prediction: A Literature Overview

Reliability of Technical Systems Estimated by Enhanced Monte Carlo Simulation

Reliability analysis of wind turbines under non‐Gaussian wind load

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