Extreme value statistics of flow-induced hydrofoil vibration and resonance

2021 ◽  
Vol 69 (1) ◽  
pp. 18-29
Author(s):  
Connor J. McCluskey ◽  
Manton J. Guers ◽  
Stephen C. Conlon
Keyword(s):  
Cyclic Loading ◽  
Maximum Load ◽  
Extreme Value ◽  
Flow Speed ◽  
Extreme Value Statistics ◽  
Extreme Value Analysis ◽  
Flow Induced Vibration ◽  
Data Set ◽  
Value Analysis ◽  
Extreme Response

Flow-induced noise and vibration produce cyclic loading on structures such as wind turbines, propellers, and vehicle control surfaces. This cyclic loading can produce fatigue damage in these structures. Additionally, large outlier loads can potentially exceed maximum design levels. Most other works have focused on the extreme value statistics of random loads, and there is limited work which considers the influence of structural resonances. The goal of this work was to study the influence of low order mode responses on extreme response statistics. To accomplish this, the flow-induced vibration response of cantilever fins forced by the wake of an upstream flow obstruction was measured in a closed-circuit water tunnel. The tunnel flow speed was increased, so the wake would excite the first bending mode. A maxima data set was determined from the measured response using the block maxima method, and the generalized extreme value (GEV) distribution was applied to each flow speed. Data were then filtered into stiffness-controlled and damping-controlled responses, and the extreme value analysis was repeated. Results indicated that the extreme response was influenced more by the damping-controlled response than the stiffness-controlled response. When excited, extreme responses from structural resonances must be considered in maximum load design.

On the Applicability of Extreme Value Statistics in the Prediction of Maximum Pit Depth in Heavily Corroded Non-Piggable Buried Pipelines

2010 ◽  
Author(s):  
L. Alfonso ◽  
F. Caleyo ◽  
J. M. Hallen ◽  
J. Araujo
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Extreme Value Analysis ◽  
Corrosion Condition ◽  
Value Analysis ◽  
Squared Error ◽  
Different Populations ◽  
Pit Depth ◽  
Selection Of

There exists a large number of works aimed at the application of Extreme Value Statistics to corrosion. However, there is a lack of studies devoted to the applicability of the Gumbel method to the prediction of maximum pitting-corrosion depth. This is especially true for works considering the typical pit densities and spatial patterns in long, underground pipelines. In the presence of spatial pit clustering, estimations could deteriorate, raising the need to increase the total inspection area in order to obtain the desired accuracy for the estimated maximum pit depth. In most practical situations, pit-depth samples collected along a pipeline belong to distinguishable groups, due to differences in corrosion environments. For example, it is quite probable that samples collected from the pipeline’s upper and lower external surfaces will differ and represent different pit populations. In that case, maximum pit-depth estimations should be made separately for these two quite different populations. Therefore, a good strategy to improve maximum pit-depth estimations is critically dependent upon a careful selection of the inspection area used for the extreme value analysis. The goal should be to obtain sampling sections that contain a pit population as homogenous as possible with regard to corrosion conditions. In this study, the aforementioned strategy is carefully tested by comparing extreme-value-oriented Monte Carlo simulations of maximum pit depth with the results of inline inspections. It was found that the variance to mean ratio, a measure of randomness, and the mean squared error of the maximum pit-depth estimations were considerably reduced, compared with the errors obtained for the entire pipeline area, when the inspection areas were selected based on corrosion-condition homogeneity.

scholarly journals Characteristics of Extreme Value Statistics of Annual Maximum Monthly Precipitation in East Asia Calculated Using an Earth System Model of Intermediate Complexity

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1273
Author(s):  
Tosiyuki Nakaegawa ◽  
Takuro Kobashi ◽  
Hirotaka Kamahori
Keyword(s):  
Time Series ◽  
Extreme Precipitation ◽  
Extreme Value ◽  
Nonstationary Time Series ◽  
Return Level ◽  
Extreme Value Statistics ◽  
Extreme Value Analysis ◽  
Monthly Precipitation ◽  
Annual Maximum ◽  
Value Analysis

Extreme precipitation is no longer stationary under a changing climate due to the increase in greenhouse gas emissions. Nonstationarity must be considered when realistically estimating the amount of extreme precipitation for future prevention and mitigation. Extreme precipitation with a certain return level is usually estimated using extreme value analysis under a stationary climate assumption without evidence. In this study, the characteristics of extreme value statistics of annual maximum monthly precipitation in East Asia were evaluated using a nonstationary historical climate simulation with an Earth system model of intermediate complexity, capable of long-term integration over 12,000 years (i.e., the Holocene). The climatological means of the annual maximum monthly precipitation for each 100-year interval had nonstationary time series, and the ratios of the largest annual maximum monthly precipitation to the climatological mean had nonstationary time series with large spike variations. The extreme value analysis revealed that the annual maximum monthly precipitation with a return level of 100 years estimated for each 100-year interval also presented a nonstationary time series which was normally distributed and not autocorrelated, even with the preceding and following 100-year interval (lag 1). Wavelet analysis of this time series showed that significant periodicity was only detected in confined areas of the time–frequency space.

Bias and Uncertainty in the Estimation of Extreme Wave Heights and Crests

2009 ◽  
Author(s):  
Richard Gibson ◽  
Colin Grant ◽  
George Z. Forristall ◽  
Rory Smyth ◽  
Peter Owrid ◽  
...  
Keyword(s):  
North Sea ◽  
Extreme Value ◽  
Accurate Prediction ◽  
Design Parameters ◽  
Extreme Value Analysis ◽  
Extreme Wave ◽  
Data Set ◽  
Value Analysis ◽  
Design Values ◽  
Wave Heights

The accurate prediction of extreme wave heights and crests is important to the design of offshore structures. For example, knowledge of the extreme crest elevation is required to set the deck elevation of the topside of a jacket structure. However, methods of extreme value analysis have an inherent bias, and the manner in which they are applied affects this bias. Furthermore, there is uncertainty in the design parameters at the time of design and the possibility that the predictions will change during the life of the structure. This paper is concerned with the accurate prediction of design values that incorporate uncertainty. In the first part of this paper the details of commonly applied extreme value analysis techniques are examined. This is achieved through analysis of simulated data of known distribution. In particular it is the application of least squares minimisation routines that is investigated; however, comparisons are made with maximum likelihood estimation. From this, preferred approaches to the analysis are recommended and their advantages and disadvantages discussed. The methods are applied to the analysis of a North Sea data set and the implications for the design values ascertained. In the second part of the paper Bayesian inference is used to consider the effect of uncertainty in the predicted wave heights and crest elevations. The practical implications are determined by the analysis of a measured North Sea data set.

Electro-Mechanical Admittance-Based Damage Detection Using Extreme Value Statistics

2008 ◽  
Vol 385-387 ◽  
pp. 561-564 ◽  
Author(s):  
Costas P. Providakis
Keyword(s):  
Health Monitoring ◽  
Extreme Values ◽  
Simulated Data ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Extreme Value Analysis ◽  
Engineering Structures ◽  
Value Analysis ◽  
Electromechanical Impedance ◽  
Tail Distribution

This paper presents the use of statistically rigorous algorithms combined with electromechanical (E/M) impedance approach for health monitoring of engineering structures. In particular, a statistical pattern recognition procedure is developed, based on frequency domain data of electromechanical impedance, to establish a decision boundary for damage identification. In order to diagnose damage with statistical confidence, health monitoring is cast in the context of outlier detection framework. Inappropriate modeling of tail distribution of outliers imposes potentially misleading behavior associated with damage. The present paper attempts to address the problem of establishing decision boundaries based on extreme value statistics so that the extreme values of outliers associated with tail distribution can be properly modeled. The validity of the proposed method is demonstrated using finite element method (FEM) simulated data while a comparison is performed for the extreme value analysis results contrasted with the standard approach where it is assumed that the damage-sensitive features are normally distributed.

scholarly journals Comparison of ice stress records in terms of extreme value analysis

2013 ◽  
Vol 54 (62) ◽  
pp. 291-298
Author(s):  
Mikko Lensu ◽  
Bruce C. Elder ◽  
Jackie Richter-Menge ◽  
Jari Haapala
Keyword(s):  
Stress State ◽  
Stress Level ◽  
Extreme Value ◽  
Arctic Sea Ice ◽  
Extreme Value Statistics ◽  
Extreme Value Analysis ◽  
Affine Scaling ◽  
Value Analysis ◽  
Pack Ice ◽  
Two Parameters

AbstractDynamic ice models use stress tensor to describe the forces arising from internal ice friction. The model stress values are typically one to two magnitudes smaller than values measured by stressmeters deployed on ice floes. The synthesis of the pack-ice stress state from the measurements has been complicated by the peaky character of stress records, and the means to connect them with spatial stress distribution of the floe system have been lacking. Here a reanalysis of Arctic Sea Ice Mechanics Initiative (SIMI) data is made in terms of extreme value statistics. The basic quantity is the maximum stress observed during a time period. The records exhibit self-affine scaling. The statistics are then determined by two parameters, the Hurst exponent H and a reference stress level. Similar analysis is possible for the kinematic data. This establishes the comparability of stress records with each other and with kinematic records. The results suggest that the exponent is related to the stress state of the regional floe system, while the stress level is determined by local floe characteristics. Based on this a characterization of spatial distribution of pack-ice stresses is given.

Extreme value analysis of wildfires in Canadian boreal forest ecosystems

2011 ◽  
Vol 41 (9) ◽  
pp. 1836-1851 ◽  
Author(s):  
Yueyang Jiang ◽  
Qianlai Zhuang
Keyword(s):  
Extreme Values ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Burned Area ◽  
Cumulative Probability ◽  
Large Fire ◽  
Data Set ◽  
Value Analysis ◽  
Gev Distribution ◽  
Large Fires

Large fires are a major disturbance in Canadian forests and exert significant effects on both the climate system and ecosystems. During the last century, extremely large fires accounted for the majority of Canadian burned area. By making an instaneous change over a vast area of ecosystems, extreme fires often have significant social, economic, and ecological consequences. Since extreme values of fire size always situate in the upper tail of a cumulative probability distribution, the mean and variance alone are not sufficient to fully characterize those extreme events. To characterize the large fire behaviors in the upper tail, the authors in this study applied three extreme value distribution functions: (i) the generalized extreme value (GEV) distribution, (ii) the generalized Pareto distribution (GPD), and (iii) the GEV distribution with a Poisson point process (PP) representation to fit the Canadian historical fire data of the period 1959–2010. The analysis was conducted with the whole data set and different portions of the data set according to ignition sources (lightning-caused or human-caused) and ecozone classification. It is found that (i) all three extreme statistical models perform well to characterize extreme fire events, but the GPD and PP models need extra care to fit the nonstationary fire data, (ii) anthropogenic and natural extreme fires have significantly different extreme statistics, and (iii) fires in different ecozones exhibit very different characteristics in the view of statistics. Further, estimated fire return levels are comparable with observations in terms of the magnitude and frequency of an extreme event. These statistics of extreme values provide valuable information for future quantification of large fire risks and forest management in the region.

Modeling European hot spells using extreme value analysis

2014 ◽  
Vol 58 (3) ◽  
pp. 193-207 ◽  
Author(s):  
C Photiadou ◽  
MR Jones ◽  
D Keellings ◽  
CF Dewes
Keyword(s):  
Extreme Value ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Hot Spells

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

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