scholarly journals Research on Wind Load Calculation Based on Identical Guarantee Rate Method

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Tao Ye ◽  
Ledong Zhu ◽  
Zhongxu Tan ◽  
Lanlan Li
Keyword(s):  
Random Variable ◽  
Wind Load ◽  
Measured Data ◽  
Wind Pressure ◽  
Extreme Value Analysis ◽  
Variable Model ◽  
Systematic Analysis ◽  
Value Analysis ◽  
Numerical Calculation Method ◽  
Guarantee Rate

Wind load on building surface is one of the main loads for structural design; scholars in this field have put forward some methods to calculate wind load, such as Simiu method and Kasperski method. Based on the basic theory of probability and the systematic analysis of the surrounding environment and turbulence, a random variable model for calculating wind load is established. According to the model, through the analysis of the relationship between guarantee rate and wind load, a numerical calculation method to calculate wind load is proposed based on extreme value analysis and polynomial fitting theory. To verify the performance of the algorithm, wind tunnel experiments were carried out to obtain a large number of first-hand measured data of high-rise building (Shanghai World Financial Center). Based on the measured data, the algorithm is simulated, and calculated results are analyzed, including wind pressure distribution on building and probability distribution of fluctuating wind pressure of some measuring points. The validity and accuracy of the proposed model and algorithm are verified by the comparative analysis and theoretical analysis of the calculation results.

A New Generation of Earthquake Recurrence Models Based on The Extreme Value Theory and Impact on Probabilistic Seismic Hazard Assessments

2021 ◽  
Author(s):  
Anne Dutfoy ◽  
Gloria Senfaute
Keyword(s):  
Seismic Hazard ◽  
Extreme Value Theory ◽  
Generalized Pareto Distribution ◽  
Random Variable ◽  
Value Theory ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Probabilistic Seismic Hazard ◽  
Universal Method ◽  
Value Analysis

Abstract Probabilistic Seismic Hazard Analysis (PSHA) procedures require that at least the mean activity rate be known, as well as the distribution of magnitudes. Within the Gutenberg-Richter assumption, that distribution is an Exponential distribution, upperly truncated to a maximum possible magnitude denoted $m_{max}$. This parameter is often fixed from expert judgement under tectonics considerations, due to a lack of universal method. In this paper, we propose two innovative alternatives to the Gutenberg-Richter model, based on the Extreme Value Theory and that don't require to fix a priori the value of $m_{max}$: the first one models the tail distribution magnitudes with a Generalized Pareto Distribution; the second one is a variation on the usual Gutenberg-Richter model where $m_{max}$ is a random variable that follows a distribution defined from an extreme value analysis. We use the maximum likelihood estimators taking into account the unequal observation spans depending on magnitude, the incompleteness threshold of the catalog and the uncertainty in the magnitude value itself. We apply these new recurrence models on the data observed in the Alps region, in the south of France and we integrate them into a probabilistic seismic hazard calculation to evaluate their impact on the seismic hazard levels. The proposed new recurrence models introduce a reduction of the seismic hazard level compared to the common Gutenberg-Richter model conventionally used for PSHA calculations. This decrease is significant for all frequencies below 10 Hz, mainly at the lowest frequencies and for very long return periods. To our knowledge, both new models have never been used in a probabilistic seismic hazard calculation and constitute a new promising generation of recurrence models.

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

Wind Pressure and Wind-Induced Vibration of Heliostat

2008 ◽  
Vol 400-402 ◽  
pp. 935-940 ◽  
Author(s):  
Ying Ge Wang ◽  
Zheng Nong Li ◽  
Bo Gong ◽  
Qiu Sheng Li
Keyword(s):  
Dynamic Response ◽  
Wind Direction ◽  
Wind Load ◽  
Wind Pressure ◽  
Induced Response ◽  
Vertical Angle ◽  
Lateral Direction ◽  
Direction Angle ◽  
Wind Pressures ◽  
Fluctuating Wind

Heliostat is the key part of Solar Tower power station, which requires extremely high accuracy in use. But it’s sensitive to gust because of its light structure, so effect of wind load should be taken into account in design. Since structure of heliostat is unusual and different from common ones, experimental investigation on rigid heliostat model using technology of surface pressure mensuration to test 3-dimensional wind loads in wind tunnel was conducted. The paper illustrates distribution and characteristics of reflector’s mean and fluctuating wind pressure while wind direction angle varied from 0° to 180° and vertical angle varied from 0° to 90°. Moreover, a finite element model was constructed to perform calculation on wind-induced dynamic response. The results show that the wind load power spectral change rulers are influenced by longitudinal wind turbulence and vortex and are related with Strouhal number; the fluctuating wind pressures between face and back mainly appear positive correlation, and the correlation coefficients at longitudinal wind direction are smaller than those at lateral direction; the fluctuating wind pressures preferably agree with Gaussian distribution at smaller vertical angle and wind direction angle. The wind-induced response and its spectrums reveal that: when vertical angle is small, the background responsive values of reflector’s different parts are approximately similar; in addition, multi-phased resonant response occurring at the bottom. With the increase of , airflow separates at the near side and reunites at the other, as produces vortex which enhances dynamic response at the upper part.

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.

Spatial stochastic simulation to aid local extreme value analysis of cyclone-induced wave heights when numerical hydrodynamic simulations are scarce

2021 ◽  
Author(s):  
Jeremy Rohmer ◽  
Rodrigo Pedreros ◽  
Yann Krien
Keyword(s):  
Confidence Interval ◽  
Stochastic Simulation ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Value Analysis ◽  
Percentage Error ◽  
Value Analysis ◽  
Return Levels ◽  
Wave Heights ◽  
Local Extreme

<p>To estimate return levels of wave heights (Hs) induced by tropical cyclones at the coast, a commonly-used approach is to (1) randomly generate a large number of synthetic cyclone events (typically >1,000); (2) numerically simulate the corresponding Hs over the whole domain of interest; (3) extract the Hs values at the desired location at the coast and (4) perform the local extreme value analysis (EVA) to derive the corresponding return level. Step 2 is however very constraining because it often involves a numerical hydrodynamic simulator that can be prohibitive to run: this might limit the number of results to perform the local EVA (typically to several hundreds). In this communication, we propose a spatial stochastic simulation procedure to increase the database size of numerical results with synthetic maps of Hs that are stochastically generated. To do so, we propose to rely on a data-driven dimensionality-reduction method, either unsupervised (Principal Component Analysis) or supervised (Partial Least Squares Regression), that is trained with a limited number of pre-existing numerically simulated Hs maps. The procedure is applied to the Guadeloupe island and results are compared to the commonly-used approach applied to a large database of Hs values computed for nearly 2,000 synthetic cyclones (representative of 3,200 years – Krien et al., NHESS, 2015). When using only a hundred of cyclones, we show that the estimates of the 100-year return levels can be achieved with a mean absolute percentage error (derived from a bootstrap-based procedure) ranging between 5 and 15% around the coasts while keeping the width of the 95% confidence interval of the same order of magnitude than the one using the full database. Without synthetic Hs maps augmentation, the error and confidence interval width are both increased by nearly 100%. A careful attention is paid to the tuning of the approach by testing the sensitivity to the spatial domain size, the information loss due to data compression, and the number of cyclones. This study has been carried within the Carib-Coast INTERREG project (https://www.interreg-caraibes.fr/carib-coast).</p>

Investigating the occurrence, likelihood and regional variability of extreme ozone pollution episodes across the UK

2021 ◽  
Author(s):  
Lily Gouldsbrough ◽  
Ryan Hossaini ◽  
Emma Eastoe ◽  
Paul J. Young
Keyword(s):  
Surface Ozone ◽  
Extreme Value Analysis ◽  
Daily Maximum ◽  
Monitoring Site ◽  
Ozone Pollution ◽  
Threshold Method ◽  
Regional Variability ◽  
Value Analysis ◽  
The Uk ◽  
Pollution Episodes

<p>Warm summer temperatures provide ideal conditions for the occurrence of extreme ground level ozone pollution episodes. Given the well-established negative impacts of ozone on human and plant health, understanding and attributing these extreme events is of importance to the scientific and wider community, particularly as heatwaves may become more frequent due to climate change. Extreme Value Analysis provides a powerful and flexible framework in which to statistically model unusually large observed values of ozone extracted from historical data. Here, a temperature dependent Peaks-Over-Threshold method based upon the Generalised Pareto Distribution is used to carry out a regional comparison of extreme ozone pollution episodes within the UK. Our analysis uses surface ozone observations from the UK’s extensive Automatic Urban and Rural Network. The statistical model was used to quantify the frequency and magnitude of extreme ozone events, including a probabilistic assessment of exceeding UK public health thresholds, conditional on temperature. Return levels are provided for each monitoring site demonstrating the expected future projections of extreme ozone pollution events across the UK. We find that across UK rural background sites, return periods for a daily maximum 8-hr ozone level of 100 ug/m3 (a 'moderate' level of air pollution in the UK's Air Quality Index) range from 32-147 days, based on analysis of the data in the decade 2010-2019. Similarly, for urban background sites the range is 36-869 days. An analysis of the spatio temporal variability in UK ozone extremes, along with their temperature dependence, will be presented.</p>

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