A Comparison of Wind Turbine Design Loads in Different Environments Using Inverse Reliability Techniques

2004 ◽  
Vol 126 (4) ◽  
pp. 1060-1068 ◽  
Author(s):  
Korn Saranyasoontorn ◽  
Lance Manuel

The influence of turbulence conditions on the design loads for wind turbines is investigated by using inverse reliability techniques. Alternative modeling assumptions for randomness in the gross wind environment and in the extreme response given wind conditions to establish nominal design loads are studied. Accuracy in design load predictions based on use of the inverse first-order reliability method (that assumes a linearized limit state surface) is also investigated. An example is presented where three alternative nominal load definitions are used to estimate extreme flapwise bending loads at a blade root for a 600 kW three-bladed, stall-regulated horizontal-axis wind turbine located at onshore and offshore sites that were assumed to experience the same mean wind speed but different turbulence intensities. It is found that second-order (curvature-type) corrections to the linearized limit state function assumption inherent in the inverse first-order reliability approach are insignificant. Thus, we suggest that the inverse first-order reliability method is an efficient and accurate technique of predicting extreme loads. Design loads derived from a full random characterization of wind conditions as well as short-term maximum response (given wind conditions) may be approximated reasonably well by simpler models that include only the randomness in the wind environment but account for response variability by employing appropriately derived “higher-than-median” fractiles of the extreme bending loads conditional on specified inflow parameters. In the various results discussed, it is found that the higher relative turbulence at the onshore site leads to larger blade bending design loads there than at the offshore site. Also, for both onshore and offshore environments accounting for response variability is found to be slightly more important at longer return periods (i.e., safer designs).

Author(s):  
Umberto Alibrandi ◽  
C. G. Koh

This paper presents a novel procedure based on first-order reliability method (FORM) for structural reliability analysis in the presence of random parameters and interval uncertain parameters. In the proposed formulation, the hybrid problem is reduced to standard reliability problems, where the limit state functions are defined only in terms of the random variables. Monte Carlo simulation (MCS) for hybrid reliability analysis (HRA) is presented, and it is shown that it requires a tremendous computational effort; FORM for HRA is more efficient but still demanding. The computational cost is significantly reduced through a simplified procedure, which gives good approximations of the design points, by requiring only three classical FORMs and one interval analysis (IA), developed herein through an optimization procedure. FORM for HRA and its simplified formulation achieve a much improved efficiency than MCS by several orders of magnitude, and it can thus be applied to real-world engineering problems. Representative examples of stochastic dynamic analysis and performance-based engineering are presented.


2003 ◽  
Vol 40 (6) ◽  
pp. 1235-1244 ◽  
Author(s):  
Anthony TC Goh ◽  
Fred H Kulhawy

Structural reliability methods are often used to evaluate the failure performance of geotechnical structures. A common approach is to use the first-order reliability method. Its popularity results from the mathematical simplicity of the method, since only second moment information (mean and coefficient of variation) on the random variables is required. The probability of failure is then assessed by an index known commonly as the reliability index. One critical aspect in determining the reliability index is the explicit definition of the limit state surface of the system. In a problem involving multi-dimensional random variables, the limit state surface is the boundary separating the safe domain from the "failure" (or lack of serviceability) domain. In many complicated and nonlinear problems where the analyses involve the use of numerical procedures such as the finite element method, this surface may be difficult to determine explicitly in terms of the random variables, and therefore the limit state can only be expressed implicitly rather than in a closed-form solution. It is proposed in this paper to use an artificial intelligence technique known as the back-propagation neural network algorithm to model the limit state surface. First, the failure domain is found through repeated point-by-point numerical analyses with different input values. The neural network is then trained on this set of data. Using the optimal weights of the neural network connections, it is possible to develop a mathematical expression relating the input and output variables that approximates the limit state surface. Some examples are given to illustrate the application and accuracy of the proposed approach.Key words: first-order reliability method, geotechnical structures, limit state surface, neural networks, reliability.


2014 ◽  
Vol 945-949 ◽  
pp. 1159-1162
Author(s):  
Wei Tao Zhao ◽  
Xiao Li ◽  
Feng Guo

Reliability of laminate structure is deeply influenced by uncertainties such as fiber properties, loads and design sizes. It is very difficult to evaluate the reliability and sensitivity of laminate structure because that laminate structure is anisotropic and the limit state function (LSF) is a high nonlinear function. In this paper, reliability and sensitivity are evaluated by using first order reliability method (FORM), response surface method (RSM) and Monte Carlo simulation (MCS). The study aims to find a numerical method to evaluate the reliability and sensitivity of laminate structures efficiently and accurately. An example of laminate with a large number of variables is analyzed. The results obtained by using different methods are compared in terms of efficiency and accuracy. It is shown that FORM is not accuracy, and RSM has a very good accuracy and efficient in terms of reliability, but the accuracy of sensitivity obtained by using RSM is not good enough.


2017 ◽  
Vol 42 (1) ◽  
pp. 51-65 ◽  
Author(s):  
Abhinav Sultania ◽  
Lance Manuel

The reliability analysis of a spar-supported floating offshore 5-MW wind turbine is the subject of this study. Environmental data from a selected site are employed in the numerical studies. Using time-domain simulations, the dynamic behavior of a coupled platform-turbine system is studied; statistics of tower and rotor loads as well as platform motions are estimated and critical combinations of wind speed and wave height identified. Long-term loads associated with a 50-year return period are estimated using statistical extrapolation based on loads derived from simulations. Inverse reliability procedures that seek appropriate fractile levels for underlying variables consistent with the target load return period are employed; these include use of (1) two-dimensional inverse first-order reliability method where extreme loads, conditional on wind speed and wave height random variables, are selected at median levels and (2) three-dimensional inverse first-order reliability method where variability in the environmental and load random variables is fully represented.


Buildings ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 119 ◽  
Author(s):  
Marin Grubišić ◽  
Jelena Ivošević ◽  
Ante Grubišić

Since the prediction of the seismic response of structures is highly uncertain, the need for the probabilistic approach is clear, especially for the estimation of critical seismic response parameters. Considering the uncertainties present in the material and geometric form of reinforced concrete (RC) structures, reliability analyses using the Finite Element Method (FEM) were performed in the context of Performance-Based Earthquake Engineering (PBEE). This study presented and compared the possibilities of nonlinear modelling of the reinforced concrete (RC) planar frame and its reliability analysis using different numerical methods, Mean-Value First-Order Second-Moment (MVFOSM), First-Order Reliability Method (FORM), Second-Order Reliability Method (SORM) and Monte Carlo simulation (MCS). The calibrated numerical models used were based on the previous experimental test of a planar RC frame subjected to cyclic horizontal load. Numerical models were upgraded by random variable (RV) parameters for reliability analysis purposes and, using implicit limit state function (LSF), pushover analyses were performed by controlling the horizontal inter-storey drift ratio (IDR). Reliability results were found to be sensitive to the reliability analysis method. The results of reliability analysis reveal that, in a nonlinear region, after exceeding the yield strength of the longitudinal reinforcement, the cross-sectional geometry parameters were of greater importance compared to the parameters of the material characteristics. The results also show that epistemic (knowledge-based) uncertainties significantly affected dispersion and on the median estimate parameter response. The MCS sampling method is recommended, but the First-Order Reliability Method (FORM) applied on a response model can be used with good accuracy. Reliability analysis using the FEM proved to be suitable for the direct implementation of geometric and material nonlinearities to cover epistemic (knowledge-based) uncertainties.


2021 ◽  
Vol 11 (2) ◽  
pp. 648
Author(s):  
Agnieszka Dudzik ◽  
Beata Potrzeszcz-Sut

The objective of the article involves presenting two approaches to the structure reliability analysis. The primary research method was the First Order Reliability Method (FORM). The Hasofer–Lind reliability index β in conjunction with transformation method in the FORM was adopted as the measure of reliability. The first proposal was combining NUMPRESS software with the non-commercial KRATA program. In this case, the implicit form of the random variables function was created. Limit state function was symbolically given in the standard math notation as a function of the basic random and external variables. The second analysis proposed a hybrid approach enabling the introduction of explicit forms of limit state functions to the reliability program. To create the descriptions of this formula, the neural networks were used and our own original FEM module. The combination of conventional and neural computing can be seen as a hybrid system. The explicit functions were implemented into NUMPRESS software. The values of the reliability index for different descriptions of the mathematical model of the structure were determined. The proposed hybrid approach allowed us to obtain similar results to the results from the reference method.


Author(s):  
Caio Cesar Cardoso da Silva ◽  
Mauro de Vasconcellos Real ◽  
Samir Maghous

abstract: The Monte Carlo simulation (MCS) and First-Order Reliability Method (FORM) provide a reliability analysis in axisymmetric deep tunnels driven in elastoplastic rocks. The Convergence-Confinement method (CV-CF) and Mohr-Coulomb (M-C) criterion are used to model the mechanical interaction between the shotcrete lining and ground through deterministic parameters and random variables. Numerical models synchronize tunnel analytical models and reliability methods, whereas the limit state functions control the failure probability in both ground plastic zone and shotcrete lining. The results showed that a low dispersion of random variables affects the plastic zone's reliability analysis in unsupported tunnels. Moreover, the support pressure generates a significant reduction in the plastic zone's failure, whereas the increase of shotcrete thickness results in great reduction of the lining collapse probability.


Author(s):  
Sheng-Tong Zhou ◽  
Di Wang ◽  
Qian Xiao ◽  
Jian-min Zhou ◽  
Hong-Guang Li ◽  
...  

Hasofer-Lind and Rackwtiz-Fiessler (HLRF) method is an efficient iterative algorithm for locating the most probable failure point and calculating the first order reliability index in structural reliability analysis. However, this method may encounter numerical instability problems for high nonlinear limit state function (LSF). In this paper, an improved HLRF-based first order reliability method is developed based on a modified Armijo line search rule and an interpolation-based step size backtracking scheme to improve the robustness and efficiency of the original HLRF method. Compared with other improved HLRF-based methods, the proposed method can not only guarantee the global convergence but also adaptively estimate some sensitive algorithm parameters, such as initial step size, step-size reduction coefficient, using the current known iterative information. Ten selected examples with high nonlinear LSFs are used to compare the robustness and efficiency of the proposed method with the original HLRF method and the improved HLRF (iHLRF) method. Results indicate that the proposed method is not only more computationally efficient but also less sensitive to the remaining user-defined algorithm parameters than the iHLRF method.


Author(s):  
Z Yanfang ◽  
Z Yanlin ◽  
Z Yimin

A new efficient and accurate method for reliability sensitivity analysis of mechanical components is proposed based on the first-order reliability method (FORM). This method provides very close approximate solutions for strongly non-linear limit state functions with independent normal random variables. But most probable point should be searched at first. Three methods are presented and investigated for reliability-based sensitivity analysis. They are, respectively, based on three different reliability analysis methods which are mean-value first-order reliability method, Monte Carlo method, and FORM. Numerical results of the three methods are calculated out by three examples. The accuracy and efficiency of the new method are demonstrated by the comparison of the numerical results.


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