Application of Orthogonal Experimental Design on Reliability and Sensitivity Analysis

2011 ◽  
Vol 211-212 ◽  
pp. 651-655 ◽  
Author(s):  
Qin Shu He ◽  
Xi Nen Liu ◽  
Shi Fu Xiao
Keyword(s):  
Sensitivity Analysis ◽  
Experimental Design ◽  
Structural Parameters ◽  
Orthogonal Experimental Design ◽  
Random Input ◽  
Range Analysis ◽  
First Order ◽  
Reliability Method ◽  
Structural Responses ◽  
Input Variables

In the present paper, the effects of four structural parameters at three levels on the reliability and sensitivity of structure are investigated. Sensitivity of parameters is achieved by the range analysis and the significance of parameters is achieved by the variance analysis. A response surface based on orthogonal experimental design and finite element calculations is elaborated so that the relation between the random input variables and structural responses could be established. The First-Order Reliability Method (FORM) as an approximated method is used here to assess the reliability. Comparing with the results of Monte Carlo simulations by ANSYS for a numerical example, the effect of sensitivity analysis has been proved, while the precision of the reliability and sensitivity should be improved in the future.

scholarly journals Use of the correlation between input variables in estimating the risk of feedlot finishing of steers and young steers

2014 ◽  
Vol 86 (2) ◽  
pp. 945-954 ◽  
Author(s):  
PAULO S. PACHECO ◽  
JOÃO RESTLE ◽  
LEONIR L. PASCOAL ◽  
FABIANO N. VAZ ◽  
RICARDO Z. VAZ ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Stochastic Dominance ◽  
Net Present Value ◽  
Latin Hypercube Sampling ◽  
Risk Estimate ◽  
Present Value ◽  
Random Input ◽  
Financial Indicator ◽  
Input Variables ◽  
Diet Intake

The objective of this study was to evaluate the risk of feedlot finishing of steers (22.8 months) and young steers (15.2 months), using or not a correlation between the random input variables (data collected from 2004 to 2010) in the simulation of the Net Present Value (NPV) financial indicator. The animals were fed a diet containing roughage:concentrate ratio of 60:40 for 34 and 143 days, respectively, until they had reached a predetermined slaughter weight of 430 kg. For the NPV simulation, Latin Hypercube sampling was used, with 2000 interactions. The stochastic dominance analysis, test of differences between pairs of curves of cumulative distributions and sensitivity analysis were carried out. The NPV simulation using the correlation resulted in the best option for risk estimate. The confinement of young steers was the alternative of investment most viable than confinement of steers (NPV ≥ 0 of 80.4 vs. 62.3% in the simulation with correlation, respectively). Sensitivity analysis determined the following items had the greatest impact on the estimate of NPV: prices of fat and thin cattle, initial and final weights, diet costs, minimum rate of attractiveness and diet intake.

scholarly journals Uncertainty and sensitivity analysis techniques for hydrologic modeling

2009 ◽  
Vol 11 (3-4) ◽  
pp. 282-296 ◽  
Author(s):  
Srikanta Mishra
Keyword(s):  
Sensitivity Analysis ◽  
Classification Tree ◽  
Sensitivity Analyses ◽  
Tree Analysis ◽  
First Order ◽  
Analysis Techniques ◽  
Point Estimate Method ◽  
Uncertainty And Sensitivity Analysis ◽  
Reliability Method ◽  
Model Predictions

Formal uncertainty and sensitivity analysis techniques enable hydrologic modelers to quantify the range of likely outcomes, likelihood of each outcome and an assessment of key contributors to output uncertainty. Such information is an improvement over standard deterministic point estimates for making engineering decisions under uncertainty. This paper provides an overview of various uncertainty analysis techniques that permit mapping model input uncertainty into uncertainty in model predictions. These include Monte Carlo simulation, first-order second-moment analysis, point estimate method, logic tree analysis and first-order reliability method. Also presented is an overview of sensitivity analysis techniques that permit identification of those parameters that control the uncertainty in model predictions. These include stepwise regression, mutual information (entropy) analysis and classification tree analysis. Two case studies are presented to demonstrate the practical applicability of these techniques. The paper also discusses a systematic framework for carrying out uncertainty and sensitivity analyses.

scholarly journals PROBABILISTIC ANALYSIS OP SEAFLOOR LIQUEFACTION

1988 ◽  
Vol 1 (21) ◽  
pp. 101 ◽  
Author(s):  
Rafael Blazquez ◽  
Felipe M. Martinez
Keyword(s):  
Soil Layer ◽  
Water System ◽  
Deterministic Models ◽  
Risk Models ◽  
First Order ◽  
Sensitivity Indices ◽  
Reliability Method ◽  
Input Variables ◽  
Relationship Of ◽  
The Relationship

To investigate the reliability of a sandy soil layer in an ocean wave environment a liquefaction model is used in conjunction with a first order reliability method. Thus, sensitivity indices of the soil-water system with respect to the uncertain strength and input variables are computed, and the relative importance of the various factors defining the problem can be determined. The relationship of this approach with more conventional design methods (deterministic models, risk models) is discussed along with the range of applicability of the different safety measurements.

scholarly journals New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2425
Author(s):  
Zdeněk Kala
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Binary Outcome ◽  
Global Sensitivity ◽  
First Order ◽  
Sensitivity Measure ◽  
Sensitivity Indices ◽  
Alternative Measures ◽  
Input Variables ◽  
Dome Shape

This article presents new sensitivity measures in reliability-oriented global sensitivity analysis. The obtained results show that the contrast and the newly proposed sensitivity measures (entropy and two others) effectively describe the influence of input random variables on the probability of failure Pf. The contrast sensitivity measure builds on Sobol, using the variance of the binary outcome as either a success (0) or a failure (1). In Bernoulli distribution, variance Pf(1 − Pf) and discrete entropy—Pfln(Pf) − (1 − Pf)ln(1 − Pf) are similar to dome functions. By replacing the variance with discrete entropy, a new alternative sensitivity measure is obtained, and then two additional new alternative measures are derived. It is shown that the desired property of all the measures is a dome shape; the rise is not important. Although the decomposition of sensitivity indices with alternative measures is not proven, the case studies suggest a rationale structure of all the indices in the sensitivity analysis of small Pf. The sensitivity ranking of input variables based on the total indices is approximately the same, but the proportions of the first-order and the higher-order indices are very different. Discrete entropy gives significantly higher proportions of first-order sensitivity indices than the other sensitivity measures, presenting entropy as an interesting new sensitivity measure of engineering reliability.

A Reliability Approach to Inverse Simulation Under Uncertainty

2012 ◽  
Author(s):  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
High Efficiency ◽  
Probability Distributions ◽  
Random Parameters ◽  
Inverse Simulation ◽  
First Order ◽  
Reliability Method ◽  
Simulation Input ◽  
Input Variables ◽  
Impact Problem

Inverse simulation is an inverse process of a direct simulation. During the process, the simulation input variables are identified for a given set of simulation output variables. Uncertainties such as random parameters may exist in engineering applications of inverse simulation. A reliability method is developed in this work to estimate the probability distributions of unknown simulation input. The First Order Reliability Method is employed and modified so that the inverse simulation is embedded within the reliability analysis algorithm. This treatment avoids the separate executions of reliability analysis and inverse simulation and consequently maintains high efficiency. In addition, the means and standard deviations of unknown input variables can also be obtained. A particle impact problem is presented to demonstrate the proposed method for inverse simulation under uncertainty.

Crashworthiness analysis and optimization of different configurations multi-layered corrugated sandwich panels under crush loading

2020 ◽  
pp. 109963622090975
Author(s):  
Chengfu Shu ◽  
Shujuan Hou ◽  
YX Zhang ◽  
Yutao Luo
Keyword(s):  
Experimental Design ◽  
Specific Energy ◽  
Sandwich Structure ◽  
Sandwich Panels ◽  
Functionally Graded ◽  
Orthogonal Experimental Design ◽  
Analysis Method ◽  
Range Analysis ◽  
Specific Energy Absorption ◽  
Graded Thickness

Multi-layered corrugated sandwich panels can be made up of different core shapes, different arrangements, the variable height, and variable thickness in every layer. In this paper, the crashworthiness behaviors of multi-layered corrugated sandwich panels with different configurations, which are controlled by these four factors, are analyzed and compared. The optimal configuration is found by adopting orthogonal experimental design and range analysis method. A novel multi-layered corrugated sandwich structure with functionally graded thickness is proposed and studied and is proved to better structural crashworthiness. First, finite element models of multi-layered corrugated sandwich panels are established and validated by experiment. Then, the effect of the four factors with three levels on crashworthiness is analyzed, and we obtain the main factor and the optimal configuration with the maximum specific energy absorption by using orthogonal experimental design and range analysis method. Finally, parametric studies and multi-objectives optimization of the proposed novel multi-layered corrugated sandwich structure with functionally graded thickness are conducted. The optimization is aimed at maximizing the specific energy absorption and minimizing the initial peak force under crush loading, based on the non-dominated sorting genetic algorithm and response surface method technique. These findings can provide valuable guidelines for the design of multi-layered corrugated sandwich panels with different configurations under crush loading.

Effect of Alkali Treatment on the Quality of Hemp Fiber

2014 ◽  
Vol 9 (2) ◽  
pp. 155892501400900 ◽  
Author(s):  
Jie Zhang ◽  
Hua Zhang ◽  
Jianchun Zhang
Keyword(s):  
Chemical Composition ◽  
Experimental Design ◽  
Average Length ◽  
Alkali Treatment ◽  
Orthogonal Experimental Design ◽  
Average Weight ◽  
Range Analysis ◽  
Experimental Conditions ◽  
Hemp Fiber

An orthogonal experimental design was employed to study the effects of the bath ratio, time, and alkali dosage of alkali treatment on the chemical composition, fineness, average length, and staple rate of hemp fiber. Through normalization and average weight distribution of multiple indices, the quality of hemp fiber was quantified. Results of range analysis showed that the optimum quality of hemp fiber can be achieved under the following conditions: alkali treatment bath ratio, 1:10; time, 5 h; alkali dosage, 10 g/L; and length of hemp fiber, 16 mm to 29 mm. The reliability and repeatability of the best experimental conditions were further confirmed.

scholarly journals Machine Learning Based Sensitivity Analysis of Aeroelastic Stability Parameters in a Compressor Cascade

2021 ◽  
Vol 6 (3) ◽  
pp. 39
Author(s):  
Marco Rauseo ◽  
Mehdi Vahdati ◽  
Fanzhou Zhao
Keyword(s):  
Sensitivity Analysis ◽  
Surrogate Model ◽  
Structural Parameters ◽  
Operating Conditions ◽  
Data Driven ◽  
Compressor Cascade ◽  
Stability Parameters ◽  
Aerodynamic Damping ◽  
Data Driven Approach ◽  
Input Variables

Aeroelastic instabilities such as flutter have a crucial role in limiting the operating range and reliability of turbomachinery. This paper offers an alternative approach to aeroelastic analysis, where the sensitivity of aerodynamic damping with respect to main flow and structural parameters is quantified through a surrogate-model-based investigation. The parameters are chosen based on previous studies and are represented by a uniform distribution within applicable intervals. The surrogate model is an artificial neural network, trained and tested to achieve an error within 1% of the test data. The quantity of interest is aerodynamic damping and the datasets are obtained from a linearised aeroelastic solver. The sensitivity of aerodynamic damping with respect to the input variables is obtained by calculating normalised gradients from the surrogate model at specific operating conditions. The results show a quantitative comparison of sensitivity across the different input parameters. The outcome of the sensitivity analysis is then used to decide the most appropriate action to take in order to induce stability in unstable operating conditions. The work is a preliminary study, carried out on a simplified two dimensional compressor cascade and it is aimed at proving the validity of a data-driven approach in studying the aeroelastic behaviour of turbomachinery. To the best of the authors’ knowledge, this is the first time a data-driven flutter model has been investigated. The initial results are encouraging, indicating that this approach is worth pursuing in the future. The presented framework can be used as a redesign tool to enhance the flutter stability of an existing blade.

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