Parameter Sensitivity and Reliability Analysis for Construction Control of Cable-Net Structure Supporting the Reflector of FAST

2013 ◽  
Vol 671-674 ◽  
pp. 529-533
Author(s):  
Xu Kong ◽  
Qi Ming Wang ◽  
Chuan Jia Liu ◽  
Zhong Yi Zhu
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Structural Parameters ◽  
Element Model ◽  
Parameter Sensitivity ◽  
Tension Force ◽  
Design Parameters ◽  
Structural Parameter ◽  
Global Sensitivity ◽  
The Impact

Five-hundred-meter Aperture Spherical radio Telescope (FAST) is supported by cable-net structure, which enables its surface to form a paraboloid in real time under active control. FAST is now entering project construction and implement stage, however there are always a considerable amount of errors existed in practice which would result in the deviation of the structure from its ideal model. Therefore, structural parameter sensitivity analysis was indispensable discussed. In the paper, the variation ranges of structural parameters were rationally determined. Base on local sensitivity analysis and global sensitivity analysis method, Using the finite element model investigated the influence of different structural parameters change on the static behavior, gets the conclusions that the impact of several key design parameters on the tension force of cable-net is large. The results indicate that of all types of the structural parameters, the error of the length of cable plays the most important role, and the global sensitivity analysis indicates that the tension force range of cable-net is -18% to 27%.

scholarly journals The Effects of Structural Parameter Variation on Cable Force of Fast Cable-Net Structure

2014 ◽  
Vol 6 ◽  
pp. 912158
Author(s):  
Qiming Wang ◽  
Peng Jiang ◽  
Xu Kong
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Structural Parameters ◽  
Element Model ◽  
Design Parameters ◽  
Structural Parameter ◽  
Global Sensitivity ◽  
Fatigue Stress ◽  
Cutting Length ◽  
Cable Force

Five-hundred-meter aperture spherical radio telescope (FAST) is supported by a cable-net structure, which enables its surface to form a real-time paraboloid by active control. FAST project is currently in the construction and implementation stage. However, there are always a considerable amount of errors that existed in practice which may result in the deviation of the structure from its ideal model or design. Therefore, structural parameter sensitivity analysis was discussed, which is indispensable. However, such deformation operation would lead to about 500 MPa of fatigue stress variation amplitude in the cable-net structure. Optimized deformation strategy is proposed to release the fatigue stress of the cable-net structure, which would be of advantage to improve the reliability of the cable-net structure. In the paper, the variation ranges of structural parameters were rationally determined. Based on local sensitivity analysis and global sensitivity analysis method, finite element model was used to study the effect of different structural parameters on the static behavior. It can be concluded that the effect of several key design parameters such as the cutting length and the elastic modulus of cable on the cable force is significant. The global sensitivity analysis indicates that the cable force range of the cable-net is −19% to 27%.

Climate change impacts parameter sensitivity - What does this mean for model calibration?

2020 ◽  
Author(s):  
Lieke Melsen ◽  
Björn Guse
Keyword(s):  
Climate Change ◽  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Parameter Sensitivity ◽  
Rate Of Change ◽  
Hydrological Models ◽  
Global Sensitivity ◽  
The Future ◽  
Sensitive Parameters ◽  
The Impact

<p>Many hydrological models that are used for long term projections require calibration of at least a few parameters. When calibrated on discharge only, a general rule of thumb is that 4 to 5 parameters can be calibrated. The general approach is to conduct a global sensitivity analysis, to determine the four to five most sensitive parameters, and to select these for calibration.</p><p>Parameter sensitivity differs over models, target variables, sensitivity analysis methods, and also over climates. This would also imply that parameter sensitivity could change in a changing climate, and that would interfere with the current standard calibration procedure for hydrological models. Therefore, the question is whether, within a plausible rate of change, climate change propagates into a change in parameter sensitivity.   </p><p>We investigated how parameter sensitivity changes as a consequence of climate change, and if and how this has consequences for the calibration strategy. We applied a hybrid local-global sensitivity analysis method to three frequently used hydrological models (SAC, VIC, and HBV) in 605 basins across the US, and link changes in sensitivity to changes in climate. Finally, we evaluated the impact on the top five most sensitive parameters.</p><p>The results show that in all three models especially snow parameters tend to become less sensitive in the future. However, the models differ in which parameters increase in sensitivity; for some models ET parameters increase, while for others deep layer parameters increase. Evaluating the top 5 most sensitive parameters per basin, we found that in 43% to 49% of the basins at least one parameter changes in the top 5 in the future, while a maximum of two parameter changes in the top 5 was observed over all basins (in 2 to 4% of the basins).</p><p>Overall, the results indicate that in about half of the investigated basins one parameter would have been chosen differently for calibration. If a particular model parameter is, within the current climate, not or hardly sensitive to discharge, it is not possible to calibrate this parameter – notwithstanding whether this parameter becomes sensitive in the future. Therefore, the consequence of these results is that for parameters that will become sensitive in the future, a range of feasible parameter values have to be sampled for future projections, thereby capturing predictive uncertainty as a consequence of changing sensitivities.</p>

Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Souransu Nandi ◽  
Tarunraj Singh
Keyword(s):  
Sensitivity Analysis ◽  
Linear Systems ◽  
Global Sensitivity Analysis ◽  
Benchmark Problems ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Time Invariant ◽  
The Impact

The focus of this paper is on the global sensitivity analysis (GSA) of linear systems with time-invariant model parameter uncertainties and driven by stochastic inputs. The Sobol' indices of the evolving mean and variance estimates of states are used to assess the impact of the time-invariant uncertain model parameters and the statistics of the stochastic input on the uncertainty of the output. Numerical results on two benchmark problems help illustrate that it is conceivable that parameters, which are not so significant in contributing to the uncertainty of the mean, can be extremely significant in contributing to the uncertainty of the variances. The paper uses a polynomial chaos (PC) approach to synthesize a surrogate probabilistic model of the stochastic system after using Lagrange interpolation polynomials (LIPs) as PC bases. The Sobol' indices are then directly evaluated from the PC coefficients. Although this concept is not new, a novel interpretation of stochastic collocation-based PC and intrusive PC is presented where they are shown to represent identical probabilistic models when the system under consideration is linear. This result now permits treating linear models as black boxes to develop intrusive PC surrogates.

Efficient and Accurate Global Sensitivity Analysis for Reservoir Simulations By Use of the Probabilistic Collocation Method

SPE Journal ◽  
2013 ◽  
Vol 19 (04) ◽  
pp. 621-635 ◽  
Author(s):  
Cheng Dai ◽  
Heng Li ◽  
Dongxiao Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Collocation Method ◽  
History Matching ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Global Sensitivity ◽  
Reservoir Simulations ◽  
Probabilistic Collocation Method ◽  
The Impact ◽  
Probabilistic Collocation

Summary Reservoir simulations involve a large number of formation and fluid parameters, many of which are subject to uncertainties owing to the combination of spatial heterogeneity and insufficient measurements. Accurately quantifying the impact of varying parameters on simulation models can reveal the importance of the parameters, which helps in designing field-characterization strategies and determining parameterization for history matching. Compared with the commonly used local sensitivity analysis (SA), global SA considers the whole variation range of the parameters and can thus provide more-complete information. However, the traditional global sensitivity analysis that is derived from Monte Carlo simulation (MCS) is computationally too demanding for reservoir simulations. In this study, we propose an alternative approach that is both accurate and efficient. In the proposed approach, the model outputs such as pressure and reservoir production quantities are expressed by polynomial chaos expansions (PCEs). The probabilistic collocation method is used to determine the coefficients of the polynomial expansions by solving outputs at different sets of collocation points by means of the original partial-differential equations. Then, a proxy is constructed with such coefficients. Accurate statistical sensitivity indices of the uncertainty parameters can be obtained by running the proxy. We validate the approach with 2D examples by comparing with the MCS-based global SA. It is found that with only a small fraction of the computational cost required by the MCS approach, the new approach gives accurate global sensitivity for each parameter. The proposed approach is also demonstrated on a large-scale 3D black-oil model, for which the MCS-based global SA is found to be computationally infeasible. It is found that the developed approach possesses the following key advantages: It requires a much smaller number of reservoir simulations for accurate global SA; it is nonintrusive and can be implemented with existing codes or simulators; and it can accommodate arbitrary distributions of parameters encountered in realistic geological situations.

scholarly journals How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model? Application to the PESHMELBA model

2021 ◽  
Author(s):  
Emilie Rouzies ◽  
Claire Lauvernet ◽  
Bruno Sudret ◽  
Arthur Vidard
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Transfer Model ◽  
Valuable Insight ◽  
Catchment Scale ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
The Impact

Abstract. Pesticide transfers in agricultural catchments are responsible for diffuse but major risks to water quality. Spatialized pesticide transfer models are useful tools to assess the impact of the structure of the landscape on water quality. Before considering using these tools in operational contexts, quantifying their uncertainties is a preliminary necessary step. In this study, we explored how global sensitivity analysis can be applied to the recent PESHMELBA pesticide transfer model to quantify uncertainties on transfer simulations. We set up a virtual catchment based on a real one and we compared different approaches for sensitivity analysis that could handle the specificities of the model: high number of input parameters, limited size of sample due to computational cost and spatialized output. We compared Sobol' indices obtained from Polynomial Chaos Expansion, HSIC dependence measures and feature importance measures obtained from Random Forest surrogate model. Results showed the consistency of the different methods and they highlighted the relevance of Sobol' indices to capture interactions between parameters. Sensitivity indices were first computed for each landscape element (site sensitivity indices). Second, we proposed to aggregate them at the hillslope and the catchment scale in order to get a summary of the model sensitivity and a valuable insight into the model hydrodynamical behaviour. The methodology proposed in this paper may be extended to other modular and distributed hydrological models as there has been a growing interest in these methods in recent years.

scholarly journals Global Sensitivity Analysis of the Vibration Reduction System with Seated Human Body

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Igor Maciejewski ◽  
Tomasz Krzyzynski
Keyword(s):  
Sensitivity Analysis ◽  
Vibration Isolation ◽  
Global Sensitivity Analysis ◽  
Operating Conditions ◽  
Design Parameters ◽  
Global Sensitivity ◽  
Seat Suspension ◽  
Sensitivity Indices ◽  
Vibration Transmissibility ◽  
Nonlinear Force

The paper deals with the global sensitivity analysis for the purpose of shaping the vibroisolation properties of suspension systems under strictly defined operating conditions. The variance-based method is used to evaluate an influence of nonlinear force characteristics on the system dynamics. The proposed sensitivity indices provide the basis for determining the effect of key design parameters on the vibration isolation performance. The vibration transmissibility behaviour of an exemplary seat suspension system is discussed in order to illustrate the developed methodology.

scholarly journals Global sensitivity-based modelling approach to identify suitable Eucalyptus traits for adaptation to climate variability and change

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Elvis Felipe Elli ◽  
Neil Huth ◽  
Paulo Cesar Sentelhas ◽  
Rafaela Lorenzato Carneiro ◽  
Clayton Alcarde Alvares
Keyword(s):  
Sensitivity Analysis ◽  
Climate Variability ◽  
Global Sensitivity Analysis ◽  
Greenhouse Gas Emission ◽  
Growth And Yield ◽  
Superior Performance ◽  
Climate Variability And Change ◽  
Genetic Traits ◽  
Global Sensitivity ◽  
The Impact

Abstract Eucalyptus-breeding efforts have been made to identify clones of superior performance for growth and yield and how they will interact with global climate changes. This study performs a global sensitivity analysis for assessing the impact of genetic traits on Eucalyptus yield across contrasting environments in Brazil under present and future climate scenarios. The APSIM Next Generation Eucalyptus model was used to perform the simulations of stemwood biomass (t ha−1) for 7-year rotations across 23 locations in Brazil. Projections for the period from 2020 to 2049 using three global circulation models under intermediate (RCP4.5) and high (RCP8.5) greenhouse gas emission scenarios were performed. The Morris sensitivity method was used to perform a global sensitivity analysis to identify the influence of plant traits on stemwood biomass. Traits for radiation use efficiency, leaf partitioning, canopy light capture and fine root partitioning were the most important, impacting the Eucalyptus yield substantially in all environments under the present climate. Some of the traits targeted now by breeders for current climate will remain important under future climates. However, breeding should place a greater emphasis on photosynthetic temperature response for Eucalyptus in some regions. Global sensitivity analysis was found to be a powerful tool for identifying suitable Eucalyptus traits for adaptation to climate variability and change. This approach can improve breeding strategies by better understanding the gene × environment interactions for forest productivity.

scholarly journals Moment-based metrics for global sensitivity analysis of hydrological systems

2017 ◽  
Vol 21 (12) ◽  
pp. 6219-6234 ◽  
Author(s):  
Aronne Dell'Oca ◽  
Monica Riva ◽  
Alberto Guadagnini
Keyword(s):  
Sensitivity Analysis ◽  
Surrogate Model ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Original System ◽  
Model Complexity ◽  
Model Output ◽  
Global Sensitivity ◽  
Reduction Techniques ◽  
The Impact

Abstract. We propose new metrics to assist global sensitivity analysis, GSA, of hydrological and Earth systems. Our approach allows assessing the impact of uncertain parameters on main features of the probability density function, pdf, of a target model output, y. These include the expected value of y, the spread around the mean and the degree of symmetry and tailedness of the pdf of y. Since reliable assessment of higher-order statistical moments can be computationally demanding, we couple our GSA approach with a surrogate model, approximating the full model response at a reduced computational cost. Here, we consider the generalized polynomial chaos expansion (gPCE), other model reduction techniques being fully compatible with our theoretical framework. We demonstrate our approach through three test cases, including an analytical benchmark, a simplified scenario mimicking pumping in a coastal aquifer and a laboratory-scale conservative transport experiment. Our results allow ascertaining which parameters can impact some moments of the model output pdf while being uninfluential to others. We also investigate the error associated with the evaluation of our sensitivity metrics by replacing the original system model through a gPCE. Our results indicate that the construction of a surrogate model with increasing level of accuracy might be required depending on the statistical moment considered in the GSA. The approach is fully compatible with (and can assist the development of) analysis techniques employed in the context of reduction of model complexity, model calibration, design of experiment, uncertainty quantification and risk assessment.

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