scholarly journals Sensitivity Analysis of Deviation Source for Fast Assembly Precision Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Jianjun Tang ◽  
Xitian Tian ◽  
Junhao Geng
Keyword(s):  
Sensitivity Analysis ◽  
Transformation Method ◽  
Assembly Sequences ◽  
Sensitivity Analysis Method ◽  
Source Dimension ◽  
Assembly Deviation ◽  
The Impact ◽  
Coupling Phenomena ◽  
Assembly Precision

Assembly precision optimization of complex product has a huge benefit in improving the quality of our products. Due to the impact of a variety of deviation source coupling phenomena, the goal of assembly precision optimization is difficult to be confirmed accurately. In order to achieve optimization of assembly precision accurately and rapidly, sensitivity analysis of deviation source is proposed. First, deviation source sensitivity is defined as the ratio of assembly dimension variation and deviation source dimension variation. Second, according to assembly constraint relations, assembly sequences and locating, deviation transmission paths are established by locating the joints between the adjacent parts, and establishing each part’s datum reference frame. Third, assembly multidimensional vector loops are created using deviation transmission paths, and the corresponding scalar equations of each dimension are established. Then, assembly deviation source sensitivity is calculated by using a first-order Taylor expansion and matrix transformation method. Finally, taking assembly precision optimization of wing flap rocker as an example, the effectiveness and efficiency of the deviation source sensitivity analysis method are verified.

scholarly journals Parameter Dependencies of a Biomechanical Cervical Spine FSU - The Process of Finding Optimal Model Parameters by Sensitivity Analysis

2021 ◽  
Author(s):  
Sabine Bauer ◽  
Ivanna Kramer
Keyword(s):  
Sensitivity Analysis ◽  
Intervertebral Discs ◽  
Model Parameters ◽  
Biomechanical Behavior ◽  
Functional Spinal Unit ◽  
Spine Model ◽  
Biomechanical Parameters ◽  
Multi Body ◽  
The Impact

The knowledge about the impact of structure-specific parameters on the biomechanical behavior of a computer model has an essential meaning for the realistic modeling and system improving. Especially the biomechanical parameters of the intervertebral discs, the ligamentous structures and the facet joints are seen in the literature as significant components of a spine model, which define the quality of the model. Therefore, it is important to understand how the variations of input parameters for these components affect the entire model and its individual structures. Sensitivity analysis can be used to gain the required knowledge about the correlation of the input and output variables in a complex spinal model. The present study analyses the influence of the biomechanical parameters of the intervertebral disc using different sensitivity analysis methods to optimize the spine model parameters. The analysis is performed with a multi-body simulation model of the cervical functional spinal unit C6-C7.

scholarly journals An Investigation of Parameter Sensitivity of Minimum Complexity Earth Simulator

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 95
Author(s):  
Jiewei Chen ◽  
Huijuan Cui ◽  
Yangyang Xu ◽  
Quansheng Ge
Keyword(s):  
Climate Change ◽  
Sensitivity Analysis ◽  
Climate Models ◽  
Climate Model ◽  
Greenhouse Gas Emission ◽  
Parameter Sensitivity ◽  
Parameter Sensitivity Analysis ◽  
Output Measurement ◽  
Sensitivity Analysis Method ◽  
The Impact

Climate change, induced by human greenhouse gas emission, has already influenced the environment and society. To quantify the impact of human activity on climate change, scientists have developed numerical climate models to simulate the evolution of the climate system, which often contains many parameters. The choice of parameters is of great importance to the reliability of the simulation. Therefore, parameter sensitivity analysis is needed to optimize the parameters for the model so that the physical process of nature can be reasonably simulated. In this study, we analyzed the parameter sensitivity of a simple carbon-cycle energy balance climate model, called the Minimum Complexity Earth Simulator (MiCES), in different periods using a multi-parameter sensitivity analysis method and output measurement method. The results show that the seven parameters related to heat and carbon transferred are most sensitive among all 37 parameters. Then uncertainties of the above key parameters are further analyzed by changing the input emission and temperature, providing reference bounds of parameters with 95% confidence intervals. Furthermore, we found that ocean heat capacity will be more sensitive if the simulation time becomes longer, indicating that ocean influence on climate is stronger in the future.

A Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Yu Liu ◽  
Xiaolei Yin ◽  
Paul Arendt ◽  
Wei Chen ◽  
Hong-Zhong Huang
Keyword(s):  
Sensitivity Analysis ◽  
Design Stage ◽  
Engineering Systems ◽  
Hierarchical Systems ◽  
Multilevel Systems ◽  
Shared Variables ◽  
Sensitivity Analysis Method ◽  
Group A ◽  
Upper Level ◽  
The Impact

Statistical sensitivity analysis (SSA) is an effective methodology to examine the impact of variations in model inputs on the variations in model outputs at either a prior or posterior design stage. A hierarchical statistical sensitivity analysis (HSSA) method has been proposed in literature to incorporate SSA in designing complex engineering systems with a hierarchical structure. However, the original HSSA method only deals with hierarchical systems with independent subsystems. For engineering systems with dependent subsystem responses and shared variables, an extended HSSA method with shared variables (named HSSA-SV) is developed in this work. A top-down strategy, the same as in the original HSSA method, is employed to direct SSA from the top level to lower levels. To overcome the limitation of the original HSSA method, the concept of a subset SSA is utilized to group a set of dependent responses from the lower level submodels in the upper level SSA and the covariance of dependent responses is decomposed into the contributions from individual shared variables. An extended aggregation formulation is developed to integrate local submodel SSA results to estimate the global impact of lower level inputs on the top level response. The effectiveness of the proposed HSSA-SV method is illustrated via a mathematical example and a multiscale design problem.

scholarly journals Error analysis for CO and CH<sub>4</sub> total column retrievals from SCIAMACHY 2.3 μm spectra

2008 ◽  
Vol 8 (2) ◽  
pp. 5183-5233
Author(s):  
A. M. S. Gloudemans ◽  
H. Schrijver ◽  
O. P. Hasekamp ◽  
I. Aben
Keyword(s):  
Sensitivity Analysis ◽  
Likelihood Method ◽  
Polarization Sensitivity ◽  
Line Intensities ◽  
Detailed Assessment ◽  
Instrument Noise ◽  
Aerosol Sources ◽  
Iterative Maximum Likelihood ◽  
The Impact

Abstract. A detailed sensitivity analysis of the Iterative Maximum Likelihood Method (IMLM) algorithm and its application to the SCIAMACHY 2.3 μm spectra is presented. The sensitivity analysis includes a detailed assessment of the impact of aerosols in the 2.3 μm range. Results show that near strong aerosol sources mineral dust and biomass aerosols can have an effect of ~7–10% on the CH4 total columns retrieved from this wavelength range. Similar but somewhat larger effects are found for CO, but due to the larger variability of CO these errors are less important. Away from strong sources much smaller effects of a few percent are found. Spectroscopic uncertainties are mostly negligible except for uncertainties in the CH4 intrinsic line intensities, which can be important. Application of the IMLM algorithm to the SCIAMACHY 2.3 μm spectra shows that the quality of the retrieved CO and CH4 total columns is good, except for a bias for large instrument-noise errors which is partly due to remaining calibration issues. Polarization sensitivity of the SCIAMACHY instrument has a negligible effect on the retrieved CO and CH4 total columns. The H2O total columns, which have to be retrieved simultaneously with CO and CH4 due to overlapping absorption lines, agree well with H2O total columns from ECMWF data. This ensures that the fit to the H2O absorptions is of sufficient quality not to hamper the retrieved CO and CH4 total columns from SCIAMACHY spectra.

Reliability sensitivity analysis method for time-dependent problem based on first-passage method

2018 ◽  
Vol 233 (4) ◽  
pp. 787-802
Author(s):  
Wenxuan Wang ◽  
Hangshan Gao ◽  
Changcong Zhou ◽  
Wanghua Xu
Keyword(s):  
Sensitivity Analysis ◽  
Time Dependent ◽  
Sensitivity Index ◽  
Analysis Method ◽  
First Passage ◽  
Reliability Sensitivity ◽  
Reliability Sensitivity Analysis ◽  
Sensitivity Analysis Method ◽  
Global Reliability Sensitivity ◽  
The Impact

The sensitivity index plays a critical role in the design of product and is used to quantify the impact degree of the uncertainty of the input variable to the uncertainty of the interest output. This paper presents a new local reliability sensitivity method and a global reliability sensitivity analysis method of time-dependent reliability problems. Firstly, according to the Poisson's assumption-based first-passage method, the local reliability sensitivity index is directly obtained by calculating the partial derivative of the failure probability to the distribution parameters of input random variable. Then, the moment-independent global reliability sensitivity index of the time-dependent problems is derived based on the concept of moment-independent. Finally, the efficiency and accuracy of the proposed method are verified with the reference results of Monte Carlo simulation.

Aquifer vulnerability to pollution of Oum El-Bouaghi region in North East of Algeria

2017 ◽  
Vol 28 (3) ◽  
pp. 384-399
Author(s):  
Nadjet Zair ◽  
Salah Chaab ◽  
Catherine Bertrand
Keyword(s):  
Sensitivity Analysis ◽  
Vadose Zone ◽  
Vulnerability Assessment ◽  
Design Methodology ◽  
Aquifer Vulnerability ◽  
Parameter Sensitivity Analysis ◽  
Content Type ◽  
North East ◽  
The Impact

Purpose The purpose of this paper is to assess the vulnerability of the aquifer using two models of analysis (DRASTIC and GOD) that were applied in practice in the regions of Bir Chouhada, Souk Naamane and Ouled Zouai in the district of Oum El-Bouaghi. Design/methodology/approach This study aims to determine the most adequate methods to ensure the protection of the Bir Chouhada, Souk Naamane and Ouled Zouai aquifer from pollution using vulnerability assessment. The application of the DRASTIC and GOD models made this evaluation possible. Findings The analysis of the both maps of vulnerability, resulting from the application of the two methods (DRASTIC and GOD), has revealed several classes of vulnerability that are no-, low-, medium- and high-vulnerable area. High DRASTIC vulnerability values vary between 145 and 178, and those of GOD vary between 0.07 and 0.57. It is observed that vulnerability increases from the center toward the eastern part of the plain; this is confirmed by the repartition of nitrate contents. The impact of the hydraulic conductivity on vulnerability to pollution is more significant than those of the vadose zone and the aquifer media. This is well observed when considering the single-parameter sensitivity analysis. Originality/value The text deepens the understanding of the vulnerability assessment and quality of the aquifer and the groundwater. The present study can be used for the assessment and the management of groundwater.

scholarly journals ANÁLISE DE SENSIBILIDADE DE PARÂMETROS HIDROLÓGICOS NA BACIA DO RIO DAS PEDRAS - GUARAPUAVA-PR

2019 ◽  
Vol 12 (5) ◽  
pp. 1746
Author(s):  
Rafael Adriano de Castro Adriano de Castro ◽  
Elias Machado
Keyword(s):  
Sensitivity Analysis ◽  
Input Data ◽  
Assessment Tool ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Land Use And Management ◽  
The Impact ◽  
Water Assessment ◽  
Soil And Water

O modelo Soil and Water Assessment Tool (SWAT) é amplamente utilizado para predizer o impacto das alterações no uso e no manejo do solo, entre outros, é extremamente sensível à qualidade dos dados de entrada.  Assim, antes da simulação é necessário que se realize uma análise de sensibilidade de tal forma que se possa dar ênfase maior à aquisição e refinamento de determinados dados, diminuir as incertezas e aumentar a confiança nos resultados gerados. Os resultados simulados na bacia do Rio das Pedras – Guarapuava, foram realizadas a análise de sensibilidade e a calibração do modelo SWAT. Após a calibração do modelo os resultados do Índice de Nash & Sutcliffe alterado (COE), do percentual de tendência (PBIAS), e o coeficiente de determinação (R²) foram, respectivamente, 0,69, -0,5 e 0,7, indicando bom ajuste entre a vazão média mensal da bacia Rio das Pedras simulada pelo modelo SWAT em relação aos dados observados.  Sensitivity analysis of hydrological parameters in the Rio das Pedras basin - Guarapuava-PR A B S T R A C TThe SWAT model is widely used to predict the impact of changes in land use and management, among others, is extremely sensitive to the quality of input data. Thus, prior to the simulation, it is necessary to perform a sensitivity analysis in such a way that greater emphasis can be placed on the acquisition and refinement of certain data, decrease uncertainties and increase confidence in the results generated. The simulated results in the Rio das Pedras - Guarapuava basin, were performed the sensitivity analysis and calibration of the SWAT model. After the calibration of the model, the results of the modified Nash & Sutcliffe Index (COE), percentage of trend (PBIAS), and coefficient of determination (R²) were, respectively, 0.69, -0.5 and 0.7, Indicating a good fit between the average monthly flow of the Rio das Pedras basin simulated by the SWAT model in relation to the observed data. 

An Extended Hierarchical Statistical Sensitivity Analysis Method for Multilevel Systems With Shared Variables

2009 ◽  
Author(s):  
Yu Liu ◽  
Xiaolei Yin ◽  
Paul Arendt ◽  
Wei Chen ◽  
Hong-Zhong Huang
Keyword(s):  
Sensitivity Analysis ◽  
Sampling Technique ◽  
Design Stage ◽  
Engineering Systems ◽  
Hierarchical Systems ◽  
Shared Variables ◽  
Sensitivity Analysis Method ◽  
Upper Level ◽  
Importance Sampling Technique ◽  
The Impact

Statistical sensitivity analysis (SSA) is an effective methodology to examine the impact of variations in model inputs on the variations in model outputs at either a prior or posterior design stage. A hierarchical statistical sensitivity analysis (HSSA) method has been proposed in literature to incorporate SSA in designing complex engineering systems with a hierarchical structure. However, the original HSSA method only deals with hierarchical systems with independent subsystems. Due to the existence of shared variables at lower levels, responses from lower level submodels that act as inputs to a higher level subsystem are both functionally and statistically dependent. For designing engineering systems with dependent subsystem responses, an extended hierarchical statistical sensitivity analysis (EHSSA) method is developed in this work to provide a ranking order based on the impact of lower level model inputs on the top level system performance. A top-down strategy, same as in the original HSSA method, is employed to direct SSA from the top level to lower levels. To overcome the limitation of the original HSSA method, the concept of a subset SSA is utilized to group a set of dependent responses from lower level submodels in the upper level SSA. For variance decomposition at a lower level, the covariance of dependent responses is decomposed into the contributions from individual shared variables. To estimate the global impact of lower level inputs on the top level output, an extended aggregation formulation is developed to integrate local submodel SSA results. The importance sampling technique is also introduced to re-use the existing data from submodels SSA during the aggregation process. The effectiveness of the proposed EHSSA method is illustrated via a mathematical example and a multiscale design problem.

scholarly journals A Novel Sensitivity Analysis Method in Structural Performance of Hydraulic Press

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Peihao Zhu ◽  
Lianhong Zhang ◽  
Rui Zhou ◽  
Lihai Chen ◽  
Bing Yu ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Structural Optimization ◽  
Machine Tools ◽  
Hydraulic Press ◽  
Structural Performance ◽  
Analysis Method ◽  
Design Variables ◽  
Sensitivity Analysis Method ◽  
Strength And Stiffness ◽  
The Impact

Sensitivity analysis plays a key role in structural optimization, but traditional methods of sensitivity analysis in strength and stiffness are time consuming and of high cost. In order to effectively carry out structural optimization of hydraulic press, this paper presents a novel sensitivity analysis method in structural performance of hydraulic press, which saves a great deal of time and design costs. The key dimension parameters of the optimization of design variables, which remarkably impact on the structural performance of hydraulic press, are efficiently selected. The impact order of various sensitivity parameters in strength and stiffness of machine tools is consistent with the sensitivity ranking of regression analysis. The research results provide the basis for the hydraulic machine design and references in research of machine tools and equipment.

scholarly journals A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1095
Author(s):  
Xiang Peng ◽  
Xiaoqing Xu ◽  
Jiquan Li ◽  
Shaofei Jiang
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Method ◽  
Transformation Method ◽  
Unscented Transformation ◽  
Performance Function ◽  
Gaussian Integration ◽  
Sensitivity Indices ◽  
Sensitivity Analysis Method ◽  
Distribution Parameters ◽  
Input Variables

For engineering products with uncertain input variables and distribution parameters, a sampling-based sensitivity analysis methodology was investigated to efficiently determine the influences of these uncertainties. In the calculation of the sensitivity indices, the nonlinear degrees of the performance function in the subintervals were greatly reduced by using the integral whole domain segmentation method, while the mean and variance of the performance function were calculated using the unscented transformation method. Compared with the traditional Monte Carlo simulation method, the loop number and sampling number in every loop were decreased by using the multiplication approximation and Gaussian integration methods. The proposed algorithm also reduced the calculation complexity by reusing the sample points in the calculation of two sensitivity indices to measure the influence of input variables and their distribution parameters. The accuracy and efficiency of the proposed algorithm were verified with three numerical examples and one engineering example.

Sign in / Sign up

Export Citation Format

Share Document