by National Aeronautics and Space Administration, Langley Research Center, For sale by the National Technical Information Service in Hampton, Va, [Springfield, Va .
Written in English
|Other titles||Dynamic test analysis correlation using ....|
|Statement||Paul E. McGowan, A. Filippo Angelucci, and Mehzad Javeed.|
|Series||NASA technical memorandum -- 107671.|
|Contributions||Angelucci, A. Filippo., Javeed, Mehzad., Langley Research Center.|
|The Physical Object|
Test data, or identified parameters, are usually used to identify a dynamic model-mass, stiffness and damping distribution or optimize an existing analytical model. In this paper, the problem of dynamic modeling of structures and model updating is discussed. The existing approaches are reviewed in order to evaluate the existing state of the by: The dynamic expansion method has been shown to produce reasonable results on actual test cases [11 ], although it is sensitive to test and analysis errors . However, it is based on a series of. A rational criterion for structural dynamic analysis-test correlation has been established by using a matrix perturbation technique. This criterion can be used for the verification of an analytical model by the test results. Also, the same technique can be applied to update the analytical results by using the test results without repeating the Cited by: from this approach by using simulated series with the same characteristics as the real data, assuming that the asymmet-ric dynamic conditional correlation (DCC) model of Cappiello, Engle,andSheppard()-tion 6 follows the same structure of Section 5, but using highly correlated assets.
a study of correlation between finite element analysis and experimental modal analysis in structural dynamic analysis May DOI: /RG 5. Test analysis correlation. Test results were compared to analysis results for both the flexible and the stiff piping systems. Comparisons of frequencies, mode shapes, and snapback responses were made. In general, it was found that the comparison between the test data and analysis results were in closer agreement for the stiff system than for. on Correlation and Regression Analysis covers a variety topics of how to investigate the strength, direction and effect of a relationship between variables by collecting measurements and using appropriate statistical analysis. Also this textbook intends to practice data of labor force survey. The correlation coefficient, r, tells us about the strength and direction of the linear relationship between x and r, the reliability of the linear model also depends on how many observed data points are in the sample. We need to look at both the value of the correlation coefficient r and the sample size n, together.. We perform a hypothesis test of the “significance of the.
The quantification and analysis of uncertainties is important in all cases where maps and models of uncertain properties are the basis for further decisions. Once these uncertainties are identified, the logical next step is to determine how they can be reduced. Information theory provides a framework for the analysis of spatial uncertainties when different subregions are considered as random. The modal test/analysis correlation problem is for- mulated as a mathematical programming (MP) primal problem solved by the ADS code, 4 and as a dual Lagran- gian problem solved by Optimality Criteria (OC) code. 2 The NASTRAN 1 FEM of the SSF Integrated Equip- ment Assembly (IEA) with the test boundary conditions is modified using design. Example 1: Repeat Example 1 of Correlation Testing via the t Test (regarding Pearson’s correlation) using the Correlation data analysis tool. To use this tool, press Ctrl-m and select Correlation from the menu of choices that appears. Fill in the dialog box that appears as shown in Figure 1 and press the OK button. Figure 1 – Correlation. This paper presents a review of structural dynamic model updating techniques. Starting with a tutorial introduction of basic concepts of model updating, the paper reviews direct and iterative techniques of model updating along with their applications to real life systems. The main objective of this paper is to review the most widely applied model updating techniques so that beginners as well.