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Dynamic Testing of Nuclear Power Plant Structures

Dynamic Testing of Nuclear Power Plant Structures
Author:
Publisher:
Total Pages:
Release: 1980
Genre:
ISBN:

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Lawrence Livermore Laboratory (LLL) evaluated the applications of system identification techniques to the dynamic testing of nuclear power plant structures and subsystems. These experimental techniques involve exciting a structure and measuring, digitizing, and processing the time-history motions that result. The data can be compared to parameters calculated using finite element or other models of the test systems to validate the model and to verify the seismic analysis. This report summarizes work in three main areas: (1) analytical qualification of a set of computer programs developed at LLL to extract model parameters from the time histories; (2) examination of the feasibility of safely exciting nuclear power plant structures and accurately recording the resulting time-history motions; (3) study of how the model parameters that are extracted from the data be used best to evaluate structural integrity and analyze nuclear power plants.


NUREG/CR.

NUREG/CR.
Author: U.S. Nuclear Regulatory Commission
Publisher:
Total Pages: 64
Release: 1981
Genre: Nuclear energy
ISBN:

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Analytical Qualification of System Identification (modal Analysis) Codes for Use in the Dynamic Testing of Nuclear Power Plant Structures

Analytical Qualification of System Identification (modal Analysis) Codes for Use in the Dynamic Testing of Nuclear Power Plant Structures
Author:
Publisher:
Total Pages:
Release: 1980
Genre:
ISBN:

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The analytical evaluation of two particular system identification codes used at Lawrence Livermore Laboratory is presented. Both codes are eigenparameter identification codes; however, one uses a time domain approach while the other a frequency domain approach. The evaluation was accomplished by analytically generating several time history signals in which the true modal parameters were known. These time histories ranged from widely spaced modes with spacing factors of 100 percent to closely spaced modes with spacing factors of 6 percent. These signals were then polluted with various levels of simulated measurement noise and the ability of our computer codes to extract the parameters from this noisy data was evaluated.