Outline

  • Abstract
  • 1. Introduction
  • 2. Description of the Structure
  • 3. Ambient Vibration Test and Numerical Modeling
  • 3.1. Test Setup and Procedure
  • 3.2. Identification of Modal Parameters
  • 3.3. Numerical Modeling
  • 3.4. Correlation Between the Experimental and Numerical Modal Parameters
  • 4. System Identification
  • 4.1. System Identification Theory
  • 4.2. System Identification Results
  • 5. Conclusions
  • Acknowledgements
  • References

رئوس مطالب

  • چکیده
  • 1. مقدمه
  • 2. تشریح سازه
  • 3. تست ارتعاش محیطی و مدل سازی عددی
  • 3.1. راه اندازی آزمایش و پروسه
  • 3.2. شناسایی پارامترهای مودال
  • 3.3. مدل سازی عددی
  • 3.4. ارتباط بین پارامترهای مودال عددی و تجربی
  • 4. شناسایی سیستم
  • 4.1. نظریه ی شناسایی سیستم
  • 4.2. نتایج شناسایی سیستم
  • 5. نتیجه گیری

Abstract

Ensuring and maintaining the structural integrity of the containment structure in nuclear power plants is essential for preserving the nuclear reactor and other safety-related systems as well as protecting plant workers and publics from hazardous radioactive materials. To date, the structural integrity of the containment has been evaluated periodically via various nondestructive inspection methods. However, these methods require considerable time and cost to estimate overall structural integrity. In this paper, the possibility of monitoring the structural integrity of the containment utilizing ambient vibration measurement is explored. The ambient vibration testing was selected because it can avoid the interruption of normal operation of power plants. To fulfill the objective, the ambient vibration of the containment of Ulchin Nuclear Power Plant Unit 5 in Korea was measured, and the modal parameters, i.e., resonant frequencies and corresponding mode shapes, were extracted using the modal identification techniques in the frequency domain, i.e., the peak picking and the frequency domain decomposition methods. Using the extracted modal parameters and the finite element model, the elastic modulus of the concrete was estimated based on the sensitivity-based system identification method.


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