Thesis

Structural health monitoring of marine structures by using inverse finite element method

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Awarding institution
  • University of Strathclyde
Date of award
  • 2017
Thesis identifier
  • T14528
Person Identifier (Local)
  • 201370958
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Abstract
  • Structural health monitoring (SHM) is a process aimed at providing accurate and real-time information concerning structural condition and performance. SHM is a very important discipline in the areas of civil, aerospace, and marine engineering because the utilization of SHM allows us to increase both human and environmental safety in conjunction with reduction in direct economic losses. A key component of the SHM process is real-time reconstruction of a structure's three-dimensional displacement and stress fields using a network of in situ strain sensors and measured strains, which is commonly referred to as "shape and stress sensing". The inverse finite element method (iFEM) is a revolutionary shape- and stress-sensing methodology shown to be fast, accurate, and robust for usage as a part of SHM systems. In the present thesis, the general framework of iFEM, i.e., least-squares variational principle, is adopted to develop unconventional and more effective shape- and stress-sensing techniques, with focus on general engineering structures and marine structures in particular. Firstly, the original iFEM formulation for plate and shell structures, developed on the basis of first-order shear deformation theory, is summarized. Then, this formulation is utilized to develop a new four-node quadrilateral inverse-shell element, iQS4, which further extends the practical utility of iFEM for shape sensing of large-scale structures including marine structures. Various numerical examples are presented and it is demonstrated that the iQS4 formulation is robust with respect to the membrane- and shear-locking phenomena. Moreover, the iFEM/iQS4 methodology is applied to various types of marine structures including a stiffened plate, a chemical tanker, and a container ship. To simulate experimentally measured strains and to establish reference displacements, a coupled hydrodynamic and high-fidelity finite element analyses are performed. Utilizing the simulated strain-sensor strains, iFEM analysis of each marine structure is performed. As a result, the optimum locations of the on-board strain sensors are determined for each marine structure.;Furthermore, a novel isogeometric Kirchhoff-Love inverse-shell element (iKLS) for more accurate shape-sensing analysis of curved/complex shell structures is presented. The new formulation employs the iFEM as a general framework and the non-uniform rational B-splines (NURBS) as the discretization technology for both structural geometry and displacement domain. Therefore, this new formulation couples the concept of isogeometric analysis with iFEM methodology and creates an innovative "isogeometric iFEM formulation". The superior shape-sensing capability of the isogeometric iFEM formulation (i.e., iKLS) is demonstrated for curved shell structures when using low-fidelity discretizations with few strain sensors. Finally, an improved iFEM formulation for dealing with shape and stress sensing of multilayered composite and sandwich plate/shell structures is described. The present iFEM formulation is based upon the minimization of a weighted-least-squares functional that uses the complete set of strain measures of refined zigzag theory (RZT). A new three-node inverse-shell element, i3-RZT, is developed based on the enhanced iFEM formulation. Various validation and demonstration problems are solved to examine the precision of the iFEM/i3-RZT methodology. The numerical results demonstrate the superior accuracy and robustness of the i3-RZT element for performing accurate shape and stress sensing of complex composite structures. In conclusion, all proposed iFEM frameworks are computationally efficient, accurate, and powerful, hence they can be helpful for shape sensing and SHM of general engineering structures, especially of marine structures.
Resource Type
DOI
Date Created
  • 2017
Former identifier
  • 9912545491002996

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