Nonlinear deformation monitoring of elastic beams based on isogeometric iFEM approach

Zhao, Feifei; Kefal, Adnan; Bao, Hong

doi:10.1016/j.ijnonlinmec.2022.104229

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…In addition, Kefal and Oterkus [ 21 ] have introduced a novel isogeometric inverse element (iKLS), which utilizes Non-Uniform Rational B-Splines (NURBS) as shape functions to achieve a geometrically exact representation of curved thin shell structures. Subsequently, this isogeometric approach has also been extended to the reconstruction of the displacement field in beam-like geometries [ 22 , 23 ]. The abovementioned elements enable real-time monitoring of isotropic structures like beams, plates, and shells by means of the inverse finite element method.…”

Section: Introductionmentioning

confidence: 99%

Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation

Del Priore,

Lampani

2024

Sensors

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The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as “shape sensing”. The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

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Section: Introductionmentioning

confidence: 99%

Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation

Del Priore,

Lampani

2024

Sensors

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“…Numerical and experimental application of the 1D iFEM feature the monitoring of circular and airfoil beams [54,57], radio telescope reflectors [58], wing structures [59], subsea pipelines [60], etc. Recent efforts have also been aimed at non-linear deformation monitoring [61] and optimal sensor placement [46,47,62] for efficient shape sensing.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Shell-Beam Inverse Finite Element Method for the Shape Sensing of Stiffened Thin-Walled Structures: Formulation and Experimental Validation on a Composite Wing-Shaped Panel

Esposito

Roy

Surace

et al. 2023

Sensors

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This work presents a novel methodology for the accurate and efficient elastic deformation reconstruction of thin-walled and stiffened structures from discrete strains. It builds on the inverse finite element method (iFEM), a variationally-based shape-sensing approach that reconstructs structural displacements by matching a set of analytical and experimental strains in a least-squares sense. As iFEM employs the finite element framework to discretize the structural domain and as the displacements and strains are approximated using element shape functions, the kind of element used influences the accuracy and efficiency of the iFEM analysis. This problem is addressed in the present work through a novel discretization scheme that combines beam and shell inverse elements to develop an iFEM model of the structure. Such a hybrid discretization paradigm paves the way for more accurate shape-sensing of geometrically complex structures using fewer sensor measurements and lower computational effort than traditional approaches. The hybrid iFEM is experimentally demonstrated in this work for the shape sensing of bending and torsional deformations of a composite stiffened wing panel instrumented with strain rosettes and fiber-optic sensors. The experimental results are accurate, robust, and computationally efficient, demonstrating the potential of this hybrid scheme for developing an efficient digital twin for online structural monitoring and control.

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“…Cerracchio et al [6] used the inverse finite element method (iFEM) to analyze the shape sensing of typical composite stiffened structures. Zhao et al [7] proposed a large deformation monitoring shape sensing method based on strain gradient theory and the iFEM method and verified the accuracy and effectiveness of the method in large deformation by using cantilever beams. Kefal et al [8,9] coupled the inverse finite element method with smoothing element analysis of the shape sensing ability of iFEM in multilayer composites and sandwich structures, coupled into battlefield dynamics and inverse finite element method to achieve crack identification of composite structures.…”

Section: Introductionmentioning

confidence: 99%

A strain reconstruction method for uncertain boundary conditions

Huo

Zhang

et al. 2023

Preprint

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Affected by complex boundary conditions and redundant structural assembly, it is difficult to collect stress and strain information on key structures of complex mechanical equipment, resulting in difficult life monitoring and large calculation errors. The purpose of this study is to provide reliable strain data for accurate life prediction and solve the problem of obtaining critical structural strain under uncertain loads and other complex boundary conditions. In this paper, a neural network training dataset is established based on finite element analysis data and experimental data, and the neural network architecture and parameter optimization are carried out for each data set. Finally, the strain reconstruction model based on a neural network is established, which solves the two problems of accurate equivalence from structure to strain and equivalence from strain to load. Feature experimental samples were designed to verify the universality of the research method. The model is experimentally verified with an average error of less than 5%. It realizes the global strain reconstruction from the local measurement point to the overall structure, which can be used for real-time and full-cycle life monitoring of the structure;

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Nonlinear deformation monitoring of elastic beams based on isogeometric iFEM approach

Cited by 14 publications

References 30 publications

Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation

Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation

Hybrid Shell-Beam Inverse Finite Element Method for the Shape Sensing of Stiffened Thin-Walled Structures: Formulation and Experimental Validation on a Composite Wing-Shaped Panel

A strain reconstruction method for uncertain boundary conditions

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