Comparison of strain pre-extrapolation techniques for shape and strain sensing by iFEM of a composite plate subjected to compression buckling

Oboe, Daniele; Colombo, Luca; Sbarufatti, Claudio; Giglio, Marco

doi:10.1016/j.compstruct.2021.113587

Cited by 39 publications

(25 citation statements)

References 40 publications

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“…However, when dealing with experimental tests, the cost of sensors, acquisition system limitations and physical constraints often limit sensor installation on the whole structure, preventing the full input strain field definition. In this case, the elements free from any sensor can leverage on pre-extrapolated strain measurements, for example, exploiting the Smoothing Element Analysis (SEA) [40,41,[45][46][47][48]. However, since pre-extrapolated strains are reasonably less accurate than sensor's strains, a small weighting coefficient (⋅) will be associated to these elements (e.g., 10 ), while unitary value is assumed for elements including physical sensors.…”

Section: Input Strain Formulationmentioning

confidence: 99%

“…In particular, if the sensor network is not optimized for the particular case under analysis, considering all the possible loading conditions, the iFEM may lead to wrong full-field reconstructions. To limit this issue, sensors must accurately describe the structure's strain field, in particular in the load direction, and input strain pre-extrapolation can increase the overall accuracy of the results, as described in [40,41]…”

Section: Input Strain Formulationmentioning

confidence: 99%

“…Thus, the model contains only rectangular elements aligned with the load direction to avoid numerical errors in the change of reference system from global to local coordinates. Finally, strains are pre-extrapolated with the Smoothing Element Analysis [35,[40][41][42] where physical sensors are not available, increasing the overall reconstruction's accuracy. The overall procedure is first assessed with a numerical study, where the structure is modeled with a direct finite element analysis to compute the reference displacement field for a specific loading condition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Shape Sensing of a Complex Aeronautical Structure with Inverse Finite Element Method

Oboe

Colombo

Sbarufatti

et al. 2021

Sensors

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The inverse Finite Element Method (iFEM) is receiving more attention for shape sensing due to its independence from the material properties and the external load. However, a proper definition of the model geometry with its boundary conditions is required, together with the acquisition of the structure’s strain field with optimized sensor networks. The iFEM model definition is not trivial in the case of complex structures, in particular, if sensors are not applied on the whole structure allowing just a partial definition of the input strain field. To overcome this issue, this research proposes a simplified iFEM model in which the geometrical complexity is reduced and boundary conditions are tuned with the superimposition of the effects to behave as the real structure. The procedure is assessed for a complex aeronautical structure, where the reference displacement field is first computed in a numerical framework with input strains coming from a direct finite element analysis, confirming the effectiveness of the iFEM based on a simplified geometry. Finally, the model is fed with experimentally acquired strain measurements and the performance of the method is assessed in presence of a high level of uncertainty.

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Section: Input Strain Formulationmentioning

confidence: 99%

Section: Input Strain Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shape Sensing of a Complex Aeronautical Structure with Inverse Finite Element Method

Oboe

Colombo

Sbarufatti

et al. 2021

Sensors

Self Cite

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“…The most attractive ones including iMIN3 [32], iQS4 [33], and iCS8 [34] inverse-shell elements employ C 0 -continuous interpolation functions in accordance with the firstorder shear deformation theory (FSDT). Particularly, the iQS4 element has recently gained a popularity for shape sensing applications on simple/complex geometries, e.g., ship and offshore structures [35][36][37][38][39] and stiffened aerospace panels [40][41], due to its merits for practical modelling of large-scale structures with low-cost sensor measurement and highly accurate displacement estimations [42][43]. Several studies have shown the superior applications of iFEM/iQS4 approach Coupling of peridynamics and inverse finite element method for shape sensing and crack propagation monitoring of plate structures for damage identification in monolithic/stiffened structures having isotropic/orthotropic material properties [44][45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Coupling of peridynamics and inverse finite element method for shape sensing and crack propagation monitoring of plate structures

Kefal

Diyaroglu

Yıldız

et al. 2022

Computer Methods in Applied Mechanics and Engineering

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“…The Inverse finite element (iFEM) method, a novel approach of SHM proposed by Tessler and Spangler [ 24 ], originally employed a three-node inverse-shell element (iMIN3) for the shape sensing of plate structures. iFEM is a strain/displacement based SHM technique which, in contrast to other available SHM methods, is suitable to monitor any displacement of complex topologies and stress fields with intricate boundary conditions by using a network of in situ strain sensors and measured strains [ 25 , 26 , 27 , 28 , 29 ]. The advantages of iFEM have recently drawn significant attention since various scientists have attempted to improve the available iFEM equations to achieve better results in the last decade.…”

Section: Introductionmentioning

confidence: 99%

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

Ghasemzadeh

Kefal

2022

Sensors

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This paper reports the first investigation of the inverse finite element method (iFEM) coupled with the genetic algorithm (GA) to optimize sensor placement models of plate/shell structures for their real-time and full-field deformation reconstruction. The primary goal was to reduce the number of sensors in the iFEM models while maintaining the high accuracy of the displacement results. Here, GA was combined with the four-node quadrilateral inverse-shell elements (iQS4) as the genes inherited through generations to define the optimum positions of a specified number of sensors. Initially, displacement monitoring of various plates with different boundary conditions under concentrated and distributed static/dynamic loads was conducted to investigate the performance of the coupled iFEM-GA method. One of these case studies was repeated for different initial populations and densities of sensors to evaluate their influence on the accuracy of the results. The results of the iFEM-GA algorithm indicate that an adequate number of individuals is essential to be assigned as the initial population during the optimization process to ensure diversity for the reproduction of the optimized sensor placement models and prevent the local optimum. In addition, practical optimization constraints were applied for each plate case study to demonstrate the realistic applicability of the implemented method by placing the available sensors at feasible sites. The iFEM-GA method’s capability in structural dynamics was also investigated by shape sensing the plate subjected to different dynamic loadings. Furthermore, a clamped stiffened plate and a curved shell were also considered to assess the applicability of the proposed method for the shape sensing of complex structures. Remarkably, the outcomes of the iFEM-GA approach with the reduced number of sensors agreed well with those of the full-sensor counterpart for all of the plate/shell case studies. Hence, this study reveals the superior performance of the iFEM-GA method as a viable sensor placement strategy for the accurate shape sensing of engineering structures with only a few sensors.

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Comparison of strain pre-extrapolation techniques for shape and strain sensing by iFEM of a composite plate subjected to compression buckling

Cited by 39 publications

References 40 publications

Shape Sensing of a Complex Aeronautical Structure with Inverse Finite Element Method

Shape Sensing of a Complex Aeronautical Structure with Inverse Finite Element Method

Coupling of peridynamics and inverse finite element method for shape sensing and crack propagation monitoring of plate structures

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

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