A smoothed iFEM approach for efficient shape-sensing applications: Numerical and experimental validation on composite structures

Kefal, Adnan; Tabrizi, Isa Emami; Yıldız, Mehmet; Tessler, Alexander

doi:10.1016/j.ymssp.2020.107486

Cited by 92 publications

(49 citation statements)

References 60 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%

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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

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et al. 2021

Sensors

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show abstract

“…It should be noted that an exact inverse is not possible since the fiber strain calculation using Equation (29) is irreversible. Previous studies have proposed the inverse method using least squares for plates [ 16 ], using inverse FEM (iFEM) for shells and plates [ 17 ] and, recently, using inverse IGA-iFEM for shells with strain gauge rosettes [ 36 ]. However, our formulations of integrated IGA and DFOS presented in Section 2 and the following inverse method are substantially different since our method explicitly considers the geometry of the fiber and does not impose any restriction on the location or resolution of sampling points.…”

Section: Estimation Of Boundary Conditions Using Inverse Igamentioning

confidence: 99%

“…In addition, there is a gap in information between directly measured strains and the necessary engineering data. The measurement data are usually sparse and limited in terms of type of information, demanding the use of numerical analysis and finite element tools in order to translate data into insights [ 16 , 17 ]. For example, DFOS can be used to obtain distributed strain measurements along the optical fiber, and numerical tools are used to estimate deformation, loading conditions and stress distributions in the structure.…”

Section: Introductionmentioning

confidence: 99%

Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing

Aung

Kishida³

et al. 2021

Sensors

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Attempts in digital management of structures are among the most popular topics in the trend of Information of Things (IoT). However, the implementation lags behind. This work recognized that Computer Aided Design (CAD) comprises the core of modern engineering; thus, most digital information can be available if CAD is used not only in design but also for life cycle structural health monitoring (SHM). Based on this concept, the newly designed method utilizes the isogeometric analysis (IGA) tool to include the Distributed Fiber Optic Sensing (DFOS) information by proposing a fiber mesh model. The IGA model can be obtained directly from CAD, and the boundary conditions can be provided directly or indirectly from DFOS in real time and remotely. Hence a practical method of SHM is able to achieve highly efficient and accurate numerical model creation, which can even accommodate non-linear constitutive property of materials. The proposed method was applied to a pipe deformation model as an example. The inverse analysis method is also shown to determine the contact force for loading on the pipe, which shows the potential for many engineering applications.

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A Full Textile Capacitive Woven Sensor

Martínez-Estrada

Ventura

Gil

et al. 2022

Adv Materials Technologies

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In this paper, a full textile capacitive woven sensor integrated over a textile substrate is presented. The sensor consists in an interdigitated capacitance prepared to measure moisture and/or presence detection. In order to evaluate the sensor response to moisture, capacitance has been measured by means of an LCR meter from 20 Hz to 20 kHz in a climatic chamber with a swept of the relative humidity (RH) from 30% to 90% at 20 °C. Subsequently, presence response is evaluated measuring the capacitance of the woven sensor meanwhile a person is sitting down and getting up. The woven sensor results demonstrate its functionality over moisture measurement where sensor capacitance changes from a minimum of 9.74 pF at 30% RH to a maximum of 2.31 uF at 90% RH. The presence detection is also demonstrated, which makes the capacitance variation change from a 10% of capacitance variation when the chair is empty, to a capacitance variation of 170% when a person is sitting on it. The adaptation of the weaving process to accomplish a fully integrated sensor provides a better repeatability than previous embroidered sensors and opens a door to being commercially produced.

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A smoothed iFEM approach for efficient shape-sensing applications: Numerical and experimental validation on composite structures

Cited by 92 publications

References 60 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

Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing

A Full Textile Capacitive Woven Sensor

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