Novel computation method of reducing ill-posedness for structural static distributed load identification by optimising strain gauge locations

Zhang, Meng; Qiu, Bingsen; Zhu, Maoyu; Qu, Xianqiang

doi:10.1016/j.ymssp.2019.01.051

Cited by 27 publications

(14 citation statements)

References 31 publications

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“…The aim of the experimental measurement was to take real boundary conditions into account, including bonds in the bearing, load dynamics and other operational impacts. The knowledge of time-dependent stress charts in selected places can be considered, among other things, as a tool for validation of numerical modelling results [ 31 ]. Deeper knowledge of these conditions will allow to draw conclusions about stress states in individual modes of operation as well as about residual life of the sluice gate.…”

Section: Experimental Determination Of Time Changes Of Stresses Inmentioning

confidence: 99%

Experimental and Numerical Analysis of 60-Year-Old Sluice Gate Affected by Long-Term Operation

et al. 2020

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Technological units of water reservoirs and power plants include sluice gates which are designed to completely seal the inflow or outflow of water in supply or discharge channels. This article describes the issue of technical assessment of a sluice gate made in the 1950s. Such structures are characterised by states of significant corrosive wear, permanent deformations of contact and sealing surfaces as well as increased levels of residual stresses. In such cases, it is difficult to determine service life using only numerical modelling methods, mainly due to problematic definition of material properties and boundary conditions. Therefore, for safety assessment, it is necessary to verify these facts experimentally. This article presents the procedure for assessing safe operation of the sluice gate on which places with permanent deformation and a broken part of the guide wheel flange were identified. By means of numerical modelling, we identified critical stress values at the locations of reinforcing elements, which were modified, and the stress values were reduced by about 15%. The results of numerical modelling were verified at select locations by experimental measurements during operation using strain gauges. The maximum values of operational normal stresses in the assessed places reached about 27 MPa. Based on the comparison of obtained results and taking into account values of residual stresses reaching up to 190 MPa made by shielded metal arc welding, it can be stated that, for safe operation of the sluice gate, it is necessary to follow the proposed procedure during its lowering and to modify the reinforcing elements structurally.

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Section: Experimental Determination Of Time Changes Of Stresses Inmentioning

confidence: 99%

Experimental and Numerical Analysis of 60-Year-Old Sluice Gate Affected by Long-Term Operation

et al. 2020

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“…Yet, even when the number of response measurements is greater than or equal to the number of desired loading outputs, the system matrix may easily be ill-conditioned, meaning small measurement errors can be dramatically amplified upon inversion [1]. Further, the quality of the force reconstruction is typically sensitive to the locations at which sensors are placed [2,3]. While algorithms for determining optimal sensor systems exist, little guidance is available for configuring sensor placement in the absence of numerical models [2,3].…”

Section: Introductionmentioning

confidence: 99%

Optimal design of strain sensor placement for distributed static load determination

Morris,

Davis

2023

Inverse Problems

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In many applications it is desirable to inverse-calculate the distributed loading on a structure using a limited number of sensors. Yet, the calculated loads can be extremely sensitive to the placement of these sensors. In the case of predicting point loading applied at a known location, best results are typically achieved when one sensor is collocated with the force. However, the extension of this rule to distributed loading remains uncertain, and even simple sensor system design scenarios often require the designer to directly optimize the sensor placements using a numerical model. In an effort to provide designers with guidance, we identify optimal sensor configurations for predicting static distributed loads on beams with classical boundary conditions. An influence coefficient method, wherein the strain is related linearly to the static load, is used to estimate the applied forces. The loading distribution on the structure is assumed to be either a piece-wise linearly-distributed load or a uniformly-distributed load, allowing for distributed loads to be estimated using the magnitudes of a small number of control points. Given the simplicity of the beam structure, the equations of the influence coefficient method are derived analytically, which allows for the sensor placement to be specified using continuous optimization methods. The condition number of the influence coefficient matrix is used as a surrogate for error during optimization. ‘Rules of thumb’ for sensor placement are presented based on the optimization results. Results show that the optimal and rule-of-thumb sensor configurations are more resistant to input noise than naïve configurations, with the rule-of-thumb configurations yielding similar force predictions relative to the optimal configurations. We expect the rules of thumb to be useful guidelines for engineers designing tests on beam-like structures such as aircraft wings or marine propellers where the inverse calculation of distributed loads is of interest.

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“…They have a range of industrial uses in addition to scientific and technological research and development [ 2 ]. Load cells have been providing quality measurements in robotics and automation [ 3 , 4 , 5 ], agriculture [ 6 , 7 ], medicine [ 8 , 9 , 10 ], industrial weighing [ 11 , 12 ], and many other applications for decades [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. The load cell can also be used to gauge the interference force in wearable robotic applications (such as exoskeletons and prostheses) or to detect the comparison force as the robot walks to provide information to the stable-controlling system [ 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Shape Optimization of Strain Gauge Load Cell for Axial Force Measurement for Test Benches

Al-Dahiree

Tokhi

Hadi

et al. 2022

Sensors

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The load cell is an indispensable component of many engineering machinery and industrial automation for measuring and sensing force and torque. This paper describes the design and analysis of the strain gauge load cell, from the conceptional design stage to shape optimization (based on the finite element method (FEM) technique) and calibration, providing ample load capacity with low-cost material (aluminum 6061) and highly accurate force measurement. The amplifier circuit of the half Wheatstone bridge configuration with two strain gauges was implemented experimentally with an actual load cell prototype. The calibration test was conducted to evaluate the load cell characteristics and derive the governing equation for sensing the unknown load depending on the measured output voltage. The measured sensitivity of the load cell is approximately 15 mV/N and 446.8 µV/V at a maximum applied load of 30 kg. The findings are supported by FEM results and experiments with an acceptable percentage of errors, which revealed an overall error of 6% in the worst situation. Therefore, the proposed load cell meets the design considerations for axial force measurement for the laboratory test bench, which has a light weight of 20 g and a maximum axial force capacity of 300 N with good sensor characteristics.

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Novel computation method of reducing ill-posedness for structural static distributed load identification by optimising strain gauge locations

Cited by 27 publications

References 31 publications

Experimental and Numerical Analysis of 60-Year-Old Sluice Gate Affected by Long-Term Operation

Experimental and Numerical Analysis of 60-Year-Old Sluice Gate Affected by Long-Term Operation

Optimal design of strain sensor placement for distributed static load determination

Design and Shape Optimization of Strain Gauge Load Cell for Axial Force Measurement for Test Benches

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