Next-Generation 2D Optical Strain Mapping: Strain-Sensing Smart Skin vs. Digital Image Correlation
| dc.contributor.advisor | Nagarajaiah, Satish | en_US |
| dc.contributor.advisor | Weisman, Bruce | en_US |
| dc.creator | Meng, Wei | en_US |
| dc.date.accessioned | 2022-09-26T19:25:44Z | en_US |
| dc.date.available | 2022-09-26T19:25:44Z | en_US |
| dc.date.created | 2022-12 | en_US |
| dc.date.issued | 2022-08-10 | en_US |
| dc.date.submitted | December 2022 | en_US |
| dc.date.updated | 2022-09-26T19:25:44Z | en_US |
| dc.description.abstract | Structures in various fields such as offshore oil platforms, air-crafts, and dams, play a vital role for the prosperity of our economy and technology. Many of them are critical to our lives and often subjected to severe environmental conditions, such as earthquakes, winds, and waves. In many accidents, destructive failure was initiated by small structural defects. Hence, an accurate local damage detection technique based on strain monitoring is important to ensure the safe functioning of these structures. Traditional techniques, such as resistance strain gauges and fiber Bragg grating (FBG) sensors, monitor only at discrete locations along a specific direction, and have limited ability to measure strains on small length scales. Moreover, practical issues such as deployment, connection and high-cost further limit its application. Over the past twenty years, new generation of 2D optical strain sensing techniques have been proposed and studied. Belonging to the category of image-based method, Digital Image Correlation (DIC) presents surface strain distribution by analysing optical features on photographs taken in different deforming states. As an indirect method, the strain map is generated from computation instead of measurement. Its accuracy heavily depends on camera quality and image-processing algorithms. For spectroscopy-based strain sensing method, the strain induced change of nanotube electronic structure can be directly measured as peak shifts in the single-walled carbon nanotubes (SWCNTs) near-infrared (NIR) fluorescence emission spectrum. Based on this property, strain-sensing smart skin (S4) technique was proposed. S4 uses SWCNTs embedded in thin polymer coatings as microscopic sensors. Strains in the specimen surface are transmitted to the nanotubes, causing systematic changes and spectral shifts in their NIR fluorescence signatures. Analysis of fluorescence spectra from S4 sensing films then gives local strain values based on non-contact optical measurements. This technique has been tested successfully on metal specimens. The agreement between S4 strain map and theoretical computation motivates us to explore more applications on other materials and compare it against the well established techniques such as DIC. In this thesis, we report refinements to the previously developed S4 method and comparisons against the established DIC method on noncontact strain sensing. The refined S4 design includes a dual-layer base coating consisting of an opaque primer that can block any interfering emission from the specimen plus another layer to provide a smooth surface for the sensing layer and protect the primer from solvent damage during sensing layer application. This coating design reduces spectral inhomogeneity in the nanotube sensors and avoids the need for annealing at elevated temperatures. Tests were performed on acrylic, concrete and aluminum specimens that had been shaped and stressed to generate particular strain patterns. The strain maps measured with the refined S4 films were compared with DIC and finite element method (FEM). The strain patterns presented by FEM simulations are more clearly revealed by S4 than DIC, particularly for sub-millistrain levels and on sub-millimeter length scales, which is critical in structural damage detection. These findings show the potential of S4 strain measurement technology as a promising alternative or complement to existing technologies for fracture mechanism studies, non-destructive evaluation and structural health maintenance. | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Meng, Wei. "Next-Generation 2D Optical Strain Mapping: Strain-Sensing Smart Skin vs. Digital Image Correlation." (2022) Diss., Rice University. <a href="https://hdl.handle.net/1911/113397">https://hdl.handle.net/1911/113397</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/113397 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | Two-dimensional (2D) strain mapping | en_US |
| dc.subject | non-contact strain sensing | en_US |
| dc.subject | single-wall carbon nanotubes | en_US |
| dc.subject | near-infrared fluorescence | en_US |
| dc.subject | digital image correlation (DIC) | en_US |
| dc.subject | strain/stress concentration | en_US |
| dc.title | Next-Generation 2D Optical Strain Mapping: Strain-Sensing Smart Skin vs. Digital Image Correlation | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Civil and Environmental Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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