Feasibility Study on the Use of the Coplanar Capacitive Sensing Technique for Underwater Non-Destructive Evaluation
Source abstract
Abstract
Abstract Recent advancements in Non-Destructive Evaluation (NDE) techniques have demonstrated potential in assessing underwater structural integrity. However, evolving maritime structures demand more efficient, user-friendly, and technologically advanced underwater NDE methods. Building on successful applications in air as a medium, this paper explores the feasibility of utilizing coplanar capacitive sensors to gauge structural integrity in underwater environments, drawing on assertions made by pioneering scholars. The study employs simulations, complemented by experimental validation, to assess its viability. With artificial surface defects in both non-conducting and conducting specimens, this study conducts a comprehensive comparison of the performance between the bare-electrode and insulated-electrode Coplanar Capacitive Sensor (CCS). The outcomes affirm the viability of utilizing the technique for underwater NDE. Notably, the study reveals that electrical conductivity is a significantly influential factor, and there are discernible differences in response between the two sensor configurations. The nature of the response in non-conducting materials is intricately tied to the dominant sensitivity value region. However, detecting defects in conducting materials poses a challenge in some instances. Overall, results show that defect detection, characterisation and imaging under water are feasible, thereby emphasizing the techniques potential for underwater NDE. This study broadens underwater NDE knowledge and offers a viable alternative for inspecting structures and equipment in underwater environments.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Non-Destructive Testing Techniques · Structural Health Monitoring Techniques · Ultrasonics and Acoustic Wave Propagation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
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