Shape Sensing 2021-2024
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 14068
Special Issue Editor
Interests: multilayered composite and sandwich structures; structural health monitoring; shape sensing; finite element method
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Structural Health Monitoring/Management (SHM) is the latest technology utilizing advanced sensor networks for real-time monitoring and assessment of structural integrity. This technology is of an increased interest for application to existing and next generation aerospace and naval vehicles and structures. Current areas for application of SHM include civilian and military aircraft, spacecraft, naval and off-shore structures, and civil engineering structures, such as bridges and tunnels. The massive amounts of sensor data can be processed and analyzed using physics-based inverse and direct discretization methods, similar to the widely used direct Finite Element Method for structural analysis and design. Damage detection can also be inferred from the processed in-situ temperature and strain-sensor data, thus enabling improved maintanance of structural components based on the actual structural loads sustained during operational service environment.
This Special Issue is focuced on bringing together the latest advances in physics-based solution methods suitable for the large-scale structural applications to enable real-time structural health monitoring of aerospace, civil, and marine structures. The topics include but are not limited to:
- Shape sensing
- Inverse Finite Element Methods
- Modal reconstruction methods
- Methods for real-time damage assessment
- Methods for real-time delamination damage assessment in laminated composite structures
- Sensor optimization methods and studies
- Advanced and affordable strain sensor technologies for large-scale applications
- Method for material characterization inferred from sensor data
Prof. Dr. Marco Gherlone
Guest Editor
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