New smart materials to address issues of structural health monitoring.
Nuclear weapons and their storage facilities may benefit from in-situ structural health monitoring systems. Appending health-monitoring functionality to conventional materials and structures has been only marginally successful. The purpose of this project was to evaluate feasibility of a new smart material that includes self-sensing health monitoring functions similar to that of a nervous system of a living organism. Reviews of current efforts in the fields of heath-monitoring, nanotechnology, micro-electromechanical systems (MEMS), and wireless sensor networks were conducted. Limitations of the current nanotechnology methods were identified and new approaches were proposed to accelerate the development of self-sensing materials. Wireless networks of MEMS sensors have been researched as possible prototypes of self-sensing materials. Sensor networks were also examined as enabling technologies for dense data collection techniques to be used for validation of numerical methods and material parameter identification. Each grain of the envisioned material contains sensors that are connected in a dendritic manner similar to networks of neurons in a nervous system. Each sensor/neuron can communicate with the neighboring grains. Both the state of the sensor (on/off) and the quality of communication signal (speed/amplitude) should indicate not only a presence of a structural defect but the nature of the defect as well. For example, a failed sensor may represent a through-grain crack, while a lost or degraded communication link may represent an inter-granular crack. A technology to create such material does not exist. While recent progress in the fields of MEMS and nanotechnology allows to envision these new smart materials, it is unrealistic to expect creation of self-sensing materials in the near future. The current state of MEMS, nanotechnology, communication, sensor networks, and data processing technologies indicates that it will take more than ten years for the technologies to mature enough to make self-sensing materials a reality. Nevertheless, recent advances in the field of nanotechnology demonstrate that nanotubes, nanorods, and nanoparticles of carbon, boron and other materials have remarkable mechanical and electrical properties. This would provide. for a plethora of potential applications including self-sensing materials. Record strength-to-weight ratios, ballistic conductivity, and sensing capabilities (i.e., piezo- resistance and piezoelectricity) have been reported for carbon nanotubes. The first transistors, sensors, and actuators have been made from the carbon nanotubes and other nanomaterials. However, nanomaterials are notoriously difficult to manipulate into useful geometries. Nano-manufacturing processes often produce bundles or random networks of nanostructured materials. Samples of the material are then manipulated with advanced microscopy tools to measure properties or to create a single device. This is a laborious and time consuming process. An often overlooked property of the manufactured nanotube bundles is their similarity to the dendritic structure of neural networks with a great quantity of interconnects that may serve as initiation sites for artificial neurons in a self-sensing material nervous system. To accelerate the development of self-sensing materials, future research should concentrate on naturally occurring dendritic nano-structures. While self-sensing materials with subgrain size sensors (scale of micrometers) remain in the realm of basic research, meso-scale (millimeters to centimeters) sensors and their networks are in the state of mature research and have begun to find their way into commercial applications. Macro-scale (centimeters to decimeters) sensors and their networks are commercially available from various sources. The majority of applications that employ sensor networks are driven by the needs of the Department of Defense. Widespread adaptation of sensor networks has been limited by, on one hand, the sensor's high cost of design, development, and deployment, and on the other hand, a lack of reliable long-term power sources. Solutions to both of these drawbacks require significant investments driven by real-life applications. Possible applications for sensor networks at Sandia National Laboratories include dense data collection techniques for validation of numerical methods and material parameter identification. For example, an array of distributed wireless macro-scale sensors can record the structural response of soils and reinforced concrete during explosive loading. Another example is an array of surface mounted micro-sensors that can record the modal response of nuclear weapon components. The collected data would be used to validate existing numerical codes and to identify new physical mechanisms to improve Sandia's computational models.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 920836
- Report Number(s):
- SAND2004-6354; TRN: US200803%%121
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE
CARBON
DATA PROCESSING
ELECTRICAL PROPERTIES
MICROSCOPY
MONITORING
NANOTUBES
NERVE CELLS
NERVOUS SYSTEM
NEURAL NETWORKS
NUCLEAR WEAPONS
PIEZOELECTRICITY
REINFORCED CONCRETE
STORAGE FACILITIES
US DOD
Smart materials.
Nanostructure materials.
Sensors.