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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

High-Temperature Sodium System Structural Health Monitoring (SHM) Technologies at the Mechanisms Engineering Test Loop (METL)

This report documents the deployment of non-destructive testing (NDT) technologies at the Mechanisms Engineering Test Loop (METL). METL became operational in September 2018 with the mission to provide an ecosystem for Advanced Reactor Development (ARD). METLs flagship facility’s primary purpose is conducting small to intermediate scale tests for Sodium Fast Reactors (SFR). Its resemblance to a commercial SFR’s intermediate heat transport system, prototypic operating conditions, and industrial construction practices/materials provides the overarching benefit of establishing a proving ground for devices of all technology readiness levels as well as commercial off-the-shelf equipment yet to be demonstrated on a sodium system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2006

Structural Health Monitoring and Impact Detection Using Neural Networks for Damage Characterization

Detection of damage due to foreign object impact is an important factor in the development of new aerospace vehicles. Acoustic waves generated on impact can be detected using a set of piezoelectric transducers, and the location of impact can be determined by triangulation based on the differences in the arrival time of the waves at each of the sensors. These sensors generate electrical signals in response to mechanical motion resulting from the impact as well as from natural vibrations. Due to electrical noise and mechanical vibration, accurately determining these time differentials can be challenging, and even small measurement inaccuracies can lead to significant errors in the computed damage location. Wavelet transforms are used to analyze the signals at multiple levels of detail, allowing the signals resulting from the impact to be isolated from ambient electromechanical noise. Data extracted from these transformed signals are input to an artificial neural network to aid in identifying the moment of impact from the transformed signals. By distinguishing which of the signal components are resultant from the impact and which are characteristic of noise and normal aerodynamic loads, the time differentials as well as the location of damage can be accurately assessed. The combination of wavelet transformations and neural network processing results in an efficient and accurate approach for passive in-flight detection of foreign object damage.

Ross, Richard W.↗
NASA NTRS2007

Optical Fiber Distributed Sensing Structural Health Monitoring (SHM) Strain Measurements Taken During Cryotank Y-Joint Test Article Load Cycling at Liquid Helium Temperatures

This paper outlines cryogenic Y-joint testing at Langley Research Center (LaRC) to validate the performance of optical fiber Bragg grating strain sensors for measuring strain at liquid helium temperature (-240 C). This testing also verified survivability of fiber sensors after experiencing 10 thermal cool-down, warm-up cycles and 400 limit load cycles. Graphite composite skins bonded to a honeycomb substrate in a sandwich configuration comprised the Y-joint specimens. To enable SHM of composite cryotanks for consideration to future spacecraft, a light-weight, durable monitoring technology is needed. The fiber optic distributed Bragg grating strain sensing system developed at LaRC is a viable substitute for conventional strain gauges which are not practical for SHM. This distributed sensing technology uses an Optical Frequency Domain Reflectometer (OFDR). This measurement approach has the advantage that it can measure hundreds of Bragg grating sensors per fiber and the sensors are all written at one frequency, greatly simplifying fiber manufacturing. Fiber optic strain measurements compared well to conventional strain gauge measurements obtained during these tests. These results demonstrated a high potential for a successful implementation of a SHM system incorporating LaRC's fiber optic sensing system on the composite cryotank and other future cryogenic applications.

Allison, Sidney G.↗
NASA NTRS2010

Optical Structural Health Monitoring Device

This non-destructive, optical fatigue detection and monitoring system relies on a small and unobtrusive light-scattering sensor that is installed on a component at the beginning of its life in order to periodically scan the component in situ. The method involves using a laser beam to scan the surface of the monitored component. The device scans a laser spot over a metal surface to which it is attached. As the laser beam scans the surface, disruptions in the surface cause increases in scattered light intensity. As the disruptions in the surface grow, they will cause the light to scatter more. Over time, the scattering intensities over the scanned line can be compared to detect changes in the metal surface to find cracks, crack precursors, or corrosion. This periodic monitoring of the surface can be used to indicate the degree of fatigue damage on a component and allow one to predict the remaining life and/or incipient mechanical failure of the monitored component. This wireless, compact device can operate for long periods under its own battery power and could one day use harvested power. The prototype device uses the popular open-source TinyOS operating system on an off-the-shelf Mica2 sensor mote, which allows wireless command and control through dynamically reconfigurable multi-node sensor networks. The small size and long life of this device could make it possible for the nodes to be installed and left in place over the course of years, and with wireless communication, data can be extracted from the nodes by operators without physical access to the devices. While a prototype has been demonstrated at the time of this reporting, further work is required in the system s development to take this technology into the field, especially to improve its power management and ruggedness. It should be possible to reduce the size and sensitivity as well. Establishment of better prognostic methods based on these data is also needed. The increase of surface roughness with fatigue is closely connected to the microstructure of the metal, and ongoing research is seeking to connect this observed evidence of the fatigue state with microstructural theories of fatigue evolution to allow more accurate prognosis of remaining component life. Plans are also being discussed for flight testing, perhaps on NASA s SOFIA platform.

Buckner, Benjamin D.↗
NASA NTRS2015

NASA Prototype All Composite Tank Cryogenic Pressure Tests to Failure with Structural Health Monitoring

This Paper will describe the results of pressurization to failure of 100 gallon composite tanks using liquid nitrogen. Advanced methods of health monitoring will be compared as will the experimental data to a finite element model. The testing is wholly under NASA including unique PZT (Lead Zirconate Titanate) based active vibration technology. Other technologies include fiber optics strain based systems including NASA AFRC technology, Acoustic Emission, Acellent smart sensor, this work is expected to lead to a practical in-Sutu system for composite tanks.

tanks↗
NASA NTRS2002

Health Monitoring for Airframe Structural Characterization

This study established requirements for structural health monitoring systems, identified and characterized a prototype structural sensor system, developed sensor interpretation algorithms, and demonstrated the sensor systems on operationally realistic test articles. Fiber-optic corrosion sensors (i.e., moisture and metal ion sensors) and low-cycle fatigue sensors (i.e., strain and acoustic emission sensors) were evaluated to validate their suitability for monitoring aging degradation; characterize the sensor performance in aircraft environments; and demonstrate placement processes and multiplexing schemes. In addition, a unique micromachined multimeasure and sensor concept was developed and demonstrated. The results show that structural degradation of aircraft materials could be effectively detected and characterized using available and emerging sensors. A key component of the structural health monitoring capability is the ability to interpret the information provided by sensor system in order to characterize the structural condition. Novel deterministic and stochastic fatigue damage development and growth models were developed for this program. These models enable real time characterization and assessment of structural fatigue damage.

Munns, Thomas E.↗
NASA NTRS2020

Utilizing Modal Testing for Monitoring the Structural Health of Wind Tunnel Facility Hardware

The 10- by 10-Foot Abe Silverstein Supersonic Wind Tunnel (10×10) is the largest and fastest wind tunnel facility at NASA’s Glenn Research Center(GRC) and is specifically designed to test supersonic propulsion components from inlets and nozzles to full-scale jet and rocket engines[1]. Recently, a critical part of the wind tunnel failed and required a redesign before reintegrating into the facility. The design requirements of this new component required that clearances between large metallic components exist, which have the potential for undesirable nonlinear dynamics to occur, in particular rattling. Rattling is feared to occur when the wind tunnel is being operated in certain flow regimes that induce cyclic aero loads on the new component near its natural frequencies. This paper describes the approach taken to better understand and resolve this vibration problem using modal testing. A modal test was developed and executed by GRC’s Structural Dynamics Lab to quantify the modal parameters of the structure, namely which specific excitation frequencies caused the structure to rattle. These results were shared with facility operators as frequency ranges that should be avoided to ensure maximum lifespan of the new structure. Additional means of structural health monitoring (SHM) as well as Vortex shedding are briefly discussed in this paper.

Pederson, Kenneth↗
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2005

Ability of Impedance-Based Health Monitoring To Detect Structural Damage of Propulsion System Components Assessed

Impedance-based structural-health-monitoring uses piezoelectric (PZT) patches that are bonded onto or embedded in a structure. Each individual patch behaves as both an actuator of the surrounding structural area as well as a sensor of the structural response. The size of the excited area varies with the geometry and material composition of the structure, and an active patch is driven by a sinusoidal voltage sweep. When a PZT patch is subjected to an electric field, it produces a mechanical strain; and when it is stressed, it produces an electric charge. Since the patch is bonded to the structure, driving a patch deforms and vibrates the structure. The structure then produces a localized dynamic response. This structural system response is transferred back to the PZT patch, which in turn produces an electrical response. The electromechanical impedance method is based on the principle of electromechanical coupling between the active sensor and the structure, which allows researchers to assess local structural dynamics directly by interrogating a distributed sensor array. Because of mechanical coupling between the sensor and the host structure, this mechanical effect is picked up by the sensor and, through electromechanical coupling inside the active element, is reflected in electrical impedance measured at the sensor s terminals.

Martin, Richard E.↗
NASA NTRS2009

Fiber Optic Thermal Health Monitoring of Aerospace Structures and Materials

A new technique is presented for thermographic detection of flaws in materials and structures by performing temperature measurements with fiber Bragg gratings. Individual optical fibers with multiple Bragg gratings employed as surface temperature sensors were bonded to the surfaces of structures with subsurface defects or thickness variations. Both during and following the application of a thermal heat flux to the surface, the individual Bragg grating sensors measured the temporal and spatial temperature variations. The investigated structures included a 10-ply composite specimen with subsurface delaminations of various sizes and depths. The data obtained from grating sensors were further analyzed with thermal modeling to reveal particular characteristics of the interested areas. These results were found to be consistent with those from conventional thermography techniques. Limitations of the technique were investigated using both experimental and numerical simulation techniques. Methods for performing in-situ structural health monitoring are discussed.

Wu, Meng-Chou↗
DOE OSTI2021

Self-sensing of printed polymer structures

A structural health monitoring method is provided that utilizes self-sensing printed polymer structures. The method is based on resistivity properties of conductive materials, which can be integrated to a 3D printed polymer structure during additive manufacturing. An article to be monitored has at least one 3D printed polymer structure including a circuit comprising at least one conductive pathway extending through a non-conductive material. The resistance across the circuit is measured during or after loading of the article to determine a resistance value. The measured resistance value is compared to a known resistance value, and based on the comparison, a defect can be detected in the 3D printed polymer structure. Structural health monitoring systems and articles with integrated structural health monitoring are also provided.

Kunc, Vlastimil↗