Search NASA⌕ Search

SEARCH · Search NASA

Results for “Structural health monitoring”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Frequency‐Locked Wireless Multifunctional Surface Acoustic Wave Sensors

Abstract Surface acoustic waves (SAWs) have shown great potential for developing sensors for structural health monitoring (SHM) and lab‐on‐a‐chip (LOC) applications. Existing SAW sensors mainly rely on measuring the frequency shifts of high‐frequency (e.g., >0.1 GHz) resonance peaks. This study presents frequency‐locked wireless multifunctional SAW sensors that enable multiple wireless sensing functions, including strain sensing, temperature measurement, water presence detection, and vibration sensing. These sensors leverage SAW resonators on piezoelectric chips, inductive coupling‐based wireless power transmission, and, particularly, a frequency‐locked wireless sensing mechanism that works at low frequencies (e.g., <0.1 GHz). This mechanism locks the input frequency on the slope of a sensor's reflection spectrum and monitors the reflection signal's amplitude change induced by the changes of sensing parameters. The proof‐of‐concept experiments show that these wireless sensors can operate in a low‐power active mode for on‐demand wireless strain measurement, temperature sensing, and water presence detection. Moreover, these sensors can operate in a power‐free passive mode for vibration sensing, with results that agree well with laser vibrometer measurements. It is anticipated that the designs and mechanisms of the frequency‐locked wireless SAW sensors will inspire researchers to develop future wireless multifunctional sensors for SHM and LOC applications.

Bo, Luyu↗
DOE OSTI2023

Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks

Abstract Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite ® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2024

Transfer learning for probabilistic localization of hidden cracks in concrete structures

Abstract The utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

Miele, S.↗
DOE OSTI2021

Effects of fiber dosage, loading orientation and stress on frequency response of enhanced Carbon Nano-Fiber Aggregates

Nanomaterial additives have been broadly used in cement and concrete-based sensors to measure the stress and strain in the structure. Most of the studies focused on measuring the resistivity of sensors with Direct Current (DC) or relatively low-frequency. Here in this work, the impedance responses of enhanced Carbon Nano-Fiber Aggregates (CNFAs) at different frequencies are rigorously studied and utilized as a tool for real-time structural health monitoring (SHM). The CNFA impedance measurements at different measurement frequencies provide an extra dimension (frequency dimension) to allow the characterization of different phenomena and multimodal measurements simultaneously. First, a comprehensive physical model is established to understand the frequency responses of the CNFA impedance. Second, COMSOL Multiphysics is used to simulate the frequency responses of the CNFA impedance, which provides more insights into the electric field and current density distribution. The impedance of CNFAs decreased with the increased dosage of carbon nanofibers (CNFs). Third, experimental studies are reported in detail. The electrical impedance variation (EZV) of the CNFA in parallel orientation is 151% higher than that of perpendicular orientation at the frequency of 2612 Hz and uniaxial compression stress of 4.65 MPa. The strain-EZV curve obtained from the response spectrum is linear for frequencies ranging from 98 Hz to 463.9 kHz. The strain-EZV curve for 5625 Hz has a linear fit with the gauge factor of 147.78. At 1000 Hz, the CNFA exhibited a repeatable behavior up to 9.35 MPa and detection limit up to the stress of 18.62 MPa.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Slow dynamic elasticity at short times

It has been reported that slow dynamic nonlinear elastic relaxations, widely thought to proceed universally in proportion to the logarithm of time after cessation of mechanical conditioning, actually recover with a smaller slope at early times, with a time of transition that varies with the grain size of the material. Furthermore, this would constitute a heretofore unreported failure of the claimed universality, while suggesting application to nondestructive evaluation and structural health monitoring. Here we present new observations at short times, in the single bead system, in cement paste, mortar, concrete, sandstone and granite. Within the limits imposed by finite duration ring-down such that the effective instant of conditioning cessation is imprecise, and the corresponding ambiguity as to the time at which relaxation begins, we find no reliable sign of such a transition, even in samples of large grain size mortar and concrete similar to those described elsewhere as having clear and late cutoffs.

58 GEOSCIENCES↗
DOE OSTI2022

Temperature and Dynamic Strain Measurements Using a Single SAWR Sensor

Dynamic strain sensing is relevant in numerous applications involving structural health monitoring, condition-based maintenance, operation efficiency preservation, and work environment safety. These sensors are particularly critical for high-temperature (HT) harsh-environment (HE) industries such as aerospace, automotive, power plants, and advanced manufacturing. In HT/HE, sensor implementation and measurement present challenges such as maintaining sensor stability, accounting for temperature cross-sensitivity, providing HT attachment, and packaging of the sensors and system. Surface acoustic wave resonator (SAWR) sensors can address these needs and offer additional benefits such as compact size and wireless interrogation capability. The SAWR sensitivity to dynamic strain is temperature dependent, making it necessary to measure the sensor temperature in order to use the correct dynamic strain calibration curve. In this work, a method for determining the temperature, dynamic strain magnitude, and dynamic strain spectral components using a single SAWR sensor is presented. Here, the established technique for determining operational temperature and dynamic strain magnitude/spectral components using only one SAWR simplifies the sensor measurement system, thus being very attractive for HT /HE applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Electromechanical response of piezoelectric thin films subjected to large strain

The use of piezoelectric thin sensors, when subjected to large static strain levels in ultrasonic applications is reported in this paper. A novel flipped beam bending method is proposed to induce static strain level nearing twice the incipient yield strain in the beam material (~0.39%). First, the effect of static large strain on ultrasonic guided wave propagation in the beam was studied using the velocity measurements obtained from a laser Doppler vibrometer. Next, the ultrasonic response of thin sensors is studied under compressive and tensile static strain (±0.72%) with the sensors symmetrically bonded on the lower and upper surfaces, and at the same axial location. The ultrasonic guided wave response to varying levels of static strain is recorded at different center frequencies (50–400 kHz) of the narrow banded ultrasonic excitation. As a case study, the variation of the ultrasonic guided wave response of polyvinylidene difluoride (PVDF) thin sensors with center frequencies at different static strain levels was analyzed and compared with that of lead zirconate titanate (PZT) thin sensors. PZT sensors show a higher variation of sensing performance with strain when compared to PVDF sensors. Apart from static strain, the possible effects on the change in performance of sensors, such as the change in the capacitance is discussed. The characterization method provides a reusable beam arrangement to study the ultrasonic sensor performance at large static strain levels. Characterization at large strain level allows the development of fail-safe sensors even under large strain and permanent plastic deformation of structure on which they are bonded, allowing accurate ultrasonic structural health monitoring even in the presence of damage progression. Piezopolymer sensor like PVDF are least susceptible to static strain due to large yield strain and low modulus and can serve as fail-safe sensors under large strain.

Rathod, Vivek↗
DOE OSTI2025

Machine learning-driven design and self-sensing capabilities of automotive bumper lattices for adaptive impact response

We present a novel approach to design an automotive bumper energy absorber using carbon fiber reinforced polymer composites, optimized to meet conflicting performance requirements for two distinct impact scenarios. The design must satisfy both a low-speed (2.5 mph) pendulum intrusion test, simulating vehicle-to-vehicle collisions, and a high-speed (25 mph) leg flexion test, replicating pedestrian impacts. These tests demand opposing deformation characteristics: high flexibility (deformation < 85 mm) for the former and high stiffness (deformation < 22 mm) for the latter. To address these contradictory requirements, we developed a machine learning (ML) framework for inverse optimization of lattice designs and material selection. Unlike traditional iterative design processes, our ML model directly outputs optimal design parameters and material choices based on target performance inputs. The energy absorber was fabricated using advanced additive manufacturing techniques, including extrusion deposition and digital light processing. The integration of carbon fibers provides multifunctionality to the bumper structure, enabling self-sensing capabilities through changes in electrical resistivity under compression. This electrical response demonstrates high repeatability under multiple cycles at 2% compression and exhibits distinct signatures during crack formation under high deformation. This research offers adaptive performance through innovative design methodologies and smart material integration. The approach has potential applications in various fields requiring adaptive energy absorption and real-time structural health monitoring.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2022

Acoustic Data Analytics For Predictive Technologies

The software utilizes frequency, amplitude, and phase features from sensor signals (either raw or processed) that are extracted using recursive frequency analysis, enabling (1) understanding of reactor operations under different operating conditions, (2) identifying of quiescent frequency ranges for sensor development, (3) system controls (4) predictive analytics, (5) structural health monitoring, and (6) loose part monitoring.

Smith, JamesA.↗
DOE OSTI2023

Development, Testing, and Validation of Fabry-Pérot Cavity Acoustic Sensors for Microreactor Applications

Nuclear microreactors are a unique reactor design which may allow for the nuclear energy industry to tap into unconventional markets, and their modularity presents some advantages over larger, conventional reactor designs. For nuclear microreactors to be economically viable, on-line structural health monitoring may be necessary to reduce operation and maintenance costs with lower power production. Optical fiber sensors, and specifically optical Fabry-Pérot cavities (FPCs), may present the opportunity to monitor vibrational frequencies of microreactor components during operation, enabling identification of the location and severity of potential damage. In this work, FPCs were designed and fabricated with materials which may be able to survive in the extremely harsh environment of nuclear microreactors. The FPCs were bonded to metal test specimens to monitor their vibrational frequencies, along with a reference piezoelectric accelerometer. Euler-Bernoulli theory was used to calculate the fundamental modes and mode-shapes to compare with experimentally measured frequency spectra. The peak frequencies detected with the FPC and piezoelectric accelerometer, and theoretical modes, all agreed well for the first four fundamental modes (within 5%). Furthermore, when the FPC and accelerometer were co-located on the test specimen, their peak frequencies all agreed within 1%.

Birri, Tony↗
DOE OSTI2020

Information integrated glass module fabricated by integrated additive and subtractive manufacturing

In this Letter, we report a novel integrated additive and subtractive manufacturing (IASM) method to fabricate an information integrated glass module. After a certain number of glass layers are 3D printed and sintered by direct C O 2 laser irradiation, a microchannel will be fabricated on top of the printed glass by integrated picosecond laser, for intrinsic Fabry–Perot interferometer (IFPI) optical fiber sensor embedment. Then, the glass 3D printing process continues for the realization of bonding between optical fiber and printed glass. Temperature sensing up to 1000°C was demonstrated using the fabricated information integrated module. In addition, the long-term stability of the glass module at 1000°C was conducted. Enhanced sensor structure robustness and harsh temperature sensing capability make this glass module attractive for harsh environment structural health monitoring.

Zhang, Qi (ORCID:0000000268655981)↗
DOE OSTI2020

Demonstrate embedding of sensors in a relevant microreactor component

This report describes the first successful embedding of sensors in stainless steel pipe specimens and a miniature hex block to support structural health monitoring of microreactor components during electrically heated testing in non-nuclear testbed facilities. Sensors were embedded with an ultrasonic additive manufacturing process that uses a combination of downward pressure and an ultrasonic scrubbing motion to bond stainless steel foils to underlying materials. After placing sensors within cavities that have been machined to the size of the sensors, the ultrasonic bonding process fills the gaps around the sensors and embeds them within the part. Both Type K thermocouples and spatially distributed fiber-optic strain sensors were successfully embedded within two pipe specimens and a miniature hex block. After the fiber-optic sensors were embedded, they were interrogated and found to have minimal signal attenuation within the embedded region—a significant improvement compared to previous trials. This is likely because of the low-bend-loss fiber that was used, which greatly reduces microbending losses that occur after the embedding process. In addition to showing minimal signal attention, the fibers also showed adequate compressive strain, which is a strong indication that the sensors are physically embedded in the parts as opposed to simply being loosely contained inside the machined cavities. The embedded fibers were able to capture the strain induced in one of the pipe specimens while the pipe was actively heated with a moving heat source.

42 ENGINEERING↗
DOE OSTI2021

Alternative Damage Tolerant Materials for Wind Turbine Blades: An Overview

Current wind turbine blade materials may not be damage tolerant to the extent necessary to optimize the Levelized Cost of Energy (LCOE) of wind energy plants. Traditionally, wind turbine blades have been designed using a safe-life approach, but advances in inspection techniques and structural health monitoring solutions give rise to the opportunity to design wind turbine blades using a damage tolerant approach. Materials selection is a key element of da mage tolerant design, so the extent of the damage tolerance of alternative materials has been analyzed through a literature review and discussions with industry leaders. Fabrics and resin selection significantly affect the damage tolerance of composites. Changes to fabric architecture may include through-the-thickness (TTT) fibers, stretch-broken carbon fiber (SBCF) composites, and aligned discontinuous fiber reinforced composites (ADFRCs). Previous research has demonstrated that using TTT fibers in creases damage tolerance, but additional research is necessary to demonstrate the effectiveness of SBCFs and ADFRCs in mitigating damage. Several studies have demonstrated increased damage tolerance when toughened resin systems are used. In addition to toughened resin systems, thermoplastics have been shown to be tougher than thermosets. However, thermosets have been traditionally preferred in wind turbine blade manufacturing due to ease of manufacturing. Thermoplastic resin system s have been developed that can be used with conventional manufacturing methods but have yet to be studied for its damage tolerant capabilities. Furthermore, cost and stress analyses on where to effectively implement TTT fibers, SBCF composites, ADFRCs, and toughened resin systems must be executed prior to incorporating new materials into wind turbine blade manufacturing.

17 WIND ENERGY↗
DOE OSTI2021

On-Line Lead/Water Heat Exchanger Sensor/System Feasibility: CRADA 462 [Abstract Only]

The purpose of this project is to develop and demonstrate the feasibility of a high-temperature sensor for on-line structural health monitoring (OLSHM) of the novel heat exchanger tubes in the Hydromine lead cooled reactor. The high temperature sensor takes advantage of the Hydromine heat exchanger tube design that has a relatively cool inlet temperature (~ 350 °C) and the tube ends are in an Argon gas blanket rather than being submerged in a corrosive fluid and the tube ends are conveniently accessible from the periphery of the heat exchanger shell.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Homomorphic Encryption for Machine Learning and Artificial Intelligence Applications

Third-party and expert analysis is a cost-effective solution for solving specialized problems or processing large datasets related to reactor structural health monitoring and nondestructive evaluation. However, when handling proprietary information, third-party and expert analysts pose a privacy risk. To address this challenge, Homomorphic Encryption (HE) permits arithmetic operations on encrypted data without exposing the underlying data. Implementations of Machine Learning (ML) and Artificial Intelligence (AI) algorithms using HE greatly enhances the capabilities of third-party analysts while maintaining a low security risk. This paper details current success in applying Principal Component Analysis (PCA) and Fully Connected Neural Networks (NN) using the Microsoft SEAL implementation of the popular CKKS Fully Homomorphic Encryption (FHE) algorithm. The MNIST Handwritten Dataset is analyzed as a proof-of-concept demonstration of the implementations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Multi-Functional Smart Structures for Smart Vehicles

This report summarizes the development of a new class of recyclable multi-functional composite materials for production of lightweight smart structures and surfaces. Functional high stiffness conductive composites were processed using molding methods that integrated continuous fiber and additively manufactured features. Methods for integration of sensing functionality and controls were also developed to reduce system cost while providing a new capability for structural health monitoring. This new class of composites is applicable to a broad range of vehicle interior, exterior and battery enclosure systems. By way of demonstration, a vehicle instrument panel cross car beam was developed that provided a 38% mass savings compared to steel while maintaining a cost penalty competitive to alternate lightweight material solutions. These technologies were validated for implementation by a uniquely qualified project team comprising a US automotive OEM, Tier 1 and Tier 2 supplier, with key contributions from Oak Ridge National Lab, Purdue University and Michigan State University.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Feasibility Assessment of Using Electrical Impedance Tomography for Damage Localization in Graphite Microreactor Components

Microreactors have great potential to decrease capital costs and construction timelines, reducing the barriers to implementing advanced nuclear reactor technologies. However, the lower power output of these microreactors introduces economic challenges if the operation and maintenance costs cannot be reduced sufficiently. Many microreactor concepts use graphite materials for in-core moderator and structural purposes, which will require periodic inspection or, ideally, in situ structural health monitoring. This work describes an initial evaluation of the feasibility of leveraging graphite’s semiconducting properties to perform electrical impedance tomography (EIT) for defect localization. First, a study was performed to identify the best methods for bonding electrical contacts to minimize the effects of contact resistance that interfere with impedance measurements of the graphite. After determining the best bonding approach, electrodes were attached to multiple graphite components with varying geometries (e.g., simple block and more representative microreactor hexagonal block). In parallel, finite element analysis approaches were developed and implemented to analyze the impact of defects and contact resistances and to inform an iterative inverse model for reconstructing the conductivity distribution. The results of this study show that defect localization in graphite components is possible using EIT if a sufficient number of electrodes (to improve spatial resolution) can be bonded using a technique with very low contact resistance and those contact resistances remain stable during reactor operation. If the reactor core and vessel design do not allow electrodes to be bonded during reactor operation, then it may be possible to detach/reattach electrodes between reactor operation and outages if a suitable mechanical connection that offers repeatable contact resistance can be identified. Alternatively, future work could focus on identifying contacts that can survive the harsh reactor operating environment so that contacts do not need to be removed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗