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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.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry

Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.

42 ENGINEERING↗
DOE OSTI2026

SpaceNet 9—Cross-Sensor Alignment of Optical and SAR Imagery

Precise registration of high-resolution synthetic aperture radar (SAR) and optical imagery is necessary for realizing the full potential and benefits of multimodal image analysis. However, two significant challenges presently exist. First, there is a lack of annotated datasets and benchmarks available for high-resolution SAR–optical image registration. Second, an assessment of efficient and reliable image registration methods that can precisely align these modalities is lacking. Here, we present a holistic description of the SpaceNet 9 Challenge and its results. We present a description of the dataset and baseline algorithm along with the results of the challenge, including a description of the winning algorithms. We release the SpaceNet 9 dataset along with open-sourcing the winning algorithms and baseline. The objective of SpaceNet 9 was to compute a dense displacement map that indicates the shift needed to align pixels in an optical image to the pixels in a SAR image. The challenge launched in April 2025 and was active for approximately two months. The top five solutions reduced image alignment error from approximately 34 m to under 13 m for public and private test data, with the best results obtaining a registration error of only 8.5 and 6.7 m on the public testing and private testing dataset, respectively. Usage of pretrained image matching models, robust outlier rejection with RANSAC, and estimating local displacement were common among the top solutions. The results of this challenge provide insight into high-resolution SAR–optical image registration and offer opportunities for future benchmarking in this domain. The baseline algorithm, winning solutions, and datasets are available at https://spacenet.ai/sn9-challenge/.

benchmark datasets↗
DOE OSTI2024

Mini-cell: Compact all optical gas monitoring sensor for persistent surveillance of complex system

Persistent surveillance of complex systems by embedded sensing is the new paradigm of aging awareness and monitoring. In any complex system with a long-term lifespan plan, the persistent surveillance of critical parameters warrants the reliability and safety/security of the system by the continuous monitoring of the state of health and aging conditions. Therefore, embedded sensors of physical, chemical, and structural features/traits are critical for uncovering occurrences of undesired/unexpected events. Gas sensing can provide early detection of a broad range of problems such as decomposing components, corrosion, failures, and leaks. For the application of interest, the sensors must be: 1/highly selective and sensitive for a broad range of gas molecules, 2/compact, 3/minimally invasive, 4/rugged for vibration and thermal excursions, and 5/ environmentally inert.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Feature Extraction for Pipeline Defects Inspection Based Upon Distributed Acoustic Fiber Optic Sensing Data

Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Pipeline Defects Inspection, Fiber-optic sensors, ↗
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 OSTI2020

Glass 3D Printing of Microfluidic Pressure Sensor Interrogated by Fiber-Optic Refractometry

This letter reports a novel fused silica microfluidic device with pressure sensing capability that is fabricated by integrated additive and subtractive manufacturing (IASM) method. The sensor consists of a capillary and a 3D printed glass reservoir, where the reservoir volume change under pressure manifests liquid level deviation inside the capillary, thus realizing the conversion between small pressure change into large liquid level variation. Thanks to the design flexibility of this unique IASM method, the proposed microfluidic device is fabricated with liquid-in-glass thermometer configuration, where the reservoir is sealed following a novel 3D printing assisted glass bonding process. And liquid level is interrogated by a fiber-optic sensor based on multimode interference (MMI) effect. This proposed microfluidic device is attractive for chemical and biomedical sensing because it is flexible in design, and maintains good chemical and mechanical stability, and adjustable sensitivity and range.

42 ENGINEERING↗
DOE OSTI2023

Development of Optical Fiber-Based Sensors for Nuclear Microreactor Structural Health Monitoring

This report provides an experimental assessment of two different optical fiber–based acoustic sensors that are being investigated for application in nuclear microreactors to enhance structural health monitoring capabilities. Optical fibers are resilient in high-temperature and high-radiation environments, have a small sensor footprint, are immune to electromagnetic interference, and are capable of spatially distributed sensing. The two sensors investigated here are (1) Fabry–Pérot Cavities (FPCs) between two copper-coated fibers, embedded in nickel capillary tubes and (2) type-I fiber Bragg grating (FBG) arrays contained within metal capillary tubes. These sensors can be interrogated using low-coherence interferometry or swept wavelength interferometry, respectively, to measure the resonant frequencies of the components or systems to which these sensors are bonded. The FPC developed herein has been subjected to temperatures up to nearly 800°C while tack-welded to a tubular test specimen. Even at the highest temperatures, the measured resonant frequencies compared well with those obtained using an accelerometer that was bonded to an unheated portion of the specimen. The FBG array was tested in multiple bonding configurations to a tubular test specimen, all at room temperature, with the understanding that high temperature (i.e., type II) FBGs could be used to obtain similar data at high temperatures; the FBG array data were validated with noncontact laser vibrometry, which is being used at Los Alamos National Laboratory to relate acoustic signatures to component stresses and/or structural defects. The goal of this work is to identify the most promising techniques that are also compatible with operation in a microreactor environment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

FiberFlex: Real-time FPGA-based Intelligent and Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.

Distributed↗
DOE OSTI2020

Characterization and equalization of intensity-modulated voltage sensors

We test fiber-optic voltage sensors based on reflection from a piezoelectric transducer. Detailed characterization confirms a 60 dB dynamic range, and our equalization approach accurately recovers transient excitations, indicating the versatility of this sensor design in electric grid monitoring.

Lukens, Joseph↗
DOE OSTI2024

Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic

Observations collected during cold-season precipitation periods at Utquagvik, Alaska and at the multidisciplinary drifting observatory for the study of Arctic climate (MOSAiC) are used to statistically analyze the relations among the atmospheric water cycle parameters including the columnar supercooled liquid and ice amounts (expressed as liquid-water and ice-water paths, i.e., LWP and IWP), the integrated water vapor (IWV) and the near-surface snowfall rate. Data come from radar and radiometer-based retrievals and from optical precipitation sensors. While the correlation between snowfall rate and LWP is rather weak, correlation coefficients between radar-derived snowfall rate and IWP are high (~0.8), which is explained, in part, by the generally low LWP/IWP ratios during significant precipitation. Correlation coefficients between snowfall rate and IWV are moderate (~0.45). Correlations are generally weaker if snowfall is estimated by optical sensors, which is, in part, due to blowing snow. Correlation coefficients between near-surface temperature and snowfall rates are low (r < 0.3). The results from the Alaska and MOSAiC sites are generally similar. These results are not very sensitive to the amount of time averaging (e.g., 15 min averaging versus daily averages). Observationally based relations among the water cycle parameters are informative about atmospheric moisture conversion processes and can be used for model evaluations.

54 ENVIRONMENTAL SCIENCES↗