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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ISOCS-based Extended In Situ Gamma Spectrometry Services Tool SuperISOCS for Waste Measurements - 20275

Canberra's In Situ Object Counting System (ISOCS) is an established tool used for gamma spectrometry. It allows physical representation of complex geometries and mathematical calculation of the calibration function while avoiding the need for radioisotope standards. ISOCS provides a large range of templates to generate the geometry for most nuclear waste components in nuclear facilities. Nonetheless, there are some complex geometries where the accuracy attainable is limited by the conventional ISOCS templates. An ISOCS-based extended in-situ Gamma Spectrometry service tool has been developed, based on the so-called SuperISOCS (SISOCS) software. This tool was developed to generate the most complex geometries. The principle of geometry construction is based on the composition of 8 different primitive geometries. The primitive object is able to be cut and to change a part of an object. An object is able to overlap to another one to form a more complex object. These functions refine the modelled geometry compared to the real objects and decrease the measurement systematic error because they allow the mathematical model to represent the real-life geometry more faithfully through reducing the approximations and simplifications that need to be made The algorithm of SISOCS is the same as that of standard ISOCS. A set of calculations was done in comparison with MCNP efficiency calculation and SISOCS results. 80 different models were compared between SISOCS and MCNP, with added 30 cases compared between ISOCS and SISOCS. The average differences were less than 2%, for all energy's efficiency calculations. In this paper we illustrate the benefits that this new tool can provide, in terms of improved accuracy for complex realistic geometries, through simulation of some example scenarios. Furthermore, we demonstrate, through SISOCS modelling of the real object data test, the comparison between SISOCS to ISOCS calculations for the same geometry. This paper describes the added functions that are easily employed using SISOCS. Our results show the potential application and benefit of this technique for real waste assay projects and the way to integrate the tool into the waste assay systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Collision avoidance based on traffic management data

A system for determining a travel direction that avoids objects when a vehicle travels from a current location to a target location is provided. The system determines a travel direction based on an attract-repel model. The system accesses external object information provided by an external object system. The external object information may include, for each of a plurality of objects, location, type, and constraint. The system assigns a repel value to the location of each object based on the type of and constraint on the object. The system assigns an attractive value to the target location. The system calculates a cumulative force based on the attractive and repulsive forces and sets the travel direction based on the cumulative force.

Beer, N. Reginald↗
DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗
DOE OSTI2021

Remote sensing detection enhancement

Big Data in the area of Remote Sensing has been growing rapidly. Remote sensors are used in surveillance, security, traffic, environmental monitoring, and autonomous sensing. Real-time detection of small moving targets using a remote sensor is an ongoing, challenging problem. Since the object is located far away from the sensor, the object often appears too small. The object’s signal-to-noise-ratio (SNR) is often very low. Occurrences such as camera motion, moving backgrounds (e.g., rustling leaves), low contrast and resolution of foreground objects makes it difficult to segment out the targeted moving objects of interest. Due to the limited appearance of the target, it is tough to obtain the target’s characteristics such as its shape and texture. Without these characteristics, filtering out false detections can be a difficult task. Detecting these targets, would often require the detector to operate under a low detection threshold. However, lowering the detection threshold could lead to an increase of false alarms. In this paper, the author will introduce a new method that improves the probability to detect low SNR objects, while decreasing the number of false alarms as compared to using the traditional baseline detection technique.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

The spectra of IceCube Neutrino (SIN) candidate sources – III. Optical spectroscopy and source characterization of the full sample

A correlation between astrophysical high-energy neutrinos and blazars has been suggested by various authors. In particular, a likely association between IceCube events and intermediate- and high-energy peaked BL Lac objects has led to a sample of 47 objects having a high probability of being neutrino sources. In the first paper of this series we reported optical spectroscopy of 17 objects, which together with data taken from the literature covered 80 per cent of the sample. Here, we present spectroscopy obtained at large aperture telescopes of a further 17 objects (plus four additional targets), which completes the sample coverage. For 12 objects we are able to determine the redshift (0.07 < z < 1.6), while for the others we set a lower limit on it, based on either the robust detection of intervening absorption systems or on an estimation derived from the absence of spectral signatures of the host galaxy. With these new data we expand and reinforce the main results of our previous papers, namely the fact that in terms of their broad-band properties our sources appear to be indistinguishable from the rest of the blazar population and the relatively large (>34 per cent and possibly as high as 80 per cent) fraction of masquerading BL Lac objects, for which the low equivalent width of the emission lines is due to the brightness of the boosted continuum, rather than being an intrinsic property, in our sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Chapter 18: Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

energy distribution↗
DOE OSTI2024

Toward Improving the Generation Quality of Autoregressive Slot VAEs

Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations (“slots”) from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multiobject relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multiobject environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.

MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement

In real-time remote sensing application, frames of data are continuously flowing into the processing system. The capability of detecting objects of interest and tracking them as they move is crucial to many critical surveillance and monitoring missions. Detecting small objects using remote sensors is an ongoing, challenging problem. Since object(s) are located far away from the sensor, the target’s Signal-to-Noise-Ratio (SNR) is low. The Limit of Detection (LOD) for remote sensors is bounded by what is observable on each image frame. In this paper, we present a new method, a “Multi-frame Moving Object Detection System (MMODS)”, to detect small, low SNR objects that are beyond what a human can observe in a single video frame. This is demonstrated by using simulated data where our technology-detected objects are as small as one pixel with a targeted SNR, close to 1:1. We also demonstrate a similar improvement using live data collected with a remote camera. The MMODS technology fills a major technology gap in remote sensing surveillance applications for small target detection. Our method does not require prior knowledge about the environment, pre-labeled targets, or training data to effectively detect and track slow- and fast-moving targets, regardless of the size or the distance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Path-Integrated X-Ray Digital Image Correlation using Synthetic Reference Images

X-rays can provide images when an object is visibly obstructed, allowing for motion measurements via x-ray digital image correlation (DIC). However, x-ray images are path-integrated and contain data for all objects between the source and detector. If multiple objects are present in the x-ray path, conventional DIC algorithms may fail to correlate the x-ray images. A new DIC algorithm called path-integrated (PI)-DIC addresses this issue by reformulating the matching criterion for DIC to account for multiple, independently-moving objects. PI-DIC requires a set of reference x-ray images of each independent object. However, due to experimental constraints, such reference images might not be obtainable from the experiment. Here, this work focuses on the reliability of synthetically-generated reference images, in such cases. A simplified exemplar is used for demonstration purposes, consisting of two aluminum plates with tantalum x-ray DIC patterns undergoing independent rigid translations. Synthetic reference images based on the “as-designed” DIC patterns were generated. However, PI-DIC with the synthetic images suffered some biases due to manufacturing defects of the patterns. A systematic study of seven identified defect types found that an incorrect feature diameter was the most influential defect. Synthetic images were re-generated with the corrected feature diameter, and PI-DIC errors were improved by a factor of 3-4. Final biases ranged from 0.00-0.04 px, and standard uncertainties ranged from 0.06-0.11 px. In conclusion, PI-DIC accurately measured the independent displacement of two plates from a single series of path-integrated x-ray images using synthetically-generated reference images, and the methods and conclusions derived here can be extended to more generalized cases involving stereo PI-DIC for arbitrary specimen geometry and motion. This work thus extends the application space of x-ray imaging for full-field DIC measurements of multiple surfaces or objects in extreme environments where optical DIC is not possible.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗
DOE OSTI2025

Monitoring of asteroids in cometary orbits and activated asteroids through archival images and new observations

Transitional objects are minor bodies that share some characteristics with asteroids and others with comets. These objects include asteroids in cometary orbits (ACOs), which behave dynamically like comets, but lack observed activity, while activated asteroids (AAs) follow typical asteroidal orbits, but have shown dust ejections. The monitoring of a set of these objects carried out in 2015 and 2016 is continued using archival images from various observatories and new data from the IMPACTON telescope in Brazil. Two techniques were applied to detect activity: (i) surface brightness profiles were compared with those of field stars to identify widening, and (ii) the magnitudes reported in the Minor Planet Center, combined with our observations, were reduced and analyzed to identify abrupt brightness increases as a function of heliocentric distance. We analyzed the surface brightness profiles of 133 ACOs and 7 AAs. To study the reduced magnitude, we obtained data from the 705 ACOs that were known at the time of the analysis. Together with the data from our previous work, our analysis covered 23% of the total known ACOs; 8 deviated slightly in the surface brightness profile, 6 brightened in the reduced magnitude, and one object is in common in both samples. A very low percentage of objects might show activity (4% of the sample with brightness profiles and <1% in the reduced magnitudes). These results would rule out a slow transition from active to inert. Regarding AAs, 4 showed activity, and 3 of them matched previously reported periods, while the data we analyzed for P/2015 X 6 were obtained 19 days before the first existing activity report. The activity episodes of these objects are very restricted in time and do not always occur in the same region of the orbit.

asteroids: general↗
DOE OSTI2023

Co-Simulation Model for Optimal Wind-Hydro Coordination Using Wind Farm Control Dynamics

The growing share of Variable Renewable Energy sources (VRES) in power systems presents challenges for regula- tors, grid operators and energy producers. The VRES’ operation has limited flexibility in their operations, as they are highly dependent on ambient environments. To address these challenges, decision-makers must consider multiple objectives, among these are revenue, power system services and mechanical load on wind turbines. Coordinated operation of power plants and different wind farm control strategies are examples of measures that can benefit these objectives. This study proposes a Multi-Objective Linear Programming (MOLP) model to simulate the optimum operation of wind and hydropower plants that share limited transmission capacity. Further, wind farm control dynamics are included to obtain realistic output power and accumulated damage. From this, a case study based on a relevant location in Norway is presented to analyze the improved effect of wind- hydro coordination and wind farm control in achieving the objectives of accumulated wind turbine damage and total revenue of the hybrid power system. In addition, the study considers the potential advantages of adding a variable-speed pump to the hydropower plant. The results demonstrate that by considering multiple objectives in the optimization, one may achieve better overall performance of the objectives. By utilizing the flexibility of hydro storage, the decision maker may adjust to obtain the most desired outcome. Moreover, the added flexibility of utilizing a pump for hydro storage shows great improvements for the combined revenue of the power plants and reduced curtailment of the wind farm. However, less impact is observed from using a variable speed pump compared to a fixed speed pump.

13 HYDRO ENERGY↗
DOE OSTI2021

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates — and to do so with enough efficiency at large scale. In this work, we propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

42 ENGINEERING↗
DOE OSTI2021

An Adaptive Tracking Algorithm for Convection in Simulated and Remote Sensing Data

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The report highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features

An essential application in intelligent transportation systems is multi-target multi-camera tracking (MTMCT), where the target’s activity is tracked from different cameras. Although the tracking-by-detection scheme is the primary paradigm in MTMCT, the object association information from the video frames is lost. This is mainly because the multi-camera multi-object matching uses the information from the video frames separately. To solve this problem and leverage this association information, we propose an MTMCT framework, where features are built in the form of a graph and a graph similarity algorithm is used to match multi-camera objects. In this paper, we focus on the real-time scenario, where only the past images are used to match an object. Our method achieves an IDF1 score (the ratio of the number of correctly identified objects to the number of ground truth and average objects) of 0.75 with a rate of 14 frames per second (fps).

Engineering↗
DOE OSTI2022

Electric Field Measurements at the Surface of a Piezoelectric Transformer for Plasma Jet Formation using the Plasma Research Facility at Sandia National Laboratory (Final Report)

The overall goal of this research is to understand how low-temperature non-equilibrium plasmas form directly off the surface of piezoelectric crystals. Specifically, this project aims to temporally and spatially resolve how the electric field evolves on and near the surface of a piezoelectric transformer (PT) at conditions that produce an atmospheric-pressure air plasma jet emanating from the corner of the PT. In this work, we used the Plasma Research Facility (PRF) at Sandia National Laboratory to conduct temporally- and spatially-resolved electric field induced second harmonic (E-FISH) measurements of the electric field at and near the surface of a piezoelectric transformer, providing a fundamental understanding of how piezoelectric crystals can be used for plasma generation. We carried out two Objectives: Objective 1 – The first objective was to measure the temporal and spatial evolution of the electric field along the surfaces approaching the corner of a rectangular PT just below conditions that lead to plasma jet formation. Such a measurement can be used to explain when and where the electric field peaks, leading to plasma formation; Objective 2 – The second objective was to measure the temporal and spatial evolution of the electric field along the surfaces approaching the corner of a rectangular PT just at conditions of plasma jet formation. This, in principle, gives a more accurate determination of how the electric field leads to plasma formation but is more difficult due to the relatively stochastic plasma jet behavior of a PT. This work was complemented by an award from the Sandia PRF, and we worked with Dr. Edward Barnat at the Sandia PRF to carry out these objectives.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Adaptive Site Management Strategies for the Hanford Central Plateau Groundwater

Adaptive site management (ASM) is a systematic and iterative management approach that can be used to expedite the remediation of large and/or complex sites. ASM is defined as a formal and systematic project or site management approach centered on rigorous site planning and a firm understanding of site conditions and uncertainties, using science and technology to routinely re-evaluate and prioritize site remedial actions and characterization activities. The goal of the approach is to create a framework of structured and continuous planning, implementation, and assessment processes that accommodates new information and changing site conditions to develop effective and efficient cleanup approaches that achieves the required outcome(s) while optimizing costs, cleanup timeframe, and performance. Central to ASM is an evolving conceptual site model (CSM). Over time, incremental reductions in uncertainty associated with the CSM will occur, while supporting continued progress toward site cleanup and closure through remedy optimization and evaluation. ASM has the potential to expedite cleanup for the Central Plateau area at Hanford through a planned and systematic approach for reducing uncertainty with targeted characterization activities, while continuing remediation activities that advance cleanup for key risk-driving contaminants. A core component of cleanup within the Central Plateau is the 200 West Pump-and-Treat (P&T) system. Even with an active P&T remedy, uncertainty exists with respect to plume distributions, total mass in the aquifer, and currently known continuing sources. Additional uncertainty is associated with multiple contaminant source locations in the vadose zone, which have the potential to create new groundwater plumes in the absence of any source control measures, although not all contaminant fluxes to groundwater will result in contaminant concentrations above cleanup levels. Collectively, these uncertainties need to be addressed in the CSM to support effective and efficient site progress toward cleanup goal(s). Other nontechnical factors that may warrant an ASM approach at the Hanford Central Plateau are associated with the formation of operable units (OUs) used to manage cleanup. With the exception of the 200-ZP-1 OU, the three remaining groundwater OUs only have interim action records of decision. Nine vadose zone source area OUs are also in the early stages of the remedial investigation and feasibility study process, with pending characterization and technology identification activities. A set of proposed site objectives for the Central Plateau are provided in this document as an initial consideration/example and basis for discussion for ASM implementation. These example site objectives were selected with the goal of maintaining protectiveness at the Columbia River through confinement of contaminant plumes within an administratively controlled area below existing surface waste sites and waste disposal facilities. Although site decision makers and regulators need to provide concurrence, these site objectives are used in this document to describe the elements of an ASM approach, including selection of interim objectives and a long-term adaptive management plan. Interim objectives are also defined to yield measurable incremental progress toward the overall site goals. This document identifies initial technical considerations for developing an ASM framework for the Central Plateau cleanup decisions. These considerations are intended to facilitate more specific decisions, such as objectives, near- and long-term actions, and performance metrics, to develop an overall approach that maintains protectiveness but recognizes the uncertainty, long timeframe, and technical challenges that need to be considered in selecting, implementing, and managing remediation at the Hanford Central Plateau.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

High-Fluence Active Irradiation and Combined Effects Testing of Sapphire Optical Fiber Distributed Temperature Sensors

The goal of this work was to investigate the in-core performance of sapphire optical fiber temperature sensors and to develop clad sapphire optical fibers for in-core instrumentation. We fabricated clad sapphire optical fibers and evaluated the distributed sensing performance of these sensors via optical backscatter reflectometry under high fluence and combined radiation and temperature effects. A series of irradiations was completed to evaluate the effect of irradiation on sapphire optical fiber temperature sensors and to determine the operational limits of these sensors. (1) Objective 1: Fabricate sapphire optical fiber sensors. (2) Objective 2: Evaluate the clad sapphire fiber to verify single-mode behavior and determine and characterize the light modes supported by optical fibers. (3) Objective 3: Characterize the in-core temperature sensing of sapphire optical fiber, as well as the combined temperature and irradiation effects. (4) Objective 4: Evaluate the lifetime and performance of the sensor under irradiation to high neutron fluence. Objectives 1, 2, and 3 were completed during the first 2 years of the project. Due to the Covid pandemic, Objective 4, a high-fluence irradiation performed at the Massachusetts Institute of Technology Research Reactor (MITR), was delayed, as partner facilities were subject to mandatory shutdowns and required a 1 year, no-cost extension. This irradiation was eventually completed on December 12, 2022. This work indicates that sapphire optical fiber sensors may be a solution for ultra-high-temperature applications in which traditional silica optical fibers are prone to fail. Sapphire sensors are potentially suitable for experiments featuring temperatures above 700°C for long periods of time, or for any length of time above 1000°C. Experiments featuring a low total fluence, such as irradiations conducted in the Transient Reactor Test (TREAT) facility, also represent good applications for sapphire optical sensors. Additional work is required to characterize the sapphire fiber cladding performance, which falls outside the scope of this project, as well as the effects of high temperatures on the response of the fiber. A comprehensive material study is recommended as future work to evaluate the attenuation in sapphire under irradiation, and how that attenuation changes with irradiation temperature. The drift and attenuation in the fiber at temperatures of up to 1600°C and a total fluence of up to 2.9 x 10 17 n/cm 2 was minimal, and the fibers returned to baseline after being heated to 1600°C under irradiation. This is promising for the future use of sapphire optical fibers in advanced reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗