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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Utilization of GIS technology to support development of flow and contaminant fate and transport models at US DOE sites - 15457

U.S. Department of Energy (DOE) sites have accumulated years of data and millions of spatial and temporal records related to the hydrological cycle, contaminant transport, and parameters of remediation technologies. In addition, numerical models which use field monitoring data to help understand flow and transport produce gigabytes of computed spatial and temporal data for each computation node. There is a need therefore for an advanced spatial data structure that can be used to address the management, processing, and analysis of spatial and temporal numerical modeling data derived from multiple sources. This can be accomplished using a geo-database. The development of an ArcSDE geo-database facilitates the centralized storage, backup, accessibility, organization, and management of model configuration and computed simulation data, putting it into a structured, coherent, and logical computer-supported system. GIS data processing and automation would enable faster and hence more complex analyses of field test data. The Applied Research Center (ARC) at Florida International University (FIU) has provided analysis of flow and transport for several watersheds at DOE's Oak Ridge Reservation (ORR), including East Fork Poplar Creek (EFPC), Upper EFPC in the Y-12 National Security Complex area (Y-12 NSC) and White Oak Creek (WOC). Integrated surface and subsurface flow, fate and transport models were developed to provide analysis of contaminant patterns within each watershed. In addition, digital monitoring data available from the Oak Ridge Environmental Information System (OREIS) related to mercury (Hg) contamination and remediation within these watersheds was used for calibration and verification of the model. Experimental studies were also carried out which provided kinetic and equilibrium data about important parameters related to Hg transport, speciation and methylation/demethylation kinetics within the watershed. Geographic Information Systems (GIS) technology was employed to support the modeling work through storage and geo-processing of spatial and temporal data required by the models and to produce hydrogeological maps for visualization. An ArcGIS geo-database was developed for centralized storage and management of experimental and computed model data and its capabilities were extended over the years using tools such as ModelBuilder combined with Python scripting to automate repetitive tasks, perform statistical analyses and generate maps and reports. The hydrologic geo-database model developed possesses a structure that enables linkage with scalable hydrologic modeling tools/applications to model hydrologic systems, and in this case, enables the testing of the potential impacts of various remediation scenarios on the ORR watersheds. The ArcSDE geo-database can also be used to automate and simplify the process of calling stored GIS and time series data. This geo-database can serve as a tool for contaminant flow and transport analyses which require large amounts of high-quality spatial and temporal data in order to ensure reliability and validity of modeling results. The existing geo-database structure developed for the hydrological modeling at ORR was designed to be replicable for application at other DOE sites. An investigation of downloadable free/open source GIS software along with required security protocols to facilitate online querying of the database was also conducted to determine methods by which project-derived data can be more easily shared with other project stakeholders such as DOE personnel and site contractors. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

LatticeAnalytics: Strut-Level Visualization and Inspection of Additively Manufactured Lattice Structures

Additive manufacturing (AM) is revolutionizing the production of custom components with complex internal geometries, essential for high-performance applications in diverse fields such as medicine and defense. These AM parts optimize strength while minimizing weight by utilizing internal lattice structures consisting of large quantities of small interconnected struts. However, the complexity of these structures, combined with the challenges of using X-ray Computed Tomography (XCT) data, makes validation of part reliability difficult. This ultimately inhibits the development of novel parts for our collaborating material scientists. Here, we introduce LatticeAnalytics, a novel framework specifically designed for visual inspection of defects in these lattice structures. Our framework offers an end-to-end solution that includes the data management of XCT scans, enables remote access for geographically dispersed teams through a web-based dashboard, and incorporates novel visualizations. Our analysis is facilitated by a coarse alignment between the lattice’s nominal model, a spatial graph, and the XCT data. We employ a simple VR-based approach for fast and rough alignment, followed by an offline registration and identification of the struts. With the nodes and struts aligned and identified in the volume, our framework allows querying of subvolumes containing a single strut at multiple resolutions. This avoids computation over the entire lattice and also allow for easy parallelization of down-stream computations, such as strut-specific metrics. To depict a fast overview of the strut quality, we introduce two innovative visual encodings, crucial for our collaborators’ research in creating novel AM parts: the Contour View and the Roughness Map, which depict critical geometrical and surface features of individual struts in standardized two 2D views. We evaluated the integrated system through expert interviews. The feedback confirms the framework’s practicality and its effectiveness in enhancing current inspection workflows. It solves major bottlenecks for our collaborators, ultimately helping them create novel parts with advanced properties.

Miao, Haichao [Lawrence Livermore National Laborat↗
DOE OSTI2019

High-performance End-to-End Integrity Verification on Big Data Transfer

The scale of scientific data generated by experimental facilities and simulations in high-performance computing facilities has been proliferating with the emergence of IoT-based big data. In many cases, this data must be transmitted rapidly and reliably to remote facilities for storage, analysis, or sharing, for the Internet of Things (IoT) applications. Simultaneously, IoT data can be verified using a checksum after the data has been written to the disk at the destination to ensure its integrity. However, this end-to-end integrity verification inevitably creates overheads (extra disk I/O and more computation). Thus, the overall data transfer time increases. In this article, we evaluate strategies to maximize the overlap between data transfer and checksum computation for astronomical observation data. Specifically, we examine file-level and block-level (with various block sizes) pipelining to overlap data transfer and checksum computation. We analyze these pipelining approaches in the context of GridFTP, a widely used protocol for scientific data transfers. Theoretical analysis and experiments are conducted to evaluate our methods. The results show that block-level pipelining is effective in maximizing the overlap mentioned above, and can improve the overall data transfer time with end-to-end integrity verification by up to 70% compared to the sequential execution of transfer and checksum, and by up to 60% compared to file-level pipelining.

data integrity↗
DOE OSTI2019

Measurement of the (e,e') cross section for12C, 48Ti, 27Al and 40Ar

Many experiments have found that neutrinos have three different flavors, and they can transform from each other. Each flavor is a mixture of three nonzero mass states. In neutrino oscillation experiments, the uncertainty of the extracted oscillation parameters is dependent on the neutrino-nucleus cross section. In recent years various experiments provided precise neutrino and anti-neutrino cross section measurements and they have shown the inability of current Monte Carlo simulations to reproduce the data. A more accurate nuclear structure model of the target employed in neutrino detector is urgently needed to improve the precision of long baseline neutrino experiments. Electron scattering has been one of the most powerful methods of obtaining information about cross sections in the past. In the upcoming deep underground neutrino experiment (DUNE), Liquid Argon Time Projection Chambers (LArTPCs) will be used as the detector technology and argon will be used as the nuclear target. There are very few data available on argon and there is not an accurate nuclear model that describes them accurately, at ~5% level. We have performed an electron-argon scattering experiment at Jefferson Lab (E12-14-012) to provide accurate data that will help build a reliable nuclear model for describing neutrino-argon scattering. The JLab E12-14-012 experiment has successfully taken data in Hall A at the Thomas Jefferson National Accelerator Facility between February and March of 2017. We collected data for the inclusive (e,e') and exclusive (e,e'p) processes for a variety of targets (argon, titanium, aluminum, carbon) at a wide range of kinematic settings. This thesis will present the results of the inclusive double differential cross sections for carbon, titanium, aluminum and argon at beam energy E = 2:222 GeV and scattering angle theta = 15:541 deg with total uncertainties smaller than 5%. The kinematic settings covered a broad range of energy transfers, including deep inelastic scattering (DIS), delta production, and quasielastic scattering.

Dai, Hongxia↗
DOE OSTI2019

3D Imaging of Geothermal Faults from a Vertical DAS Fiber at Brady Hot Spring, NV USA

In March 2016, arguably the most ambitious 4D (3D space + over time) active-source seismic survey for geothermal exploration in the U.S. was acquired at Brady Natural Laboratory, outside Fernley, Nevada. The four-week experiment included 191 vibroseis source locations, and approximately 130 m of distributed acoustic sensing (DAS) in a vertical well, located at the southern end of the survey area. The imaging of the geothermal faults is done with reverse time migration of the DAS data for both P-P and P-S events in order to generate 3D models of reflectivity, which can identify subsurface fault locations. Three scenarios of receiver data are explored to investigate the reliability of the reflectivity models obtained: (1) Migration of synthetic P-P and P-S DAS data, (2) migration of the observed field DAS data and (3) migration of pure random noise to better assess the validity of our results. The comparisons of the 3D reflectivity models from these three scenarios confirm that sections of three known faults at Brady produce reflected energy observed by the DAS. Two faults that are imaged are ~1 km away from the DAS well; one of these faults (middle west-dipping) is well-constructed for over 400 m along the fault’s strike, and 300 m in depth. These results confirm that the DAS data, together with an imaging engine such as reverse time migration, can be used to position important geothermal features such as faults.

10 SYNTHETIC FUELS↗
DOE OSTI2025

Navigating Integration: Key Challenges for Data Centers, Nuclear Stakeholders, and Utility Operators

The rapid expansion of data centers, driven by the exponential growth in data-processing and storage needs, presents significant challenges and opportunities for various stakeholders, including data center developers, nuclear energy providers, and utility companies. Data centers are projected to consume 6.7–12% of United States (U.S.) electricity by 2028, driven by artificial intelligence (AI) and cloud-computing demands. Nuclear energy offers reliability and dispatchable baseload power, but data centers need power now while nuclear still needs time to address siting, fast power ramping, and regulatory hurdles. Utilities must keep pace with the unprecedented acceleration of large load interconnection requests and urgently adapt to high-density loads while maintaining grid stability, reliability, and accelerating interconnection timelines. This report dives into these challenges and proposes key collaboration strategies to streamline data center integration that aligns with recent federal initiatives like America’s AI Action Plan and related executive orders that emphasize the importance of data center growth, nuclear energy expansion, and maintaining a competitive edge in the global AI race.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

Modeling Framework for Data Center

This chapter highlights the critical need for advanced modeling of data centers due to their rapidly increasing energy consumption and impact on grid reliability. Driven by the demand for AI applications, data centers are projected to consume a significant portion of US energy by 2028, putting stress on an already challenged power grid. The chapter emphasizes the importance of "fast" time-scale models to understand the dynamic interactions between data centers and the grid, especially given the rapid power fluctuations of AI workloads. It outlines a modeling framework that includes both offline and real-time EMT domain simulations, detailing the necessary representations for various components like utility interfaces, transformers, IT loads, UPS, cooling loads, Battery Energy Storage Systems (BESS), generators, protection systems, and higher-level control systems. While standard simulation tools like PSCAD offer basic models, custom development is often required to accurately capture the unique and fast-changing behaviors of modern data centers. The chapter also discusses key metrics and test cases for validating these models, focusing on transient load responses, protection relay coordination, and demand flexibility. Finally, it addresses the challenges of modeling large-scale data centers, such as computational complexity and the trade-off between model fidelity and practicality, suggesting hybrid modeling approaches as a solution. The overarching goal is to create a robust framework that helps assess data center impacts on grid stability, identify vulnerabilities, and inform the development of standards for reliable integration of these large loads into the bulk power system.

25 ENERGY STORAGE↗
DOE OSTI2015

PESSTO: survey description and products from the first data release by the Public ESO Spectroscopic Survey of Transient Objects

Context. The Public European Southern Observatory Spectroscopic Survey of Transient Objects (PESSTO) began as a public spectroscopic survey in April 2012. PESSTO classifies transients from publicly available sources and wide-field surveys, and selects science targets for detailed spectroscopic and photometric follow-up. PESSTO runs for nine months of the year, January - April and August - December inclusive, and typically has allocations of 10 nights per month. Aims. We describe the data reduction strategy and data products that are publicly available through the ESO archive as the Spectroscopic Survey data release 1 (SSDR1). Methods. PESSTO uses the New Technology Telescope with the instruments EFOSC2 and SOFI to provide optical and NIR spectroscopy and imaging. We target supernovae and optical transients brighter than 20.5m for classification. Science targets are selected for follow-up based on the PESSTO science goal of extending knowledge of the extremes of the supernova population. We use standard EFOSC2 set-ups providing spectra with resolutions of 13-18 Å between 3345-9995 Å. A subset of the brighter science targets are selected for SOFI spectroscopy with the blue and red grisms (0.935-2.53 μm and resolutions 23-33 Å) and imaging with broadband JHKs filters. Results. This first data release (SSDR1) contains flux calibrated spectra from the first year (April 2012-2013). A total of 221 confirmed supernovae were classified, and we released calibrated optical spectra and classifications publicly within 24 h of the data being taken (via WISeREP). The data in SSDR1 replace those released spectra. They have more reliable and quantifiable flux calibrations, correction for telluric absorption, and are made available in standard ESO Phase 3 formats. We estimate the absolute accuracy of the flux calibrations for EFOSC2 across the whole survey in SSDR1 to be typically ~15%, although a number of spectra will have less reliable absolute flux calibration because of weather and slit losses. Acquisition images for each spectrum are available which, in principle, can allow the user to refine the absolute flux calibration. The standard NIR reduction process does not produce high accuracy absolute spectrophotometry but synthetic photometry with accompanying JHKs imaging can improve this. Whenever possible, reduced SOFI images are provided to allow this. Conclusions. Future data releases will focus on improving the automated flux calibration of the data products. The rapid turnaround between discovery and classification and access to reliable pipeline processed data products has allowed early science papers in the first few months of the survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data

Abstract We propose a new approach to generate a reliable reduced model for a parametric elliptic problem, in the presence of noisy data. The reference model reduction procedure is the directional HiPOD method, which combines Hierarchical Model reduction with a standard Proper Orthogonal Decomposition, according to an offline/online paradigm. In this paper we show that directional HiPOD looses in terms of accuracy when problem data are affected by noise. This is due to the interpolation driving the online phase, since it replicates, by definition, the noise trend. To overcome this limit, we replace interpolation with Machine Learning fitting models which better discriminate relevant physical features in the data from irrelevant unstructured noise. The numerical assessment, although preliminary, confirms the potentialities of the new approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Estimating influenza incidence using search query deceptiveness and generalized ridge regression

Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response. The current state of the art is to gather this knowledge using in-person patient contact. While accurate, this is time-consuming and expensive. This has motivated inquiry into new approaches using internet activity traces, based on the theory that lay observations of health status lead to informative features in internet data. These approaches risk being deceived by activity traces having a coincidental, rather than informative, relationship to disease incidence; to our knowledge, this risk has not yet been quantitatively explored. We evaluated both simulated and real activity traces of varying deceptiveness for influenza incidence estimation using linear regression. We found that deceptiveness knowledge does reduce error in such estimates, that it may help automatically- selected features perform as well or better than features that require human curation, and that a semantic distance measure derived from the Wikipedia article category tree serves as a useful proxy for deceptiveness. This suggests that disease incidence estimation models should incorporate not only data about how internet features map to incidence but also additional data to estimate feature deceptiveness. By doing so, we may gain one more step along the path to accurate, reliable disease incidence estimation using internet data. This capability would improve public health by decreasing the cost and increasing the timeliness of such estimates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Temperature Rise on Liner Surfaces of Fuel Cell Electric Vehicle Tanks during Fueling Process

Herein, the temperature of the inner tank walls, or plastic liners, of composite pressure tanks in fuel cell electric vehicles during fueling using three‐dimensional computational fluid dynamics (CFD) models is evaluated. The liner materials are limited to 85.0 °C to prevent thermal stress causing material failure that would result in a hydrogen leak. Therefore, the temperature of hydrogen gas must be limited to below 85.0 °C during the fueling process as dictated by the current fueling protocol. However, there are limited experimental or simulation data confirming that the temperature changes do not exceed the threshold. Herein, the liner temperatures with CFD tank models for two sizes of type IV tanks representative of the upper and lower system bounds that are close to the SAE J2601 fueling protocol (36.0 and 244.0 L) are evaluated. First, each model's reliability is validated with experimental data and then analyzed, and the data are used to evaluate the maximum hydrogen and liner temperatures under real‐world fueling conditions. The evaluation shows that the maximum liner surface temperature of each tank model is at least 7 °C lower than that of the hydrogen. Additionally, there is at least 12 °C difference found between the upper limit and actual liner temperatures.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Abstract for CRADA between the National Energy Technology Laboratory and Strategic Power Systems, Inc. (AGMT-0884)

This project will utilize field data, available through the Operational Reliability Analysis Program (ORAP®), to assess the operation of natural gas combined cycle (NGCC) and coal fired plants in cyclic duty. The emphasis will be on Heat Recovery Steam Generator (HRSG) and coal-fired boiler operating conditions and failure experience related to tube failures. The project will be based on high-fidelity data available in ORAP through the collection of near real time process data characterizing the operating environment and dynamic conditions, from start up to shut-down, that affect the HRSG and coal-fired boiler (i.e. the boiler tubes). It is important to understand the operating environment to assess the influence of the cyclic start-stop cycles, time at temperature, and other operating conditions that can be seen in the data, with their possible impact on the life and reparability of the boiler tubes. This knowledge is important to influence/develop a predictive operations and maintenance strategy to improve performance, increase life, and reduce cost.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2019

Recommended Nuclear Data for the Production of Selected Therapeutic Radionuclides

Increasing interest in the treatment of human disease by means of targeted radionuclide-based therapies requires an accurate understanding of achievable yields and quantification of realisable degrees of purity. Important studies within a more extensive Coordinated Research Project of the International Atomic Energy Agency (IAEA CRP) have included in-depth assessments and evaluations of all available measured cross-section data for ten nuclear reactions leading to the formation of 131 Cs, 131 Ba, 225 Ac, 225 Ra, 227 Th, 227 Ac, 230 U and 230 Pa for various on-going and proposed therapeutic applications in nuclear medicine. Equivalent studies were also undertaken of possible reactions to produce the 178W /low-spin 178m Ta generator system that has been clinically applied in diagnosis by means of SPECT - these particular cross-section studies and recommendations are included in this paper for completeness. All available measured data have been critically reviewed and assessed to produce statistically consistent datasets. Selected datasets have been fitted by a least-squares method with Padé approximations of variable order to evaluate the production cross sections and corresponding uncertainties. New measurements have been made in some instances, and reported data have been adjusted to accommodate new nuclear decay or monitor reaction data which altered the original reported quantities adversely. Insufficient measured data are available in many cases to permit a reliable evaluation of cross sections, and these instances are discussed. Finally, recommended therapeutic radionuclide production reaction data and their uncertainties are available on the Web page of the IAEA Nuclear Data Section at www-nds.iaea.org/radionuclides/ and also at the IAEA medical portal www-nds.iaea.org/medportal/.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Towards a study protocol: A data-driven workflow to identify error sources in direct ink write mechatronics

Abstract Using Direct Ink Write (DIW) technology in a rapid and large-scale production requires reliable quality control for printed parts. Data streams generated during printing, such as print mechatronics, are massive and diverse which impedes extracting insights. In our study protocol approach, we developed a data-driven workflow to understand the behavior of sensor-measured X- and Y- axes positional errors with process parameters, such as print velocity and velocity control. We uncovered patterns showing that instantaneous changes in the velocity, when the build platform accelerates and decelerates, largely influence the positional errors, especially in the X- axis due to the hardware architecture. Since DIW systems share similar mechatronic inputs and outputs, our study protocol approach is broadly applicable and scalable across multiple systems. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Retractable Sensors for In-Core Use in Material Test Reactors - conf paper

Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 - 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on friction drive wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 - 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology's MITR reactor, followed by deployment in an ATR irradiation experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Retractable Sensors for In-Core Service in Material Test Reactors

Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 - 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on counter-rotating wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 - 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology's MITR reactor, followed by deployment in an ATR irradiation experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Retractable Sensors for In-Core Service in Material Test Reactors

Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 – 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on counter-rotating wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 – 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology’s MITR reactor, followed by deployment in an ATR irradiation experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗