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NASA NTRS2010

A Framework for Propagation of Uncertainties in the Kepler Data Analysis Pipeline

The Kepler space telescope is designed to detect Earth-like planets around Sun-like stars using transit photometry by simultaneously observing 100,000 stellar targets nearly continuously over a three and a half year period. The 96-megapixel focal plane consists of 42 charge-coupled devices (CCD) each containing two 1024 x 1100 pixel arrays. Cross-correlations between calibrated pixels are introduced by common calibrations performed on each CCD requiring downstream data products access to the calibrated pixel covariance matrix in order to properly estimate uncertainties. The prohibitively large covariance matrices corresponding to the ~75,000 calibrated pixels per CCD preclude calculating and storing the covariance in standard lock-step fashion. We present a novel framework used to implement standard propagation of uncertainties (POU) in the Kepler Science Operations Center (SOC) data processing pipeline. The POU framework captures the variance of the raw pixel data and the kernel of each subsequent calibration transformation allowing the full covariance matrix of any subset of calibrated pixels to be recalled on-the-fly at any step in the calibration process. Singular value decomposition (SVD) is used to compress and low-pass filter the raw uncertainty data as well as any data dependent kernels. The combination of POU framework and SVD compression provide downstream consumers of the calibrated pixel data access to the full covariance matrix of any subset of the calibrated pixels traceable to pixel level measurement uncertainties without having to store, retrieve and operate on prohibitively large covariance matrices. We describe the POU Framework and SVD compression scheme and its implementation in the Kepler SOC pipeline.

Clarke, Bruce D.↗

DOE OSTI2017

Applied metrology in the production of superconducting model magnets for particle accelerators

The production of superconducting magnets for particle accelerators involves high precision assemblies and tight tolerances, in order to achieve the requirements for their appropriate performance. It is therefore essential to have a strict control and traceability over the geometry of each component of the system, and also to be able to compensate possible inherent deviations coming from the production process.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

Trace impurity analysis in uranium oxide via hybrid quantification techniques—gravimetric standard addition and isotope dilution mass spectrometry

Measurement methods for the analysis of trace impurities in uranium materials, essential in nuclear fuel production and nuclear forensics, are continuously improving. Analytical methods were developed with the goal of lowering uncertainties of next generation certified uranium oxide reference materials for trace impurity concentrations. Through addition of a traceable standard directly into the sample, gravimetric standard addition and isotope dilution followed by analysis on an inductively coupled plasma mass spectrometer can achieve lower uncertainties. In conclusion, results for 28 impurities in CRM 124-1 and 124-6 from NBL Program Office were used for validation of accuracy and comparisons of uncertainties.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Automation of Solid Oxyde Electrolyzer Cell (SOEC) & Stack Assembly

The work performed under this agreement before the No/Go decision amounted to the following: 1. Preliminary manufacturing requirements defined by business sensitivity, baseline processes & risk assessment; 2. Process & materials development, in-line gauge exploration, and preliminary automation work used to reduce risk & further refine equipment specifications; 3. “Request for Quote” issued to multiple suppliers for major equipment; 4. Application specific equipment, hardware, and fixturing will be more beneficial to fabricate inhouse; 5.Current proposals & estimates meet cycle time, capital spend, & direct labor; space is on target but requires awareness; 6. Team will continue to explore opportunities to reduce risk (dry time, traceability); 7. Go / No-Go, Purchase Orders, Equipment build & commissioning next All other future task were not completed because during the Go /No-Go decision it was confirmed this project was a "No-Go" and did not proceed past BP1; these included the following: 1. Preliminary manufacturing requirements defined by business sensitivity, baseline processes & risk assessment; 2. Process & materials development, in-line gauge exploration, and preliminary automation work used to reduce risk & further refine equipment specifications; 3. “Request for Quote” issued to multiple suppliers for major equipment; 4. Application specific equipment, hardware, and fixturing will be more beneficial to fabricate inhouse; 5. Current proposals & estimates meet cycle time, capital spend, & direct labor; space is on target but requires awareness; 6. Team will continue to explore opportunities to reduce risk (dry time, traceability) In final preparation of the termination of the Automation of Solid Oxyde Electrolyzer Cell (SOEC) & Stack Assembly, Cummins has purchased no equipment with government funds, and there is no government-owned property in Cummins possession related to this project. Minimal labor and travel were completed and paid. This acts as the Final technical report and concludes our business with DOE on this grant.

36 MATERIALS SCIENCE↗

NASA NTRS2009

Technology Infusion Challenges from a Decision Support Perspective

In a restricted science budget environment and increasingly numerous required technology developments, the technology investment decisions within NASA are objectively more and more difficult to make such that the end results are satisfying the technical objectives and all the organizational constraints. Under these conditions it is rationally desirable to build an investment portfolio, which has the highest possible technology infusion rate. Arguably the path to infusion is subject to many influencing factors, but here only the challenges associated with the very initial stages are addressed: defining the needs and the subsequent investment decision-support process. It is conceivable that decision consistency and possibly its quality suffer when the decision-making process has limited or no traceability. This paper presents a structured decision-support framework aiming to provide traceable, auditable, infusion- driven recommendations towards a selection process in which these recommendations are used as reference points in further discussions among stakeholders. In this framework addressing well-defined requirements, different measures of success can be defined based on traceability to specific selection criteria. As a direct result, even by using simplified decision models the likelihood of infusion can be probed and consequently improved.

Adumitroaie, V.↗

NASA NTRSDate not supplied

Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models

The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.

systems engineering↗

NASA NTRSDate not supplied

Managing the Digital Thread for Structural Applications With Fit for Purpose Materials

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Consequently, at NASA Glenn Research Center a robust information management system that manages the digital thread across the full material life (i.e., capture, analysis, maintenance, and dissemination of data) cycle directed at the design of ‘fit-for-purpose materials’ is under development. To this end the Application Table has been incorporated within NASA Glenn Research Center’s ICME Information Management framework within the ANSYS Granta MI tool. The Application Table provides a place where material and structural application information/requirements can be linked to marry the “design-the-material” (structural engineering) and the “design-with-material” (material science) paradigms and thereby enable application-driven design and optimization of materials and structures. In additional several associated toolsets, specifically: AIMAOS (Automated Information Management Across Organizations and Scales), Py MILab, and JARIMIS (Just A Rather Intelligent Material Interrogation System) are also under development to assist in the judicious automation of this process. AIMOAS offers users an interactive graphical user interface for connecting material information management systems with both commercial and in-house simulation tools at various length scales to enable such automation in the handoff across scales and maintenance of material digital twins and the digital thread. Py MILab, is an automatic framework for the capture, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. Finally, JARIMIS is an expert system that integrates various materials informatics tools (e.g., MicroNet, Surrogate ML models, ANSYS Granta MI, etc.) to enable inverse design of materials and facilitate the application of machine learning (ML) and data science with human in the loop decision making to rapidly discover and optimize new materials.

Digital Transformation↗

DOE OSTI2015

Improvements to the operational efficiency of the Portsmouth 'Blue Box' Assay system - 15162

The 'Blue Box' in Building 326 at Portsmouth comprises a dual cargo container lined with high density polyethylene with 5% (elemental) boron that acts as a neutron shield. This construction provides a low environmental neutron background to support the capability to attain low values of minimal detectable activity (MDA). A Non-destructive Assay (NDA) measurement platform known as the Fluor B and W Portsmouth Slab Counter (FBPSC) is housed inside the Blue Box. This comprises four neutron slab detectors supported with an electronic system supplying power and associated signal processing. The system is operated by a laptop computer which has data acquisition and analysis capabilities. Each slab contains five {sup 3}He proportional counters embedded in polyethylene. The system is designed to measure the mass of U-235 in coolers from the Portsmouth site using passive neutron counting. The system was calibrated by taking measurements with {sup 252}Cf sources attached to various positions on the outside surfaces of a surrogate cooler. In addition, Monte-Carlo N-Particle (MCNP) simulations were performed to extend the baseline calibration for coolers of various sizes used in the gaseous-diffusion process. The basic principle of operation is to measure the total neutron emission from the item and determine the mass of {sup 235}U using the system efficiency (determined from MCNP calculations combined with the calibration measurements) combined with known isotopic information ({sup 235}U enrichment and U-235/U-234 ratio), assumed chemical composition, and gross cooler mass. The FBPSC analysis program calculates the mass of U-235 within the cooler being assayed, together with the associated Total Measurement Uncertainty (TMU). A background correction is performed that accounts for the measured environmental background with a cosmic-ray spallation correction (based on the cooler's gross weight). The software suite developed for the system also includes the capability to track and trend measurement control data in order to quickly identify conditions where the system has drifted outside of its quality control envelope. The MDA of the system has been determined to be less the 15 g of U-235 for count times that are typically less than 5 minutes (depending on the uranium isotopics). This is primarily the result of the low neutron background rate inside the Blue Box (approximately 3 times lower than at nearby locations outside the box). Additionally, various software features have been designed to yield optimum counting performance for the system including count time adjustment and interval filtering to reject statistical outliers that are usually associated with cosmic spallation events. The operation of the system has proven to be very efficient, with the short measurement time providing a high throughput rate of coolers through the Blue Box NDA system. Significant operational benefits and improved data traceability have been yielded compared to the previous measurement process, which involved operators taking measurement with counter/scalers and manually entering data into spreadsheets. As of August 2014, this system successfully measured all coolers required to support completion of a contractually-required Performance Milestone. There are approximately 1900 coolers available for measurement. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Improving the way land use change is handled in economic models

Sound economic modelling of land use in global economic models is critical for evaluating agricultural, biofuel, and climate policies. Current approaches do not preserve physical land area, do not account for the fact that land is of different qualities, or do not explicitly include the cost of converting land from one use to another. This study proposes a land use modelling framework building on the additive form of the constant elasticity of transformation (ACET) approach. We demonstrated that the framework could (1) directly provide traceable physical land use results, (2) flexibly handle land productivity differences based on biophysical information, (3) explicitly introduce land conversion cost, and (4) provide welfare decomposition in light of land heterogeneity and conversion cost. An experiment of mandating a 10 percent increase in grain consumption in the US food sector showed that ignoring land heterogeneity and conversion cost would underestimate the welfare loss by 28 percent.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Integrated emergency control board including design extended conditions for Krsko NPP - 297

In the aim of implementing post-Fukushima safety measures, Krsko Nuclear Power Plant is developing a Safety Upgrade Program. Krsko NPP is upgrading the remote shutdown capabilities and implementing ways to prevent and mitigate the consequences of potential Severe Accidents. This modification enhances remote shutdown capabilities of the plant to prevent potential severe accidents and improve the ways to mitigate their consequences. Within the set of plant design modifications, addressed the design, construction, and commissioning, of a new Emergency Control Room (EMCB. Tecnatom is the responsible of the design modification of the plant together with Idom, that participates with Tecnatom in the project as a joint venture, who provides a new set of field instrumentation, including its design and the corresponding wiring. EMCB integrates Human- System Interface throughout centralizing capabilities to safety shutdown and cooldown the plant as well as to control the new Design Extension Systems. The process relies on a Human Factors Engineering (HFE) analysis applying a specific methodology based on NUREG-0711 . Tecnatom has developed a specific HFE Program Management Plan to proceed with the analysis in a documented, traceable and reliable way. HFE activities ends with an Integrated System Validation in the actual full scope simulator involving MCR crews. This paper describes different phases of the project, the integrated schedule between HFE and I and C activities, benefits of upgrading main and emergency control rooms beyond design bases and addresses Tecnatom lessons learned obtained during the project execution. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

SU-F-T-669: Commissioning of An Electronic Brachytherapy System for Targeted Mouse Irradiation

Purpose: The aim of this study was to commission the Xoft Axxent™ electronic brachytherapy (eBT) source and 10 mm diameter surface applicator with NIST traceability for targeted irradiations of mouse anal carcinomas. Methods: The Xoft Axxent™ electronic brachytherapy (eBT) and 10 mm diameter surface applicator was chosen by the collaborating physician as a radiation delivery mechanism for mouse anal carcinomas. The target dose was 2 Gy at a depth of 3 mm in tissue to be delivered in a single fraction. To implement an accurate and reliable irradiation plan, the system was commissioned by first determining the eBT source output and corresponding dose rate at a depth of 3 mm in tissue. This was determined through parallel-plate ion chamber measurements and published conversion factors. Well-type ionization chamber measurements were used to determine a transfer coefficient, which correlates the measured dose rate at 3 mm to the NIST-traceable quantity, air-kerma rate at 50 cm in air, for eBT sources. By correlating these two quantities, daily monitoring in the well chamber becomes an accurate and efficient quality assurance technique. Once the dose-rate was determined, a treatment recipe was developed and confirmed with chamber measurements to deliver the requested dose. Radiochromic film was used to verify the dose distribution across the field. Results: Dose rates at 3 mm depth in tissue were determined for two different Xoft Axxent™ sources and correlated with NIST-traceable well-type ionization chamber measurements. Unique transfer coefficients were determined for each source and the treatment recipe was validated by measurements. Film profiles showed a uniform dose distribution across the field. Conclusion: A Xoft Axxent™ eBT system was successfully commissioned for use in the irradiation of mouse rectal tumors. Dose rates in tissue were determined as well as other pertinent parameters to ensure accurate delivery of dose to the target region.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

In this project we have developed atmospheric measurement capabilities and a suite of atmospheric modeling and analysis tools that are well suited for verifying emissions of green- house gases (GHGs) on an urban-through-regional scale. We have for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate atmospheric CO 2 . This will allow for the examination of regional-scale transport and distribution of CO 2 along with air pollutants traditionally studied using CMAQ at relatively high spatial and temporal resolution with the goal of leveraging emissions verification efforts for both air quality and climate. We have developed a bias-enhanced Bayesian inference approach that can remedy the well-known problem of transport model errors in atmospheric CO 2 inversions. We have tested the approach using data and model outputs from the TransCom3 global CO 2 inversion comparison project. We have also performed two prototyping studies on inversion approaches in the generalized convection-diffusion context. One of these studies employed Polynomial Chaos Expansion to accelerate the evaluation of a regional transport model and enable efficient Markov Chain Monte Carlo sampling of the posterior for Bayesian inference. The other approach uses de- terministic inversion of a convection-diffusion-reaction system in the presence of uncertainty. These approaches should, in principle, be applicable to realistic atmospheric problems with moderate adaptation. We outline a regional greenhouse gas source inference system that integrates (1) two ap- proaches of atmospheric dispersion simulation and (2) a class of Bayesian inference and un- certainty quantification algorithms. We use two different and complementary approaches to simulate atmospheric dispersion. Specifically, we use a Eulerian chemical transport model CMAQ and a Lagrangian Particle Dispersion Model - FLEXPART-WRF. These two models share the same WRF assimilated meteorology fields, making it possible to perform a hybrid simulation, in which the Eulerian model (CMAQ) can be used to compute the initial condi- tion needed by the Lagrangian model, while the source-receptor relationships for a large state vector can be efficiently computed using the Lagrangian model in its backward mode. In ad- dition, CMAQ has a complete treatment of atmospheric chemistry of a suite of traditional air pollutants, many of which could help attribute GHGs from different sources. The inference of emissions sources using atmospheric observations is cast as a Bayesian model calibration problem, which is solved using a variety of Bayesian techniques, such as the bias-enhanced Bayesian inference algorithm, which accounts for the intrinsic model deficiency, Polynomial Chaos Expansion to accelerate model evaluation and Markov Chain Monte Carlo sampling, and Karhunen-Lo %60 eve (KL) Expansion to reduce the dimensionality of the state space. We have established an atmospheric measurement site in Livermore, CA and are collect- ing continuous measurements of CO 2 , CH 4 and other species that are typically co-emitted with these GHGs. Measurements of co-emitted species can assist in attributing the GHGs to different emissions sectors. Automatic calibrations using traceable standards are performed routinely for the gas-phase measurements. We are also collecting standard meteorological data at the Livermore site as well as planetary boundary height measurements using a ceilometer. The location of the measurement site is well suited to sample air transported between the San Francisco Bay area and the California Central Valley.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Mobile platform for radiological characterization of sites under or after decommissioning - 15139

Nowadays, nuclear industry is facing a crucial need in establishing radiological characterization for the assessment and the monitoring of any remediation work. Regarding its experience in this domain, the French Commission for Atomic Energy and Alternative Energies (CEA) of Fontenay-aux-Roses, established an important feedback and developed a sound methodology for radiological characterization. A clean-up project is managed from the initial surface characterization to the final one, once volumes of contaminated soil have been removed. Samples are collected via recognized methods and data is gathered by real-time acquisition with different conveyance such as all-in-vehicles, cart, walking, helicopter or drone. Accurate radiological analyses are performed on site thanks to the mobile laboratories cluster, called SMaRT. It enables to realize long-time gamma spectroscopy, chemical preparation for alpha spectroscopy or beta liquid scintillation counting. Data is processed with geo-statistics via the software Kartotrak, 2D or 3D mapping are created with the associated uncertainty and probability map. Drilling profiles with geological data (layers composition, particles size, etc.) are studied in order to understand the behavior of the contamination in depth. Source term, contaminated volumes and sanitary impact are calculated in order to determine the optimum excavation depth. Finally, data is stored in an online site that insures the traceability of the whole process. This just-in-time production model allows a fast response and control of costs and avoids the disadvantages of radioactive material transport. This whole innovative process, developed inside the mobile platform, requires all these tools that have been used in France and continuously evolving for more than 10 years thanks to a learning organization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Shortwave Spectrometer (SWS) zenith radiance swsrad.b1 v3 and higher

Hyperspectral zenith radiances from the SWS instrument reported at 1 Hz. This data stream contains calibrated, dark-subtracted, radiances from two grating array spectrometers: a Si-detector based spectrometer denoted by SW and an InGaAs based detector denoted by LW. Spectra from both spectrometers are calibrated based on the spectral responsivity determined by reference against NIST-traceable light sources are reported independently as separate arrays having the same time record but wavelength dimension specified for each detector. Because the LW spectrometer is more susceptible to temperature-induced changes, a scale factor adjustment is applied the the LW spectra to yield agreement with the SW spectra over the wavelength range where they overlap. The final radiances incorporate QC to flag saturated values, periods where house-keeping fields fall outside acceptable bounds, and to flag pixels for which the spectral responsivity is not acceptable.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A Plan to Qualify New Fuel for the High Flux Isotope Reactor for Material Minimization

The High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) is one of five high power research reactors the Office of Material Management and Minimization (M3) Program, Office of Conversion is working to convert from using highly enriched uranium (HEU) fuel to using low-enriched uranium (LEU) fuel. This effort stems from the primary objective within the U.S. Department of Energy (DOE) National Nuclear Security Administration (NNSA) to achieve permanent threat reduction by minimizing, and when possible, eliminating weapon-usable nuclear material around the world. Under M3’s Office of Conversion, the U.S. High Performance Research Reactor (USHPRR) Project is pursuing fuel qualification and licensing of LEU fuels to support the high-performance reactors. All high-performance reactors except HFIR will be converted to LEU monolithic uranium-molybdenum alloy fuel. HFIR will be evaluated for conversion to LEU using a uranium silicide fuel, namely, U3Si2-Al dispersion fuel. The mission of the USHPRR Project is to develop the technology needed to reduce, and eventually eliminate, worldwide use of HEU in civilian applications. The goal is to develop the technical means needed to use low enriched uranium (LEU) instead of HEU fuel in research and test reactors without significant penalties in performance, economics, or safety of the reactors. The USHPRR Project has four major elements, called Pillars: Fuel Qualification (FQ) managed at Idaho National Laboratory (INL), Fuel Fabrication (FF) managed at Pacific Northwest National Laboratory (PNNL), Reactor Conversion (RC) managed at Argonne National Laboratory (Argonne), and Cross-Cutting (CC) managed at Savannah River National Laboratory (SRNL). FQ is responsible for the qualification of the fuel type. RC is responsible for supporting reactor conversion analysis and overseeing licensing submittals leading to conversions of domestic reactors to LEU fuel. For the FQ effort, FQ (INL) worked in collaboration with RC (Argonne) and ORNL to develop the plan for the uranium silicide fuel qualification for HFIR. The resulting HFIR Fuel Qualification Plan provides the general approach for the USHPRR team to move the selected uranium silicide fuel design for HFIR conversion through qualification. Authorization and use in HFIR will be approved through the DOE’s Office of Science. Uranium silicide fuel was previously qualified in NUREG-1313 at an approximate maximum heat flux of 1.4 MW/m2 and a maximum fuel section temperature of about 130°C. In addition to the different regulator process utilized by DOE, these upper limits will be exceeded in HFIR; therefore, further testing will be necessary to ensure the fuel can meet HFIR qualification requirements. The HFIR fuel loading may exceed 4.8 gU/cm3 which was determined in the NUREG-1313 safety evaluation to be acceptable for use in non-power NRC-licensed reactors provided there exist no other safety considerations. In addition, the uranium silicide fuel will need to be qualified in a HFIR-specific design. This plan includes the currently available information from the USHPRR Project Functions and Requirements document and expands these requirements to ensure that planned tests have traceable results providing evidence that the requirements have been met. Data collection methods are discussed as well as the process to show that the requirements have been met. This document is designed to provide a pathway for researchers to obtain data necessary and at the appropriate quality level for HFIR fuel qualification

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

A Plan to Qualify New Fuel for the High Flux Isotope Reactor for Material Minimization

The High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) is one of five high power research reactors the Office of Material Management and Minimization (M3) Program, Office of Conversion is working to convert from using highly enriched uranium (HEU) fuel to using low-enriched uranium (LEU) fuel. This effort stems from the primary objective within the U.S. Department of Energy (DOE) National Nuclear Security Administration (NNSA) to achieve permanent threat reduction by minimizing, and when possible, eliminating weapon-usable nuclear material around the world. Under M3’s Office of Conversion, the U.S. High Performance Research Reactor (USHPRR) Project is pursuing fuel qualification and licensing of the high-performance reactors to operate with LEU fuels. All high-performance reactors except HFIR will be converted to LEU monolithic uranium-molybdenum alloy fuel. HFIR will be evaluated for conversion to LEU using a uranium silicide fuel, namely, U3Si2-Al dispersion fuel.The mission of the USHPRR Project is to develop the technology needed to reduce, and eventually eliminate, worldwide use of HEU in civilian applications. The goal is to develop the technical means needed to use low enriched uranium (LEU) instead of HEU fuel in research and test reactors without significant penalties in performance, economics, or safety of the reactors. The USHPRR Project has four major elements, called Pillars: Fuel Qualification (FQ) managed at Idaho National Laboratory (INL), Fuel Fabrication (FF) managed at Pacific Northwest National Laboratory (PNNL), Reactor Conversion (RC) managed at Argonne National Laboratory (Argonne), and Cross-Cutting (CC) managed at Savannah River National Laboratory (SRNL). FQ is responsible for the qualification of the fuel type. RC is responsible for supporting reactor conversion analysis and overseeing licensing submittals leading to conversions of domestic reactors to LEU fuel. For the FQ effort, FQ (INL) worked in collaboration with RC (Argonne) and ORNL to develop the plan for the uranium silicide fuel qualification for HFIR.The resulting HFIR Fuel Qualification Plan provides the general approach for the USHPRR team to move the selected uranium silicide fuel design for HFIR conversion through qualification. Authorization and use in HFIR will be approved through the DOE’s Office of Science. Uranium silicide fuel was previously qualified in NUREG-1313 at an approximate maximum heat flux of 1.4 MW/m2 and a maximum fuel section temperature of about 130°C. In addition to the different regulator process utilized by DOE, these upper limits will be exceeded in HFIR; therefore, further testing will be necessary to ensure the fuel can meet HFIR qualification requirements. The HFIR fuel loading may exceed 4.8 gU/cm3 which was determined in the NUREG-1313 safety evaluation to be acceptable for use in non-power NRC-regulated reactors provided there exist no other safety considerations. In addition, the uranium silicide fuel will need to be qualified in a HFIR-specific design. This plan includes the currently available information from the USHPRR Project Functions and Requirements document and expands these requirements to ensure that planned tests have traceable results providing evidence that the requirements have been met. Data collection methods are discussed as well as the process to show that the requirements have been met. This document is designed to provide a pathway for researchers to obtain data necessary and at the appropriate quality level for HFIR fuel qualification.

Shokes, Tamara↗

DOE OSTI2014

Predicting the sensitivity of the beryllium/scintillator layer neutron detector using Monte Carlo and experimental response functions

A methodology for obtaining empirical curves relating absolute measured scintillation light output to beta energy deposited is presented. Output signals were measured from thin plastic scintillator using NIST traceable beta and gamma sources and MCNP5 was used to model the energy deposition from each source. Combining the experimental and calculated results gives the desired empirical relationships. To validate, the sensitivity of a beryllium/scintillator-layer neutron activation detector was predicted and then exposed to a known neutron fluence from a Deuterium-Deuterium fusion plasma (DD). The predicted and the measured sensitivity were in statistical agreement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2007

Collaborative Resource Allocation

Collaborative Resource Allocation Networking Environment (CRANE) Version 0.5 is a prototype created to prove the newest concept of using a distributed environment to schedule Deep Space Network (DSN) antenna times in a collaborative fashion. This program is for all space-flight and terrestrial science project users and DSN schedulers to perform scheduling activities and conflict resolution, both synchronously and asynchronously. Project schedulers can, for the first time, participate directly in scheduling their tracking times into the official DSN schedule, and negotiate directly with other projects in an integrated scheduling system. A master schedule covers long-range, mid-range, near-real-time, and real-time scheduling time frames all in one, rather than the current method of separate functions that are supported by different processes and tools. CRANE also provides private workspaces (both dynamic and static), data sharing, scenario management, user control, rapid messaging (based on Java Message Service), data/time synchronization, workflow management, notification (including emails), conflict checking, and a linkage to a schedule generation engine. The data structure with corresponding database design combines object trees with multiple associated mortal instances and relational database to provide unprecedented traceability and simplify the existing DSN XML schedule representation. These technologies are used to provide traceability, schedule negotiation, conflict resolution, and load forecasting from real-time operations to long-range loading analysis up to 20 years in the future. CRANE includes a database, a stored procedure layer, an agent-based middle tier, a Web service wrapper, a Windows Integrated Analysis Environment (IAE), a Java application, and a Web page interface.

Wang, Yeou-Fang↗