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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

New approach to precisely measure γ-ray intensities for long-lived fission products, with results for the decay of 95 Zr

For many fission products, the rays emitted following decay provide an easily-detectable signature that can be used to identify their quantities and distributions in a sample. As a result, γ-ray spectroscopy is often exploited to study fission-product yields, provided sufficiently accurate information on the γ-ray intensity is available. However, in many cases, the uncertainties in the existing nuclear data are large enough that they compromise the precision achievable for modern experiments and applications. To address this need, we have developed a new experimental method that is well suited to precisely measure absolute γ-ray intensities in the β decay of long-lived fission products. The approach involves the production of a radiopure sample by implantation of a mass-separated ion beam from the CAlifornium Rare Isotope Breeder Upgrade (CARIBU) facility on a thin carbon foil. The emitted β-decay radiation is detected with a 4π gas proportional counter and a meticulously efficiency-calibrated high-purity germanium (HPGe) detector. As a first measurement to demonstrate the approach, we studied the absolute γ-ray intensities of the strongest transitions following the β decay of 95 Zr and its decay-daughter 95 Nb, and determined them to fractional precisions of better than 1–2%. In addition, with a larger sample of activity produced through neutron irradiation of an isotopically-enriched Zr foil, we performed a high-precision measurement of the relative γ-ray intensities following the decay of 95 Zr with just the HPGe detector. Here, the sample-production method at CARIBU and the coincidence detection approach demonstrated here can be applied to study fission products with half-lives longer than a day, which includes isotopes important not only for nuclear-energy and national-security applications, but also for medical-isotope research and environmental monitoring.

4π↗
DOE OSTI2025

Phase-Imaging Ion-Cyclotron-Resonance mass spectrometry with the Canadian Penning Trap at CARIBU

The Canadian Penning Trap mass spectrometer (CPT) has conducted precision mass measurements of neutron-rich nuclides from the CAlifornium Rare Isotope Breeder Upgrade (CARIBU) of the Argonne Tandem Linac Accelerator System (ATLAS) facility at Argonne National Laboratory using the Phase-Imaging Ion-Cyclotron-Resonance (PI-ICR) technique for over half a decade. Here, in this study, we discuss the CPT system, and methods to improve accuracy and precision in mass measurement using PI-ICR including some optimization techniques and recently studied systematic effects.

Penning trap mass spectrometry↗
DOE OSTI2021

FIPD: The SFR metallic fuels irradiation & physics database

The DOE Advanced Reactor Technology (ART) program has supported efforts to recover and preserve metallic fuel data generated throughout the US sodium-cooled fast reactor (SFR) program. Those efforts have been focused on establishing databases of the experimental data that were mainly generated during the Integral Fast Reactor (IFR) program including data generated at Experimental Breeder Reactor-II (EBR-II), Fast Flux Test Facility (FFTF), and Transient Reactor Test Facility (TREAT) reactors, as well as out of pile data. The data is essential for future licensing activities of metallic fuel based advanced fast reactors. This paper describes the development of the SFR Metallic Fuels Irradiation & Physics Database (FIPD) and covers the scientific knowledge available in the database. Furthermore, the architecture of the FIPD is described by showing the available reactor operation data, fabrication, and post-irradiation examination (PIE) data, and other documents. The applications of the fuel database are also discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Modeling molybdeneum-99 production in molten salt reactors

Molten salt reactors (MSRs) are being investigated as a possible source of 99 Mo for medical uses. This paper describes computational modeling being performed to determine the feasibility for use of several MSR concepts in 99 Mo production: the Molten Salt Breeder Reactor (MSBR), the Molten Salt Demonstration Reactor (MSDR), and the Molten Salt Reactor Experiment (MSRE). This modeling is accomplished with SCALE/TRITON using a three-mixture system and estimated removal terms. Results showed that the MSBR, MSDR and MSRE were capable of supplying an estimated 35,800,000, 13,700,000, and 132,000 six-day Ci/week of 99 Mo, respectively, which is far more than the global demand. In conclusion, a range of operating parameters has been determined in which each reactor type is expected to have sufficient production capability to meet global demand.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Initial demonstration of automated fuel performance modeling with 1977 EBR-II metallic fuel pins using BISON code with FIPD and IMIS databases

Using the BISON fuel performance code, simulations were conducted using an automated process to read initial and operating conditions from the Fuels Irradiation and Physics Database (FIPD) and Integral Fast Reactor materials information system (IMIS) database, which contains metallic fuel data from the Experimental Breeder Reactor-II (EBR-II). This work demonstrates use of an integrated framework to access the vast majority of EBR-II experimental fuel pin data to support rapid development of fuel performance models for next-generation metallic fuel systems. With this capability, validation for fuel qualification can be performed rapidly. Between IMIS and FIPD, there is enough information to conduct 1977 unique EBR-II metallic fuel pin histories from 24 different experiments, at varying levels of detail between the two databases. Each of these histories includes a high-resolution power history, flux history, coolant channel flow rates, and coolant channel temperatures. Fission gas release (FGR), cumulative damage fraction (CDF), fuel axial swelling, cladding profilometry, and burnup were all simulated in BISON. The results were compared to post-irradiation examination (PIE) results for the initial demonstration of automated BISON modeling. BISON simulations conducted with IMIS and FIPD were in rough agreement with PIE measurements and calculations. Cladding profilometry, FGR, and fuel axial swelling were found to be in rough agreement with PIE measurements, depending on the physics used within the BISON input files. Here, the mechanical contact solver chosen was found to significantly impact axial fuel swelling and cladding strain predictions. CDF values were assessed to see whether pin failure may have been predicted (CDF ≥ 1). This work suggests that continued development of an automated tool for BISON should focus on inclusion of the Fast Flux Test Facility (FFTF) experimental data for a larger database for metallic fuel, improved physical models to better capture fuel performance, such as fuel-cladding interactions, and a more detailed comparison with available PIE data to further the BISON model development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Development and preliminary validation of a mechanistic multiscale model for fuel-cladding chemical interaction in metallic nuclear fuels

Despite decades of fuel rod material and design improvements, fuel-cladding chemical interaction (FCCI) remains the single-most lifetime-limiting behavior for modern metallic fuel rods. Constraining fuel lifetime increases operating costs, limiting the economic viability of commercializing metallic nuclear fuel technology. A mechanistic multiscale model utilizing the finite element method-based MARMOT and BISON codes was developed to more confidently predict cladding-side FCCI and its impact on fuel performance. The new BISON model incorporates mesoscale models for the effects of fuel microstructure evolution on the transport of wastage-inducing lanthanides through the fuel and for the kinetics of cladding wastage layer growth. The mesoscale models, in turn, build on lanthanide transport property data obtained from the atomistic scale. Preliminary validation studies using wastage thickness and cladding profilometry data from four fuel rods irradiated in Experimental Breeder Reactor II experiment X447 and one fuel rod from Fast Flux Test Facility experiment IFR1 show that the new model predicts cladding wastage and its effects on cladding deformation as well as existing empirical FCCI correlations. The new model is expected to aid in the design of new metallic fuel concepts, including fuel additives, cladding liners, and sodium-free annular fuel geometries. In conclusion, future work will focus on broader validation and refinement of the model’s treatment of different fuel alloys and cladding materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Phosphate-Based Approaches for Dechlorination and Treatment of Salt Waste from Electrochemical Processing of Used Nuclear Fuel: A Perspective on Recent Work

Phosphate-based reagents are being considered by the U.S. Department of Energy (DOE) Office of Nuclear Energy to process halide salt-based nuclear wastes for stabilization prior to disposal. As evidenced by the Experimental Breeder Reactor-II (EBR-II) project, electrochemical processing (pyroprocessing) can be employed to recover uranium and other actinides for reintegration into the nuclear fuel cycle from metallic fuels. The resultant salt-based wastes generated from electrochemical processing of EBR-II fuel contains fission products within a LiCl–KCl eutectic salt that necessitate appropriate disposal. This paper provides an overview of recent efforts to support halide-based salt waste treatment for disposition, as well as a basis for comparison with other related efforts in salt waste treatment through salt partitioning initiatives. The U.S. DOE has selected a phosphate waste form reference material for further investigation and longer-term studies.

electrorefiner↗
DOE OSTI2022

Chromosome evolution and the genetic basis of agronomically important traits in greater yam

The nutrient-rich tubers of the greater yam, Dioscorea alata L., provide food and income security for millions of people around the world. Despite its global importance, however, greater yam remains an orphan crop. Here, we address this resource gap by presenting a highly contiguous chromosome-scale genome assembly of D. alata combined with a dense genetic map derived from African breeding populations. The genome sequence reveals an ancient allotetraploidization in the Dioscorea lineage, followed by extensive genome-wide reorganization. Using the genomic tools, we find quantitative trait loci for resistance to anthracnose, a damaging fungal pathogen of yam, and several tuber quality traits. Genomic analysis of breeding lines reveals both extensive inbreeding as well as regions of extensive heterozygosity that may represent interspecific introgression during domestication. These tools and insights will enable yam breeders to unlock the potential of this staple crop and take full advantage of its adaptability to varied environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Grain Boundary Effects on Radiation Damage and Tritium Diffusion in Li–Al–O Ceramics from Molecular Dynamics and Experiments

Understanding irradiation damage and tritium transport in LiAlO2 ceramics is essential for their deployment in tritium-producing burnable absorber rods (TPBARs). Grain boundaries (GBs) play an important role in governing radiation response and tritium transport in LiAlO2 and its secondary phase LiAl5O8. Using molecular dynamics simulations, we investigated defect evolution and tritium diffusion during displacement cascades in single-crystal and bicrystal LiAlO2 and LiAl5O8 at 600 K. The results reveal that GBs suppress the damage in the LiAlO2 bicrystals as compared to single crystals, via interstitial emission. The effect is more pronounced for Li defects, than for Al and O. In LiAl5O8 the damage reduction due to introduction of GB is less pronounced overall. Tritium diffusion coefficients at GBs are higher by factors of 2–10 relative to diffusion inside grain bulk. The effect is particularly pronounced in LiAlO2, where S5, S17, and S25 GBs promote rapid tritium transport, whereas LiAl5O8 exhibits slower diffusion due to reduced free volume at its GBs. These findings evince a trade-off that while GBs mitigate radiation damage by absorbing interstitials, they simultaneously provide fast pathways for tritium migration which is not desirable for TPBAR application. The mechanistic insights gained here establish a foundation for microstructural design strategies to balance radiation tolerance and tritium retention in ceramic breeder materials.

Roy, Ankit↗
DOE OSTI2021

Genomic mechanisms of climate adaptation in polyploid bioenergy switchgrass

Long-term climate change and periodic environmental extremes threaten food and fuel security and global crop productivity. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation—knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass ( Panicum virgatum ). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate–gene–biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene–trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.

09 BIOMASS FUELS↗
DOE OSTI2024

The complex polyploid genome architecture of sugarcane

Sugarcane, the world’s most harvested crop by tonnage, has shaped global history, trade and geopolitics, and is currently responsible for 80% of sugar production worldwide. While traditional sugarcane breeding methods have effectively generated cultivars adapted to new environments and pathogens, sugar yield improvements have recently plateaued. The cessation of yield gains may be due to limited genetic diversity within breeding populations, long breeding cycles and the complexity of its genome, the latter preventing breeders from taking advantage of the recent explosion of whole-genome sequencing that has benefited many other crops. Thus, modern sugarcane hybrids are the last remaining major crop without a reference-quality genome. Here we take a major step towards advancing sugarcane biotechnology by generating a polyploid reference genome for R570, a typical modern cultivar derived from interspecific hybridization between the domesticated species (Saccharum officinarum) and the wild species (Saccharum spontaneum). In contrast to the existing single haplotype (‘monoploid’) representation of R570, our 8.7 billion base assembly contains a complete representation of unique DNA sequences across the approximately 12 chromosome copies in this polyploid genome. Using this highly contiguous genome assembly, we filled a previously unsized gap within an R570 physical genetic map to describe the likely causal genes underlying the single-copy Bru1 brown rust resistance locus. This polyploid genome assembly with fine-grain descriptions of genome architecture and molecular targets for biotechnology will help accelerate molecular and transgenic breeding and adaptation of sugarcane to future environmental conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Advanced characterization-informed machine learning framework and quantitative insight to irradiated annular U-10Zr metallic fuels

Abstract U-10Zr Metal fuel is a promising nuclear fuel candidate for next-generation sodium-cooled fast spectrum reactors. Since the Experimental Breeder Reactor-II in the late 1960s, researchers accumulated a considerable amount of experience and knowledge on fuel performance at the engineering scale. However, a mechanistic understanding of fuel microstructure evolution and property degradation during in-reactor irradiation is still missing due to a lack of appropriate tools for rapid fuel microstructure assessment and property prediction based on post irradiation examination. This paper proposed a machine learning enabled workflow, coupled with domain knowledge and large dataset collected from advanced post-irradiation examination microscopies, to provide rapid and quantified assessments of the microstructure in two reactor irradiated prototypical annular metal fuels. Specifically, this paper revealed the distribution of Zr-bearing secondary phases and constitutional redistribution across different radial locations. Additionally, the ratios of seven different microstructures at various locations along the temperature gradient were quantified. Moreover, the distributions of fission gas pores on two types of U-10Zr annular fuels were quantitatively compared.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

Here, in this work, we introduce a novel method to develop whole system digital twins (DTs) for advanced nuclear reactors. This method treats a complex reactor system as a heterogeneous graph: with the system components as different types of graph nodes and their physical interconnections as edges. Based on the heterogeneous graph, a graph neural network combining graph convolution and temporal node attention is developed as the DT, facilitating a comprehensive understanding of the system's dynamic behavior. By utilizing the System Analysis Module (SAM) code for simulating various operational transients, we develop a graph-based database that trains the DT. This DT is characterized by two primary functions: It can infer the entire system's status using sparse node information, and it can predict the progress of transients based on current and historical system information. Our approach is validated through case studies on the Experimental Breeder Reactor II (EBR-II) system and a generic Fluoride-salt-cooled High-temperature Reactor (gFHR), demonstrating the DT's accuracy in forecasting operational transients. The DT's rapid computation capabilities enhance its potential for supporting advanced reactor operations, offering benefits in intelligent simulation, autonomous control, and anomaly detection, paving the way for improved safety analysis and intelligent component health management for advanced reactor systems and reducing their operations and maintenance cost.

EBR-II↗
DOE OSTI2023

$\mathrm{EBR-II MOX}$ Fuel Characterization Enabling ARES Phase I Testing

Pretransient characterization was performed for the Experimental Breeder Reactor II (EBR-II) mixed-oxide (MOX) fuel pellets from the SPA-2/-2B Operational Reliability Testing collaboration between Japan and the United States. Continued collaboration under the Advanced Reactor Experiments for Sodium Fast Reactor Fuels project will investigate the transient performance of these rods in the Transient Reactor Test facility at Idaho National Laboratory in the MOXTOP-THOR experiment. The results will fill a gap in existing transient performance data for MOX as these rods have a peak burnup of 14.3 at. % (~134.4 GWd/t) in the EBR-II. Fuel pellet properties were gathered from available resources and their irradiation and decay history evaluated. Further reactor physics calculations were performed to support the experiment design, reactor operations, and safety analyses necessary to enable the programmatic success of this effort. Of the three irradiated fuel pins, two will undergo transient testing, and all three will undergo post-irradiation examination. The methodology development and analysis activities utilized we report enable current experiment design work and provide the pathway through which measured data of this type can be further evaluated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Conceptual Design of a Thermal–to-14-MeV Neutron Conversion Device for Use in the Advanced Test Reactor

Here, this paper details the conceptual design of a thermal-to-14-MeV neutron converter consisting of a mixture of lithium and deuterium in a blanket material. Such a device operates a two-step reaction, first generating tritons via thermal neutron absorption in the tritium breeding material, and in the second step, high-energy neutrons are produced either via deuterium-tritium fusion reaction or with tritium reacting with lithium. A thermal-to-14-MeV neutron converter significantly hardens the neutron spectrum by virtually removing thermal neutrons and adding a high-energy 14 MeV component to the neutron spectrum. While similar concepts have been previously proposed and tested in other reactors, the unique characteristics of the Advanced Test Reactor (ATR), namely its important thermal flux (up to $10^{15}$ n.cm$^{-2}$.s$^{-1}$) makes it markedly attractive for obtaining a very large fast neutron flux, usable for irradiation studies under neutron flux conditions prototypical of fusion reactors. The paper provides a description of a new computational scheme developed for handling the coupled neutron-triton transport mechanism using the Geant4 toolkit. Resulting neutron spectra and high-energy neutron yields are summarized for different irradiation positions and potential neutron breeder materials. Maximum predicted thermal-to-14-MeV neutron yields are on the order of $2.10^{-4}$, which is consistent with previous studies found in the literature. Thus, when placed inside the ATR, such a neutron converter will be providing the largest high-energy neutron source available for activation and irradiation studies of materials foreseen for use in fusion reactors. Future steps will involve qualifying the computational scheme using the ATR critical facility using activation foil measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Validation of Fast Reactor Depletion Tools Using EBR-II Measured Data

The validation of simulation tools for calculating fuel depletion and evolution in fast reactors is vital for design, licensing, deployment, operations, and material accountancy. The Physics Analysis Database (PADB) and Analytical Laboratory (AL) database contain measured data collected from Experimental Breeder Reactor II (EBR-II) and were used to validate the most recent versions of the Argonne Reactor Computation (ARC) tool suite and ORIGEN-S for calculating isotopic compositions in irradiated fast reactor fuel. The PADB contains important modeling and operational information about the EBR-II core design, fuel cycle, and analytical results from the legacy versions of the ARC tool suite. The AL database contains the measured isotopic compositions of irradiated samples taken from core subassemblies. A new procedure was developed for the ARC tool suite to perform the EBR-II depletion simulation, as well as to perform more detailed isotopic calculations using ORIGEN-S calculations by coupling it with the ARC suite. Both the ARC and ARC-ORIGEN results were compared with the AL measured data for all relevant samples and showed good agreement for the major actinides. Good agreement with measured data was also achieved using the ARC-ORIGEN approach for several fission products.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗