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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Handheld Medical Diagnostic Device for Harsh Environments

Medical diagnostic capabilities for extreme environments, are currently limited in nature to single marker type assays. We propose to develop a handheld device for medical diagnostics in extreme environments that provide sensitive and specific detection of biomarkers with an emphasis initially on radiation exposure. Such a device will have multiple uses, and will be easily adaptable to many applications. For example, first responders, the battlefield, in low resource settings in developing countries facing emerging disease outbreaks, and during long-duration space flights. To this end, we will develop highly adaptable microfluidic devices (cards) for profiling different biomarkers with an emphasis on those related to radiation exposure. We will leverage the previous centrifugal separator work from LLNL and Sandia National Laboratory. The radiation exposure biomarker work at LLNL will provide a biological platform for demonstrating the utility of the device. We will develop a fluidic system using a pump-less mechanical method (centrifugal) to move, control, order and layer fluids in the platform. This decreases complexity, while enabling simultaneous cell separation and plasma generation for two uses: the first is differential blood cell counts and the second is biomarker assay panels. This work will be accomplished in three Aims: Aim 1) Test multiplex microfluidic cards and integrated reader. Aim 2) Demonstrate system capabilities to take and process blood samples for multi-endpoint analysis. Aim 3) Test multiplex detection based on measuring differential blood cell counts, while measuring plasma protein biomarkers. The proposed work addresses the mission research challenge of chemical and biological countermeasure in that our work aims to provide new research capabilities to respond to man-made or natural outbreaks of pandemic diseases in particular in low resource and triage environments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗
DOE OSTI2021

Informing nuclear physics via machine learning methods with differential and integral experiments

Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Genome-Wide Analysis of Terpene Synthase Gene Family in Mentha longifolia and Catalytic Activity Analysis of a Single Terpene Synthase

Terpenoids are a wide variety of natural products and terpene synthase (TPS) plays a key role in the biosynthesis of terpenoids. Mentha plants are rich in essential oils, whose main components are terpenoids, and their biosynthetic pathways have been basically elucidated. However, there is a lack of systematic identification and study of TPS in Mentha plants. In this work, we genome-widely identified and analyzed the TPS gene family in Mentha longifolia, a model plant for functional genomic research in the genus Mentha. A total of 63 TPS genes were identified in the M. longifolia genome sequence assembly, which could be divided into six subfamilies. The TPS-b subfamily had the largest number of genes, which might be related to the abundant monoterpenoids in Mentha plants. The TPS-e subfamily had 18 members and showed a significant species-specific expansion compared with other sequenced Lamiaceae plant species. The 63 TPS genes could be mapped to nine scaffolds of the M. longifolia genome sequence assembly and the distribution of these genes is uneven. Tandem duplicates and fragment duplicates contributed greatly to the increase in the number of TPS genes in M. longifolia. The conserved motifs (RR(X)8W, NSE/DTE, RXR, and DDXXD) were analyzed in M. longifolia TPSs, and significant differentiation was found between different subfamilies. Adaptive evolution analysis showed that M. longifolia TPSs were subjected to purifying selection after the species-specific expansion, and some amino acid residues under positive selection were identified. Furthermore, we also cloned and analyzed the catalytic activity of a single terpene synthase, MlongTPS29, which belongs to the TPS-b subfamily. MlongTPS29 could encode a limonene synthase and catalyze the biosynthesis of limonene, an important precursor of essential oils from the genus Mentha. This study provides useful information for the biosynthesis of terpenoids in the genus Mentha.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Utah FORGE: Seismic DAS and Geophone Borehole Data Processing and 3D Imaging of Vp/Vs Ratio in the 2024 Stimulated Reservoir

This dataset includes a final report and a 3D velocity model derived from seismic DAS and geophone borehole data collected during the April 2024 stimulation of the reservoir at Utah FORGE. The report details the processing of over 50,000 P- and S-wave travel times used in a tomographic inversion to estimate the Vp/Vs ratio, revealing anomalies adjacent to wells 16A and 16B that may be associated with injected fracturing fluids. Additionally, Wadati analysis of more than 27,000 pairs of differential P- and S-wave travel times supports this interpretation with a high Vp/Vs estimate of 1.86, averaged over the dimensions of the earthquake cluster. The accompanying data file provides a 3D model of P- and S-wave velocities and Vp/Vs ratios, structured with spatial coordinates and velocity values.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Probing thermodynamics of radiogenic helium and defects in $δ$-plutonium alloys and interactions with adsorbed environmental gases

Differential scanning calorimetry coupled with simultaneous evolved gas analysis (DSC-EGA) on aged δ-Pu samples shows that most radiogenic helium remains trapped within the Pu matrix at temperatures very close to, or slightly above, the melting temperature. Our results indicate that helium release from 50-year-old δ-Pu occurs as a burst just below the melting temperature (>0.994 T m ), with subsequent pressure oscillations as temperature increases. Subordinate quantities of H 2 were also released along with helium. Here, the helium emission tails off and ceases above ~720 – 750°C. In a δ-Pu alloy aged 6 years, the initial helium burst occurs slightly above melting (~1.015 – 1.042 T m ), with a discrete, larger helium spike occurring between 670 and 686°C. The proximity of helium release to the liquidus transition presented challenges in the deconvolution of overlapping process enthalpies, the liquidus endotherm, and the exotherm resulting from bubble collapse, annealing and gas expulsion. Helium’s strong affinity for vacancy binding in a 2He-vac configuration is predicted by Density Functional Theory (DFT) modeling. The measured stored energy associated with the He release events in a 50-year-old δ alloy is on the order of ~ 10–11 J/g, which is significantly higher than stored energies measured in the sub-solidus regimes (~2 J/g) that are related to the solid-state annealing of processing- and radiation-induced defects. This implies that aged δ Pu alloys have a remarkable resilience to accommodate the lattice strain produced by the internal pressure of the helium bubbles and provides further insight into the thermodynamic behavior of aged δ Pu.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Gastrointestinal stromal tumor risk classification: spectral CT quantitative parameters

Purpose: To examine the value of spectral CT quantitative parameters in gastrointestinal stromal tumor (GIST) risk classification. Materials and methods: This retrospective study was approved by the institutional review board. The requirement for informed consent was signed. The authors evaluated 86 patients (30 high risk, 22 medium risk, 28 low risk, and 6 very low risk; mean age: 59 years [range 19–83 years]) with pathologically confirmed GIST who underwent plain and triple-phase contrast-enhanced CT with spectral CT imaging mode from March 2015 through September 2017, with manual follow-up. Quantitative parameters including the CT value of 70 keV monochromatic images, the slope of spectral curves, and the normalized iodine concentration (NIC) and water (iodine) concentrations were measured and calculated, and conducted a power analysis of the above data. Results: (1) The CT values at 70 keV of the high-risk group were higher than the intermediate and low groups in each of the enhanced phases (P ≤ 0.001), no significant differences in the intermediate-risk and low-risk groups were noted (P = 0.874, 0.871, 0.831, respectively). (2) The slope of the spectral curve of the high-risk group was higher than those of the intermediate and low groups in each of the enhanced phases (P ≤ 0.001), and there were no significant differences between the intermediate- and low-risk groups (P = 0.069, 0.466, 0.840, respectively). (3) The NIC of the high-risk group significantly differed from the lower risk groups (P ≤ 0.001). There was also no significant difference observed between the intermediate- and low-risk groups (P = 0.671, 0.457, 0.833, respectively). (4) The power analysis results show that only the low-risk group with delay period is 0.530, the rest groups are all greater than 0.999. Conclusion: Dual-energy spectral CT with quantitative analysis may help to increase the accuracy in differentiating the pathological risk classification of GIST between high risk and non-high risk, preoperatively. There were limitations for distinguishing the intermediate- and low-risk groups.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Hydromechanical reactivation of natural discontinuities: mesoscale experimental observations and DEM modeling

Fracture interaction mechanisms and reactivation of natural discontinuities under fluid pressurization conditions can represent critical issues in risk assessment of caprock integrity. A field injection test, carried out in a damage fault zone at the decameter scale, i.e., mesoscale, has been studied using a distinct element model. Given the complex structural nature of the damage fault zone hydraulically loaded, the contribution of fracture sets on the bulk permeability has been investigated. It has been shown that their orientation for a given in situ stress field plays a major role. Based on these results, a simpler model with a fluid-driven fracture intersecting a second fracture has been set up to perform a sensitivity analysis. It is in presence of a minimum differential stress value with a minimum angle with the maximum principal stress that the second fracture could be both, hydraulically and mechanically reactivated. Results also showed that in the vicinity of the fluid-driven fracture, a natural fracture will offer contrasted hydromechanical responses on each side of the intersection depending on the stress conditions and its orientation with respect to the stress field. In this case, we show that a hydromechanical decoupling can occur along the same plane. These results provide insights into fracture-controlled permeability of fault zones depending on the properties of the fractures and their hydromechanical interactions for a given in situ stress field.

58 GEOSCIENCES↗
DOE OSTI2019

MicroRNA expression profiling provides novel insights into immune-related pathways involved in gastric cancer

Gastric cancer is one of the most common cancers, and an increasing number of studies have found that microRNAs (miRNAs) play essential roles in gastric cancer progression; however, the roles of specific miRNAs involved in the immune response to this disease remain unclear. We compared the miRNA expression in tissues from primary gastric cancer patients and healthy controls to find miRNAs dysregulated in gastric cancer and used bioinformatics tools to determine potential roles of these miRNAs in the immune system. We evaluated 25 primary gastric cancer tissues and five healthy gastric tissues. Quantitative real-time polymerase chain reaction was performed for a set of miRNAs, followed by the prediction of their target genes and functional enrichment analysis of these targets. Analysis of a microarray dataset showed that the miRNA miR-196a-5p was significantly upregulated, while miR-374a-5p and miR-375 were downregulated in gastric cancer patients. In addition, miR-374-5p was significantly downregulated in patients with metastasis compared with its expression levels in non-metastatic patients (p = 0.03). Bioinformatics analysis suggested that the pathways regulated by these differentially expressed miRNAs were related to the immune response, cell adhesion, and cell migration. Most importantly, this study provides a new insight into the potential use of multiple miRNAs to find distinct pathways of immune regulation in gastric cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

An extension of Thwaites’ method for turbulent boundary layers

Thwaites developed an approximate method for determining the evolution of laminar boundary layers. The approximation follows from an assumption that the growth of a laminar boundary layer in the presence of pressure gradients could be parameterized solely as a function of the Holstein–Bohlen flow parameter, thus reducing the von Kármán momentum integral to a first-order ordinary differential equation. This method is useful for the analysis of laminar flows, and in computational potential flow solvers to account for the viscous effects. In this work, an approximate method for determining the momentum thickness of a two-dimensional, turbulent boundary layer is proposed following Thwaites’ work. It is shown that the method provides good estimates of the momentum thickness for multiple boundary layers, including both favourable and adverse pressure gradient effects, up to the point of separation. In the limit of high Reynolds numbers, it is possible to derive a criterion for the onset of separation from the proposed model, which is shown to be in agreement with prior empirical observations. The sensitivity of the separation location with respect to upstream perturbations is also analysed through this model for the NASA/Boeing speed bump and the transonic Bachalo–Johnson bump.

17 WIND ENERGY↗
DOE OSTI2025

DONUT: physics-aware machine learning for real-time X-ray nanodiffraction analysis

Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Materials science↗
DOE OSTI2023

Tripogon loliiformis tolerates rapid desiccation after metabolic and transcriptional priming during initial drying

Abstract Crop plants and undomesticated resilient species employ different strategies to regulate their energy resources and growth. Most crop species are sensitive to stress and prioritise rapid growth to maximise yield or biomass production. In contrast, resilient plants grow slowly, are small, and allocate their resources for survival in challenging environments. One small group of plants, termed resurrection plants, survive desiccation of their vegetative tissue and regain full metabolic activity upon watering. However, the precise molecular mechanisms underlying this extreme tolerance remain unknown. In this study, we employed a transcriptomics and metabolomics approach, to investigate the mechanisms of desiccation tolerance in Tripogon loliiformis , a modified desiccation-tolerant plant, that survives gradual but not rapid drying. We show that T. loliiformis can survive rapid desiccation if it is gradually dried to 60% relative water content (RWC). Furthermore, the gene expression data showed that T. loliiformis is genetically predisposed for desiccation in the hydrated state, as evidenced by the accumulation of MYB, NAC, bZIP, WRKY transcription factors along with the phytohormones, abscisic acid, salicylic acid, amino acids (e.g., proline) and TCA cycle sugars during initial drying. Through network analysis of co-expressed genes, we observed differential responses to desiccation between T. loliiformis shoots and roots. Dehydrating shoots displayed global transcriptional changes across broad functional categories, although no enrichment was observed during drying. In contrast, dehydrating roots showed distinct network changes with the most significant differences occurring at 40% RWC. The cumulative effects of the early stress responses may indicate the minimum requirements of desiccation tolerance and enable T. loliiformis to survive rapid drying. These findings potentially hold promise for identifying biotechnological solutions aimed at developing drought-tolerant crops without growth and yield penalties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Neural network surrogate models for equations of state

Equation of state (EOS) data provide necessary information for accurate multiphysics modeling, which is necessary for fields such as inertial confinement fusion. Here, we suggest a neural network surrogate model of energy and entropy and use thermodynamic relationships to derive other necessary thermodynamic EOS quantities. We incorporate phase information into the model by training a phase classifier and using phase-specific regression models, which improves the modal prediction accuracy. Our model predicts energy values to 1% relative error and entropy to 3.5% relative error in a log-transformed space. Although sound speed predictions require further improvement, the derived pressure values are accurate within 10% relative error. Our results suggest that neural network models can effectively model EOS for inertial confinement fusion simulation applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Integration of high-density genetic mapping with transcriptome analysis uncovers numerous agronomic QTL and reveals candidate genes for the control of tillering in sorghum

Phenotypes such as branching, photoperiod sensitivity, and height were modified during plant domestication and crop improvement. Here, we perform quantitative trait locus (QTL) mapping of these and other agronomic traits in a recombinant inbred line (RIL) population derived from an interspecific cross between Sorghum propinquum and Sorghum bicolor inbred Tx7000. Using low-coverage Illumina sequencing and a bin-mapping approach, we generated ~1920 bin markers spanning ~875 cM. Phenotyping data were collected and analyzed from two field locations and one greenhouse experiment for six agronomic traits, thereby identifying a total of 30 QTL. Many of these QTL were penetrant across environments and co-mapped with major QTL identified in other studies. Other QTL uncovered new genomic regions associated with these traits, and some of these were environment-specific in their action. To further dissect the genetic underpinnings of tillering, we complemented QTL analysis with transcriptomics, identifying 6189 genes that were differentially expressed during tiller bud elongation. We identified genes such as Dormancy Associated Protein 1 (DRM1) in addition to various transcription factors that are differentially expressed in comparisons of dormant to elongating tiller buds and lie within tillering QTL, suggesting that these genes are key regulators of tiller elongation in sorghum. Our study demonstrates the usefulness of this RIL population in detecting domestication and improvement-associated genes in sorghum, thus providing a valuable resource for genetic investigation and improvement to the sorghum community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis

SF-25-088 Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Zhou, Tao [Argonne National Laboratory (ANL), Argo↗
DOE OSTI2024

Nitrogen starvation causes lipid remodeling in Rhodotorula toruloides

Abstract Background The oleaginous yeast Rhodotorula toruloides is a promising chassis organism for the biomanufacturing of value-added bioproducts. It can accumulate lipids at a high fraction of biomass. However, metabolic engineering efforts in this organism have progressed at a slower pace than those in more extensively studied yeasts. Few studies have investigated the lipid accumulation phenotype exhibited by R. toruloides under nitrogen limitation conditions. Consequently, there have been only a few studies exploiting the lipid metabolism for higher product titers. Results We performed a multi-omic investigation of the lipid accumulation phenotype under nitrogen limitation. Specifically, we performed comparative transcriptomic and lipidomic analysis of the oleaginous yeast under nitrogen-sufficient and nitrogen deficient conditions. Clustering analysis of transcriptomic data was used to identify the growth phase where nitrogen-deficient cultures diverged from the baseline conditions. Independently, lipidomic data was used to identify that lipid fractions shifted from mostly phospholipids to mostly storage lipids under the nitrogen-deficient phenotype. Through an integrative lens of transcriptomic and lipidomic analysis, we discovered that R. toruloides undergoes lipid remodeling during nitrogen limitation, wherein the pool of phospholipids gets remodeled to mostly storage lipids. We identify specific mRNAs and pathways that are strongly correlated with an increase in lipid levels, thus identifying putative targets for engineering greater lipid accumulation in R. toruloides . One surprising pathway identified was related to inositol phosphate metabolism, suggesting further inquiry into its role in lipid accumulation. Conclusions Integrative analysis identified the specific biosynthetic pathways that are differentially regulated during lipid remodeling. This insight into the mechanisms of lipid accumulation can lead to the success of future metabolic engineering strategies for overproduction of oleochemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data Mining – Image Analysis of Radiography for Zr Redistribution

The research effort described in this report represents a first attempt to investigate the radial redistribution of Zr in ternary fuel alloys U-xPu-10Zr (x = 0, 8, 19) irradiated in the in-reactor fuel experiments during the operation of the U.S. Department of Energy’s (DOE’s) Experimental Breeder Reactor II (EBR-II) at Idaho National Laboratory (INL) using methods of image processing on post-irradiation neutron radiographs. Approximately 130,000 metal fuel pins were irradiated in EBR-II during its 30 years of operation to develop and characterize existing and prospective fuels. For many of the metal fuel irradiation experiments, neutron radiography imaging was performed historically now allowing application of modern image analysis techniques to characterize fuel behavior, such as fuel swelling, fluff formation, and now, fuel alloy constituent redistribution. The redistribution of fuel components depends on the temperature field, radially, within the fuel. Specifically, Zr is expected to redistribute radially towards the center of the pin, as well as towards the outer zones. Currently, direct imaging of a pin cross-section through optical methods or scanning electron microscopy (SEM) is used to study the constituents’ redistribution, which is very time-consuming and can only be applied to a limited number of pins. An automated image processing technique allowing for the investigation of fuel radial redistribution zones would significantly accelerate data analysis. In general, if the fuel temperature is hot enough, three redistribution zones are expected, corresponding to the main fuel components (e.g., U, Pu, Zr). While more assessment will be performed in fiscal year (FY)-2023, it seems possible to differentiate the pins according to their fuel composition using image analysis techniques on neutron radiographs of metallic fuel pins based on the analysis to date.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗