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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Resolving atomic transport through iron oxide under irradiation using isotopic tracers

Structural materials in nuclear environments are exposed to complex and harsh conditions, including high temperatures, corrosive solutions, and irradiation. Material stability and likewise degradation are fundamentally controlled by atomic transport mechanisms. Here, we investigate the influence of radiation-induced, non-equilibrium point defect populations on mass transport within model oxide systems using isotopic tracers and atom probe tomography. An isotopically labelled hematite (a-Fe2O3) film was synthesized with 16O- and 18O-rich layers (>30nm and 10 nm, respectively) sequentially deposited via molecular beam epitaxy. One region of the film was proton irradiated to ~0.1 dpa at 450°C, and regions outside the beam spot provided a non-irradiated control of thermal annealing only. 3D APT was used to directly visualize the elemental and O-isotopic redistribution following both thermal and combined thermal and irradiation conditions. Complementary scanning transmission electron microscopy (STEM) was also employed to study the microstructural evolution. Error function fits of the 18O-concentration profiles enabled quantification of diffusion coefficients.

isotopic tracers, diffusion, proton irradiation, h↗
DOE OSTI2015

SU-E-J-116: Uncertainties Associated with Dose Summation of High-Dose Rate Brachytherapy and Intensity Modulated Radiotherapy for Gynecological Cases

Purpose: Determining the cumulative dose distribution (CDD) for gynecological patients treated with both high-dose rate (HDR) brachytherapy and intensity-modulated radiotherapy (IMRT) is challenging. The purpose of this work is to study the uncertainty of performing this with a structure-guided deformable (SGD) approach in Velocity. Methods: For SGD, the Hounsfield units inside specified contours are overridden to set uniform values. Deformable image registration (DIR) is the run on these process images, which forces the DIR to focus on these contour boundaries. 18 gynecological cancer patients were used in this study. The original bladder and rectum planning contours for these patients were used to drive the SGD. A second set of contours were made of the bladder by the same person with the intent of carefully making them completely consistent with each other. This second set was utilized to evaluate the spatial accuracy of the SGD. The determined spatial accuracy was then multiplied by the local dose gradient to determine a dose uncertainty associated with the SGD dose warping. The normal tissue complication probability (NTCP) was then calculated for each dose volume histogram (DVH) that included four different probabilistic uncertainties associated with the spatial errors (e.g., 68.3% and 95.4%). Results: The NTCPs for each DVH (e.g., NTCP-−95.4%, NTCP-−68.3%, NTCP-68.3%, NTCP-95.4%) differed amongst patients. All patients had an NTCP-−95.4% close to 0%, while NTCP-95.4% ranged from 0.67% to 100%. Nine patients had an NTCP-−95.4% less than 50% while the remaining nine patients had NTCP-95.4% greater than 50%. Conclusion: The uncertainty associated with this CDD technique renders a large NTCP uncertainty. Thus, it is currently not practical for clinical use. The two ways to improve this would be to use more precise contours to drive the SGD and to use a more accurate DIR algorithm.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Magnetic exchange interactions in binuclear and tetranuclear iron(III) complexes described by spin‐flip DFT and Heisenberg effective Hamiltonians

Abstract Low‐energy spectra of single‐molecule magnets (SMMs) are often described by Heisenberg Hamiltonians. Within this formalism, exchange interactions between magnetic centers determine the ground‐state multiplicity and energy separation between the ground and excited states. In this contribution, we extract exchange coupling constants ( J ) for a set of iron (III) binuclear and tetranuclear complexes from all‐electron calculations using non‐collinear spin‐flip time‐dependent density functional theory (NC‐SF‐TDDFT). For 12 binuclear complexes with J ‐values ranging from −6 to −132 cm −1 , our benchmark calculations using the short‐range hybrid ω PBEh functional and 6‐31G(d,p) basis set agree well with the experimentally derived values (mean absolute error of 4.7 cm −1 ). For the tetranuclear SMMs, the computed J constants are within 6 cm −1 from the experimentally derived values. We explore the range of applicability of the Heisenberg model by analyzing bonding patterns in these Fe(III) complexes using natural orbitals (NO), their occupations, and the number of effectively unpaired electrons. The results illustrate the efficiency of the spin‐flip protocol for computing the exchange couplings and the utility of the NO analysis in assessing the validity of effective spin Hamiltonians.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

How does a probe inserted into the discharge influence the plasma structure?

Shielding the bias applied to the probe by the sheath formed around it and determination of parameters of unperturbed plasmas are in the basis of the probe diagnostics. The results from a two-dimensional model of a discharge with a probe inserted in it show that the probe influences the spatial distribution of the plasma parameters in the entire discharge. The increase (although slight) in the electron temperature, due to the increased losses of charged particles on the additional wall in the discharge (mainly the probe holder), leads to redistribution of the plasma density and plasma potential, as shown by the results obtained at the floating potential of the probe. The deviations due to the bias applied to the probe tip are stronger in the ion saturation region of the probe characteristics. The pattern of the spatial redistribution of the plasma parameters advances together with the movement of the probe deeper in the discharge. Although probe sheaths and probe characteristics resulting from the model are shown, the study does not aim at discussions on the theories for determination of the plasma density from the ion saturation current. Regardless of the modifications in the plasma behavior in the entire discharge, the deviations of the plasma parameters at the position of the probe tip and, respectively, the uncertainty which should be added as an error when the accuracy of the probe diagnostics is estimated do not exceed 10%. Consequently, the electron density and temperature obtained, respectively, at the position of the plasma potential on the probe characteristics and from its transition region are in reasonable agreement with the results from the model of the discharge without a probe. Being in the scope of research on a source of negative hydrogen ions with the design of a matrix of small radius inductive discharges, the model is specified for a low-pressure hydrogen discharge sustained in a small-radius tube.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Simulation of carbon dioxide injection at the FutureGen2.0 site: Class VI permit model and local sensitivity analysis

Numerical simulation was conducted to evaluate supercritical CO 2 (scCO 2 ) storage in the Mt. Simon sandstone at the FutureGen 2.0 site in Morgan County, Illinois. The simulations illustrate the recent modeling effort conducted to successfully obtain a Class VI permit under the Underground Injection Control Program of the U.S. Environmental Protection Agency. The scCO2 was injected into a highly stratified reservoir, distributed among four lateral wells. Results show that each of the multiple reservoir layers with low permeability traps a fraction of the scCO 2 beneath it so that the injected scCO 2 does not reach the top of the reservoir by the end of the simulation period. Hence, all of the injected scCO 2 is considered to be safely trapped by the stratified structure within the reservoir. A local sensitivity analysis (LSA) was conducted for injectivity and plume size at the end of the 20 year injection period. The LSA indicates that the imposed initial conditions and the hydraulic properties of the injection layer contribute the most to the sensitivity. Overall, the simulation outputs are very sensitive to only a small fraction of the inputs. However, the parameters that are important for controlling CO 2 injectivity are not the same as those controlling the plume area. The three most sensitive inputs for injectivity are the horizontal permeability and the residual aqueous saturation of the injection layer, and the initial fracture-pressure gradient. For the plume area, the most sensitive inputs are the horizontal permeability of the injection layer and the residual aqueous saturation of the two adjacent layers above the injection layer. Furthermore, the advantages of requiring just a single set of simulation results, scalability to the proper parameter errors, and straightforward calculation of the composite sensitivities make the proposed analysis attractive for guiding site characterization, injection well design, estimating AoR uncertainty and, and monitoring network design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-F-J-21: Clinical Evaluation of Surface Scanning Systems in Different Treatment Locations

Purpose: To reduce imaging dose in fractionated IGRT, the ability of optical surface imaging systems (OSIS) to detect setup errors was tested. Therefore, clinical studies to evaluate for different treatment locations setup corrections derived by OSIS in comparison with x-ray image guidance in fractionated radiation therapy was performed. Methods: The setup correction accuracy of an OSIS system (AlignRT, VisionRT, London, UK) will be analysed for the 4 tumour locations Pelvis, Upper Abdomen, Thorax and Breast, 20 patients for each location in comparison to a different system (Sentinel, C-RAD, SE). For each patient, the setup corrections of the cone-beam computed tomography (CBCT) of an Elekta Versa HD linear accelerator (Elekta, Crawley, UK) is considered as gold-standard and then compared with those of the OSIS for the first ten fractions retrospectively. There were no clinical decisions made based on the surrogate system. For the OSIS, the reference surface is highly important as it represents the actual ground truth. It can be obtained either with the system itself or the surface structure delineated in the planning CT can be imported via DICOM interface. In this paper, the first results for the treatment region thorax are presented. The reference image modalities were compared. Results: Table 1 displays the difference between the setup corrections obtained with OSIS and CBCT in lateral (LAT), longitudinal (LNG) and vertical (VRT) direction for the DICOM reference image. While the median deviations are within a few millimeters, some outliers showed large deviations. Generally, the mean deviation as well as the spread was smallest in lateral and largest in vertical direction. Conclusion: Although the system allows fast, simple and non-invasive determination of setup corrections, it should be evaluated treatment region dependant. Therefore, the study is ongoing. The application of OSIS may help to reduce the imaging dose for the patient. We gratefully acknowledge the support by our colleagues from the Radiological University Clinic Heidelberg, where the study was performed. This work was funded by the Federal Ministry of Education and Research (BMBF) Germany, grant number 01IB13001B.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Modeling land use and land cover change: using a hindcast to estimate economic parameters in gcamland v2.0

Abstract. Future changes in land use and cover have important implications for agriculture, energy, water use, and climate. Estimates of future land use and land cover differ significantly across economic models as a result of differences in drivers, model structure, and model parameters; however, these models often rely on heuristics to determine model parameters. In this study, we demonstrate a more systematic and empirically based approach to estimating a few key parameters for an economic model of land use and land cover change, gcamland. Specifically, we generate a large set of model parameter perturbations for the selected parameters and run gcamland simulations with these parameter sets over the historical period in the United States to quantify land use and land cover, determine how well the model reproduces observations, and identify parameter combinations that best replicate observations, assuming other model parameters are fixed. We also test alternate methods for forming expectations about uncertain crop yields and prices, including adaptive, perfect, linear, and hybrid approaches. In particular, we estimate parameters for six parameters used in the formation of expectations and three of seven logit exponents for the USA only. We find that an adaptive expectation approach minimizes the error between simulated outputs and observations, with parameters that suggest that for most crops, landowners put a significant weight on previous information. Interestingly, for corn, where ethanol policies have led to a rapid growth in demand, the resulting parameters show that a larger weight is placed on more recent information. We examine the change in model parameters as the metric of model error changes, finding that the measure of model fitness affects the choice of parameter sets. Finally, we discuss how the methodology and results used in this study could be used for other regions or economic models to improve projections of future land use and land cover change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Validation of Scale-Derived Ages in Wild Juvenile and Adult Steelhead Using Parental-Based Tagging

Abstract Accurate age information is a critical component in fisheries monitoring, management, and research. The ability to assign accurate ages to fish by using nonlethal structures is vital in calculating cohort productivity for fish species with low abundances and variable life histories. A common nonlethal method to assign ages is by interpretation of patterns in scales. Validation of ages is not easily obtained for free-ranging fishes; however, the development of genetic “tags” has expanded age validation opportunities. In this study, we used parental-based genetic tagging to validate ages determined from scales collected from juvenile and adult steelhead Oncorhynchus mykiss in two streams within the Snake River basin. Juvenile scale ages were in agreement with 93% of the known ages. Adult scale ages were in agreement with 89% of the known ages. Although overall bias in juvenile ages was very low, we saw a slight positive bias in younger fish (young of the year and age 1) and a small negative bias in older juveniles (ages 2–4). A small negative bias in ages of adults was a consequence of errors in freshwater age. The errors observed did not significantly bias age compositions because the 90% confidence intervals about the age proportions based on scales contained the known proportions; therefore, scale analysis is an acceptable method for assigning ages to Snake River steelhead. We discuss the value of validated known-age scales as a reference collection, and as an illustrative use, we constructed a circulus count guide to aid technicians in identifying missing annuli and distinguishing true from false annuli. To demonstrate the use of the circulus guidelines, we reanalyzed samples with age discrepancies, applying the circulus count data and the known age to identify mistakes.

Reinhardt, Leslie↗
DOE OSTI2019

From the Arctic to the tropics: multi-biome prediction of leaf mass per area using leaf reflectance

Leaf-mass-per-area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. In this work, we explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 g m -2 to 393 g m -2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needle-leaf species, and upper and lower canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age, and canopy position, from diverse biomes. Our model captures LMA variability with high accuracy and low error (R 2 =0.89; RMSE=15.45 g m -2 ). Lastly, our finding highlights that the leaf economic spectrum is mirrored by the leaf optical spectrum paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Leveraging 3D CAD and Virtual Reality in Design of the Calcine Disposition Project - 20512

Fluor Idaho's Calcine Disposition Project (CDP) is leveraging the power of three-dimensional (3D) visualization, light detection and ranging (Lidar), and virtual reality to improve designs and worker safety. With the use of computer-aided design (CAD) modeling software, the CDP has created 3D models of the facility and systems to aid in the design, development, and operation of its retrieval and transfer system. This by itself is nothing new and has been done in industry for years. The CDP project however is taking this a few steps farther with the use of Lidar and virtual reality software. The CRP is tasked with removing a radioactive granular material from stainless steel bins located in concrete vaults that were constructed in the late 1950's and early 1960's. While construction drawings are available, it is not certain that the drawings are as-built or how accurate they truly are. Part of the project requires precise placement of equipment on to bins around multiple pipe, electrical lines, and bin stiffeners. Relying solely on the existing drawings and models created from these drawings is highly risky and prone to failure. The first step to determine the precise location and orientation of all obstructions in the vault the project will use a Lidar system to scan the vault structure and precisely locate all vault and bin components. Once this information is obtained, the data will be integrated into the CAD model. The model will then be verified, and a true as-built model developed. Precise component location, size, and orientation will be used for final design and placement of retrieval components. The second step will be to take the verified 3D model and scan data and import it into virtual reality software creating a virtual world. While some work on the project requires all work to be completed remotely due to high radiation fields, some areas have low enough radiation levels that personnel can enter and perform work. With the use of a virtual reality headset, operators and technicians will be able to enter our virtual world and become familiar with the surroundings and perform work prior to ever entering the radiation environment. This training is expected to pay dividends by improving worker efficiency, reducing errors, and improving confidence that the work can be performed as expected. It will play a key aspect in as low as reasonably achievable (ALARA) principles. An additional aspect in using the Lidar scans and virtual environment, is that it will give engineers and managers an opportunity to revisit the facility, especially radiation areas, at any time to obtain design information and measurements that may be costly or impossible to obtain once the facility transitions into radiation operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Arabinose substitution effect on xylan rigidity and self-aggregation

Substituted xylans play an important role in the structure and mechanics of the primary cell wall of plants. Arabinoxylans (AX) consist of a xylose backbone substituted with arabinose, while glucuronoarabinoxylans (GAX) also contain glucuronic acid substitutions and ferulic acid esters on some of the arabinoses. Here, we provide a molecular-level description on the dependence of xylan conformational, self-aggregation properties and binding to cellulose on the degree of arabinose substitution. Molecular dynamics simulations reveal fully solubilized xylans with a low degree of arabinose substitution (lsAX) to be stiffer than their highly substituted (hsAX) counterparts. Small-angle neutron scattering experiments indicate that both wild-type hsAX and debranched lsAX form macromolecular networks that are penetrated by water. In those networks, lsAX are more folded and entangled than hsAX chains. Increased conformational entropy upon network formation for hsAX contributes to AX loss of solubility upon debranching. Furthermore, simulations show the intermolecular contacts to cellulose are not affected by arabinose substitution (within the margin of error). Ferulic acid is the GAX moiety found here to bind to cellulose most strongly, suggesting it may play an anchoring role to strengthen GAX-cellulose interactions. The above results suggest highly substituted GAX acts as a spacer, keeping cellulose microfibrils apart, whereas low substitution GAX is more localized in plant cell walls and promotes cellulose bundling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry

Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.

42 ENGINEERING↗
DOE OSTI2015

SU-E-T-657: Quantitative Assessment of Plan Robustness for Helical Tomotherapy for Head and Neck Cancer Radiotherapy

Introduction: Geometric uncertainties in daily patient setup can lead to variations in the planned dose, especially when using highly conformal techniques such as helical Tomotherapy. To account for the potential effect of geometric uncertainty, our clinical practice is to expand critical structures by 3mm expansion into planning risk volumes (PRV). The PRV concept assumes the spatial dose cloud is insensitive to patient positioning. However, no tools currently exist to determine if a Tomotherapy plan is robust to the effects of daily setup variation. We objectively quantified the impact of geometric uncertainties on the 3D doses to critical normal tissues during helical Tomotherapy. Methods: Using a Matlab-based program created and validated by Accuray (Madison, WI), the planned Tomotherapy delivery sinogram recalculated dose on shifted CT datasets. Ten head and neck patients were selected for analysis. To simulate setup uncertainty, the patient anatomy was shifted ±3mm in the longitudinal, lateral and vertical axes. For each potential shift, the recalculated doses to various critical normal tissues were compared to the doses delivered to the PRV in the original plan Results: 18 shifted scenarios created from Tomotherapy plans for three patients with head and neck cancers were analyzed. For all simulated setup errors, the maximum doses to the brainstem, spinal cord, parotids and cochlea were no greater than 0.6Gy of the respective original PRV maximum. Despite 3mm setup shifts, the minimum dose delivered to 95% of the CTVs and PTVs were always within 0.4Gy of the original plan. Conclusions: For head and neck sites treated with Tomotherapy, the use of a 3mm PRV expansion provide a reasonable estimate of the dosimetric effects of 3mm setup uncertainties. Similarly, target coverage appears minimally effected by a 3mm setup uncertainty. Data from a larger number of patients will be presented. Future work will include other anatomical sites.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

WE-DE-BRA-03: Construction of An Ultrasound Guidance Platform for Image-Guided Radiotherapy with the Intent to Treat Transitional Cell Carcinoma

Purpose: Ultrasound (US) is a noninvasive, nonradiographic imaging technique with high spatial and temporal resolution that can be used for localizing soft-tissue structures and tumors in real-time during radiotherapy (inter- and intra-fraction). A detailed methodology integrating 3D-US within RT is presented. This method is easier to adopt into current treatment protocol than current US based systems and reduces user variability for image acquisition, thus eliminating transducer induced changes that limit CT planning system. Methods: We designed an in-house integrated US manipulator and platform to relate CT, 3D-US and linear accelerator coordinate systems. To validate the platform, an agar-based phantom with measured densities and speed-of-sound consistent with tissues surrounding the bladder, was rotated (0–45°) resulting in translations (up to 55mm) relative to the CT and US coordinate systems. After acquiring and integrating CT and US images into the treatment planning system, US-to-US and US-to-CT images were co-registered to re-align the phantom relative to the linear accelerator. Errors in the transformation matrix components were calculate to determine precision of this method under different patient positions. Results: Statistical errors from US-US registrations for different patient orientations ranged from 0.06–1.66mm for x, y, and z translational components, and 0.00–1.05° for rotational components. Statistical errors from US-CT registrations were 0.23–1.18mm for the x, y and z translational components, and 0.08–2.52° for the rotational components. Conclusion: Based on our result, this is consistent with currently used techniques for positioning prostate patients if couch re-positioning is less than a 5 degree rotation. We are now testing this on a dog patient to obtain both inter and intra-fractional positional errors. Additional design considerations include the future use of ultrasound-based functionality (photoacoustics, radioacoustics, Doppler) to monitor blood flow and hypoxia and/or in-vivo dosimetry for applications in other therapeutic techniques, such as hyperthermia, anti-angiogenesis, and particle therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Accelerated fission rate irradiation design, pre-irradiation characterization, and adaptation of conventional PIE methods for U-10Mo and U-17Mo

Metallic U alloys have high U density and thermal conductivity and thus have been explored since the beginning of nuclear power research. Alloys of U with modest amounts of Mo, such as U-10 wt % Mo (U-10Mo), are of particular interest because the γ-U crystal structure in this alloying addition shows prolonged stability in reactor service. Historically, radiation data on U-10Mo fuels were collected in Na fast reactors or lower temperature research reactor conditions, but little is known about irradiation behavior, particularly swelling and creep, at irradiation temperatures between 250 and 500°C. This work discusses the methodology and pre-irradiation characterization results from a U-Mo irradiation campaign performed in the High Flux Isotope Reactor at Oak Ridge National Laboratory. U-10Mo and U-17Mo samples irradiations are being completed at temperatures ranging from 250 to 500°C to three targeted fission densities between 2 × 10 20 and 1.5 × 10 21 fissions per cubic centimeter. Swelling measurement of the specimen sizes studied here required development and assessment of new methods for volume determination before and after irradiation. Laser profilometry and X-ray computation tomography (XCT) were used to provide preirradiation characterization of samples to determine the error and applicability of each to determine swelling following irradiation. These outcomes are contextualized through use of BISON simulations performed to assess the predicted expansion of U-Mo fuels subjected to the irradiation conditions of this work. Use of existing BISON fuel performance models predicted a maximum of 7% swelling under the irradiation conditions of this study. Pre-irradiation characterization revealed the as-cast U-Mo fuel samples were uniformly large-grained fully cubic U crystals with small U-C/N bearing precipitates and pores distributed throughout. Samples were found to contain a bulk porosity between .4 and 3% because of the casting process. Local porosity in areas far from large, interconnected pores was found by Slice-and-View to be under .2%. Nanometer-sized precipitates rich in C and N were identified in all samples, likely because of impurities during the fabrication process. Dendritic bands were also observed throughout the samples. These bands were characterized by variable Mo content that deviated from the overall Mo content by 2–3 wt %. No other microstructural features were correlated to these bands. Mechanical properties were found to be slightly strengthened compared to literature reports of bulk U-Mo fuels due to the nano-scale precipitates throughout the sample.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

42 ENGINEERING↗
DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗