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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The influence of thermochemistry on the reactivity of propane, the pentane isomers and $n$-heptane in the low temperature regime

In this study, the influence of thermochemistry on the reactivity of fuels at low temperatures (600–1000 K) is studied here. Specifically, the effect of different sets of thermochemistry on chemical model predictions is explored, where various sets are calculated at different levels of theory in addition to recently updated group additivity values. Experimentally measured ignition delay times for propane, the pentane isomers and n-heptane are simulated using NUIGMech1.2 and replacing the thermochemistry of the low-temperature species with the calculated values. For propane, three different thermochemistry sets were calculated, namely CCSD(T)-F12/TZ-F12//B2PLYP-D3/TZ//B2PLYP-D3/TZ (QM1), CCSD(T)-F12/TZ-F12//B2PLYP-D3/TZ//ωB97X-D/TZ (QM2) and B2PLYP-D3/TZ/ωB97X-D/6-31G*//ωB97X-D/6-31G* (QM3). The QM2 results provide parameters to optimize new group additivity (NGA) values which are used to calculate the fourth set of thermochemistry. The model predictions using these four sets are compared to those using NUIGMech1.2 for propane. As the QM1 and QM2 calculations are expensive, the thermochemistry calculated from the QM3 and NGA calculations are used in the pentane isomer and n-heptane models. For all of the models, it is found that the thermochemistry of the species involved in the low-temperature reaction sequence ($RH$, $\dot{R}$, $RO_2H$, $R\dot{O}_2$, $\dot{Q}OOH$ and $\dot{O}_2QOOH$ species) significantly affect fuel reactivity. The NGA values were developed based on all of these species except $\dot{O}_2QOOH$ radicals. The thermochemistry of $\dot{O}_2QOOH$ species cannot be accurately calculated with the NGA representations due to the importance of non-next-nearest neighbor interactions of –OOH substitution. Further development of the NGA method to capture such interactions is in progress. Overall, the model developed using the NGA thermochemistry shows better agreement with experimental data than the model using thermochemistry from affordable and prominent QM methods, such as QM3. Based on the results presented for propane, the pentane isomers and n-heptane, the thermochemistry calculated using the NGA method can be used to model the oxidation of higher order hydrocarbons at low temperatures.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Robust Parameter Design on Dual Stochastic Response Models With Constrained Bayesian Optimization

In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. Further, the performance of our proposed method is demonstrated by the extensive numerical and case studies. Note to Practitioners-Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.

42 ENGINEERING↗
DOE OSTI2024

Using Likwid and Byfl to Benchmark Hardware Performance

This paper outlines a benchmarking study conducted during my internship at LANL, focusing on CPU (Computer Processing Unit) and program performance assessment. The primary goal was to gather memory access data using three methods across five polybench kernels The data gathered would then be used to compare and contrast to one another and calculate operational intensity for performance comparisons. Benchmarking tools like Byfl and Likwid were employed, with Byfl offering hardware-independent data through LLVM compiler communication and Likwid directly interacting with computer hardware. The study considered various benchmarking factors, including optimization levels, Big O notation ((n)), CPU diversity and specific kernel equations. Big O notation was utilized to simplify code complexity, with detailed breakdwons of operations and memory components for each polybench application. Specific O(n) equations enabled nuanced kernel compariosns, facilitating the identification of performance variations. CPU efficiency assessments were conducted using Likwid tests on two CPUs. The central focus on code optimization aimed at achieving higher speeds and reduced memory usage through streamlined code. Future work propsoes creating a roofline model, synthesizing benchmarking data into a comprehensive data graph to assist in optimizing code and improving hardware performance. The potential impact on the laboratory or national mission was underscored, emphasizing the importance of optimizing applications and hardware to conserve resources and accelerate program execution. The specific relevance to LANL’s operations in math-intensive fields such as Nuclear Fission, Space Exploration, and Nanotechnology highlights the necessity of efficient benchmarking for resource conservation and proram speed. Overall, this study contributes to the understanding of CPU and program performance, providing insights for future optimization efforts in a laboratory setting

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fostering Nuclear Security Culture through Effective Leadership: An Operational Perspective

Security culture plays a critical role in determining the effectiveness of an organization's security performance, making its significance impossible to overemphasize. It encompasses the collective values, shared perceptions, and habitual actions embraced by all individuals within a nuclear organization—from leadership to frontline staff. When the entire workforce recognizes the reality of potential threats, accepts that security is a shared duty, and integrates security-minded behavior into everyday routines, it fosters an environment where strong security practices are the norm. In such a setting, everyone can take pride and feel reassured in being part of an organization where a strong security culture is deeply embedded. Security culture is based on the broader concept of organizational culture. All organizations—whether families, social clubs, religious institutions, businesses, non-governmental organizations, or governments—possess an underlying culture shaped by core values and beliefs. These values and beliefs influence attitudes and drive behavior throughout the organization. While multiple factors contribute to the development of a strong security culture, leadership plays a particularly pivotal role. In organizations where security culture is well-established, leaders go beyond rhetoric; they demonstrate a genuine commitment to security through their actions. They implement policies and procedures that actively engage all employees, foster open dialogue around security concerns, and encourage teamwork in resolving issues. Furthermore, they reward proactive behavior and ensure that corrective actions are taken promptly. Regular assessments of the organization's security culture allow such leaders to gauge its effectiveness and take strategic steps to strengthen it when necessary. This paper leverages practical, real-world experience to guide leadership and senior management within nuclear organizations through the foundational steps of cultivating a robust, organization-wide culture of nuclear security. It emphasizes the critical importance of early leadership engagement in shaping this culture and outlines a comprehensive approach that includes strategic, tactical, and operational measures. Additionally, it explores methods for fostering a unified vision across all levels of the organization to ensure alignment, commitment, and continuous improvement in nuclear security practices.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗
DOE OSTI2025

The Singlet–Triplet Gap of Cyclobutadiene: The CIPSI-Driven CC( P ; Q ) Study

An accurate determination of singlet−triplet gaps in biradicals, including cyclobutadiene in the automerization barrier region where one has to balance the substantial nondynamical many-electron correlation effects characterizing the singlet ground state with the predominantly dynamical correlations of the lowest-energy triplet, remains a challenge for many quantum chemistry methods. High-level coupled-cluster (CC) approaches, such as the CC method with a full treatment of singly, doubly, and triply excited clusters (CCSDT), are often capable of providing reliable results, but routine application of such methods is hindered by their high computational costs. We have recently proposed a practical alternative to converging the CCSDT energetics at small fractions of the computational effort, even when electron correlations become stronger and connected triply excited clusters are larger and nonperturbative, by merging the CC(P;Q) moment expansions with the selected configuration interaction methodology abbreviated as CIPSI. We demonstrate that one can accurately approximate the highly accurate CCSDT potential surfaces characterizing the lowest singlet and triplet states of cyclobutadiene along the automerization coordinate and the gap between them using tiny fractions of triply excited cluster amplitudes identified with the help of relatively inexpensive CIPSI Hamiltonian diagonalizations.

Basis sets↗
DOE OSTI2023

A multiscale landscape approach for prioritizing river and stream protection and restoration actions

River and stream conservation programs have historically focused on a single spatial scale, for example, a watershed or stream site. Recently, the use of landscape information (e.g., land use and land cover) at multiple spatial scales and over large spatial extents has highlighted the importance of incorporating a landscape perspective into stream protection and restoration activities. Previously, we developed a novel framework that links information about watershed-, catchment-, and reach-scale integrity with stream biological condition using scatterplots and a landscape integrity map. Here we examined an application of this approach for streams in urban and other settings in King County, Washington State, United States, where we related stream macroinvertebrate condition to two indices of landscape integrity, the US Environmental Protection Agency's (USEPA) nationally available Index of Watershed Integrity (IWI) and Index of Catchment Integrity (ICI). We generated a scatterplot of IWI versus ICI for sample sites, where points represented site macroinvertebrate condition from poor to good. The same data were also visualized as a landscape integrity map that displayed catchments of King County according to the level of watershed and catchment integrity (high or low IWI/ICI). Almost three-quarters of poor-condition sites were associated with high-integrity watersheds and catchments (i.e., underperforming sites), which suggested that either one or both national indicators were insufficient for this area, and that sites underperformed because of local-scale factors. In response, we used a catchment-scale indicator related to forest condition (PctForestCat) after examining several GIS-based dispersal indicators from the National Hydrography Dataset and other candidates from the USEPA's StreamCat dataset. We then compared the results of the scatterplots and maps based on the current and original analyses and found that many of the sites previously classified as underperforming now performed as expected, that is, they were poor-condition sites in poor-condition catchments. This analysis demonstrates how results based on a national dataset can be improved by developing an alternative that represents regionally important stressors. The methods used to develop an effective landscape indicator based on StreamCat datasets, and the utility of the multiscale approach, could provide important tools for prioritizing, optimizing, and communicating stream conservation actions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Stacking AsFMT overexpression with BdPMT loss of function enhances monolignol ferulate production in Brachypodium distachyon

To what degree can the lignin subunits in a monocot be derived from monolignol ferulate (ML-FA) conjugates? This simple question comes with a complex set of variables. Three potential requirements for optimizing ML-FA production are as follows: (1) The presence of an active FERULOYL-CoA MONOLIGNOL TRANSFERASE (FMT) enzyme throughout monolignol production; (2) Suppression or elimination of enzymatic pathways competing for monolignols and intermediates during lignin biosynthesis; and (3) Exclusion of alternative phenolic compounds that participate in lignification. A 16-fold increase in lignin-bound ML-FA incorporation was observed by introducing an AsFMT gene into Brachypodium distachyon. On its own, knocking out the native p-COUMAROYL-CoA MONOLIGNOL TRANSFERASE (BdPMT) pathway that competes for monolignols and the p-coumaroyl-CoA intermediate did not change ML-FA incorporation, nor did partial loss of CINNAMOYL-CoA REDUCTASE1 (CCR1) function, which reduced metabolic flux to monolignols. However, stacking AsFMT into the Bdpmt-1 mutant resulted in a 32-fold increase in ML-FA incorporation into lignin over the wild-type level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

3013 Inner Container Closure Weld Region (ICCWR) Characterization by Wide Area 3D Measurement System (WAMS) Analysis (FY21 Progress Report)

As part of the 3013 Surveillance Program, through-wall penetration from stress corrosion cracking (SCC) of the 3013 inner containers has been identified as the most credible condition for failure withing the 50- years lifetime. Chlorides contained in Pu-bearing material, together with intra-canister humidity levels, metallurgical conditions, and internal stresses have been found to produce corrosion in the Inner Container Closure Weld Region (ICCWR) of the 3013 canister system. A Laser Confocal Microscope (LCM) is used as part of the 3013 Surveillance Program protocol to identify the prevalence of corrosion and corrosionrelated cracking in the ICCWR2. With the LCM, a close visual examination is made of the ICCWR surface along with measurements of corrosion-related features. LCM inspections produce immense amounts of image data that is time intensive to analyze. There is also a 9-year backlog of images, with approximately 49 canisters that must be evaluated. To expedite data analysis and reduce the amount of generated data, a Wide Area 3D Measurement System (WAMS) microscope has been added to the examination protocol. Although WAMS is a lower resolution microscope, small features of interest can be still identified. The advantage of collecting data for the full circumference using the WAMS is that it can take about 1/16 of the time needed with the LCM. Both systems offer capabilities that combined can be utilized to expedite the examination of the ICCWR. The WAMS is an efficient system for screening and identification of corrosion features while the LCM can be utilized to obtain higher resolution images areas identified by the WAMS. This report explains and justifies the data collection methods used with the WAMS. It includes a summary of the data generated by WAMS in FY21. The goals set for data collection in FY21 were met. A total of twenty-two DE’s were imaged, and analysis was carried out on eleven samples. The analyzed samples showed large numbers of potential cracks and pits distributed throughout the surfaces. Lastly, to explain the advantages of the WAMS, its capabilities were compared to those of a simpler microscope. Micrographs of the samples imaged in FY21 and those analyzed are included in the appendices.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Geophysical Signatures of Crack Network Coalescence in Rocks at Multiple Length Scales

The main goal of the research project was to identify the geophysical signatures of fracture growth in natural rocks by utilizing novel geophysical techniques. The research objectives were to (a) investigate the potential for geophysical methods to determine when cracks initiate, the types and locations of propagated cracks, and the coalescence of networks of cracks in natural rocks at multiple scales, (b) determine how damage at the microscale evolved into damage at the macroscale and then link the microscopic and macroscopic observations, (c) quantify crack coalescence in rocks under realistic stress conditions using coupled mechanical-geophysical-optical visualization, and (d) identify the precursors in geophysical signals to crack coalescence. The following research thrusts were explored to achieve the research objectives: (1) uniaxial compression testing of rock specimens with and without a set of pre‐existing flaws and (2) triaxial compression testing of natural rock specimens. These thrusts allowed for exploring fracturing in rocks under realistic in situ environments and at multiple scales. This project provided educational opportunities for nine graduate and undergraduate students and resulted in 27 peer-reviewed publications. This first research thrust focused on investigating the micromechanics of fractures in rocks through uniaxial compression testing combined with advanced geophysical and imaging techniques, specifically acoustic emission (AE) monitoring, ultrasonic imaging, and 2-dimensional Digital Image Correlation (2D-DIC). By examining damage processes under time-independent and time-dependent loading conditions, insights into damage localization, crack initiation, and fracturing mechanisms were gained. It was observed that the AE signals and the strain-based measurements directly reflect the state of damage in the rock specimen and could be used to identify the cracking levels, such as the crack initiation (CI) and crack damage (CD), and the mode of deformation. A novel calibration apparatus was developed to enhance the accuracy of AE sensors, allowing for the estimation of key parameters such as magnitude, source dimension, stress drop, and radiated seismic energy associated with the fractures. The findings highlighted significant variations in the temporal evolution of AE source parameters during the primary, secondary, and tertiary stages of creep, identifying tensile cracking as the primary deformation mode. The second research thrust focused on enhancing the understanding of fracturing processes in natural rocks through triaxial compression testing, real-time AE monitoring, and ultrasonic monitoring. We investigated the impact of various factors such as fracture propagation regimes, injection parameters, rock types, and pre-existing conditions on the hydraulic fracture (HF) behavior using scaled true-triaxially loaded specimens of Barre granite and Lyons sandstone. Custom sensor housing facilitated concurrent active and passive monitoring to analyze hydro-mechanical responses and microseismicity associated with different HF scenarios. A coupled investigation of passive microseismicity and active signal attributes permitted a detailed comprehension of the various HF processes (aseismic deformation, fracture initiation and propagation, fluid permeation, and leak-off) and their dependence on the specific rock type. The findings of this research demonstrated the effectiveness of AE monitoring techniques in providing valuable insights into the impact of various factors on the behavior and dynamics of HF processes. The advancements in monitoring techniques, offering a more thorough and precise approach, represent a significant step towards optimizing HF practices and ensuring sustainable resource extraction.

58 GEOSCIENCES↗
DOE OSTI2021

High-level coupled-cluster energetics by Monte Carlo sampling and moment expansions: Further details and comparisons

We recently proposed a novel approach to converging electronic energies equivalent to high-level coupled-cluster (CC) computations by combining the deterministic CC(P;Q) formalism with the stochastic configuration interaction (CI) and CC Quantum Monte Carlo (QMC) propagations. This article extends our initial study [J. E. Deustua, J. Shen, and P. Piecuch, Phys. Rev. Lett. 119, 223003 (2017)], which focused on recovering the energies obtained with the CC method with singles, doubles, and triples (CCSDT) using the information extracted from full CI QMC and CCSDT-MC, to the CIQMC approaches truncated at triples and quadruples. It also reports our first semi-stochastic CC(P;Q) calculations aimed at converging the energies that correspond to the CC method with singles, doubles, triples, and quadruples (CCSDTQ). The ability of the semi-stochastic CC(P;Q) formalism to recover the CCSDT and CCSDTQ energies, even when electronic quasi-degeneracies and triply and quadruply excited clusters become substantial, is illustrated by a few numerical examples, including the F–F bond breaking in F 2 , the automerization of cyclobutadiene, and the double dissociation of the water molecule.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cascades between Light and Heavy Fermions in the Normal State of Magic-Angle Twisted Bilayer Graphene

We present a framework for understanding the cascade transitions and the Landau level degeneracies of twisted bilayer graphene. The Coulomb interaction projected onto narrow bands causes the charged excitations at an integer filling to disperse, forming new bands. If the excitation moves the filling away from the charge neutrality point, then it has a band minimum at the moiré Brillouin zone center with a small mass that compares well with the experiment; if towards the charge neutrality point, then it has a much larger mass and a higher degeneracy. At a nonzero density away from an integer filling the excitations interact. The system on the small mass side has a large bandwidth and forms a Fermi liquid. On the large mass side the bandwidth is narrow, the compressibility is negative and the Fermi liquid is likely unstable. This explains the observed sawtooth features in compressibility, the Landau fans pointing away from charge neutrality and their degeneracies. The framework sets the stage for superconductivity at lower temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-treated Pancreatic Beta Cells

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse post-translational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated ß-cells. We observed a swift response in global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-?B signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of a complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple co-localized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

proteomics, PTM, automation, SP3, cysteine thiol o↗
DOE OSTI2022

Equibiaxial Flexure Strength of a Superfine-Grained Nuclear Graphite

The strength of advanced graphite is reported in accordance with ASTM D7846-16 using a two-parameter Weibull distribution for uniaxial strength testing. The rule of thumb for Weibull distributions is to use a minimum of 30 strength measurements to have a high level of confidence in the Weibull characteristic strength and the Weibull modulus. These large sample sets for statistical confidence are easily obtained for as-manufactured graphite, but determination of the Weibull two-factor parameters on graphite that has been exposed to neutron irradiation is nearly impossible due to irradiation testing restraints on specimen size and space limitations in test reactors for the accommodation of specimens. These restrictions have resulted in irradiation programs that use sub-sized specimens to measure uniaxial strength change, but the specimen geometries still hinder the number of replicate specimens for each irradiation condition. In contrast, this work used an advanced ceramic standard, ASTM C1499-05, as the foundation for equibiaxial strength testing of a superfine-grained nuclear graphite to investigate the irradiation-induced strength change and any changes to the two-parameter Weibull distribution. The goal was to investigate the effect of specimen size on the two-parameter Weibull distribution parameters in support of the small specimens and geometry desired for irradiation. A study of the effect of a reduced sample population was also undertaken to investigate the reliability of using the two-parameter Weibull distribution to study changes to the materials properties caused by neutron irradiation damage. The results of this work suggest that these equibiaxial specimens would be ideal for statistically significant studies of the effects of irradiation on the Weibull characteristic strength (but not the modulus) and for providing a method for surveillance specimen campaigns for future operating commercial reactors.

Campbell, Anne↗
DOE OSTI2020

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

58 GEOSCIENCES↗
DOE OSTI2024

Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP)

OBJECTIVE To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. RESEARCH DESIGN AND METHODS Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). RESULTS T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. CONCLUSIONS Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.

Yang, Peter K. (ORCID:0000000193796981)↗
DOE OSTI2025

Deep Learning Super-Resolution X-Ray Computed Tomography Algorithms for Additive Manufacturing

Industrial X-ray computed tomography (XCT) is a nondestructive method for inspection and characterization of additively manufactured (AM) materials and parts. In practice, the resolution of XCT can be limited by factors such as detector binning, restricted field of view for large-scale objects, system blur, motion during scanning, and acquisition settings. These limitations can reduce the detectability of critical flaws such as pores, cracks, and lack of fusion. Super-resolution (SR) techniques offer a promising solution for improving the effective resolution and image quality of XCT reconstructions without the need for expensive hardware upgrades or laborious, time-consuming scans. In particular, deep learning-based SR methods have garnered attention in recent years as powerful tools for reconstructing high-resolution volumes from low-resolution inputs. In this work, a novel deep learning-based SR method is proposed for XCT scans of AM parts, and compared against several existing state-of-the-art (SOTA) methods. The proposed method, Simurgh-SR, is built on the pre-existing Simurgh framework and consists of a 2.5D U-Net trained to map low-quality inputs containing noise and artifacts to high-quality reconstructions characterized by higher flaw contrast, better noise texture, and reduced artifacts. The experimental results demonstrate superior performance of Simurgh-SR in performing 4× SR on real industrial XCT scans of thick 316L components, enhancing the structural similarity score and peak signal-to-noise ratio (>7dB) compared to the LR counterpart while improving the F1-score for flaw detection by more than 2.3× when compared to alternative SOTA SR methods. This improvement enables more accurate and significantly faster characterization of metal AM components. Additionally, Simurgh-SR was trained for both 2X and 4X SR and performs effectively at both levels, enabling the use of a single model for various SR factors.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
DOE OSTI2022

Developing Fuel Cell Electric Powertrain Architectures for Commercial Vehicles

Here, this article addresses the architecture development for a commercial vehicle fuel cell electric powertrain by establishing a clear multi-step formalized workflow that employs a unique technoeconomic solution for architecture selection. The power capability of the fuel cell, the energy capacity and chemistry of the electrical energy storage (battery), the DC-DC converter (including the input current rating and isolation resistance requirements), the traction drive solution, the on-board hydrogen storage solution, and the real-time power-split management of the fuel cell and the battery are all considered and developed in this effort. The methods were used to select architecture for Class 8 urban, regional, and line haul applications. When compared to traditional load-following power-split controllers, an energy management power-split controller can increase system energy efficiency by up to 19.5%. The energy-efficient power-split controller may increase the required battery capacity for an equivalent life by up to 2.6 times. The impact on the total cost of ownership (TCO) for a variety of financial cases demonstrates that high C-rate capable batteries have the potential to provide better TCO solutions over a six-year vehicle life than low C-rate capable batteries. To achieve TCO parity with the 600 A non-isolated DC-DC converter case, the specific choice of the fuel cell DC-DC converter to achieve a target power output based on current levels (from 500 A to 2400 A) shows that efficiency decreases and cost increases due to the higher current, requiring fuel cell prices to decrease by $50–$100/kW, $60–$110/kW, and $100–$220/kW for urban, regional, and line haul applications, respectively. Key recommendations for powertrain system architectures are provided, with specifics based on vehicle dynamics, mission and application characteristics, end customer use-case profile, critical powertrain component costs, and architecture selection cost function. This study rigorously demonstrates the interplay of the above parameters, with a focus on TCO, and provides application decision-makers with a mechanism and well-defined set of impact factors to consider as part of their architecture selection process.

25 ENERGY STORAGE↗