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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Dynamic Charging Rendezvous and Motion Planning for a Multi-AGV Team Including a Mobile Charging Host

Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.

Energy constraints↗

DOE OSTI2021

Analyses of transcriptomes and the first complete genome of Leucocalocybe mongolica provide new insights into phylogenetic relationships and conservation

In this study, we report a de novo assembly of the first high-quality genome for a wild mushroom species Leucocalocybe mongolica (LM). We performed high-throughput transcriptome sequencing to analyze the genetic basis for the life history of LM. Our results show that the genome size of LM is 46.0 Mb, including 26 contigs with a contig N50 size of 3.6 Mb. In total, we predicted 11,599 protein-coding genes, of which 65.7% (7630) could be aligned with high confidence to annotated homologous genes in other species. We performed phylogenetic analyses using genes form 3269 single-copy gene families and showed support for distinguishing LM from the genus Tricholoma (L.) P.Kumm., in which it is sometimes circumscribed. We believe that one reason for limited wild occurrences of LM may be the loss of key metabolic genes, especially carbohydrate-active enzymes (CAZymes), based on comparisons with other closely related species. The results of our transcriptome analyses between vegetative (mycelia) and reproductive (fruiting bodies) organs indicated that changes in gene expression among some key CAZyme genes may help to determine the switch from asexual to sexual reproduction. Taken together, our genomic and transcriptome data for LM comprise a valuable resource for both understanding the evolutionary and life history of this species.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗

DOE OSTI2024

Evaluation of the Self Retrieval Augmented Generation Technique on Common Security Advisory Framework Data

This small experimental report evaluates a variation of Retrieval Augmented Generation (RAG), called Self-RAG. This method uses a generative language model that incorporates retrieved facts into its generation and is explicitly trained to be able to determine whether retrieved information is enough to answer the input query, with a user-defined threshold for confidence. We performed an experiment using data from the publicly available CISA Common Security Advisory Framework (CSAF) repository (https://github.com/cisagov/CSAF) as the database of facts to be used in retrieval. Qualitative results from the experiment demonstrate that the Self-RAG method has some ability to provide reasonable answers to queries that are in the dataset and will often ignore irrelevant information when asked outside of domain questions (e.g., general facts). In settings with deliberately confusing questions (the question is within domain, but asks about a fabricated advisory), it was able to refuse 40% of the time without further adjustments to the original framework. While this performance is not sufficient for current practical use, further improvements to data formatting, disambiguating results, and leveraging threshold values could improve performance significantly. However, evaluating this will require more extensive evaluations on larger datasets and potentially better models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics for extracting flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. In order to demonstrate the utility of BCNNs for the PIV task, we compare the performance of three distinct BCNN models with simple architectures. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network infers velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full synthetic test image pair and a real image pair from the 1st International PIV Challenge. We show that ~98% of true particle displacements from the full synthetic image pair can be captured within the BCNN's 95% confidence intervals, and that the BCNN's performance on the real image pair is quantitatively similar to that of algorithms tested in the 1st International PIV Challenge. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. So to our knowledge, this is the first use of Bayesian neural networks to perform PIV.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Pressure-dependent phase transitions in hybrid improper ferroelectric Ruddlesden-Popper oxides

The temperature-dependent phase transitions in Ruddlesden-Popper oxides with perovskite bilayers have been under increased scrutiny in recent years due to the so-called hybrid improper ferroelectricity that some chemical compositions exhibit. However, little is currently understood about the hydrostatic pressure dependence of these phase transitions. Herein we present the results of a combined high-pressure powder synchrotron x-ray diffraction experiment and a b i n i t i o study on the bilayered Ruddlesden-Popper phases Ca 3 Mn 2 O 7 and Ca 3 Ti 2 O 7 . In both compounds we observe a first-order phase transition, that in combination with our density functional theory calculations, we can confidently assign as being between polar A 2 1 a m and nonpolar A c a a structures. Interestingly, we show that while the application of pressure ultimately favors a nonpolar phase, as is commonly observed for proper ferroelectrics, regions of response exist where pressure actually acts to increase the polar mode amplitudes. The reason for this can be untangled by considering the varied response of octahedral tilts and rotations to hydrostatic pressure and their trilinear coupling with the polar instability. Published by the American Physical Society 2024

Materials Science↗

DOE OSTI2023

U-rich U-Mo Solidus, Liquidus, Enthalpy, and Thermal Diffusivity

Several material properties have been studied as a function of composition as part of the USHPRR effort to use U-10Mo for LEU monolithic fuel foils, with special attention to areas where simulation outcomes can be improved. The liquidus and solidus phase boundaries are important for engineering design of processing parameters and for predicting the degree of microsegregation within initial castings. The numerous phase diagram constructions available in the literature differ significantly in their solid/liquid boundary definitions, therefore, an experimental strategy was employed in order to resolve some of the discord. That strategy focuses not on proving any singular point, but instead to provide data with highly characterized uncertainty. The data presented here is the most consistent with the U-rich liquidus and solidus boundaries provided by H. Okamoto (2012), Berche et al. (2011), and Mardon et al. (1959). The recently suggested change in the peritectic reaction temperature (to 1302 K) suggested by Berchè and adopted by Okamoto is also supported, but with lower confidence. The enthalpy of fusion is a basic characteristic of the solidification reaction property for which is required for both research-oriented predictions of behavior and development of applied design strategies for casting engineering. It has been determined that an admixture model for the heat of fusion between U BCC and Mo BCC is a reasonable assumption within the composition region of interest, though limitations of the measurement accuracy impacts the ability to support any trends more detailed than this baseline behavior. A limited number of thermal diffusivity measurements were also performed into the gamma region to provide additional information for solidification modeling and simulations as well as a check on the component properties of thermal conductivity, density, and heat capacity.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Quantifying the impact of temporal analysis of products reactor initial state uncertainties on kinetic parameters

The temporal analysis of products (TAP) reactor, a transient kinetic tool, provides users with information as the catalyst state evolves. However, the state of the art for TAP uncertainty quantification only considers the experimental noise present in the outlet flow signal. Additional sources of uncertainty, including, but not limited to, initial surface coverages, the catalyst zone location, the inert void fraction, and the gas pulse intensity and pulse delay, are not included. For this reason, a framework for quantifying all uncertainty sources present in TAP experiments is presented and applied to a carbon monoxide oxidation case study. Herein, two methods for quantifying these sources of uncertainty are introduced. The first utilizes initial state sensitivities to approximate the parameter variances, as well as to provide insights into the structural certainty of the model. The second generates parameter confidence distributions through an ensemble-based sampling algorithm. This initial state covariance matrix can ultimately be merged with the experimental noise covariance matrix, providing a unified description of the parameter uncertainties for a single TAP experiment.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Evaluating Mean State Cloud Properties in the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM)

Accurately simulating clouds remains a key challenge in global climate models, primarily because cloud formation involves sub-grid processes that are parameterized and crudely represented in models. This study examines the performance of DOE's Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) in simulating cloud properties and their spatio-temporal distribution by comparing against satellite observations. Two horizontal resolutions of SCREAM (3 and 12 km) are examined, and both depict a realistic spatial structure of mean-state cloud cover but underestimate its global mean magnitude. SCREAM 3 km reasonably reproduces the distribution of mean-state cloud properties across various cloud optical thickness and cloud-top pressure regimes, with performance comparable to CMIP5 and CMIP6 ensemble and marginally outperforming SCREAM 12 km. Still, SCREAM 3 km tends to underpredict low clouds and optically thin clouds, highlighting the need for continued improvement in representing unresolved processes. This study provides a basis for confidence in the representation of clouds in SCREAM, as simulating mean-state clouds is a necessary prerequisite for trusting its cloud responses to changes in aerosols and greenhouse gases.

Geosciences↗

DOE OSTI2025

Announcing the Biomedical Data Translator: Initial Public Release

ABSTRACT The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

Research & Experimental Medicine↗

DOE OSTI2024

What Are Ontologies and When Should They Be Used?

Data without description is at best unusable, and at worst, misused. If we do not understand the assumptions and meaning of our data, we are unable to confidently use it. Data today is largely described within a database’s schema, detailing structure and primitive datatypes as part of a relational model, but if we require assurance some data value can be correctly evaluated alongside others beyond the immediate systems in which they are defined, a more portable, richer semantics is needed. Ontologies define knowledge unambiguously across systems and establish the means to reason upon said knowledge using logical inference. They model neutral domains of information rather than data definitions from software or databases that would only serve to enrich a single system’s idiosyncrasies. In this paper, we take a casual stance to explore what ontologies are, how they are built, why they are useful, and when they should be used.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

FY23 Progress Report on Viscosity and Thermal Conductivity Measurements of Molten Salts

As presented in this report, thermal conductivity and viscosity measurements were performed on key chloride pseudo-binary molten salt systems of relevance to molten salt reactor developers. Thermal conductivity measurements were conducted with a variable gap technique, in which a known heat flux is driven across a molten salt specimen and the temperature difference is measured, allowing calculation of the thermal conductivity. This is achieved by establishing a small gap between the bottom of a cylindrical inner containment, which houses electrical heating elements, and an outer containment, which houses cooling channels; the gap size can be varied by compression of a formed bellows. A new calibration scheme was developed herein, involving a correction factor to the heat flux based on He measurements at various temperatures. Furthermore, the data processing methodology was improved to minimize the impact of radiative heat transfer in the calculation of salt specimen thermal conductivity from the temperature difference measurements. Viscosity measurements were conducted with a rolling ball viscometer, in which a ball rolls some known distance in an angled tubular crucible, and the terminal velocity can be used to calculate the viscosity of the salt. The measurement can be performed in a quartz crucible, with which a standard camera can be used to track the ball, or in a metal crucible, with which x-ray radiography is required to track the ball. A new custom x-ray system was made and dedicated to the rolling ball viscometer to enable high throughput automated measurements with salts which require containment with metal. Both systems have been integrated with a ventilation stack which allows for off-gassing of radioactive material, enabling future measurements with U-bearing salts. The thermal conductivity measurements performed herein were with NaCl-KCl (44 mol% NaCl). This salt system was measured in the previous fiscal year, however the thermal conductivity values obtained were comparatively low, and so the system was remeasured with the aforementioned calibration scheme and improved post-processing techniques. The newly obtained thermal conductivity values for NaCl-KCl (44 mol% NaCl) indicate good agreement with kinetic theory and ab-initio models (within 5–10 %). The viscosity measurements performed herein were with three different compositions of NaCl-KCl: 75, 50, and 25 mol% NaCl; a new calibration scheme was applied to account for variable flow effects in the laminar regime. The results show reasonable agreement with literature (5–20 %, depending on the temperature, composition, and study); however, literature values are likely higher than true values based on pure end-member measurements performed in the comparative studies. The measurements conducted herein do show a trend such that viscosity increases with increasing NaCl concentration, which agrees well with one of two comparative studies. In general, the measurements conducted herein gives confidence in the capability to use these systems to accurately measure thermal conductivity and viscosity of actinide-bearing salts within the next fiscal year.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Effects of spatial energy distribution-induced porosity on mechanical properties of laser powder bed fusion 316L stainless steel

Laser powder bed fusion (LPBF) additive manufacturing (AM) offers a variety of advantages over traditional manufacturing, however its usefulness for manufacturing of high-performance components is currently hampered by internal defects (porosity) created during the LPBF process that have an unknown impact on global mechanical performance. By inducing porosity distributions through variations in print energy density and inspecting the resulting tensile samples using computed tomography, nearly 50,000 pores across 75 samples were identified. Porosity characteristics were quantitatively extracted from inspection data and compared with mechanical properties to understand the strength of relationships between porosity and global tensile performance. Useful porosity characteristics were identified for prediction of part performance. Results indicate that ductility and strain at ultimate tensile strength are the global tensile properties most significantly impacted by porosity and can be predicted with reasonable accuracy using simple porosity shape descriptors such as volume, diameter, and surface area. Moreover, it was found that the largest pores influenced behavior most significantly. Specifically, pores in excess of 125 µm in diameter were found to be a sufficient threshold for property estimation. These results establish an initial understanding of the complex defect-performance relationship in AM 316L stainless steel and can be leveraged to develop certification standards and improve confidence in part quality and reliability for the broader set of engineering alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2025

MODIS Aerosol and Low‐Cloud Retrievals: Orographic Effects in the Wake of Macquarie Island

Abstract Visible/infrared imagery from passive satellites is commonly relied upon to study low cloud microphysics over oceanic regions, including for the Southern Ocean (SO), but relatively little validation has been undertaken for the SO. In this article, we compare low‐cloud effective radius ( r e ), cloud droplet number concentration ( N d ) and cloud liquid water path (LWP) retrievals from the NASA Moderate Imaging Spectroradiometer (MODIS) with surface measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE). MODIS 3.7‐μm band retrievals show little bias and moderately good correlation relative to MICRE retrievals for liquid‐phase low clouds when restricted to Solar Zenith Angles <65° on spatial scales of 50–100 km. However, the low overall bias in 3.7‐μm band effective radius ( r e 3.7 ) retrievals partly results from cancellation of errors: r e 3.7 is overestimated for non‐to‐lightly precipitating clouds, and underestimated for heavier drizzling clouds by ∼1–1.5 μm. 1.6‐μm and 2.1‐μm band r e retrievals are biased high. N d may likewise be slightly under‐ or overestimated depending on the concentration, but there is insufficient data to provide confidence in this result. Interestingly, a composite of MODIS retrievals from 2002 to 2020 show a distinct region of enhanced cloud cover and N d (and lower r e ) in the wake of Macquarie Island associated with orographic cloud formation. MODIS retrievals of aerosol optical depth (AOD) and Angstrom Exponent (AE) upwind and downwind of the island do not differ significantly. Comparison with MICRE measurements suggests that MODIS Collection 6 AOD retrievals are reasonable, while AE is problematically large.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Bayesian View on the Dr. Evil Scenario

Abstract In Defeating Dr. Evil with Self-Locating Belief , Adam Elga proposes and defends a principle of indifference for self-locating beliefs: if an individual is confident that his world contains more than one individual who is in a state subjectively indistinguishable from his own, then he should assign equal credences to the hypotheses that he is any one of these individuals. Through a sequence of thought experiments, Elga in effect claims that he can derive the credence function that should apply in such situations, thus justifying his principle of indifference. Here we argue, using a Bayesian approach, that Elga’s reasoning is circular: in analyzing the third of his thought experiments, he uses an assertion that is justifiable only if one assumes, from the start, the principle of indifference that he is attempting to justify. We agree with Elga that the assumption of equal credences is a very reasonable principle, in the absence of any reason to assign unequal credences, but we do not agree that the equality of credences can be so derived.

Azhar, Feraz↗

DOE OSTI2020

Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of-concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

42 ENGINEERING↗

DOE OSTI2023

Probing the consistency of cosmological contours for supernova cosmology

As the scale of cosmological surveys increases, so does the complexity in the analyses. This complexity can often make it difficult to derive the underlying principles, necessitating statistically rigorous testing to ensure the results of an analysis are consistent and reasonable. This is particularly important in multi-probe cosmological analyses like those used in the Dark Energy Survey (DES) and the upcoming Legacy Survey of Space and Time, where accurate uncertainties are vital. In this paper, we present a statistically rigorous method to test the consistency of contours produced in these analyses and apply this method to the Pippin cosmological pipeline used for type Ia supernova cosmology with the DES. We make use of the Neyman construction, a frequentist methodology that leverages extensive simulations to calculate confidence intervals, to perform this consistency check. A true Neyman construction is too computationally expensive for supernova cosmology, so we develop a method for approximating a Neyman construction with far fewer simulations. We find that for a simulated dataset, the 68% contour reported by the Pippin pipeline and the 68% confidence region produced by our approximate Neyman construction differ by less than a percent near the input cosmology; however, they show more significant differences far from the input cosmology, with a maximal difference of 0.05 in $Ω$ M and 0.07 in w. In conclusion, this divergence is most impactful for analyses of cosmological tensions, but its impact is mitigated when combining supernovae with other cross-cutting cosmological probes, such as the cosmic microwave background.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Validation of MARS-F modeling of plasma response to RMPs using internal measurements on DIII-D

The linear resistive plasma response model is validated against the plasma internal measurement data from DIII-D edge-localized mode (ELM) control experiments with applied resonant magnetic perturbation (RMP). Considered are DIII-D discharges where the n = 1, 2, and 3 (n is the toroidal mode number) RMP field was applied. Experimental data for the plasma boundary displacement, as well as the three-dimensional (3D) pressure perturbation in the edge pedestal region, are deduced from the vertical Thomson scattering (TS) system and the horizontal charge exchange recombination (CER) system on DIII-D. The linear response model produces results that are in reasonable quantitative agreement with the DIII-D internal measurements. The plasma boundary displacement of up to 15 mm is modeled, with the pedestal pressure perturbation reaching 3 kPa. As an important insight, the larger plasma displacement measured by the vertical TS system, as compared to that measured by the horizontal CER system, is due to the contribution from the tangential component of the plasma displacement to the former. This mixing of displacement components is also found to influence the sensitivity of the CER measurement comparisons. The results of this study provide further confidence in the linear resistive plasma response model for analyzing ELM control experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗