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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Thermal feedback coupling in a transient Monte Carlo high-order/low-order scheme

Transient simulations of nuclear systems face the computational challenge of resolving both space and time during reactivity changes. A common strategy for tackling this issue is to split the neutron flux into shape and amplitude functions. This split can be solved with high- order/low-order methods. While this multi-fidelity approach has traditionally been reserved for deterministic methods, it is also possible to implement in Monte Carlo as an efficient alternative to Dynamic Monte Carlo. This work implements the frequency transform method with thermal feedback in high-order/low-order Monte Carlo by blending static coupling methods such as single-batch Monte Carlo, with a simple thermal-fluids calculation. While previous work focused solely on prescribed transients, the addition of time-dependent thermal-fluids allows transients to be self-propagating. Tests were run in a fluids-initiated transient to showcase the basic functionalities of this methodology. Preliminary results behave as expected, paving the way for studying more sophisticated thermal-fluids coupling methods. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Neutronic analysis of a PWR-type SMR core using duplex ThO{sub 2}-UO{sub 2} in TRISO fuel particles

Currently, studies on small modular reactors (SMRs) present an important development due to the potential they represent in terms of safety, operational flexibility, economy, and non-energy applications. Furthermore, there is increasing interest in studying the use of thorium as fuel, as an effective way to solve problems such as the shortage of uranium reserves, reduction of nuclear waste and nuclear proliferation. Also, the use of thorium combined with highly enriched uranium TRISO particles has been studied, showing proper performance. In this work, the concept of ThO{sub 2}-UO{sub 2} duplex fuel is used, for the core configuration of a PWR type SMR that uses TRISO fuel, designed to achieve extended fuel cycles. Three distribution cases of ThO{sub 2} and UO{sub 2} in TRISO particles inside the fuel rods are compared. First, particles composed by a homogeneous mixture of ThO{sub 2} and UO{sub 2} are distributed inside of fuel elements. Second, the fuel zone of the fuel elements is divided into two radially, an internal one where the TRISO particles composed of ThO{sub 2} are distributed and the external one where the TRISO particles composed of UO{sub 2} are distributed. The third case is like the previous one, except that the particles that contain UO{sub 2} are distributed in the inner zone and those that contain ThO{sub 2} in the outer zone of the fuel elements. The comparison of the cases is carried out in terms of cycle main isotopes' mass transmutation, power distribution and temperature reactivity coefficients. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Feasibility Study for Cathodic Protection of Waste Tanks at Hanford

The Hanford Nuclear Reservation site contains approximately 211 million liters of radioactive and chemically hazardous waste arising from nuclear weapons production, beginning with World War II, and continuing through the Cold War. The waste is stored in 177 carbon-steel underground storage tanks, of which 149 are single-shell tanks (SSTs) and the remaining are double-shell tanks (DSTs). The mission of an ongoing River Protection Project is to retrieve the waste from the underground storage tanks and then treat and immobilize (i.e., vitrify) it for disposal. Waste from the older SSTs is being progressively retrieved into the newer DSTs for storage pending treatment, immobilization, and disposal. Waste chemistry controls are in place to mitigate general corrosion, pitting corrosion, and stress corrosion cracking (SCC) and minimize corrosion risks to the carbon steel tank liners of DST at the Hanford Site. The chemistry control program focuses on preserving liner integrity while balancing the need for caustic/nitrite additions that have large downstream impacts on vitrification. Some of the DSTs store salt cake or sludge waste layers contain out-of-specification interstitial liquids that contact the primary tank bottom or sidewall. Remediation of such layers through chemical additions is not practical. At the present time, the most significant leak integrity concern at Hanford is for corrosion of the tank bottoms of DSTs where the solids layer is out of specification because of hydroxide ion depletion and consequently, pH values that are less than 12. Cathodic protection (CP) has been proposed as a possible way to protect the liners of such tanks against corrosion. The objective of this project was to investigate the feasibility of applying CP to Hanford DSTs to mitigate internal corrosion of the carbon steel tank primary liners. The scope of the project included (1) a literature review on the application of CP to storage tank systems, in general, and to waste tanks at the Savannah River Site (SRS) and Hanford, (2) a review of the finite element analysis (FEA) model recently developed to evaluate potentials for a DST, and (3) the development of a boundary element analysis (BEA) model for CP for a DST. Tank AN-107, one of the Hanford DSTs, was selected for this work because (1) the bottommost waste layer in the tank is out-of-specification, and (2) electrochemical and resistivity data, based on recent waste sampling and testing, are available for this tank. The overarching conclusion of this study is that, based on the available information, it is feasible to apply CP to mitigate internal corrosion of the primary liners of the Hanford DSTs. However, significant additional information and work are required before a CP system can be designed. Recommendations for next steps in the design of the CP system will be presented.

WIERSMA, BRUCE↗
DOE OSTI2020

Leveraging Machine Learning Capabilities for the Characterization of Irradiated Uranium: A Case Study of Analysis Methods for Nuclear Safeguards and Nuclear Forensics

Nondestructively determining the initial enrichment of irradiated uranium is a complex and laborious multivariable problem due to the presence of fission products. This work demonstrates the capabilities of machine learning to analyze gamma-ray spectral data to determine initial enrichment without knowledge of the decay time of the sample. The approach developed is agnostic to the particular scenario and is applicable to a wide variety of applications in nuclear forensics and nuclear safeguards. We irradiated 5 mg uranium standard reference materials at discrete enrichment values ranging from 0.02% to 97% 235 U (weight percent) in UT Austin’s Nuclear Engineering Teaching Laboratory TRIGA Mark II 1.1 MW research reactor, allowed each to decay for 8 hours, and then measured each sample via gamma-ray spectrometry for 50 hours post-irradiation yielding 1,400 individual gamma-ray spectra discretized into 8,192 energy bins. We then trained decision trees models to analyze individual gamma-ray spectra and estimate the associated initial enrichment without knowledge of the time since end of irradiation. We evaluated the performance of the models with a reserved test set not used for training or calibrating the model. A decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 2.3% (weight percent 235 U). Next, we implemented a principal component analysis pre-processing routine of the gamma-ray spectrometry data to reduce the dimensionality of the dataset from 8,192 channels in the spectrum to 10 principal components while retaining over 99% of the inherent variance in the data. Decision tree models constructed with these data demonstrated decreased mean absolute error in enrichment determination, reduced computational time, and decreased complexity. A single decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 0.05% (weight percent 235 U). Furthermore, we analyzed these models with learning curves to ensure that overfitting did not occur. The capabilities provided by these models can be naturally extended to other application-focused measurements in the fields of nuclear safeguards, nuclear forensics, and nuclear non-proliferation.

Drescher, Adam↗
DOE OSTI2023

A Comparison of As-loaded Dose Calculations to Measured Dose Rates

Demonstrating that the radiation dose fields surrounding spent nuclear fuel (SNF) storage and transportation systems meet the applicable limits set forth in 10 CFR 72 for storage and 10 CFR 71 for transportation is essential for the safe handling of radioactive material. The Used Nuclear Fuel-Storage, Transportation, and Disposal Analysis Resource and Data System (UNF-ST&DARDS) [2] is used to provide realistic estimates of SNF-related safety margins. The UNF-ST&DARDS dose rate analysis approach differs from that used in typical licensing approaches, which use design-basis assemblies with bounding source term characteristics to demonstrate the packaging design complies with the regulations. These bounding licensing approaches can increase the time needed to qualify fuel for loading into dry storage and the time a loaded system must wait prior to transportation. UNF-ST&DARDS dose rate assessments allow quantification of realistic, uncredited safety margins associated with actual fuel loading compared with the regulatory limits. While realistic estimation of the dose field surrounding SNF systems may allow for additional flexibility in operations, it is essential to understand how these predictions compare to measured doses. The U.S. Department of Energy Office of Integrated Waste Management and the Prairie Island Indian Community conducted a transportation dose assessment to estimate the site-specific incident-free radiation doses from shipping SNF by rail from the Prairie Island Nuclear Generating Plant (PINGP) through the Prairie Island Indian Community Reservation and Trust Land [5,6]. For that effort, the dose rates were obtained for 50 TN-40 and TN-40HT systems in storage configurations. This work compares the predicted dose rates from UNF-ST&DARDS as-loaded calculations with dose rates measured from 50 SNF storage systems at PINGP. The remainder of this paper discusses the data obtained for the evaluation, the modeling methods, and the results of the calculations.

spent nuclear fuel (SNF), UNF-ST&DARDS, Validation↗
DOE OSTI2024

Interregional Transmission Operational Coordination

This presentation will discuss challenges and opportunities on interregional coordination in the areas of interchange transaction, congestion management, reserve sharing and deliverability across multi-stage operational processes. It will then provide an overview of the interregional transmission operational coordination (IRTOC) project that currently focuses on congestion management, including Inter-regional transmission coordination in real time operations and operational planning, HVDC optimization (intra- and interregional), ancillary service deliverability (intra- and interregional). In addition, it will discuss the Sienna-Decomposition tool development to systematically study interregional coordination methods.

congestion management↗
DOE OSTI2025

Fostering Geothermal Machine Learning Success: Elevating Big Data Accessibility and Automated Data Standardization in the Geothermal Data Repository

The Department of Energy's (DOE's) Geothermal Data Repository (GDR) has implemented improvements to both its data lakes and its data standards and automated data pipelines. The GDR data lakes have reduced storage and compute-related barriers to using large geothermal datasets, enabling these large datasets to be accessed by anyone with a modern computer and internet access. More recently, the GDR has been working to further reduce barriers through streamlining the data intake process, educating users on the process and requirements, and helping users access data from the data lakes. These improvements have augmented the quantity of datasets the GDR is able to accept into its data lakes and have enabled users who are new to cloud tools to access these datasets more easily, overall increasing the accessibility of big geothermal data for use in machine learning and other projects. In addition, the GDR now has built-in data standards and pipelines for drilling data, geospatial data, and distributed acoustic sensing (DAS) data. These standardization efforts aim to enhance the real-world applicability of geothermal machine learning outcomes by improving the quality of training data. Specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, thus allowing more time for actual research. By automating this process, the burden of standardization is lifted from the user, ultimately increasing the availability of standardized data. This paper provides an update on recent improvements made to the GDR's data lakes and automated data pipelines, including: (1) streamlining the data lake intake process, (2) better educating users on the process and requirements through a new data lakes page, (3) adding data lake direct access links to GDR data lake submission pages, (4) implementing a DAS data pipeline to convert DAS data uploaded in SEG-Y format to a standardized hierarchical data format v5 (HDF5), (5) extending this pipeline to encompass data in the GDR data lake, (6) adding metadata requirements for geospatial data, (7) making user interface/user experience (UX) enhancements to the data pipelines' documentation pages, and (8) improving the GDR's data standards and pipelines pages to better guide users in ensuring that their data is standardized by the GDR's automated data pipelines. 2024 Geothermal Resources Council. All rights reserved.

accessibility↗
DOE OSTI2025

Power Quality and Load Capacity Evaluations of an Electric Vehicle for Multi-Robot System Applications

This paper evaluates the capability of a fully electric pickup truck, using the Ford F-150 Lightning as an example, to provide power to the circuit of a multi-robot system. The case study was conducted on a simulated INL Autonomous Pit Exploration System (APES) designed for the inspection of nuclear waste tank pits. Through a series of controlled tests, the vehicle’s power delivery consistency, load-handling capability, and battery performance were assessed under various conditions. First of all, the load test demonstrated that the vehicle provided stable power with low distortion and no unexpected interruptions. Second, during the operational limit test, the 240V system sustained loads up to 7.4 kW before tripping, providing insights into its operational limits. Last but not least, during a simulated full-scale APES operation, the vehicle’s battery depleted by only 6% over an hour, indicating sufficient capacity for extended use while retaining reserve power for transportation needs. This study highlights the potential of electric vehicles as reliable power sources for field operations, contributing to the advancement of sustainable technologies by reducing reliance on traditional fossil fuel generators and promoting the integration of clean energy solutions in remote and challenging environments.

Electric vehicle↗
DOE OSTI2025

Frequency Control and Disturbance Containment Using Grid-Forming Embedded Storage Networks

The paper presents a distributed approach for operating a network of inverter-based energy storage resources embedded in a bulk power system. Departing from their traditional role of steady-state reserves, the storage assets in the network are utilized as frequency-responsive resources shaping system dynamics. The power electronics converter systems interfacing the storage resources are equipped with local controllers designed to respond under transient disturbances. To this end, a safety-constrained distributed control strategy is explored. The paper compares the performance of converter-interfaced grid-forming and grid-following storage networks for fast frequency control and disturbance containment/localization. Sensitivity studies are performed to study the impact of storage size, steady-state dispatch, and controller design on dynamic performance. The findings are presented through case studies with results from the IEEE test systems.

Chatterjee, Kaustav (ORCID:0000000153273860)↗
DOE OSTI2025

IEEE SusTech 2025 Presentation

This paper evaluates the capability of a fully electric pickup truck, using the Ford F-150 Lightning as an example, to provide power to the circuit of a multi-robot system. The case study was conducted on a simulated INL Autonomous Pit Exploration System (APES) designed for the inspection of nuclear waste tank pits. Through a series of controlled tests, the vehicle’s power delivery consistency, load-handling capability, and battery performance were assessed under various conditions. First of all, the load test demonstrated that the vehicle provided stable power with low distortion and no unexpected interruptions. Second, during the operational limit test, the 240V system sustained loads up to 7.4 kW before tripping, providing insights into its operational limits. Last but not least, during a simulated full-scale APES operation, the vehicle’s battery depleted by only 6\% over an hour, indicating sufficient capacity for extended use while retaining reserve power for transportation needs. This study highlights the potential of electric vehicles as reliable power sources for field operations, contributing to the advancement of sustainable technologies by reducing reliance on traditional fossil fuel generators and promoting the integration of clean energy solutions in remote and challenging environments.

42 - ENGINEERING↗
DOE OSTI2025

Insights Into On-Demand Transit: A Case Study of Houston METRO's curb2curb Transit Services: Preprint

As modern on-demand transit (ODT) deployments increase, the insights gained from studying real-world systems prove more useful. In this study, a shared ODT system called "METRO curb2curb" in the suburbs of Houston, Texas was studied, with a particular focus on the Missouri City zone, which has the highest ridership out of the four total zones in which the system operates. The Missouri City zone spans 18 square miles with an average daily ridership of nearly 400 as of November 2024. In order to gain a passenger perspective, data collection was performed through using the ODT system first-hand, completing 50 rides in March and April of 2023. These rides included a mix of three different types of on-demand travel accommodations - 1.) advance bookings (30 min - 2 hours ahead), 2.) as-soon-as-possible / real-time dispatch bookings, and 3.) no-reservation trips at two designated "anchor points." Wait and travel time were collected for each ride. Anecdotal findings related to ease of locating the driver/vehicle, the process of booking a ride, and popular trip purposes were also documented. This study provides an overview of the METRO curb2curb system, including its history, service provider, service area, ridership, booking options, wait time, and travel time. Intricacies related to different definitions of wait time are included. Finally, relevant considerations for automating the on-demand transit system and discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Microstructural Topology as a Prescriptor for Quantum Coherence: Towards A Unified Framework for Decoherence in Superconducting Qubits

In superconducting quantum circuits, decoherence improvements are frequently obtained through process interventions that simultaneously modify surface chemistry, microstructural topology, and device geometry, leaving mechanistic attribution structurally underdetermined. Predictive materials engineering requires measurable structural statistics to be separated from geometry-dependent coupling coefficients into independently testable factors. We introduce the concept of classical and quantum microstructure. In that context, we formulate a channel-wise separable framework for decoherence in superconducting transmon qubits in which each loss channel is described by a reduced prescriptor. Here, a channel-specific microstructural state variable is determined independently of device geometry, and a geometry-dependent coupling functional is computable from field solutions without reference to surface chemistry. We derive this product form from a spatially resolved kernel representation and establish a perturbative separability criterion that defines the regime where independent variation of the variables is valid. The framework specifies five prescriptor classes for dominant loss pathways in transmon-class devices. Falsifiability is operationalized through a pre-committed 2x2 experimental protocol in which the variables must satisfy independent ratio checks within propagated uncertainty. A Minimum-Dataset Specification standardizes reporting for cross-laboratory inference. Part I establishes the conceptual and mathematical architecture; coordinated experimental validation is reserved for Part II.

Dravid, Vinayak P. [Northwestern U.]↗
DOE OSTI2025

Materials Data Science Ontology(MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science

Ontologies have gained popularity in the scientific community as a way to standardize terminologies in organizations’ data. Although certain cohorts have created frameworks with rules and guidelines on creating ontologies, there exist significant variations in how Materials Science ontologies are currently developed. We seek to provide guidance in the form of a unified automated framework for developing interoperable and modular ontologies for Materials Data Science that simplifies the ontology terms matching by establishing a semantic bridge up to the Basic Formal Ontology(BFO). This framework provides key recommendations on how ontologies should be positioned within the semantic web, what knowledge representation language is recommended, and where ontologies should be published online to boost their findability and interoperability. Two fundamental components of the MDS-Onto framework are the bilingual package called FAIRmaterials for ontology creation and FAIRLinked, for FAIR data creation. To showcase the practical capabilities of FAIRmaterials, we present two exemplar domain ontologies of MDS-Onto: Synchrotron X-Ray Diffraction and Photovoltaics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Toward Rapid Actinium-225 Purification via Membrane Adsorbers with Covalently Tethered Diglycolamide Ligands

Extractive diglycolamide (DGA) resins are used in several state-of-the-art techniques for purifying 225 Ac, a promising radiometal for targeted alpha therapy. Unfortunately, separation processes that rely on resins are often limited to slow flow rates, high elution volumes, and long processing times. Membrane adsorbers functionalized with DGA ligands are an alternative separation material that may overcome these challenges. This work presents (1) the synthesis of an aminated tetrahexyldiglycolamide ligand, (2) the covalent tethering of the ligand to electrospun poly(vinylbenzyl chloride) fiber mats, and (3) the adsorption and desorption of La(III) and 225 Ac. Chemical and physical characterization supports the covalent tethering of the ligand to the fiber mat, as well as the preservation of the fiber surface area and porosity after functionalization. Equilibrium adsorption experiments were performed with stable La(III) and radioactive 225 Ac. Trends in affinity are consistent between commercial resins and the synthesized membrane adsorbers; however, the Langmuir constants and the maximum binding capacity of the membrane adsorbers were generally lower than the resins. Despite these differences, the modeled selectivity for an equimolar solution of La(III)/ 225 Ac in 10 M nitric acid is 57. Furthermore, 225 Ac is rapidly desorbed from the fibers in 10 M nitric acid (<20 min). The La(III)/ 225 Ac selectivity and rapid 225 Ac desorption indicate this class of materials is promising for rapid radioanalytical separations.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

FAIRmaterials: Ontology Tools with Data FAIRification in Development

The bilingual FAIRmaterials package simplifies the creation and visualization of materials and data science ontologies. FAIRmaterials, available in the Python and R languages, addresses the complexities associated with traditional ontology editors based on manual user input such as Protege with an intuitive workflow and easy-to-use templates, making it accessible to users both experienced and inexperienced with ontologies. The FAIRmaterials package is its ability to programatically convert simple and structured CSV inputs into rich, well-defined ontologies. This capability is designed to support the findability, accessibility, interoperability, and reusability (FAIR) of research data and serve as a tool in the process of data FAIRification. Its additional features, such as automated ontology merging, static visualizations, and comprehensive documentation for outputs extend its utility, making it a valuable tool for any researcher engaged in knowledge management.

Bradley, Alexander Harding [Case Western Reserve U↗
DOE OSTI2026

Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy

High-energy Two dimensional (2D) synchrotron x-ray diffractometry provides important insights into the atomistic structure and phase evolution of materials, yet traditional analysis methods remain complex, knowledge-intensive, and computationally demanding. Deep-learning models offer a powerful alternative for automating their analysis. Institutions that hold these datasets may be unwilling to share their data due to privacy and security policies, as well as the challenges associated with large-scale data transfer. As a result, models trained on local datasets often perform well only on their own data but exhibit bias and poor generalization across different instruments or facilities. To overcome these limitations, we explore federated learning (FL) for 2D synchrotron diffractograms, enabling collaborative model training without exchanging raw data. In this study, 2D synchrotron diffractograms of Ti–6Al–4V alloy collected from two independent facilities are used to train convolutional neural networks for predicting the β-phase volume fraction. Experimental results show that federated global models significantly outperform locally trained models in terms of generalization and achieve accuracy comparable to centralized trained models. These findings demonstrate the potential of FL to enable secure, cross-institutional collaboration and enhance the scalability of deep-learning-based materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

FAIRLinked: Data FAIRification Tools for Materials Data Science

FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of data from CSV into JSONLDs and vice versa in a way that captures the semantics of the data using MDS-Onto. Lastly, QBWorkflow is a serialization and deserialization workflow that incorporates RDF Data Cube vocabulary, useful for working with multidimensional datasets. By offering these packages, FAIRLinked lowers the barrier of creating FAIR, machine-actionable data for researchers in the materials science community.

FAIR↗
DOE OSTI2025

Side Chain Engineering of Near-IR Aza-BODIPY Dyes Enables Processable Films with High Hole Mobility in Diodes

Boron chelated azadipyrromethene (Aza-BODIPY) derivatives are promising candidates for near-infrared optoelectronic applications due to their tunable absorption, electron-deficient core, and strong π-conjugation. Here, in this work, we synthesized and studied a series of 1-naphthylethynyl-substituted aza-BODIPY molecules featuring hexyl or hexyloxy solubilizing groups at either the proximal or the distal phenyl positions. These substitutions were designed to improve the solubility, film formation, and optoelectronic properties. We found that solubilizing groups not only enabled uniform solution-processed films but also fine-tuned key properties, such as absorption onset, oxidation potential, and energy levels. Charge transport analysis showed consistently high hole mobilities (∼10 –3 cm 2 /V s) across all substituted derivatives when CuI was used as the hole transport layer, regardless of film morphology. In contrast, electron mobilities tended to be low and inconsistent, partly because of the relatively poor-quality films on the ZnO electron transport layer. Overall, we have shown that solubilizing groups enable film solution-casting and can fine-tune the properties of these near-infrared aza-BODIPY derivatives.

36 MATERIALS SCIENCE↗