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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Parametric and Robustness Studies of Qudit Mediated Quantum State Transfer and Entanglement Creation

The large number of available states participating in quantum state evolution in a system of qudits translates into better thermal resilience, for quantum state transfer (QST) and distributed entangled states, than a system of 2-level qubits. In this report we study the dependence of fidelities of the above processes on various system parameters. Sensitivity to modes of dissipation and errors is also studied. An alternative and potentially more efficient scheme to create Bell states is used.

Chao, Yu-Chiu [Fermilab] (ORCID:000900039584159X)↗
DOE OSTI2025

Vehicle-Cycle Inventory for Type C School Buses & Intra-City Transit Buses

This report documents the new inventory incorporated into the Research and Development version of Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) 2025 model for the vehicle cycle of Type C school buses and intracity transit buses. The transportation sector contributes significantly to the United States’ energy consumption and resultant emissions (EPA, 2025a). However, public transit plays an important role in mitigating these impacts because it consumes a relatively low amount of energy per passenger (Congressional Budget Office, 2022). Public transit is widely used in the United States; more than 500,000 school buses (EPA, 2025b) and ~75,000 service buses (American Public Transportation Association, 2025) operate in the nation. These are primarily internal combustion engine vehicles (ICEVs) powered by diesel. Original equipment manufacturers (OEMs) are making efforts to electrify U.S. bus fleets by using batteries as a propulsion system to replace internal combustion engines. Electrification can reduce tailpipe emissions, such as particulate matter (with a diameter ≤10 µm [PM 10 ] and with a diameter ≤2.5 µm [PM 2.5 ]) and nitrogen oxides (Jonas et al., 2025; Martinez and Samaras, 2024; EPA, 2025b; Wayne et al., 2009). Hence, any energy and emission impact analysis of public transit must consider both conventional ICEVs and upcoming electric vehicle (EV) options for the school and transit buses that dominate this landscape. To understand the detailed environmental impact profiles of ICEV and EV school and transit buses, it is necessary to conduct a thorough analysis covering both vehicle manufacturing and vehicle use stages. The current literature lacks a detailed vehicle-cycle inventory for school and transit buses, which makes this kind of comparison difficult. To overcome this gap, we developed a comprehensive vehicle-cycle model for school and transit buses in Argonne’s R&D GREET 2025 model. The model is flexible in handling user inputs for key assumptions, such as component weights and material compositions, upstream energy sources for material processing, and vehicle operating parameters, to understand their impacts on energy use and emissions for both school and transit buses. This report is organized as follows: Section 2 provides details on the modeling approach and vehicle specifications (weights and composition of different vehicle components, and vehicle operating parameters) for both school and transit buses. Section 3 provides details on vehicle assembly, disposal, and recycling (ADR) approaches for the two buses. Section 4 includes details about their incorporation into the R&D GREET model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Astrophysical S -factors, Thermonuclear Rates, and Electron Screening Potential for the 3 He(d,p) 4 He Big Bang Reaction via a Hierarchical Bayesian Model

Here, we developed a hierarchical Bayesian framework to estimate S-factors and thermonuclear rates for the 3 He(d,p) 4 He reaction, which impacts the primordial abundances of 3 He and 7 Li. The available data are evaluated and all direct measurements are taken into account in our analysis for which we can estimate separate uncertainties for systematic and statistical effects. For the nuclear reaction model, we adopt a single-level, two-channel approximation of R-matrix theory, suitably modified to take the effects of electron screening at lower energies into account. In addition to the usual resonance parameters (resonance location and reduced widths for the incoming and outgoing reaction channel), we include the channel radii and boundary condition parameters in the fitting process. Our new analysis of the 3 He(d,p) 4 He S-factor data results in improved estimates for the thermonuclear rates. This work represents the first nuclear rate evaluation using R-matrix theory embedded into a hierarchical Bayesian framework, properly accounting for all known sources of uncertainty. Therefore, it provides a test bed for future studies of more complex reactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Multiwavelength Afterglow Analysis of GRB 221009A: Unveiling the Evolution of a Cooling Break in a Wind-like Medium

Gamma-ray bursts (GRBs) are the most energetic explosions in the Universe, and their afterglow emission provides an opportunity to probe the physics of relativistic shock waves in an extreme environment. Several key pieces for completing the picture of GRB afterglow physics are still missing, including jet properties, the emission mechanism, and particle acceleration. Here, we present a study of the afterglow emission of GRB 221009A, the most energetic GRB ever observed. Using optical, X-ray, and gamma-ray data up to approximately 2 days after the trigger, we trace the evolution of the multiwavelength spectrum and the physical parameters behind the emission process. The broadband spectrum is consistent with the synchrotron emission emitted by relativistic electrons with its index of p = 2.29 ± 0.02. We identify a break energy at keV and an exponential cutoff at GeV in the observed multiwavelength spectrum. The break energy increases in time from $16.0^{+7.1}_{-4.9}$ keV at 0.65 days to $46.8^{+25.0}_{-15.5}$ keV at 1.68 days, favoring a stellar-wind-like profile of the circumburst medium with k = 2.4 ± 0.1 as in ρ(r) ∝ r -k . The high-energy attenuation at around 0.4 to 4 GeV is attributed to the maximum of the particle acceleration in the relativistic shock wave. This study confirms that the synchrotron process can explain the multiwavelength afterglow emission and its evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Direction-optimizing Label Propagation and its Application for Community Detection

Label Propagation is a machine learning algorithm typically used for classification. It has also been found to be an effective method for detecting communities in networks. It has two attractive features as a community detection method: it has nearly linear runtime and it requires no \textit{a priori} community information. We propose a new Direction Optimizing Label Propagation Algorithm (DOLPA) that relies on the use of {\em frontiers} and alternates between label {\em push} and label {\em pull} operations to enhance the performance of LPA. Specifically, DOLPA has parameters for tuning the processing order of vertices in a graph. This reduces the number of edges visited and improves the quality of solution. We apply DOLPA to community detection and present the design and implementation of the algorithm as well as its shared-memory parallelization using OpenMP. Empirically, we evaluate our algorithm using synthetic graphs as well as real-world networks. Compared with the state-of-art \textit{Parallel Label Propagation} algorithm, we achieve at least two times the F-Score while reducing the runtime by 50\% for synthetic graphs with overlapping communities. We also compare DOLPA against the state-of-art parallel implementation of the Louvain method using the same graphs and show that DOLPA achieves about three times the F-Score at 10\% the runtime. On real-world graphs, we get a speedup of up to $10\times$ using 64 threads.

Liu, X↗
DOE OSTI2024

Digital Twin Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derate while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Digital-Twin-Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable improved scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. Digital twins for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. However, digital twins may also provide additional insights by combining and interpreting various sources of information. These insights may be used for preventative maintenance scheduling optimization or early fault detection and are projected to be valuable for meeting requirements under 10 CFR 50.55a. However, digital twin technologies are still under significant development; quantifying model uncertainties, improving unique fault identification, and multimodal sensor fusion are some areas under investigation. Therefore, in this work, we discuss and review the various enabling technologies, in the form of advanced sensors, instrumentation, and modelling methods, that may be used to implement and enhance digital twins for online condition monitoring. A potential use case for pump-motors is presented to demonstrate how these various pieces of enabling digital twin technologies may integrated together for online condition monitoring. Challenges and opportunities associated with the pump-motor digital twin enabling technologies are also identified and discussed.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

The effect of varying powder feedstock chemistry and printing atmosphere on the microstructure of additively manufactured nickel-based ODS alloys: Role on stabilization of cellular structures vs. oxide dispersion formation

Nickel-based alloys have a wide variety of structural applications due to their high corrosion resistance and mechanical strength which depend on solid solution strengthening, or the formation of oxides and/or intermetallic precipitation for their properties. In this study, the microstructure of six Ni–Cr–Y–Ti–Al powder batches designed for the production of oxide dispersion strengthened nickel were compared. These batches varied in chemistry and atomization technique used which included Gas Atomization Reactive Synthesis (GARS). The batches of powder were then consolidated via Additive Manufacturing (AM) Powder Bed Fusion using Laser Beam (PBF-LB) and characterized via transmission electron microscopy to elucidate the influence of powder feedstock (i.e. synthesis methodology and chemistry) on the PBF-LB microstructure. The study investigates (i) how the amount of yttrium and titanium additions in the powder feedstock and the addition of oxygen during the processing (through GARS) affect the microstructure of the powder itself and the AM printed microstructure, and (ii) how the control of oxygen addition in the printing atmosphere during the PBF-LB printing process itself is another important parameter for achieving the formation of the wanted oxide dispersion versus the stabilization of the cellular structure (often observed in AM processed alloys). Microstructural characterization of both powder particles and additively manufactured nickel alloys in this study provide important insights into the movement of yttrium within the material upon solidification, particularly along cell boundaries, and how yttrium behaves depending on alloy chemistry. When a threshold of yttrium content is reached within the system, yttrium consistently reacts to form an intermetallic along cell boundaries instead of forming oxide nanoparticles.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Failure analysis of solid oxide fuel cells nickel-yttria stabilized zirconia anode under siloxane contamination

In this study, the failure process of the solid oxide fuel cell (SOFC) Ni-yttria-stabilized zirconia (YSZ) anode is investigated with D4 siloxane (octamethylcyclotetrasiloxane) contamination. In order to evaluate the influence of the electrochemical reaction on the siloxane deposition process, the SOFC experiments were operated at open circuit voltage (OCV) and 50 mA cm -2 conditions at 800°C. During the failure process, electrochemical, morphology and exhaust gas component analysis testing are conducted at the critical points. An equivalent circuit model and corresponding microstructure parameter calculations for separated physicochemical processes were utilized for the quantitative analysis of the failure process. Further, the results confirm the siloxane chemical adsorption deposition mechanism proposed in previous work. As a result, the failure of the anode was attributed to the gas diffusion blockage by dense silicon dioxide layer formation. The anode failure process with siloxane contamination is faster when the anode is operated under polarization.

25 ENERGY STORAGE↗
DOE OSTI2019

A direct filter method for parameter estimation

Parameter estimation is an important research topic in data assimilation. In this paper, a novel parameter estimation method is introduced, where the parameter is considered as the state process in a nonlinear filtering problem and the state model that contains the parameter is used to construct a pseudo-observation. This approach is named the direct filter method since nonlinear filtering algorithms are used to estimate the parameter directly without estimating the state model as part of the solution in the nonlinear filtering problem. Numerical experiments are carried out to examine the effectiveness and accuracy of the direct filter method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Application of Advanced Materials Processing to Enable Direct Production of Fast Reactor Fuel Alloys

Argonne National Laboratory (the Contractor), located in Lemont, IL, and Oklo, Inc. (the Participant), headquartered in Sunnyvale, CA, entered into a Cooperative Research and Development Agreement (CRADA) to integrate advanced electrorefining co-deposition and molten-salt monitoring technologies to produce a uranium-transuranic (U/TRU) alloy within a controlled composition range that can be used as fast reactor fuel for Oklo, Inc.’s advanced reactor technology. Argonne performed the integration of electrorefining and process monitoring technologies, determined operating parameters for producing U/TRU alloys, and developed optimized design and operating parameters for co-deposition of U/TRU alloys. Oklo, Inc. worked with Argonne and the Nuclear Regulatory Commission (NRC) to provide the necessary process documentation to begin implementing pyroprocessing technology in the fast reactor fuel production process. Outcomes of this project included a pilot-scale co-deposition cathode design and technical basis for the operation of the co-deposition electrorefiner to produce U/TRU alloys with controlled composition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

In‐mold rheology and automated process control for injection molding of recycled polypropylene

Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.

Krantz, Joshua↗
DOE OSTI2023

Model Agnostic Bayesian Framework for Online Anomaly/Event Detection in PMU Data

Phasor measurement units (PMU) are integral to the modernization and automation plan of the electric power industry. A PMU data signature contains system-level events (e.g., faults, generation/load change, etc.) and any measurement/device-related errors. Therefore, the reliable and resilient operation of power systems is equivalent to the quality of the PMU data and the situation awareness provided by its data signature. Despite recent progress, current state-of-the-art methods are not fool-proof and have certain limitations tracing an error/abnormality to sensor sub-components and grid systems. This is because of technical challenges imposed by the scarcity of the labeled information, loss of data quality, and non-stationarity of data. In this paper, we consider the online PMU data stream as an output of a stochastic process and pose the anomaly/event detection as a changepoint detection problem dealing with detecting parameter changes in the underlying stochastic processes. The proposed model-agnostic framework relies on: (a) feature extraction utilizing the minimum volume enclosing ellipsoids (MVEE) method from raw PMU observations and (b) a Bayesian framework of changepoint detection. The validity of the proposed methodology is discussed through numerical experiments on real-world utility-scale PMU data.

Hossain, Ramij Raja↗
DOE OSTI2022

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis to Support Risk Model Parameter Estimations

This set of slides has been prepared for a talk at the INL AI/ML Symposium held on September 8, 2022. Presentation outline: Background - Nuclear power plant operating experience data sources • Research focus and motivation - Analyzing free-text operating experience data: present and future • Research method - Input - Methodological steps - Output • Conclusions and next steps

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
DOE OSTI2026

A Parallel Kinetic Model for Surface and Bulk Charge Storage in ε -MnO 2 Pseudocapacitors

Pseudocapacitive materials such as manganese dioxide (MnO 2 ) are attractive for energy storage applications due to their ability to combine the fast kinetics of capacitors with the higher energy density of battery-type systems. However, the electrochemical behavior of MnO 2 remains difficult to interpret mechanistically, in part because existing models often fail to distinguish between surface-based redox processes and bulk intercalation mechanisms. In this work, we develop a physics-based model that represents MnO 2 pseudocapacitance as a linear combination of two independent, parallel electrochemical processes: (i) the surface or near-surface redox storage and (ii) lithium ion intercalation into the bulk material. These two processes are treated with distinct kinetic and thermodynamic parameters and are assumed to proceed independently. The total measured current is assumed to be the sum of these two partial currents. We validate the model using rate-dependent cyclic voltammetry experiments, demonstrating that it captures key trends and provides physically interpretable parameters reflecting the relative contributions of surface and bulk processes. By enabling a clear separation between these mechanisms, the model offers a useful framework for analyzing pseudocapacitive materials and can guide the rational design of high-performance energy storage electrodes.

Energy - Storage↗
DOE OSTI2020

Understanding the eco-geomorphologic feedback of coastal marsh under sea level rise: vegetation dynamic representations, processes interaction, and parametric sensitivity

A growing number of coastal eco-geomorphologic modeling studies have been conducted to understand coastal marsh evolution under sea-level rise (SLR). Although these models quantify marsh topographic change as a function of sedimentation and erosion, their representations of vegetation dynamics that control organic sedimentation differ. How vegetation dynamic schemes contribute to simulation outcomes is not well quantified. Additionally, the sensitivity of modeling outcomes to parameter selection in the available formulations has not been rigorously tested to date, especially under the influence of an accelerating SLR. In this paper, we used a coastal eco-geomorphologic model with different vegetation dynamic schemes to investigate the eco-geomorphologic feedbacks of coastal marshes and parametric sensitivity under SLR scenarios. We found that marsh platform relief increased with sea level rise rate. The simulations with different vegetation schemes exhibited different spatial-temporal variations in elevation and biomass. The nonlinear Spartina scheme presented the most resilient prediction with generally the highest marsh accretion and vegetation biomass, and the least elevation relief under SLR. But the linear Spartina scheme predicts the lowest unvegetated-vegetated ratio. We also found that vegetation-related parameters and sediment diffusivity, which were not well measured or discussed in previous studies, were identified as some of the most critical parameters. Additionally, the model sensitivity to vegetation-related parameters increased with SLR rates. The identified most sensitive parameters may inform how to appropriately choose modeling representations of key processes and parameters for different coastal marsh landscapes under SLR, and demonstrate the importance of future field measurements of these key parameters.

58 GEOSCIENCES↗
DOE OSTI2019

Probability of Electron Capture by {sup 3}He{sup 2+} Ions from Argon Atoms at Different Impact Parameters

We have measured the absolute values of total cross sections of capture of one and two electrons by He{sup 2+} ions from argon atoms. The differential scattering cross sections have been determined for fast atoms and singly charged helium ions formed in each of these processes (without and with additional ionization of the formed slow argon ion). Measurements have been taken for He{sup 2+} ions with kinetic energy of 6 keV in scattering angle range 0–2.5°. Based on the measured differential cross sections using different model atomic particle interaction potentials, we have calculated the cross sections of these processes as functions of the impact parameter. The probabilities of realization of these processes with electron density distribution in different shells in the target atom have been compared. The applicability of the expressions for the screened Coulomb interaction potentials in the description of scattering of particles that have captured electrons has been demonstrated.

74 ATOMIC AND MOLECULAR PHYSICS↗