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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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1,038 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Roadmap and Benchmarking: Privacy in Federated Load Forecasting

Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.

Abebe, Waqwoya [Oak Ridge National Laboratory (ORN↗
DOE OSTI2026

Compositional Homogeneity in Rapidly Solidified 316L stainless steel produced by laser powder-bed fusion processing

The segregation of alloying elements in rapidly solidified 316L stainless steel produced using additive manufacturing technologies is widely reported throughout literature. The microstructure of thin walls manufactured from 316L stainless steel using laser powder bed fusion are examined for the presence of alloy segregation, i.e. the rejection of solutes to grain boundaries. Metallographic methods including mechanical polishing and chemical etching are used to prepare samples for microanalysis m

Jankowski, Alan F. [Sandia National Laboratories (↗
DOE OSTI2026

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li + , by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH – dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li + -coupled electron transfer along the PAN backbone, causing ∼10 4 times faster sequential ring-formation of the remaining nitriles. PAN’s extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

Chahal-Crockett, Rajni [Oak Ridge National Laborat↗
DOE OSTI2026

Active learning using hybrid surrogate tool life modeling for machining process optimization

Here, this paper describes an active learning approach for part-to-part iterative machining process optimization using a hybrid surrogate tool life model. A probabilistic interpolating tool life model is developed by combining the empirical Taylor-type tool life equation and the model fit error. The probabilistic tool life model is then used to calculate the machining cost per part distribution. The optimal machining parameters are selected using an expected improvement in machining cost per part criterion. The method is validated numerically using experimental results; the results show a median convergence error of 2.2% after three tests over 400 simulations. The method is validated experimentally on two industrial applications for Ti-6Al-4V roughing resulting in a cost per part reduction greater than 23% after two tests. The described method is a robust solution for rapid convergence to optimal machining parameters in an industrial production environment.

Active learning↗
DOE OSTI2026

Plasma flow generation and particle acceleration from expanding magnetic bubbles

Impulsive plasma dynamics in the laboratory are often driven by rising electric currents, yet their quantitative plasma response has not been well established. By means of fully kinetic particle-in-cell simulations and laser-driven capacitor-coil experiments, we show that a rising current expels plasma, forming an expanding magnetic bubble and accelerating particles. The expansion front velocity scales with the Alfvén speed determined by the magnetic field at its inner edge and the plasma density at its outer edge. This mechanism establishes impulsive current drive as a fundamental way that generates plasma flows and accelerates particles in laboratory plasmas, with potential relevance to astrophysics.

Zhang, Yang [Princeton University, NJ (United Stat↗
DOE OSTI2026

Additively manufactured refractory high-entropy alloys with superior radiation resistance

Refractory high-entropy alloys (RHEAs) are promising candidates for next-generation nuclear and high-temperature applications. Among many approaches to manufacture RHEAs, additive manufacturing (AM) represents the most recent and advanced metal manufacturing method which allows near-net-shape manufacturing to reduce material waste and post-processing time. However, performance of AM RHEAs under complex irradiation conditions remains largely unexplored. Here, in this study, we demonstrate for the first time the response of directed energy deposition (DED) AM quaternary RHEAs (HfTaVW, CrTaVW) subjected to sequential dual-beam ion irradiation, consisting of helium pre-implantation followed by high-dose heavy ion bombardment. Compositions of DED AM RHEAs were selected using Monte Carlo (MC) simulations based on a cluster expansion (CE) Hamiltonian parameterized by density functional theory (DFT). Post-irradiation microstructural characterization revealed that the AM RHEA maintained remarkable stability, with suppressed helium bubble growth and reduced defect accumulation compared to conventional alloys. Even at high doses (∼100 dpa), the alloy exhibited no void swelling, a low density of dislocation loops, and no evidence of severe degradation. These results highlight the intrinsic ability of AM-derived microstructures and multicomponent chemistry to synergistically mitigate irradiation effects. Our findings establish AM RHEAs as a class of materials with superior resistance to radiation damage under conditions relevant to advanced fusion and fission environments and demonstrate the importance of sequential ion beam studies in evaluating their long-term performance.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Reduced fast-ion transport calculations of m = n = 1 fishbone-like instabilities in MAST-U

Fast-ion transport associated with an m = n = 1 fishbone-like burst in MAST-U discharge 47128 is investigated using a reduced guiding-center-based transport model (ORBIT-Kick) constrained by multi-diagnostic measurements. The two-dimensional beam-emission spectroscopy system provides measurements of the core poloidal mode structure and fluctuation amplitude, while EFIT++ reconstructions constrained by the motional Stark effect diagnostic indicate a flat q-profile with q 0 > 1⁠, indicating the absence of a resonant q = 1 surface and supporting a pressure-driven infernal-mode interpretation. Analytic m = n = 1 displacement profiles consistent with the measured core mode structure and equilibrium constraints are used as the mode structure inputs to ORBIT-Kick. The calculations show that the dominant resonances occur between the mode and co-passing fast ions, producing redistribution localized near the magnetic axis. Synthetic neutron camera signals from TRANSP-Kick recover up to 90% of the experimentally observed neutron deficit at the time of peak mode amplitude, indicating that the measured m = n = 1 mode is a dominant contributor to core fast-ion transport. However, the synthetic neutron signals recover rapidly, whereas the measured neutron emission continues to decrease after the peak amplitude. In conclusion, the remaining discrepancy may arise from contributions not included in the present single-harmonic model, including higher-m and higher-n harmonics, multi-harmonic interactions, and additional transport mechanisms, motivating future diagnostic development and modeling efforts to resolve and incorporate these additional contributions.

Wong, Henry H. [University of California, Los Ange↗
DOE OSTI2026

Assessing the Impact of a Forest Canopy on Near-Surface Wind Statistics

Representing the forest canopy in atmospheric numerical models should improve simulated winds within and above the canopy up to a few hundred meters above the ground. Here, in this study, we implement a forest canopy parameterization into the Weather Research and Forecasting (WRF) Model in a large-eddy simulation (LES) mode by applying drag forces across multiple layers within the canopy height. We use unique observations from the Lidar Experiments for Assessing Flow over Forests (LEAFF) field campaign at the Wind River Experimental Forest (WREF) in the U.S. Pacific Northwest to evaluate model performance. In a 2-day case study, the canopy parameterization improved wind predictions both within and above the canopy, particularly during the daytime and at finer grid resolution. Without it, winds were frequently overpredicted above the canopy. Similarly, derived quantities such as the wind shear index also yielded estimates closer to observations with the canopy parameterization implemented. These findings suggest that representing the canopy using drag forces alone can improve simulated mean winds up to 200 m above the surface. Furthermore, second-order statistical moments of wind were more sensitive to canopy density than first-order moments, especially during the daytime. This increased sensitivity and the improved daytime performance in wind speed—evidenced by the lowest bias from observations (3% compared to 20% over diurnal cycle)—imply that winds above the canopy layer are strongly influenced by how well turbulence above the canopy is modeled. The results of this study can serve as a foundation for parameterizing forest canopy effects in coarser weather forecast models.

Energy - Wind↗
DOE OSTI2026

Justification that the Thermo-Fisher Scientific 241 Am Residues Were Generated by Atomic Energy Defense Activities

The Waste Isolation Pilot Plant (WIPP) Land Withdrawal Act (LWA) as amended by the National Defense Authorization Act for Fiscal Year 1997 (1) requires that for Transuranic (TRU) waste to be eligible for disposal at WIPP, it must have been generated by atomic energy defense activities. The definition of “atomic energy defense activity” is defined in the Nuclear Waste Policy Act of 1982 (NWPA) (2).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti3C2TX MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures-from isolated vacancies to nanopores-revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.

2D materials↗
DOE OSTI2026

Calibration of the Nonlinear Viscoelastic SPECtacular Model for 828-CTBN/DEA/GMB and 828/Z/AlOx

Nonlinear viscoelastic model calibrations are developed for two filled epoxies: an adduct of Epon 828 DGEBA and CTBN cross-linked with diethanolamine (DEA) filled with glass microballoons, called 828-CTBN/DEA/GMB, and Epon 828 DGEBA cross-linked with Ancamine Z and filled with alumina, called 828/Z/AlOx. The thermo-mechanical responses of these materials are represented using the SPECtacular constitutive model, an extension of the Simplified Potential Energy Clock (SPEC) model. The model couples

Cundiff, Kenneth Noel [Sandia National Laboratorie↗
DOE OSTI2026

First Wall Design of a Tokamak Pilot Plant Using a Monte Carlo Model for 3-D Heat Flux Deposition

We present a method for calculating the heat fluxes deposited on nonaxisymmetric tokamak first wall components, allowing for a first-of-its-kind model for power handling in the tokamak far scrape-off layer (SOL). The DIV3D Monte Carlo model features strict global power conservation and can calculate the finite cross-field plasma transport into magnetically-shadowed regions, which is significant when dealing with meter-scale shadows introduced by components such as poloidal limiters or antennas. As a case study, we apply the DIV3D model to inform the distribution of first wall poloidal limiters in an ARC-class reactor device. We demonstrate that discrete protection limiters can efficiently reduce peak heat fluxes on recessed breeder wall components in the presence of significant far-SOL plasma fluxes. By varying the toroidal periodicity and radial standoff depth of the limiters, we demonstrate one of the tradeoffs that must be considered in first wall design: more limiters provide greater protection, but at the cost of reduced breeding performance. We also present the impact that radial misalignments between limiters would have on first wall power loading.

Monte Carlo methods↗
DOE OSTI2026

A stable high-humidity bubbler design to supply steam for solid oxide electrolysis cells

A stable steam supply is required for the operation of solid oxide electrolysis cells. Heated water bubblers are the most common method due to the simplicity and inherent safety of the method, however, several design challenges hinder effective implementation. Here, a stable and validated bubbler design is presented, capable of achieving very high steam concentrations, relatively high flow rates, and continuous operation. A piping and instrumentation diagram and bill of materials are provided to enable easy duplication. Critical design parameters are discussed, including safety considerations and materials requirements, which are applicable to any bubbler design. The practical implementation of bubblers is also presented, including methods to prevent condensation instability and reduce backpressure to achieve a stable steam supply. The 3″ x 6″ (7.6 cm × 15.2 cm) bubbler achieves up to 98% steam balance hydrogen at 200 sccm and up to 1 slpm at 96% steam.

08 - HYDROGEN↗