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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

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗
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

Adapter Signaling Evaluations on EVs [Slides]

In case J3400 and J3400/1 define a basic analog signaling approach for DC charging adapters to communicate over-temperature events to both the EV and EVSE. They also require the EV to implement mitigation and corrective actions in cases where the EVSE does not respond to adapter thermal signals. In this study, multiple production vehicles were evaluated for responsiveness to assess field readiness and ensure consistent performance in accordance with the SAE J1772, J3400 and J3400/1 standards. A series of test cases were developed and executed across different vehicles, using an adapter thermal breakout fixture designed by NLR for evaluations. The results revealed notable variations in behavior: some vehicles expected to comply with J1772 were found to be non-responsive under certain conditions. Additionally, while some EV OEMs appear to align with the J3400 standard, discrepancies exist due to its evolving nature, resulting in inconsistent implementation of the latest requirements. These findings highlight the need for alignment within standards organizations to ensure consistent interpretation, encourage compliance, and reduce potential confusion across implementations.

33 ADVANCED PROPULSION SYSTEMS↗
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

Direct Observation of Elusive (DTBM‐SEGPHOS)CuH Monomer Enables Mechanistic Insights Into Hydrocupration, Aggregation, and Dynamics of Alkene Functionalization Catalysis

The bulky diphosphine DTBM-SEGPHOS is widely employed in CuH-catalyzed transformations as it provides remarkably active catalyst systems. The transient (DTBM-SEGPHOS)CuH monomer (LCuH) is the often-invoked active species. However, its instability has prevented spectroscopic characterization and mechanistic elucidation, hindering mechanistic understanding. We report low-temperature NMR spectroscopic characterization of LCuH, enabling quantitative kinetic analysis of the stoichiometric hydrocupration and catalytic hydroboration of cyclopentene, as well as the structural identification of two CuH clusters. LCuH inserts cyclopentene at −43°C, reaffirming its high reactivity toward olefins. LCuH deactivates to form L 2 Cu 3 H 3 and L 2 Cu 4 H 4 clusters, in which LCuH dimerization initiates aggregation. Kinetic analysis of reactions of unactivated alkenes indicates that competing on-cycle alkene hydrocupration and LCuH dimerization impact performance, as catalyst deactivation and turnover occur on comparable timescales. Structure–activity analysis using atomistic simulations shows that the steric profile of DTBM-SEGPHOS increases the CuH dimerization barrier by ∼7.7 kcal mol−1 compared to that of SEGPHOS, rationalizing the unique ability of DTBM-SEGPHOS to stabilize a reactive monomer for hydrocupration of broader alkene substrates. These findings illustrate the fundamental design principle that steric control of aggregation governs CuH catalyst performance, explaining both the exceptional activity of (DTBM-SEGPHOS)CuH and the limitations imposed by competing deactivation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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

Progress and Perspectives: Zirconium Electrodeposition from Different Electrolytes

Zirconium (Zr) possesses outstanding properties, including exceptional chemical resistance and a high melting point, and is therefore desirable for use in a wide variety of challenging environments, such as in nuclear reactors and the chemical processing industry. To minimize the amount of Zr required for any given application, developing methods for generating metallic Zr coatings is highly advantageous. Owing to Zr’s highly negative reduction potential, electrodeposition in traditional solvents at near ambient temperatures remains challenging and thus not well understood. Due to the extreme conditions required to deposit this metal, extensive work has been conducted in molten salt electrolytes, however the broad applicability of this methodology is limited due to its corrosivity. The primary focus of this article is to present an overview of Zr’s electrochemical behavior and to consolidate the efforts of researchers in exploring electrodeposition techniques for Zr involving aqueous, organic, ionic liquid, deep eutectic, and molten salt solvents. With this information, we highlight trends across solvent systems and opportunities for future research.

42 ENGINEERING↗
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

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

Magnetism and magnetoelastic effect in the two-dimensional van der Waals multiferroic CuCrP 2 ⁢S 6

Here, we report a magnetic and neutron diffraction study on the ground state magnetism and field evolution of single-crystal van der Waals multiferroic CuCrP 2 ⁢S 6 . The ordered moments align along the 𝑏 axis in the "𝐴-type" antiferromagnetic configuration with a spin-flop transition along the same direction. Field application along 𝑎 introduces a smooth transition to a fully polarized ferromagnetic state via in-plane spin rotation. These findings resolve the ambiguity of the ground state magnetization direction in CuCrP 2 ⁢S 6 and uncover its field responses, providing a firm basis for future magnetoelectric study. A magnetoelastic coupling effect connecting the interlayer spacing and the magnetic order was further revealed, highlighting the out-of-plane strain as an effective control knob for tuning magnetism both in this system and in related van der Waals magnets.

Guo, Jiasen [Oak Ridge National Laboratory (ORNL),↗
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↗