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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗
DOE OSTI2025

Probing Electrode Transformation under Dynamic Operation for Alkaline Water Electrolysis

Alkaline water electrolyzers (AWEs) play a pivotal role in the realm of large-scale hydrogen production. However, AWEs face significant challenges in electrode degradation particularly under dynamic operating conditions, induced by reverse current phenomenon during frequent startup/shutdown. Herein, this study aims to rationalize the degradation mechanisms of AWEs under these conditions. A three-electrode membrane electrode assembly (MEA) setup is first utilized to decouple polarization behaviors of anode and cathode in AWEs. Following a proposed accelerated stress testing protocol, the setup allows for tracking individual electrode performance transformations during frequent reverse current operation. Integrating operando cell studies with in situ and post-mortem characterizations, it is showed that continuous formation of highly active species, nickel (oxy)hydroxides, improves the anode performance for oxygen evolution reaction. On the contrary, irreversible oxidation of nickel to β-nickel hydroxide results in a severe degradation of cathode, leading to material dissolution, poor electrical conductivity and loss of catalytic activity for hydrogen evolution reaction. These results provide insights in nickel-based electrode transformation mechanisms for alkaline water electrolysis and indicate that cathode with higher redox reversibility can potentially improve durability of AWEs under dynamic conditions.

08 HYDROGEN↗
DOE OSTI2020

An ideal MHD δW stability analysis that bypasses the Newcomb equation

In the following work, we demonstrate the efficacy of a Riccati δW ideal MHD stability analysis that bypasses the numerically intractable integration of the Newcomb equation. By transforming the linear Newcomb equation into a quadratic Riccati equation, an accurate and equivalent δW analysis is performed that is shown to enjoy some numerical advantages. Additionally, we demonstrate that the Riccati approach is better conditioned than its Newcomb counterpart at the magnetic axis, and we apply dynamical systems insights to examine its behavior at singular points. We further discuss the constraints involved in solving for the extremal admissible perturbations of a δW analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

From Cryogenic to High Temperatures: A Microstructural Evolution of δ-phase Pu-Ga Alloys Investigated by Time-of-Flight High-resolution Neutron Diffraction

Phase stability and phase transformations in binary Pu alloys are critical for multiple applications. Long-term phase stability affects mechanical properties, microstructure, corrosion behavior, and structural integrity. A clear and detailed understanding of phase transformations and phase stability mechanisms is necessary to assess the behavior and response to unexpected stimuli. Gaining advanced knowledge on the behavior of Pu and Pu alloys at a variety of temperatures, pressures, and time will ensure better predictions and control on evolution outside laboratory measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Review of quantitative methods to assess impacts of changing climate and socioeconomic conditions on Arctic transportation systems

Rapid climate and socioeconomic changes are transforming Arctic human- Earth systems. An integral part of these systems is mobility, which encompasses the transport of humans and goods into, out of, and between Arctic regions. Impacts of climate and socioeconomic drivers on Arctic mobility are heterogenous. Methodologies are needed to quantify these impacts in measures that can be linked with broader socioeconomic systems. This article reviews existing such methods and organizes them into a conceptual framework to understand trends and gaps in the literature. We found methods quantifying impacts of a range of climate drivers on most transportation modes present in the Arctic, but few methods focused on socioeconomic drivers. Also underrepresented were methods explicitly considering adaptive capacity of transportation systems. In conclusion, we provide insight into the data and relationships relevant to understanding impacts of Arctic change on transportation systems and how these impacts fit into broader human- Earth systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An immersed interface method for the 2D vorticity-velocity Navier-Stokes equations with multiple bodies

We present an immersed interface method for the vorticity-velocity form of the 2D Navier Stokes equations that directly addresses challenges posed by nonconvex immersed bodies, multiply connected domains, and the calculation of force distributions on immersed surfaces. The immersed interface method is re-interpreted as a polynomial extrapolation of flow quantities and boundary conditions into the immersed solid bodies, reducing computational cost and enabling simulations with nonconvex bodies that could not be discretized with previous immersed interface methods. In the flow, the vorticity transport equation is discretized using a conservative finite difference scheme and explicit Runge-Kutta time integration. The velocity reconstruction problem is transformed to a scalar Poisson equation that is discretized with conservative finite differences, and solved using an FFT-accelerated iterative algorithm. The use of conservative differencing throughout leads to exact enforcement of a discrete Kelvin's theorem, allowing for simulations with multiply connected domains and outflow boundaries that have challenged other immersed interface vortex methods. We also explore novel methods for recovering time-dependent pressure distributions on immersed bodies within a vorticity-based method and present a novel control volume formulation for recovering aerodynamic moments from only the vorticity and velocity fields. The method achieves second order spatial accuracy and third order temporal accuracy, and is validated on a variety of 2D flows in internal and free-space domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Introducing metal–sulfur active sites in metal–organic frameworks via post-synthetic modification for hydrogenation catalysis

Metal–sulfur active sites play a central role in catalytic processes such as hydrogenation and dehydrogenation, yet the majority of active sites in these compounds reside on the surfaces and edges of catalyst particles, limiting overall efficiency. Here we present a strategy to embed metal–sulfur active sites into metal–organic frameworks (MOFs) by converting bridging or terminal chloride ligands into hydroxide and subsequently into sulfide groups through post-synthetic modification. We apply this method to two representative MOF families: one featuring one-dimensional metal–chloride chains and another containing discrete multinuclear metal clusters. Crystallographic and spectroscopic analyses confirm structural integrity and sulfide incorporation, and the transformation is monitored by in situ total scattering methods. The sulfided MOFs display enhanced catalytic activity in the selective hydrogenation of nitroarenes using molecular hydrogen. Density functional theory calculations indicate that sulfur incorporation promotes homolytic metal–ligand bond cleavage and facilitates H 2 activation. This work establishes an approach to construct MOFs featuring accessible metal–sulfide sites.

Catalyst synthesis↗
DOE OSTI2023

Influence of aluminum incorporation and aqueous conditions on metal ion release of high-Ni transition metal oxide nanomaterials

Developing a materials perspective of how to control the degradation and negative impact of complex metal oxides requires an integrated understanding of how these nanomaterials transform in the environment and interact with biological systems. Doping with aluminum is known to stabilize oxide materials, but has not been assessed cohesively from synthesis to environmental fate and biological impact. In the present study, the influence of aluminum doping on metal ion release from transition metal oxides was investigated by comparing aqueous transformations of lithium nickel cobalt aluminum oxide (LiNi 0.82 Co 0.15 Al 0.03 O 2 ; NCA) and lithium nickel cobalt oxide (LiNi 0.80 Co 0.20 O 2 ; NC) nanoparticles and by calculating the energetics of metal release using a density functional theory (DFT) and thermodynamics method. Two model environmental organisms were used to assess biological impact, and metal ion release was compared for NCA and NC nanoparticles incubated in their respective growth media: moderately hard reconstituted water (MHRW) for the freshwater invertebrate Daphnia magna (D. magna) and minimal growth medium for the Gram-negative bacterium Shewanella oneidensis MR-1 (S. oneidensis). Further, the amount of metal ions released was reduced for NCA compared to NC in MHRW, which correlated to changes in the modeled energetics of release upon Al substitution in the lattice. In minimal medium, metal ion release was approximately an order of magnitude higher compared to MHRW and was similar to the stoichiometry of the bulk nanoparticles for both NCA and NC. Interpretation of the release profiles and modeling indicated that the increase in total metal ion release and the reduced influence of Al doping arises from lactate complexation of metal ions in solution. The relative biological impacts of NC and NCA exposure for both S. oneidensis and D. magna were consistent with the metal release trends observed for minimal medium and MHRW, respectively. Together, these results demonstrate how a combined experimental and computational approach provides valuable insight into the aqueous transformations and biological impacts of complex metal oxide nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A hybrid coupler for directing quantum light emission with high radiative Purcell enhancement to a dielectric metasurface lens

Quantum photonic technologies such as quantum sensing, metrology, and simulation could be transformatively enabled by the availability of integrated single photon sources with high radiative rates and photon collection efficiencies. We address these challenges for quantum emitters formed from color center defect sites such as those in hexagonal boron nitride, which are promising candidates as single photon sources due to their bright, stable, polarized, and room temperature emission. We report design of a nanophotonic coupler from color center quantum emitters to a dielectric metasurface lens. The coupler is comprised of a hybrid plasmonic–dielectric resonator that achieves a large radiative Purcell enhancement and partial control of far-field radiation. We report radiative Purcell factors up to 285 and photon collection efficiencies up to 89% for a lossless metasurface, applying a continuous hyperboloidal phase-front. Our hybrid plasmonic–dielectric coupler interfacing two nanophotonic elements is a compound optical element, analogous to those found in microscope objective lenses, which combine multiple optical functions into a single component for improved performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Effects of 9.5 years warming on SOC concentration and composition in bulk soil and density fractions

Original data of whole-soil warming experiment after 9.5 years at Blodgett Forest Research Station. The Blodgett Forest is a mixed coniferous temperate forest with Mediterranean climate. The annual air temperature is 12.5℃ and the annual precipitation is 1774 mm yr-1- The soil is mesic ultic Alfisol of granitic origin, equivalent to Dystric Cambisol according to The World Reference Base for Soil Resources (WRB) system. The soil is warmed down to 1 m at + 4℃ by vertically installed heating cables. At the time of soil sampling on 1 May 2023, the whole-soil warming experiment had been running for approximately 9.5 years, from January 2014 to May 2023. The dataset includes: - Bulk_EA: C, N content, δ13C, and CN ratio of bulk soil; - Density_fractionation: organic carbon concentration, δ13C, and C/N ratio of free light fraction (fLF), occluded ligh fraction (oLF), and heavy fraction (HF); - PCA_DRIFT_AUC: original data of area under the curve (AUC) values of eight carbon bond types integrated on diffuse reflectance infrared fourier transform spectroscopy for each soil sample and soil fraction, which are consequently used for principal component analysis (PCA); - DRIFTS_stability_index: the calculation of aliphatic C–H (3000–2800 cm-1) to aromatic C=C (1670–1600 cm-1) ratios for each bulk soil sample and soil fraction. All data are provided in CSV format and can be viewed using Microsoft Excel.

Climate change↗
DOE OSTI2021

ERAS: Enabling the Integration of Real-World Intellectual Properties (IPs) in Architectural Simulators

Sandia National Laboratories is investigating scalable architectural simulation capabilities with a focus on simulating and evaluating highly scalable supercomputers for high performance computing applications. There is a growing demand for RTL model integration to provide the capability to simulate customized node architectures and heterogeneous systems. This report describes the first steps integrating the ESSENTial Signal Simulation Enabled by Netlist Transforms (ESSENT) tool with the Structural Simulation Toolkit (SST). ESSENT can emit C++ models from models written in FIRRTL to automatically generate components. The integration workflow will automatically generate the SST component and necessary interfaces to ’plug’ the ESSENT model into the SST framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Distributed IELI, Rebuilding IELI for Scalability

IELI is an NLP-based system designed to transform text into structured knowledge graphs, integrate domain-specific ontologies, and answer conceptual logic-based queries. This poster talks about how redesigning IELI can help address scalability and modularity challenges, as well as improving responsiveness and health monitoring of the system.

Trejo, Edwin Horacio [Sandia National Laboratories↗
DOE OSTI2025

Hydrological connectivity: a review and emerging strategies for integrating measurement, modeling, and management

This review synthesizes methods for measuring, modeling, and managing hydrologic connectivity, offering pathways to improve practices and address environmental challenges (e.g., climate change) and sustainability. As a key driver of water movement and nutrient cycling, hydrologic connectivity influences flood mitigation, water quality regulation, and biodiversity conservation. However, traditional field-based methods (e.g., dye tracing), indirect measurements (e.g., runoff analysis), and remote sensing techniques (e.g., InSAR) often struggle to capture the complexity of catchment-scale interactions. Similarly, modeling approaches—including process-based and percolation theory-based models, graph theory, and entropy-based metrics—face limitations in fully representing these interconnected processes. Both modeling and measurement techniques are constrained by inadequate spatial and temporal coverage, high data demands, computational complexity, and difficulties in representing subsurface connectivity. Subsequently, we critique current management practices that prioritize isolated variables (e.g., streamflow, sediment transport) over system-wide strategies and emphasize the need for adaptive, connectivity-based approaches in water resource planning and restoration. Moving forward, we highlight the importance of interdisciplinary collaboration, technological innovations (e.g., AI-driven modeling, real-time monitoring), and integrated frameworks to improve connectivity measurement, modeling, and adaptive management to restore fragmented hydrologic networks. This integrated approach sets the stage for transformative water resource management, fostering proactive policy development and stakeholder engagement.

Dwivedi, Dipankar↗
DOE OSTI2024

South Asia Group for Energy - Sri Lanka

Sri Lanka set a target of generating 70% of its electricity from renewable energy sources by 2030. This goal includes the addition of 5.8 GW of renewable power capacity, comprising hydropower, solar, wind, and biomass, between 2023 and 2030, with an interim target of adding 2.5 GW of renewable capacity by 2026. To accomplish this, Sri Lanka's power grid needs significant transformation and modernization to handle the integration of variable renewable energy sources effectively. The South Asia Group for Energy (SAGE) is helping Sri Lanka's grid operator, the Ceylon Electricity Board (CEB), understand the gaps for operating the grid with higher renewable share and identifying technology and research requirements to establish a variable renewable energy control center.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗
DOE OSTI2026

Harnessing photoenzymatic reactions for unnatural biosynthesis in microorganisms

Photobiocatalysis provides a powerful strategy for integrating light and biological catalysts to drive abiological transformations. However, its scalability is hindered by high enzyme loading, reliance on costly cofactors and instability under radical-generating conditions. Here we report the integration of light-driven enzymatic reactions into the cellular metabolism of Escherichia coli, bridging flavin-based photobiocatalysis with biosynthesis. Using synthetic biology strategies, we engineered microbial cells to continuously produce olefin substrates and ene-reductase while regenerating cofactors directly from glucose. By externally supplying radical precursors or introducing synthetic pathways for their in situ production, we enabled fermentation-based microbial photobiosynthesis, achieving high titres and demonstrating feasibility for scale-up in a bioreactor. This approach extends photobiocatalysis from in vitro applications to in vivo semi- and complete biosynthesis, revealing its full potential for integrating light-driven reactions into cellular metabolism.

Biocatalysis↗
DOE OSTI2024

Empowering Scientific Innovation Through An Integrated Research Infrastructure: The Role of the Advanced Computing Ecosystem

As the landscape of computational science evolves, the Department of Energy (DOE) is reimagining the roles of its large-scale computing facilities to meet emerging research challenges. The Integrated Research Infrastructure (IRI) program aims to transform how experiments are designed, conducted, and shared, with significant impacts on all stakeholders. In response, the Oak Ridge Leadership Computing Facility (OLCF) has established the Advanced Computing Ecosystem (ACE), a strategic framework to prepare its hardware, software, and experimental capabilities for the IRI era. ACE focuses on integrating novel compute environments, orchestrating advanced workflows, and developing foundational technologies, ensuring a seamless transition to IRI while accelerating scientific discovery. This paper outlines ACE's role in advancing OLCF's mission and its impact on the future of computational science.

Widener, Patrick↗
DOE OSTI2021

Of Actors, Cities and Energy Systems: Advancing the Transformative Potential of Urban Electrification

The electrification of transportation and the integration of electric vehicles (EVs) with buildings connected to clean grids has been touted as one of the key solutions to the global decarbonization challenge. Cities are on the frontlines of current and future electrification, as they depend on and drive electricity generation, distribution, and use. City actors also occupy a central role in the actions to enable electrification to support energy transitions in efficient, equitable, environmentally sound, and resilient ways. Currently, however, research and development on the interactions between actors, cities and energy systems is predominantly conducted in disciplinary siloes. This topical review analyzes the transformational potential of urban electrification. It focuses on efforts to electrify transportation and integrate EVs with buildings connected to a clean grid. We find that actions in these area are driving change; they are adopted by wealthier populations and on an experimental basis by specific communities. Their larger-scale growth is constrained by institutional, behavioral, and infrastructural factors. We also find that existing siloed disciplinary approaches are often incompatible with advancing holistic research. To achieve that, divergent communities of scholars need to come together to integrate their research and create broader perspectives. Through incorporation of the social sciences, these perspectives need to consider the societal limits and potentials brought to bear by human behavior and decision making. Only then can urban electrification be understood as the empirically rich and socially complex topic that it is. And only with this understanding will innovations and smart policy actions be able to tap into the transformational potential of urban electrification.

cities↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗