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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order non-linear system with multi-time-scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, the reduced-order WECC composite load model is derived by using the proposed algorithm. Overall, simulation results show that the reduced-order large-signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Temporally-consistent koopman autoencoders for forecasting dynamical systems

Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-order feature space with simpler, linear dynamics. However, the effectiveness of KAEs is hindered by limited and noisy training datasets, leading to poor generalizability. To address this, we introduce the Temporally-Consistent Koopman Autoencoder (tcKAE), designed to generate accurate long-term predictions even with limited and noisy training data. This is achieved through a consistency regularization term that enforces prediction coherence across different time steps, thus enhancing the robustness and generalizability of tcKAE over existing models. We provide analytical justification for this approach based on Koopman spectral theory and empirically demonstrate tcKAE’s superior performance over state-of-the-art KAE models across a variety of test cases, including simple pendulum oscillations, kinetic plasma, and fluid flow data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Scout Benchmark Scenarios for U.S. Building Energy and CO2 Emissions to 2050

Overview and Intended Use Cases: These scenarios establish a range of futures for U.S. buildings sector energy use and CO2 emissions to 2050 using Scout (scout.energy.gov), a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO). Scout benchmark scenario data are suitable for the following example use cases: setting high-level policy goals for the U.S. buildings sector to 2050 (e.g., X% building CO2 emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050); exploring the effects of key dynamics driving U.S. buildings sector energy and CO2 emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market); determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO2 emissions reductions to 2050 under a given set of assumptions; and/or identifying the energy and emissions impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio. Scenario Summary: A total of 8 scenarios explore the effects of changes across both the demand- and supply-side of building energy use on annual U.S. building energy use and CO2 emissions from 2022–2050. Scenarios are organized into three groups representing low, moderate, and best-case potentials for building decarbonization, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints

Abstract Dimension reduction techniques are an essential part of the climate analyst’s toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative , sparse , and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection.

Weylandt, Michael↗
DOE OSTI2021

Mechanistic Insights into CO 2 Electroreduction on Ni 2 P: Understanding Its Selectivity toward Multicarbon Products

Recently, nickel phosphides (Ni x P y ) have been reported to enable selective electrochemical formation of multicarbon products (C 3 and C 4 ) via the CO 2 reduction reaction (CO 2 RR); nevertheless, their activities remain low. In order to understand the roots of their high selectivity and low activity and to direct the design of more active Ni x P y -based CO 2 RR catalysts, we investigate the CO 2 RR mechanism on Ni 2 P using density functional theory (DFT) calculations. Iin this work, we reveal that the reaction proceeds through the formate pathway, followed by formaldehyde (H 2 CO*) formation and self-condensation. Moreover, we demonstrate that surface hydride transfer steps, along with surface-mediated C–C coupling, are essential in order to avoid C 1 product formation and boost selectivity toward multicarbon products. In addition, we find that the thermal surface hydride transfer from the surface to the physisorbed CO 2 is one of the key rate-limiting steps, and since it is not electroactive, it cannot be accelerated by applying an overpotential. Finally, our results also show that the hydrogen affinity of the surface and the dynamic surface reconstruction via H adsorption facilitate selective CO 2 reduction and C–C coupling on Ni 2 P. These findings provide an impetus for exploring materials design space to identify the physical principles that govern the thermodynamics of rate-limiting thermal steps in electrocatalytic processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-pressure reduction of carbon dioxide in reactive liquid mixtures

Density-functional theory based molecular-dynamics simulations were used to investigate high-pressure chemical reactions in liquid mixtures of CO 2 with several elements (Si, Mn, and Fe) at high temperatures of 2000-3000 K. Our ab initio simulations indicate that these reactant elements can reduce CO 2 to C at high pressures (20 GPa) leading to the formation of C-C chains, with Si by far the most effective carbon-reducing agent. A combined chemical analysis using Bader charge analysis and Crystal Orbital Hamilton Population (COHP) on simulation snapshots shows that significant charge transfer from the reducing element to the C atoms creates instability in the C-O covalent bonds. COHP analysis further shows that Mn/Fe-O and Mn/Fe-C bonding interactions are weaker compared to the Si counterparts. These results further our understanding of the redox chemistry of CO 2 at conditions relevant to planetary mantle interiors and demonstrate the effectiveness of high pressure in the reduction of CO 2 directly to solid carbon.

Ab initio molecular dynamics↗
DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator: Preprint

To reduce the variance of rare event probability estimator, genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. It is shown that a random perturbation strategy may be effective for some systems but may also fail for others, thereby preventing variance reduction in the probability estimate. To address this limitation, it is proposed to use a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗
DOE OSTI2023

The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations

As the climate system warms, megafires have become more frequent with devastating effects. A byproduct of these events is the creation of smoke plumes that can rise into the stratosphere and spread across the globe where they reside for many months. To gain a deeper understanding of the plume dynamics, global climate simulations of a megafire were performed at a wide range of grid spacings from 2.0° down to 7 km, including a 7 km nonhydrostatic experiment. The analysis focuses on how the resolved dynamics affects the specification of the plume characteristics such as injection height and black carbon (BC) mass. Prior studies initialize the smoke plume at one or a few grid points and this is shown here to produce severely dissipative dynamics. In order to validate such simulations with observations, enhancements of the plume characteristics to offset the dissipation is necessary. Using a numerically converged simulation, sensitivity tests show that to approximate the observed stratospheric lifetime, a reduction in BC fraction by 50% is necessary for external mixtures. The vorticity dynamics of the plume is also analyzed with a Lagrangian budget to understand the mechanisms responsible for the evolution of a collocated anticyclonic vortex. The results can be distilled down into a simple conceptual model. As the plume rises, the air diverges at the top of the updraft where the largest concentrations of smoke are found. This divergence induces a dilution of the background cyclonic absolute vorticity producing an anticyclonic vortex. Vortex decay occurs from opposite arguments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Redox poise in R. rubrum phototrophic growth drives large-scale changes in macromolecular pathways

During photoheterotrophic growth on organic substrates, purple nonsulfur photosynthetic bacteria like Rhodospirillum rubrum can acquire electrons by multiple means, including oxidation of organic substrates, oxidation of inorganic electron donors (e.g., H 2 ), and by reverse electron flow from the photosynthetic electron transport chain. These electrons are stored as reduced electron-carrying cofactors (e.g., NAD(P)H and ferredoxin). The overall ratio of oxidized to reduced cofactors (e.g., NAD(P)+:NAD(P)H), or ’redox poise’, is difficult to understand or predict, as are the cellular processes for dissipating these reducing equivalents. Using physics-based models that capture mass action kinetics consistent with the thermodynamics of reactions and pathways, a range of redox conditions for heterophototrophic growth are evaluated, from conditions in which the NADP+/NADPH levels approach thermodynamic equilibrium to conditions in which the NADP+/NADPH ratio is far above the typical physiological values. Modeling predictions together with experimental measurements indicate that the redox poise of the cell results in large-scale changes in the activity of biosynthetic pathways and, thus, changes in cell macromolecule levels (DNA, RNA, proteins, and fatty acids). Furthermore, modeling predictions indicate that during phototrophic growth, reverse electron flow from the quinone pool is a minor contributor to the production of reduced cofactors (e.g., NAD(P)H) compared to other oxidative processes (H 2 and carbon substrate oxidation). Instead, the quinone pool primarily operates to aid ATP production. The high level of ATP, in turn, drives reduction processes even when NADPH levels are relatively low compared to NADP+ by coupling ATP hydrolysis to the reductive processes. The model, in agreement with experimental measurements of macromolecule ratios of cells growing on different carbon substrates, indicates that the dynamics of nucleotide versus lipid and protein production is likely a significant mechanism of balancing oxidation and reduction in the cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Redox Poise during Rhodospirillum rubrum Phototrophic Growth Drives Large-scale Changes in Macromolecular Synthesis Pathways

During photoheterotrophic growth on organic substrates, purple nonsulfur photosynthetic bacteria like Rhodospirillum rubrum can acquire electrons by multiple means, including oxidation of organic substrates, oxidation of inorganic electron donors (e.g., H2), and by reverse electron flow from the photosynthetic electron transport chain. These electrons are stored as reduced electron-carrying cofactors (e.g., NAD(P)H and ferredoxin). The overall ratio of oxidized to reduced cofactors (e.g., NAD(P)+:NAD(P)H), or ’redox poise’, is difficult to understand or predict, as are the cellular processes for dissipating these reducing equivalents. Using physics-based models that capture mass action kinetics consistent with the thermodynamics of reactions and pathways, a range of redox conditions for heterophototrophic growth are evaluated, from conditions in which the NADP+/NADPH levels approach thermodynamic equilibrium to conditions in which the NADP+/NADPH ratio is far above the typical physiological values. Modeling predictions together with experimental measurements indicate that the redox poise of the cell results in large-scale changes in the activity of biosynthetic pathways and, thus, changes in cell macromolecule levels (DNA, RNA, proteins, and fatty acids). Furthermore, modeling predictions indicate that during phototrophic growth, reverse electron flow from the quinone pool is a minor contributor to the production of reduced cofactors (e.g., NAD(P)H) compared to other oxidative processes (H2 and carbon substrate oxidation). Instead, the quinone pool primarily operates to aid ATP production. The high level of ATP, in turn, drives reduction processes even when NADPH levels are relatively low compared to NADP+ by coupling ATP hydrolysis to the reductive processes. The model, in agreement with experimental measurements of macromolecule ratios of cells growing on different carbon substrates, indicates that the dynamics of nucleotide versus lipid and protein production is likely a significant mechanism of balancing oxidation and reduction in the cell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Engineering metal–metal oxide surfaces for high-performance oxygen reduction on Ag–Mn electrocatalysts

Understanding fundamental material–property relationships in mixed-element catalyst systems is crucial to advancing the viability of renewable electrochemical energy technologies, an important part of creating a more sustainable future. Herein, we report our insight on the nature and dynamics of highly active silver–manganese oxide (Ag–MnOx) catalyst surfaces for the oxygen reduction reaction (ORR) via a combined experimental–theoretical approach. Experimentally, we synthesize well-mixed Ag–Mn co-deposited thin films that are measurably flat and smooth, despite Mn surface migration and oxidation upon air exposure and electrochemical measurements. Cyclic voltammetry in 0.1 M KOH demonstrates up to 10-fold specific activity enhancements over pure Ag at 0.8 V vs. RHE for Ag-rich films (70–95% Ag in bulk). To further understand the Ag–Mn system, separate samples were synthesized with small amounts of Mn sequentially deposited onto the surface of a pure Ag thin film (Mn@Ag), ranging from partial to full surface coverage (down to 0.3 nm Mn $cm^{-2}_{geo}$ ~ 0.2 μg Mn $cm^{-2}_{geo}$). These sequentially deposited Mn@Ag films show analogous performance to their co-deposited counterparts indicating similar enhanced active sites. With density functional theory (DFT), we calculate that this enhancement arises from the tuned d-band of these material surfaces owing to the optimal hybridization of the electronic structures in specific Ag and MnO x geometries. Together, electrochemical measurements, DFT calculations, X-ray absorption spectroscopy, and valence-band X-ray photoelectron spectroscopy suggest synergistic electronic interactions between Ag and MnOx yield enhanced oxygen adsorption, and thus ORR activity, with DFT highlighting the Ag–MnO x interface sites as the most enhanced. This work demonstrates how combined experimental–theoretical methods can help design electrocatalysts with enhanced electrocatalytic properties and understand the nature of complex mixed metal–metal oxide surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗
DOE OSTI2025

Atomically Revealing Bulk Point Defect Dynamics in Hydrogen‐Driven γ‐Fe 2 O 3 → Fe 3 O 4 → FeO Transformation

Understanding how point defects in the bulk govern redox transformations is essential for advancing hydrogen-based metal production and designing high-performance oxide materials. This study reveals the atomic-scale mechanisms driving hydrogen-induced reduction of γ-Fe 2 O 3 to Fe 3 O 4 , focusing on how bulk vacancy dynamics dictate structural evolution and reaction kinetics. A key finding is the pronounced contrast in defect behavior between the two oxides: in γ-Fe 2 O 3 , intrinsic Fe vacancies promote oxygen vacancy clustering, destabilizing the local lattice and driving nanopore formation. In contrast, Fe 3 O 4 exhibits a higher oxygen vacancy formation energy and lacks intrinsic Fe vacancies, suppressing vacancy aggregation and maintaining a dense, pore-free structure. This divergence governs distinct reduction pathways—γ-Fe 2 O 3 undergoes an interface-reaction-limited transformation confined to the γ-Fe 2 O 3 /Fe 3 O 4 boundary, while Fe 3 O 4 supports a uniform increase in oxygen vacancy concentration, enabling bulk-phase reduction to lower-oxide FeO. Integrated in situ electron microscopy and density functional theory modeling uncover a vacancy-mediated mechanism, where synergistic cation-anion vacancy dynamics steer microstructure evolution and phase progression. These insights highlight the critical role of vacancy dynamics in controlling oxide reactivity and offer a pathway toward vacancy engineering to enhance reduction kinetics in hydrogen metallurgy and to tailor porosity, reactivity, and structural resilience in oxide-based catalysts and energy materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Interplay of water and a supramolecular capsule for catalysis of reductive elimination reaction from gold

Abstract Supramolecular assemblies have gained tremendous attention due to their ability to catalyze reactions with the efficiencies of natural enzymes. Using ab initio molecular dynamics, we identify the origin of the catalysis by the supramolecular capsule Ga 4 L 6 12− on the reductive elimination reaction from gold complexes and assess their similarity to natural enzymes. By comparing the free energies of the reactants and transition states for the catalyzed and uncatalyzed reactions, we determine that an encapsulated water molecule generates electric fields that contributes the most to the reduction in the activation free energy. Although this is unlike the biomimetic scenario of catalysis through direct host-guest interactions, the electric fields from the nanocage also supports the transition state to complete the reductive elimination reaction with greater catalytic efficiency. However it is also shown that the nanocage poorly organizes the interfacial water, which in turn creates electric fields that misalign with the breaking bonds of the substrate, thus identifying new opportunities for catalytic design improvements in nanocage assemblies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Reduction-Controlled Atomic Migration for Single Atom Alloy Library

Here, picturing the atomic migration pathways of catalysts in a reactive atmosphere is of central significance for uncovering the underlying catalytic mechanisms and directing the design of high-performance catalysts. Here, we describe a reduction-controlled atomic migration pathway that converts nanoparticles to single atom alloys (SAAs), which has remained synthetically challenging in prior attempts due to the elusive mechanism. We achieved this by thermally treating the noble-metal nanoparticles M (M = Ru, Rh, Pd, Ag, Ir, Pt, and Au) on metal oxide (CuO) supports with H2/Ar. Atomic-level characterization revealed such conversion as the synergistic consequence of noble metal-promoted H2 dissociation and concomitant CuO reduction. The observed atomic migration pathway offers an understanding of the dynamic mechanisms study of nanomaterials formation and catalyst design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

System and method for high dynamic range waveform digitization

Diverse applications from particle physics experiments to lidar are driving cost and current reduction in giga-hertz sampling rate high-resolution data conversion. Multiple imagers captures a single pixel of data and require processing at very high speed. High-bandwidth high-rate signal sampling, analog-to-digital conversion, and transfer of large amounts of data to a digital data acquisition block are required in such systems. Dynamic range, power consumption, and transfer of high-speed, high-bit width data are key implementation challenges. Data acquisition architectures optimized for specific requirements of such systems may facilitate system implementation and reduce overall system cost.

Mostafanezhad, Isar↗
DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator

Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗