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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Health and Climate Benefits of Economic Dispatch in China’s Power System

China’s power system is highly regulated and uses an “equal-share” dispatch approach. However, market mechanisms are being introduced to reduce generation costs and improve system reliability. Here, we quantify the climate and human health impacts brought about by this transition, modeling China’s power system operations under economic dispatch. We find that significant reductions in mortality related to air pollution (11%) and CO 2 emissions (3%) from the power sector can be attained by economic dispatch, relative to the equal-share approach, through more efficient coal-powered generation. Additional health and climate benefits can be achieved by incorporating emission externalities in electricity generation costs. However, the benefits of the transition to economic dispatch will be unevenly distributed across China and may lead to increased health damage in some regions. Our results show the potential of dispatch decision-making in electricity generation to mitigate the negative impacts of power plant emissions with existing facilities in China.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Optimizing Renewable Ammonia Production for a Sustainable Fertilizer Supply Chain Transition

Local renewable ammonia production using electrolytic hydrogen is an emerging approach to alleviate emissions attributed to synthetic nitrogen fertilizer production while also insulating against fluctuations in fertilizer prices and mitigating transportation costs and emissions. However, replacing ammonia currently produced using fossil fuels will not be immediate. To this end, we develop a supply chain transition model, which first optimizes the design and hourly operation of new renewable ammonia facilities to minimize production costs and then optimizes the annual installation timing, production scale, and location of these new renewable facilities along with ammonia transportation to meet county resolution demands. The objective is to augment and eventually replace conventional ammonia market imports in an economically competitive manner. We performed a case study for Minnesota's ammonia supply chain and found that a full transition to in-state renewable production by 2032 is optimal. This is incentivized by the U.S. federal government's clean hydrogen production credits. This transition results in 99 % reduction in carbon intensity along with stable supply costs below $475 per metric tonne. New renewable production facilities are an order of magnitude smaller than existing conventional plants. They use both wind and solar resources and operate dynamically to minimize expensive battery and hydrogen storage capacities.

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

Ultrafast x-ray pump x-ray probe transient absorption spectroscopy: A computational study and proposed experiment probing core-valence electronic correlations in solvated complexes

Femtosecond X-ray pump X-ray probe experiments are currently possible at free electron lasers such as the linac coherent light source (LCLS), which opens new opportunities for studying solvated transition metal-ligand complexes. In order to make the most effective use of these kinds of experiments, it is necessary to determine which chemical properties an X-ray pump pulse will measure. In this paper, we have combined electron cascade calculations and excited-state time-dependent density functional theory calculations to predict the initial state prepared by an X-ray pump and the subsequent X-ray probe spectra at the Fe K-edge in the solvated model transition metal complex, K 4 Fe II (CN) 6 . We find several key spectral features that report on the ligand-field splitting, solvent-solute interactions, and the 3p and 3d electron interactions. We then show how these features could be measured in an experiment.

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

Quantum phase transitions in a model Hamiltonian exhibiting entangled simultaneous fermion-pair and exciton condensations

Quantum states of a novel Bose-Einstein condensate, in which both fermion-pair and exciton condensations are simultaneously present, have recently been realized theoretically in a model Hamiltonian system. Here, in this study, we identify quantum phase transitions in that model between fermion-pair and exciton condensations based on a geometric analysis of the convex set of ground-state two-particle reduced density matrices (2-RDMs). The 2-RDM set provides a finite representation of the infinite parameter space of Hamiltonians that readily reveals a fermion-pair condensate phase and two distinct exciton condensate phases, as well as the emergence of first- and second-order phase transitions as the particle number of the system is increased. The set, furthermore, shows that the fermion-exciton condensate (FEC) lies along the second-order phase transition between the exciton and fermion-pair condensate phases. The detailed information about the exciton and fermion-pair phases, the forces behind these phases, as well as their associated transitions provides additional insight into the formation of the FEC condensate, which we anticipate will prove useful in its experimental realization.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

DriveSense: A Noise-Resilient Framework for Driving Mode Identification

Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.

Kumar, Praveen [Oak Ridge National Laboratory (ORN↗
DOE OSTI2023

Tailoring light-induced charge transfer and intersystem crossing in FeCO using time-dependent spin–orbit configuration interaction

Real-time (RT) electronic structure methods provide a natural framework for describing light–matter interactions in arbitrary time-dependent electromagnetic fields (EMF). Optically induced excited state transitions are of particular interest, which require tuned EMF to drive population transfer to and from the specific state(s) of interest. Intersystem crossing, or spin-flip, may be driven through shaped EMF or laser pulses. These transitions can result in long-lived “spin-trapped” excited states, which are especially useful for materials requiring charge separation or protracted excited state lifetimes. Time-dependent configuration interaction (TDCI) is unique among RT methods in that it may be implemented in a basis of eigenstates, allowing for rapid propagation of the time-dependent Schrödinger equation. The recent spin–orbit TDCI (TD-SOCI) enables a real-time description of spin-flip dynamics in an arbitrary EMF and, therefore, provides an ideal framework for rational pulse design. The present study explores the mechanism of multiple spin-flip pathways for a model transition metal complex, FeCO, using shaped pulses designed to drive controlled intersystem crossing and charge transfer. These results show that extremely tunable excited state dynamics can be achieved by considering the dipole transition matrix elements between the states of interest.

Chemistry↗
DOE OSTI2025

Many-body expansion based machine learning models for octahedral transition metal complexes

Abstract Graph-based machine learning (ML) models for material properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not include any 3D information and are unable to distinguish stereoisomers such as those arising from different orderings of ligands around a metal center in coordination complexes. In this work we present a modification to revised autocorrelation descriptors, a molecular graph featurization method, for predicting spin state dependent properties of octahedral transition metal complexes (TMCs). Inspired by analytical semi-empirical models for TMCs, the new modeling strategy is based on the many-body expansion (MBE) and allows one to tune the captured stereoisomer information by changing the truncation order of the MBE. We present the necessary modifications to include this approach in two commonly used ML methods, kernel ridge regression and feed-forward neural networks. On a test set composed of all possible isomers of binary TMCs, the best MBE models achieve mean absolute errors (MAEs) of 2.75 kcal mol −1 on spin-splitting energies and 0.26 eV on frontier orbital energy gaps, a 30%–40% reduction in error compared to models based on our previous approach. We also observe improved generalization to previously unseen ligands where the best-performing models exhibit MAEs of 4.00 kcal mol −1 (i.e. a 0.73 kcal mol −1 reduction) on the spin-splitting energies and 0.53 eV (i.e. a 0.10 eV reduction) on the frontier orbital energy gaps. Because the new approach incorporates insights from electronic structure theory, such as ligand additivity relationships, these models exhibit systematic generalization from homoleptic to heteroleptic complexes, allowing for efficient screening of TMC search spaces.

Meyer, Ralf (ORCID:0000000322360261)↗
DOE OSTI2020

Nickel-rich Nickel Manganese Cobalt (NMC622) Cathode Lithiation Mechanism and Extended Cycling Effects Using Operando X-ray Absorption Spectroscopy

Ni-rich NMC materials are a particularly promising class of Li-ion cathodes for various applications. LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) offers a unique balance of thermal stability and energy density, thus attracting attention for electric vehicle implementation. However, upon extended cycling, capacity fade is prevalent due to structural degradation, which is a major drawback for layered oxide cathodes. Therefore, exploring the underlying phenomena that drive detrimental structural response can lead to future improvements. For the first time, operando X-ray absorption spectroscopy (XAS) was performed on NMC622 pouch cells at three different stages. An extensive description of the first cycle (de)lithiation mechanisms was achieved through X-ray absorption near-edge structure analyses and extended X-ray absorption fine structure modeling. Transition metal specific electrochemical participation and structural variation revealed that much of the delivered capacity and distortion is a result of Ni redox behavior, while the local structure of Co and Mn are impacted due to their interdependencies. Key mechanistic components were identified, as the local structural variation from redox processes and Ni 3+ Jahn–Teller distortion were decoupled. Further, operando XAS was used to investigate the structural response of NMC622 to extended cycling and was supported by X-ray diffraction, X-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy analyses. In this work, reduced capacity was found in the cell after 100 cycles and is attributed to structural degradation and cathode–electrolyte interphase buildup from repeated Li (de)insertion processes, which limit the electrochemical reversibility of Ni and Co through increased polarization. These results expand the understanding of a Ni-rich NMC material under extended cycling, which is vital to the future design of electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Manipulating topological Hall-like signatures by interface engineering in epitaxial ruthenate/manganite heterostructures

Topologically protected non-trivial spin textures (e.g. skyrmions) give rise to a novel phenomenon called the topological Hall effect (THE) and have promising implications in future energy-efficient nanoelectronic and spintronic devices. Here, we have studied the Hall effect in SrRuO 3 /La 0.42 Ca 0.58 MnO 3 (SRO/LCMO) bilayers. Our investigation suggests that pure SRO has hard and soft magnetic characteristics but the anomalous Hall effect (AHE) in SRO is governed by the high coercivity phase. Here, we have shown that the proximity effect of a soft magnetic LCMO on SRO plays a critical role in interfacial magnetic coupling and transport properties in SRO. Upon reducing the SRO thickness in the bilayer, the proximity effect becomes the dominant feature, enhancing the magnitude and temperature range of THE-like signatures. The THE-like features in bilayers can be explained by a diffusive Berry phase transition model in the presence of an emergent magnetic state due to interface coupling. This work provides an alternative understanding of THE-like signatures and their manipulation in SRO-based heterostructures, bilayers and superlattices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Properties of 187 Ta Revealed through Isomeric Decay

Mass-separated 187 Ta114 in a high-spin isomeric state has been produced for the first time by multi-nucleon transfer reactions, employing an argon gas stopping cell and laser ionisation. Here, internal $γ$ rays revealed a $T$ 1/2 = 7.3±0.9 s isomer at 1778±1 keV, which decays through a rotational band with perturbations associated with the approach to a prolate-oblate shape transition. Model calculations show less influence from triaxiality compared to heavier elements in the same mass region. The isomer decay reduced $E$2 hindrance factor, $f$ $ν$ = 27±1, supports the interpretation that axial symmetry is approximately conserved.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

First Measurement of Missing Energy due to Nuclear Effects in Monoenergetic Neutrino Charged-Current Interactions

We present the first measurement of the missing energy due to nuclear effects in monoenergetic, muon neutrino charged-current interactions on carbon, originating from 𝐾 + → 𝜇 + ⁢𝜈 𝜇 decay at rest (𝐸 𝜈 𝜇 = 235.5 MeV), performed with the J-PARC Sterile Neutrino Search at the J-PARC Spallation Neutron Source liquid scintillator based experiment. Toward characterizing the neutrino interaction, ostensibly 𝜈 𝜇 ⁢𝑛 → 𝜇 − ⁢𝑝 or 𝜈 𝜇 ⁢ 12 C → 𝜇 − ⁢ 12 N, we define the missing energy as the energy transferred to the nucleus (𝜔) minus the kinetic energy of the outgoing proton(s), 𝐸 𝑚 ≡ 𝜔−∑ 𝑇 𝑝 , and relate this to visible energy in the detector, 𝐸 𝑚 = 𝐸 𝜈 𝜇 ⁡(235.5 MeV) − 𝑚 𝜇⁡ (105.7 MeV) + [𝑚 𝑛 − 𝑚 𝑝⁡ (1.3 MeV)] − 𝐸 vis . The missing energy, which is naively expected to be zero in the absence of nuclear effects (e.g., nucleon separation energy, Fermi momenta, and final-state interactions), is uniquely sensitive to many aspects of the interaction, and has previously been inaccessible with neutrinos. The shape-only, differential cross section measurement reported, based on a (77 ± 3)% pure double-coincidence kaon decay-at-rest signal (621 total events), provides detailed insight into neutrino-nucleus interactions, allowing even the nuclear orbital shell of the struck nucleon to be inferred. The measurement provides an important benchmark for models and event generators at hundreds of MeV neutrino energies, characterized by the difficult-to-model transition region between neutrino-nucleus and neutrino-nucleon scattering, and relevant for applications in nuclear physics, neutrino oscillation measurements, and Type-II supernova studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Mauka Energy FEVER Tool Dataset

Mauka Energy’s dataset, developed under the Forestry Electric Vehicle Energy Routing (FEVER) project and funded by the U.S. Department of Energy’s Small Business Innovation Research program, is a high-resolution geospatial resource designed to support energy modeling for electric log trucks in complex forestry environments. The dataset integrates detailed spatial and road network data to enable accurate simulation of vehicle performance across varied terrain. At its core, the dataset incorporates lidar-derived elevation models, road alignments, and surface classifications from Oregon State University’s McDonald-Dunn Research Forest. These data capture fine-scale variations in slope, curvature, and surface conditions across forest road systems, allowing for vehicle-level analysis of energy consumption and recovery. The dataset also includes data collected on the surrounding public and private road networks in Benton County, Oregon, used in real-world haul routes. These connecting segments provide critical context for modeling transitions between forest operations and regional transportation infrastructure, incorporating attributes such as grade profiles, elevation change, and speed constraints. This combined dataset underpins the development of Mauka Energy’s rolldown tool, which quantifies energy use and regenerative braking potential on downhill and variable-grade segments. By leveraging high-resolution terrain and road data, the FEVER project enables more accurate assessment of electric vehicle feasibility and performance in forestry applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Advanced Fuel Cycle Cost Basis Report: Supporting Document 5 Considerations on Learning

Learning refers here to the increased experience associated to each new build of a given type of fuel cycle facility, which results generally in design improvements, increased construction and operational efficiencies and reduced mistakes, and therefore in reduced costs. These are important considerations, for example, when modeling transitions from first-of-a-kind (FOAK) to nth-of-a-kind (NOAK) nuclear facilities and deciding the magnitude of the cost reduction (if any) to be applied to each successive build. Different assumptions on learning can significantly affect the results of the economic analyses during transitions. Other competing electricity-generation technologies, such as wind and solar power for example, have demonstrated substantial cost reductions through learning during the last few decades.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A Strategic Condition Framework for Energy Innovation

Building upon previous work in innovation, technology, and economic fields, we propose a conceptual framework to address the interplay dynamics of public policy and the value chains of clean energy technology innovation systems, by focusing on market structural and strategic conditions in relation to the rate and direction of knowledge diffusion within activity-based value chains. Our framework extends beyond socio-technical transition models by considering such nuances as: 1) the implications of geography, 2) the locus of knowledge types, 3) the dynamics of financial and knowledge flow, and 4) emphasizing the importance of both market structure and institutional conditions. At the firm level, we consider dynamics of interactions between suppliers and customers, innovation activities, the competitive environment, and the firm’s broader strategic relationship to governance activities and structures; we allow for the fact that each of these aspects of a firm’s identity may have a strategically relevant geographic dimension (e.g., service territories, locational concentration of input resources, etc.). We provide a proof-of-concept application of the U.S. utility-scale solar photovoltaic (PV) and onshore wind sectors, including their interactions with the U.S. power market, which we in present in a web-based information platform (Energy I-SPARK, ei-spark.lbl.gov).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Demonstration of Temperature Compensation Techniques for SPNDs Operating in High Temperatures

This report presents the testing results of rhodium-based self-powered neutron detectors (Rh-SPND) irradiated in a furnace dry tube from ambient temperature to 850°C at the Ohio State University Research Reactor. The purpose of the experiment is to demonstrate the technique and application of a temperature compensation technique for the Rh-SPND. This is performed by characterizing the temperature effects observed in past experiments—a displacement current and a stabilized dark current—of the Rh-SPND as a function of temperature under the models of shifting space charges as a product of photoconductivity properties. Low-power irradiation at the OSURR was performed with stabilized temperatures of ambient, 550, 575, 600, 625, 650, 675, and 700°C were first performed to obtain the curve fit parameters that describes the temperature effects. The results provided further insight for the behavior of the SPND at high temperatures in accordance with available insulation conductivity models. Transition points from photoconductivity to ionic conductivity were identified in the range of 550–600°C. Additionally, transition points ionic to electric conductivity were observed in the range of 675–700°C, however, the data was not able to fully capture the transition and did not have enough resolution to provide predictive compensation based only on temperature readings.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

INTEGRATE – Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project developed a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. Training data was obtained from improved turbulence and transition models for RANS and hybrid RANS/LES solvers with machine-learned physics-based data-augmented corrections and then using the resulting neural-network(s) augmented RANS model to run thousands of 2-D and 3-D CFD simulations.

17 WIND ENERGY↗
DOE OSTI2025

Primordial Black Hole Triggered Type Ia Supernovae. I. Impact on Explosion Dynamics and Light Curves

Primordial black holes (PBHs) in the asteroid-mass window are compelling dark matter candidates, made plausible by the existence of black holes and by the variety of mechanisms of their production in the early Universe. If a PBH falls into a white dwarf (WD), the strong tidal forces can generate enough heat to trigger a thermonuclear runaway explosion, depending on the WD’s mass and the PBH’s orbital parameters. In this work, we investigate the WD explosion triggered by the passage of a PBH. We perform 2D simulations of the WD undergoing thermonuclear explosion in this scenario, with the predicted ignition site as a parameter assuming the deflagration–detonation transition model. We study the explosion dynamics, and predict the associated light curves and nucleosynthesis. We find that the model sequence predicts light curves which align with the Phillips relation (B max versus ΔM 15 ). Our models hint at a unifying approach in triggering Type Ia supernovae without involving two distinctive evolutionary tracks.

Dark matter↗
DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗