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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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DOE OSTI2022

Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)

Abstract. High-resolution gridded datasets of meteorological variables are needed in order to resolve fine-scale hydrological gradients in complex mountainous terrain. Across the United States, the highest available spatial resolution of gridded datasets of daily meteorological records is approximately 800 m. This work presents gridded datasets of daily precipitation and mean temperature for the East–Taylor subbasin (in the western United States) covering a 12-year period (2008–2019) at a high spatial resolution (400 m). The datasets are generated using a downscaling framework that uses data-driven models to learn relationships between climate variables and topography. We observe that downscaled datasets of precipitation and mean temperature exhibit smoother spatial gradients (while preserving the spatial variability) when compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are upscaled to the original resolution (800 m), the mean residual error is almost zero, ensuring no bias when compared with the original data. Furthermore, the downscaled datasets are observed to be linearly related to elevation, which is consistent with the methodology underlying the original 800 m product. Finally, we validate the spatial patterns exhibited by downscaled datasets via an example use case that models lidar-derived estimates of snowpack. The presented dataset constitutes a valuable resource to resolve fine-scale hydrological gradients in the mountainous terrain of the East–Taylor subbasin, which is an important study area in the context of water security for the southwestern United States and Mexico. The dataset is publicly available at https://doi.org/10.15485/1822259 (Mital et al., 2021).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Energy Model to Evaluate Thermal Energy Storage Integrated with Air Source Heat Pumps: Preprint

Full decarbonization in buildings requires the replacement of combustion appliances with electric ones, and air source heat pumps (ASHP) are a candidate alternative. However, technical limitations, such as the efficiency decrease when operating in cold weather, limit their adoption in the global heating market. Among several options to improve ASHP efficiency operating in colder climates, thermal energy storage (TES) has been considered, as it may provide heating when it is cold and shift ASHP operation to times when the weather is warmer. It may also take advantage of time of use electricity rates and support defrosting when necessary. The evaluation of ASHP-TES systems, however, is still limited because traditional metrics do not capture their full economic and environmental benefits. In this work, a python framework is presented to model the ASHP with and without the presence of TES. Metrics are proposed to analyze the system performance in terms of costs, equivalent CO2 emission, and efficiency metrics to evaluate and compare alternative systems. Model validation against experimental data obtained for a commercial heat pump is provided, as well as an application example using Denver, Colorado, to highlight the model capabilities.

cold climate↗

DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services: Preprint

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

closed-loop validation↗

DOE OSTI2021

iSPECTRON: a simulation interface for linear and nonlinear spectra with ab-initio quantum chemistry software

We introduce iSPECTRON, an open source (under the Educational Community License version 2.0) program that parses data from common quantum chemistry software (NWChem, OpenMolcas, Gaussian, Cobramm, etc.), produces the input files for the simulation of linear and nonlinear spectroscopy of molecules with the Spectron code, and analyzes the spectra with a broad range of tools. Vibronic spectra are expressed in term of the electronic eigenstates, obtained through quantum chemistry computations, and vibrational/bath effects are incorporated in the framework of the displaced harmonic oscillator model, where all required quantities are computed at the Franck-Condon point. The code capabilities are illustrated by simulating linear absorption, transient absorption and two dimensional electronic spectra of the pyrene molecule. Two levels of electronic structure theory, TDDFT (with NWChem) and RASSCF/RASPT2 (with OpenMolcas), are compared where possible. Acknowledgements: F.S., A.N., D.R.N., N.G., S.M, M.G. acknowledge support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division under Award Nos. DE-SC0019484, KC-030103172684. The Spectron code was developed with support from the National Science Foundation (Grant CHE- 1953045). This research benefited from computational resources provided by EMSL, a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research and located at PNNL. PNNL is operated by Battelle Memorial Institute for the United States Department of Energy under DOE Contract No. DE-AC05-76RL1830.

Segatta, Francesco↗

DOE OSTI2020

Irradiation enhanced diffusion and diffusional creep in U₃Si₂

U₃Si₂ is a candidate as an advanced fuel for light water reactors (LWRs). Compared to UO₂, there are significant data gaps for the thermophysical and thermomechanical properties of U₃Si₂. Atomic scale simulations can help fill in those gaps, while also providing a more mechanistic understanding of the material properties. Creep is an important property for fuel performance as it influences the pellet-cladding gap, which in turn affects the temperature of the fuel (a key parameter in many other aspects of fuel behavior). In this work, we employed a DFT-informed cluster dynamics framework to predict point defect concentrations in U₃Si₂. The concentrations, diffusivities, and volumes of these defects were used to develop a diffusional creep model based on bulk (Nabarro-Herring) and grain boundary (Coble) diffusion processes. It was found that irradiation enhanced Nabarro-Herring creep is dominant at low temperatures, while Coble creep dominates for high temperatures. The model compares well against available experimental data and has been implemented in BISON. A demonstration case using simple power profiles has been carried out, showing that negligible creep occurs due to the low temperatures experienced by U₃Si₂, a consequence of its high thermal conductivity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗

DOE OSTI2025

Collision-Energy Dependence in Heavy-Ion Collisions from Nonlinear QCD Evolution

We explore the effects of including the energy dependence determined from evolution equations within the color glass condensate framework on observables in ultrarelativistic heavy-ion collisions. This amounts to integrating the JIMWLK evolution equations into the impact parameter-dependent glasma model, which is then coupled to viscous relativistic hydrodynamics. This methodology allows for a systematic representation of nuclei at specific Bjorken-x values, which are probed at different center-of-mass energies of the collision and rapidities of final state particles. Comparing the methodology to the conventional impact parameter-dependent glasma model, we find significant effects on multiplicity distributions and particle spectra, especially in smaller collision systems at the highest center-of-mass energies. Our results highlight the importance of incorporating nonlinear QCD evolution in the description of heavy-ion collisions at varying center-of-mass energies, as the precise extraction of transport coefficients will be affected. This Letter establishes a robust framework for understanding the quark gluon plasma and nuclear structure at high energy, integrating small-x physics into the initial conditions of heavy-ion collisions.

Mäntysaari, Heikki↗

DOE OSTI2026

Discrete versus continuous: Enhancing battery optimization in capacity expansion models

This study compares two battery modeling approaches for capacity expansion models: discrete-duration and continuous-duration formulations. In the discrete approach, battery duration is fixed, and power capacity is optimized. In the continuous approach, both power and energy capacities are decision variables, allowing storage duration to be optimized endogenously. Although both discrete-duration and continuous-duration battery formulations are used in long-term power system planning models, the literature has provided limited direct, systematic comparisons of their implications within a common modeling framework. To address this gap, this study implements both approaches in the Regional Energy Deployment System (ReEDS TM ) capacity expansion model using two resource adequacy methods, across a range of future system conditions, and with varying battery cost projections. Results show continuous-duration and high-resolution discrete approaches produce similar capacity expansion outcomes. The continuous formulation achieves faster runtimes compared to discrete-duration runs with many discrete-duration options. However, the discrete-duration approach allows users to choose to have limited fidelity for storage duration options, which in some cases can outperform the continuous formulation. The continuous formulation has the lowest overall system costs, indicating its ability to fine-tune storage duration to better meet specific system needs. This study's findings provide a side-by-side evaluation of discrete and continuous battery modeling approaches and offer guidance for improving the representation of real-world systems, flexibility, and computational efficiency for representing energy storage in long-term power system planning models.

25 ENERGY STORAGE↗

DOE OSTI2021

How structural differences influence cross-model consistency: An electric sector case study

Multiple models are often employed to describe a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are a common topic of such efforts due to the economic influence of the electric sector, but model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between input harmonization and cross-model scenario consistency under disparate electric sector scenarios. We compare cross-model consistency between two state-of-the-art electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios comprising alternate assumptions about fossil fuel resource availability, technology innovation, and long-term economy-wide transitions under four harmonization configurations varying model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific if comparable cross-model consistency is desired. Model structural differences can hinder consistency, and the impact of these differences can depend on the scenario. Ultimately, thorough harmonization can reveal insights into cross-model consistency, which can be used to tighten uncertainty bounds and improve the decision-making implications of multi-model activities.

Cohen, Stuart↗

DOE OSTI2023

Divergent Stabilities of Tetravalent Cerium, Uranium, and Neptunium Imidophosphorane Complexes**

Abstract The study of the redox chemistry of mid‐actinides (U−Pu) has historically relied on cerium as a model, due to the accessibility of trivalent and tetravalent oxidation states for these ions. Recently, dramatic shifts of lanthanide 4+/3+ non‐aqueous redox couples have been established within a homoleptic imidophosphorane ligand framework. Herein we extend the chemistry of the imidophosphorane ligand (NPC=[N=P t Bu(pyrr) 2 ] − ; pyrr=pyrrolidinyl) to tetrahomoleptic NPC complexes of neptunium and cerium ( 1‐M , 2‐M , M=Np, Ce) and present comparative structural, electrochemical, and theoretical studies of these complexes. Large cathodic shifts in the M 4+/3+ (M=Ce, U, Np) couples underpin the stabilization of higher metal oxidation states owing to the strongly donating nature of the NPC ligands, providing access to the U 5+/4+ , U 6+/5+ , and to an unprecedented, well‐behaved Np 5+/4+ redox couple. The differences in the chemical redox properties of the U vs . Ce and Np complexes are rationalized based on their redox potentials, degree of structural rearrangement upon reduction/oxidation, relative molecular orbital energies, and orbital composition analyses employing density functional theory.

Otte, Kaitlyn S.↗

DOE OSTI2023

Divergent Stabilities of Tetravalent Cerium, Uranium, and Neptunium Imidophosphorane Complexes

The study of the redox chemistry of mid-actinides (U–Pu) has historically relied on cerium as a model, due to the accessibility of trivalent and tetravalent oxidation states for these ions. Recently, dramatic shifts of lanthanide 4+/3+ non-aqueous redox couples have been established within a homoleptic imidophosphorane ligand framework. Herein we extend the chemistry of the imidophosphorane ligand (NPC=[N=P t Bu(pyrr) 2 ] – ; pyrr=pyrrolidinyl) to tetrahomoleptic NPC complexes of neptunium and cerium (1-M, 2-M, M=Np, Ce) and present comparative structural, electrochemical, and theoretical studies of these complexes. Large cathodic shifts in the M 4+/3+ (M=Ce, U, Np) couples underpin the stabilization of higher metal oxidation states owing to the strongly donating nature of the NPC ligands, providing access to the U 5+/4+ , U 6+/5+ , and to an unprecedented, well-behaved Np 5+/4+ redox couple. Furthermore, the differences in the chemical redox properties of the U vs. Ce and Np complexes are rationalized based on their redox potentials, degree of structural rearrangement upon reduction/oxidation, relative molecular orbital energies, and orbital composition analyses employing density functional theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Prediction of residual stresses in additively manufactured parts using lumped capacitance and classical lamination theory

Several industries are interested in Laser Powder Bed Fusion (L-PBF) Additively Manufactured (AM) metal parts because their designs can be made arbitrarily complex while retaining bulk-type material properties. However, the residual stresses (RS) and distortions caused by the heat gradients inherent to L-PBF processes are detrimental to the structural integrity of the parts and must be taken into consideration during the part design cycle. Predicting the state of stresses in as-built 3D printed parts is a difficult problem that is typically approached with the use of transient thermomechanical Finite Element Models (FEMs). However, the nonlinearities associated with AM processes are difficult to capture in these FEMs without increasing the computational cost of the simulation, limiting their ability to be incorporated into practical design cycles. This work presents a novel analytical framework that combines lumped capacitance nonlinear heat transfer with time dependent classical lamination theory to efficiently and accurately predict RS in as-built L-PBF parts without the need of FEMs. The simulation was compared to Neutron Diffraction (ND) residual strain measurements taken at Oak Ridge National Laboratories (ORNL) as well as Synchrotron X-ray Diffraction (XRD) strain data published by the National Institute of Standards and Technology (NIST). The simulation predictions and the experimental data showed excellent agreement for the in-plane strain directions, and general agreement for the out of plane strain component, highlighting an area where further development can be implemented.

42 ENGINEERING↗

DOE OSTI2026

A scalable framework for efficient coupling of thermal and microstructural simulations in additive manufacturing

Predicting microstructure evolution in metal additive manufacturing (AM) is important for process optimization, but spatiotemporal scale disparities between thermal transport and microstructure evolution create significant challenges for efficient data transfer between simulation codes. To address this, we present Stork, a scalable framework for coupling thermal and microstructural simulations. Stork uses a sparse data representation to identify and store active solidification sub-volumes, enabling highly parallel quad-linear interpolation from coarse thermal grids to fine microstructure grids without large intermediate storage. We demonstrate the framework by coupling the semi-analytic heat transfer code 3DThesis with the time-parallel cellular automata code Toucan. This approach achieves over two orders of magnitude reduction in data generation time and file size compared to prior workflows. Numerical studies show that quad-linear interpolation preserves grain morphology and crystallographic texture in laser powder bed fusion (LPBF) simulations for coarsening ratios up to 16. Overall, Stork provides a scalable pathway for high-throughput, component-scale AM simulations on modern high-performance computing systems.

36 MATERIALS SCIENCE↗

DOE OSTI2024

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI-based services be placed across Function-as-a-Service (FaaS) based edge computing systems in order to maximize total Quality-of-Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time-scales (one for placement and one for scheduling). Here we first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP-hard. We then propose a novel placement algorithm that places services while considering device-to-device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI-based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning-based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Cluster structure of light nuclei

We review recent studies of the cluster structure of light nuclei within the framework of the algebraic cluster model (ACM) for nuclei composed of k α-particles and within the framework of the cluster shell model (CSM) for nuclei composed of k α-particles plus x additional nucleons. The calculations, based on symmetry considerations and thus for the most part given in analytic form, are compared with experiments in light cluster nuclei. The comparison shows evidence for Z 2 , D 3h and T d symmetry in the even–even nuclei 8 Be (k = 2), 12 C (k = 3) and 16 O (k = 4), respectively, and for the associated double groups Z' 2 and D' 3h in the odd nuclei 9 Be, 9 B (k = 2, x = 1) and 13 C (k = 3, x = 1), respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Life Cycle Analysis of Electrofuels: Fischer–Tropsch Fuel Production from Hydrogen and Corn Ethanol Byproduct CO 2

Electrofuels from renewable H 2 and waste CO 2 streams are of increasing interest because of their CO 2 emissions reduction potentials compared to fossil counterparts. This study evaluated the well-to-wheel (WTW) greenhouse gas (GHG) emissions of Fischer-Tropsch (FT) fuels from various electrolytic H 2 pathways and CO 2 sources, using various process designs (i.e., with and without H 2 recycle) and system boundaries. Two systems with different boundaries were considered: a stand-alone plant (with CO 2 from any source) and an integrated plant with corn ethanol production (supplying CO 2 ). The FT fuel synthesis process was modeled using Aspen Plus, which showed that 45% of the carbon in CO 2 can be fixed in the FT fuel, with a fuel production energy efficiency of 58%. Using nuclear or solar/wind electricity, the stand-alone FT fuel production from various plant designs can reduce WTW GHG emissions by 90-108%, relative to petroleum fuels. When integrating the FT fuel production process with corn ethanol production, the WTW GHG emissions of FT fuels are 57-65% lower compared to petroleum counterparts. This study highlights the sensitivity of the carbon intensity of FT fuels to the system boundary selection (i.e., stand-alone vs integrated), which has different implications under various GHG emission credit frameworks.

08 HYDROGEN↗

DOE OSTI2023

Thermodynamic Band Gap Model for Photoinduced Phase Segregation in Mixed-Halide Perovskites

Provided is a comprehensive description of a band gap thermodynamic model, which predicts and explains key features of photosegregation in lead-based, mixed-halide perovskites. The model gives a prescription for illustrating halide migration driven by photocarrier energies. Where possible, model predictions are compared with experimental results. Free energy derivations are provided for three assumptions: (1) halide mixing in the dark, (2) a fixed number of photogenerated carriers funneling to and localizing in low band gap inclusions of the alloy, and (3) the statistical occupancy of said inclusions from a bath of thermalized photocarriers in the parent material. Model predictions include excitation intensity (I exc )-dependent terminal halide stoichiometries (x terminal ), temperature-independent x terminal , excitation intensity thresholds (I exc,threshold ) below which photosegregation is suppressed, and reduced segregation in nanocrystals as compared to thin films. Here, the model also offers insight into kinetically manipulating photosegregation rates via control of underlying mediators and rationalizes asymmetries in forward and reverse photosegregation rate constants/activation energies. What emerges is a cohesive framework for understanding ubiquitous photosegregation in mixed-halide perovskites and a rational basis by which to manage the phenomenon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗