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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fast-ion transport in quasisymmetric equilibria in the presence of a resonant Alfvénic perturbation

Significant progress has been made in designing magnetic fields that provide excellent confinement of the guiding-centre trajectories of alpha particles using quasisymmetry (QS). Given the reduction in this transport channel, we assess the impact of resonant Alfvén eigenmodes (AEs) on the guiding-centre motion. The AE amplitudes are chosen to be consistent with experimental measurements and large-scale simulations. We evaluate the drift resonance condition, phase-space island width and island overlap criterion for quasisymmetric configurations. Kinetic Poincaré plots elucidate features of the transport, including stiff transport above a critical perturbation amplitude. Our analysis highlights key departures from the AE-driven transport in tokamaks, such as the avoidance of phase-space island overlap in quasihelical configurations and the enhanced transport due to wide phase-space islands in low magnetic shear configurations. In configurations that are closer to QS, with QS deviations $\delta B/B_0 \lesssim 10^{-3}$ , the transport is primarily driven by the AE, while configurations that are further from QS, $\delta B/B_0 \sim 10^{-2}$ , experience significant transport due to the QS-breaking fields in addition to the AE.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Quantum-classical embedding via ghost Gutzwiller approximation for enhanced simulations of correlated electron systems

Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. Here, this work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation and the derived spectral function is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

Chen, I-Chi [Ames Laboratory (AMES), Ames, IA (Uni↗
DOE OSTI2023

Effects of surface acidity on the structure of organometallics supported on oxide surfaces

Well-defined organometallics supported on high surface area oxides are promising heterogeneous catalysts. An important design factor in these materials is how the metal interacts with the functionalities on an oxide support, commonly anionic X-type ligands derived from the reaction of an organometallic M–R with an –OH site on the oxide. The metal can either form a covalent M–O bond or form an electrostatic M + $\cdots$ – O ion-pair, which impacts how well-defined organometallics will interact with substrates in catalytic reactions. However, a less common reaction pathway involves the reaction of a Lewis site on the oxide with the organometallic, resulting in abstraction to form an ion-pair, which is relevant to industrial olefin polymerization catalysts. This Feature Article views the spectrum of reactivity between an organometallic and an oxide through the prism of Brønsted and/or Lewis acidity of surface sites and draws analogies to the molecular frame where Lewis and Brønsted acids are known to form reactive ion-pairs. Applications of the well-defined sites developed in this article are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Confronting interacting dark radiation scenarios with cosmological data

Dark radiation (DR) is generally predicted in new physics scenarios that address fundamental puzzles of the Standard Model or tensions in the cosmological data. Cosmological data have the sensitivity to constrain not only the energy density of DR but also whether it is interacting. In this paper, we present a systematic study of five types of interacting DR (free-streaming, fluid, decoupling, instantaneous decoupling, and recoupling DR) and their impact on cosmological observables. We modify the Boltzmann hierarchy to describe all these types of interacting DR under the relaxation time approximation. We, for the first time, robustly calculate the collision terms for recoupling scalar DR and provide a better estimation of the recoupling transition redshift. We demonstrate the distinct features of each type of DR on the cosmic microwave background and matter power spectra. We perform Markov-chain Monte Carlo scans using the Planck 2018 data and baryon acoustic oscillation data. Assuming no new physics in the standard model neutrino sector, we find no statistically significant constraints on the couplings of DR, although there is a slight preference for the fluidlike limit of all the cases. In the case of instantaneous decoupling DR, this limit corresponds to a late transition redshift around recombination. Furthermore, the ΔN eff constraint varies marginally depending on the type of DR.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Introducing SpaceNet 9 - Cross-Modal Satellite Imagery Registration for Natural Disaster Responses

Computer vision algorithms are increasingly leveraged to accelerate geospatial analysis for disaster response and recovery. As the diversity of remote sensing imagery grows with optical, SAR, and other modalities, a perquisite for analytics is cross-modal image registration. There is a high potential to harness computer vision for this pre-processing requirement toward enabling downstream analytics such as heterogeneous change detection, automated feature extraction, and data fusion. Advancement in these areas has the potential to simplify data wrangling tasks and further accelerate disaster response timelines. The SpaceNet 9 challenge (launching in mid-2024) focuses on addressing the cross-modal image registration problem and demonstrating the utility of such modules on earthquake impacted scenarios. This paper describes the motivation for the SpaceNet 9 and provides a first overview of the dataset, the baseline algorithm, and implications for seeking cross-modal image registration in Earth observation. Code is available at https://github.com/SpaceNetChallenge/SpaceNet9.

Hansch, Ronny↗
DOE OSTI2022

Fluid Dynamics of Axial Turbomachinery: Blade- and Stage-Level Simulations and Models

The current generation of axial turbomachines is the culmination of decades of experience, and detailed understanding of the underlying flow physics has been a key factor for achieving high efficiency and reliability. Driven by advances in numerical methods and relentless growth in computing power, computational fluid dynamics has increasingly provided insights into the rich fluid dynamics involved and how it relates to loss generation. This article presents some of the complex flow phenomena occurring in bladed components of gas turbines and illustrates how simulations have contributed to their understanding and the challenges they pose for modeling. The interaction of key aerodynamic features with deterministic unsteadiness, caused by multiple blade rows, and stochastic unsteadiness, i.e., turbulence, is discussed. High-fidelity simulations of increasingly realistic configurations and models improved with help of machine learning promise to further grow turbomachinery performance and reliability and, thus, help fluid mechanics research have a greater industrial impact.

Mechanics↗
DOE OSTI2024

Utah FORGE 5-2428: Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip - 2024 Annual Workshop Presentation

This is a presentation on the Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior by Lawrence Livermore National Laboratory, presented by Kayla Kroll. This presentation addresses testing and modelling related to the dependency of EGS production on sustained permeability and sufficient fluid flow through pre-existing or induced fractures in hot rock. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 13, 2024.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Hydromechanical impact of basement rock on injection-induced seismicity in Illinois Basin

Abstract The common explanation of observed injection-induced microseismicity is based on the measured stress state at the injection interval and the assumption that it remains the same in the vicinity. We argue here that representing the stress state in different geologic formations over the injection site with the single Mohr’s circle is insufficient due to local stratigraphic features and contrast in compressibilities of the involved formations. The role of hydromechanical coupling in the microseismic response is also crucial for the proper assessment of the problem. Thoroughly monitored Illinois Basin Decatur Project revealed the majority of CO 2 injection-associated microseismic events being originated in the crystalline basement. Even though basement faults can serve as the conduits for fluid flow—the predicted pressure increase seems to be insufficient to trigger seismicity. To address this issue, accurate laboratory measurements of rock properties from the involved formations are conducted. The pre-injection stress state and its evolution are evaluated with the hydromechanically coupled numerical model. It appears that the presence of an offset in a stiff competent layer affects the stress state in its vicinity. Therefore, both the pre-injection stress state and its evolution during the fluid injection should be addressed during the induced seismicity assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

CyBERT: Cybersecurity Claim Classification by Fine-Tuning the BERT Language Model

We introduce CyBERT, a cybersecurity feature claims classifier based on bidirectional encoder representations from transformers and a key component in our semi-automated cybersecurity vetting for industrial control systems (ICS). To train CyBERT, we created a corpus of labeled sequences from ICS device documentation collected across a wide range of vendors and devices. This corpus provides the foundation for fine-tuning BERT’s language model, including a prediction-guided relabeling process. We propose an approach to obtain optimal hyperparameters, including the learning rate, the number of dense layers, and their configuration, to increase the accuracy of our classifier. Fine-tuning all hyperparameters of the resulting model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original architecture to 94.4% obtained with CyBERT. Furthermore, we evaluated CyBERT for the impact of randomness in the initialization, training, and data-sampling phases. CyBERT demonstrated a standard deviation of ±0.6% during validation across 100 random seed values. Finally, we also compared the performance of CyBERT to other well-established language models including GPT2, ULMFiT, and ELMo, as well as neural network models such as CNN, LSTM, and BiLSTM. The results showed that CyBERT outperforms these models on the validation accuracy and the F1 score, validating CyBERT’s robustness and accuracy as a cybersecurity feature claims classifier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Multifunctional Charge and Hydrogen‐Bond Effects of Second‐Sphere Imidazolium Pendants Promote Capture and Electrochemical Reduction of CO 2 in Water Catalyzed by Iron Porphyrins**

Abstract Microenvironments tailored by multifunctional secondary coordination sphere groups can enhance catalytic performance at primary metal active sites in natural systems. Here, we capture this biological concept in synthetic systems by developing a family of iron porphyrins decorated with imidazolium (im) pendants for the electrochemical CO 2 reduction reaction (CO 2 RR), which promotes multiple synergistic effects to enhance CO 2 RR and enables the disentangling of second‐sphere contributions that stem from each type of interaction. Fe‐ ortho ‐im(H) , which poises imidazolium units featuring both positive charge and hydrogen‐bond capabilities proximal to the active iron center, increases CO 2 binding affinity by 25‐fold and CO 2 RR activity by 2000‐fold relative to the parent Fe tetraphenylporphyrin ( Fe‐TPP ). Comparison with monofunctional analogs reveals that through‐space charge effects have a greater impact on catalytic CO 2 RR performance compared to hydrogen bonding in this context.

Narouz, Mina R.↗
DOE OSTI2023

Mitigation of spatial nonstationarity with vision transformers

Spatial nonstationarity, the location variance of features’ statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in mineral deposits grades vary due to sedimentation and concentration processes, in hydrology rainfall varies due to the atmosphere and topography interactions, and in metallurgy crystalline structures vary due to differential cooling. Conventional geostatistical modeling workflows rely on the assumption of stationarity to be able to model spatial features for geostatistical inference. Nevertheless, this is often not a realistic assumption when dealing with nonstationary spatial data and this has motivated a variety of nonstationary spatial modeling workflows such as trend and residual decomposition, cosimulation with secondary features, and spatial segmentation and independent modeling over stationary subdomains. The advent of deep learning technologies has enabled new workflows for modeling spatial relationships. However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context. We demonstrate the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance and propose the mitigation of such impacts using self-attention (vision transformer) models. We demonstrate the utility of vision transformers for the mitigation of nonstationarity with relative errors as low as 10%, exceeding the performance of alternative deep learning methods such as convolutional neural networks. We establish best practice by demonstrating the ability of self-attention networks for modeling large-scale spatial relationships in the presence of commonly observed geospatial nonstationarity.

58 GEOSCIENCES↗
DOE OSTI2022

Soil Carbonyl Sulfide (OCS) Fluxes in Terrestrial Ecosystems: An Empirical Model

Measurements of carbonyl sulfide (OCS) enable independent estimates of regional stomatal conductance provided that non-stomatal OCS fluxes are well constrained. OCS is taken up through plant leaves, following the same pathway as CO 2 ; in contrast to CO 2 , OCS is irreversibly destroyed in plant leaves and plants do not typically exhibit OCS emissions. Ecosystem uptake of OCS can indicate changes in stomatal opening. Here we present an empirical model to assess the potential impact of soil OCS exchange, the non-Stomatal OCS exchange Empirical Model (SOCSEM, version 0). We created biome-specific response curves characterizing soil OCS exchange and restricted the model design to require only knowledge of soil moisture and surface temperature because remote sensing observations are available for these two features. Comparing the model to field-based chamber observations reveal deviations that can be attributed to missing complexity of the ground surface (having excluded litter and plants without regulated stomata), shortwave radiation, or the soil environment. For agricultural regions with known net emissions, we use remotely-sensed surface temperature data and demonstrate that data resolution can affect anticipated fluxes. We further investigate the influence of regions with unknown soil OCS responses, for example, Arctic tundra. We compare our model to a process-based and respiration-based soil OCS exchange model that has been implemented in a land surface model. Further field study of tropical and arctic ecosystems in conjunction with studies of non-stomatal surfaces in addition to soil (e.g., bryophytes) will increase confidence in applying OCS as a regional tracer for stomatal conductance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2021

Cavern Leaching Monitoring CY18 And CY19

The U.S. Strategic Petroleum Reserve (SPR) is a crude oil storage system run by the U.S. Department of Energy (DOE). The reserve consists of 60 active storage caverns spread across four sites in Louisiana and Texas, near the Gulf of Mexico. Beginning in 2016, the SPR began executing U.S. congressionally mandated oil sales. The configuration of the reserve, with a total capacity of greater than 700 MMB, requires raw water to be used instead of saturated brine for oil withdrawals such as for sales. All sales will produce leaching within the caverns used for oil delivery. Thirty-six caverns had a combined total of over 29 MMB of water injected from CY18-CY19 for mandatory sales. Leaching effects were monitored in these caverns to understand how the sales operations may impact the long-term integrity of the caverns. While frequent sonars are the best way to monitor changes in cavern shape, they can be resource intensive for the number of caverns involved in sales and exchanges. An intermediate option is to model the leaching effects and see if any concerning features develop. The leaching effects were modeled here using the Sandia Solution Mining Code (SANSMIC). The results indicate that leaching induced features are not of concern in the majority of the caverns, 32 of 36. Four caverns, BH-107, BH-108, BH-114 and WH-114 have features that may grow with additional leaching and should be monitored as leaching continues in those caverns. Six caverns had post sale sonars which were compared with SANSMIC results. SANSMIC was able to capture the leaching well. A deviation in the SANSMIC and sonar cavern shapes was observed near the cavern floor in caverns with significant floor rise, a process not captured by SANSMIC. These results suggest SANSMIC is a useful tool for monitoring changes in cavern shape due to leaching effects related to sales and exchanges.

02 PETROLEUM↗
DOE OSTI2021

Strategic Petroleum Reserve Cavern Leaching Monitoring CY20

The U.S. Strategic Petroleum Reserve is a crude oil storage system run by the U.S. Department of Energy. The reserve consists of 60 active storage caverns spread across four sites in Louisiana and Texas, near the Gulf of Mexico. Beginning in 2016, the SPR began executing U.S. congressionally mandated oil sales. The configuration of the reserve, with a total capacity of greater than 700 MMB, requires raw water to be used instead of saturated brine for oil withdrawals such as for sales. All sales will produce leaching within the caverns used for oil delivery. Twenty-five caverns had a combined total of over 39 MMB of water injected in CY 20 as part of the Exchange for Storage program; oil was withdrawn in the same manner as for congressionally mandated sales. Leaching effects were monitored in these caverns to understand how the oil withdrawals may impact the long-term integrity of the caverns. While frequent sonars are the best way to monitor changes in cavern shape, they can be resource intensive for the number of caverns involved in sales and exchanges. An intermediate option is to model the leaching effects and see if any concerning features develop. The leaching effects were modeled here using the Sandia Solution Mining Code (SANSMIC) . The results indicate that leaching induced features are not of concern in the majority of the caverns, 19 of 25. Six caverns, BH-107, BH-113, BH-114, BM-4, BM-106, and WH-114 have features that may grow with additional leaching and should be monitored as leaching continues in those caverns. Ten caverns had post sale sonars that were compared with SANSMIC results. SANSMIC was able to capture the leaching well , particularly the formation of shelves and flares. A deviation in the SANSMIC and sonar cavern shapes was observed near the cavern floor in caverns with significant floor rise, a process not captured by SANSMIC. These results suggest SANSMIC is a useful tool for monitoring changes in cavern shape due to leaching effects related to sales and exchanges.

02 PETROLEUM↗
DOE OSTI2022

Evidence for Impact of Galaxy Mergers on Stellar Kinematics of Early-type Galaxies

We provide observational evidence that galaxy mergers significantly affect stellar kinematics of early-type galaxies (ETGs) such as specific stellar angular momentum within the half-light radius (${\lambda }_{{R}_{e}}$) and kinematic misalignment (ψ mis ), using MaNGA integral field unit spectroscopic data that are in the Stripe 82 region of the Sloan Digital Sky Survey. In this study, tidal features around ETGs, which are detected in deep coadded images, are used as direct evidence for mergers that occurred recently. In the case of ETGs that do not have dust lanes, ${\lambda }_{{R}_{e}}$ is lower in ETGs with tidal features than in those without tidal features (median ${\lambda }_{{R}_{e}}$: 0.21 versus 0.39) in all stellar mass and Sérsic index ranges except the most massive bin, so that the fraction of ETGs with tidal features in slow rotators is more than twice as large as that in fast rotators (42% versus 18%). Moreover, ETGs with tidal features have larger ψ mis than those without tidal features (mean ψ mis : 28° versus 15°). By contrast, ETGs with dust lanes are fast rotators, and ETGs with both dust lanes and tidal features have the highest ${\lambda }_{{R}_{e}}$ (median ${\lambda }_{{R}_{e}}$: 0.59) among all ETG categories. In addition, ETGs with dust lanes have small ψ mis regardless of the existence of tidal features (Ψ mis <7$^{°}_{.}$5). Our results can be explained if mergers with different gas fractions generate merger remnants that have different kinematic properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m -2 with the area-weighted average value of 1.5 W m -2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m -2 and -29.7 to 12.2 W m -2 , respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change.

58 GEOSCIENCES↗
DOE OSTI2021

Impacts of Mode Mixity on Controlled Spalling of (100)-Oriented Germanium

Controlled spalling is a technology to prepare single-crystal thin films of semiconductors by fracture with a subsurface crack propagating nearly parallel to the substrate surface. Practical applications require uniform thickness and a smooth surface across the whole film. Both wafer-scale and patterned-stressor-defined small-area spalling of germanium substrates are conducted experimentally and numerically. River line features are observed on spalled surfaces close to lateral edges of the spall, regardless of the spall direction and the size of the spalled area. Three-dimensional finite element method modeling shows the river lines are caused by mixed mode I?+?III loading near the lateral edges of spall and predicts a spall depth variation near the lateral edges of spall due to mixed mode I?+?II loading. The absolute range of river lines increases with lateral size of spall, while the relative range of river lines decreases, consistent with variations in mode mixity.

36 MATERIALS SCIENCE↗