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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Comparison of edge turbulence characteristics between DIII-D and C-Mod simulations with XGC1

The physical processes taking place at the separatrix and scrape-off layer regions are crucial for the operation of tokamaks as they govern the interaction of hot plasma with the vessel walls. Numerical modeling of the edge with state-of-the-art codes attempts to elucidate the complex interactions between neoclassical drifts, turbulence, poloidal and parallel flows that control the physical set-up of the SOL region. Here, we present post-processing analysis of simulation results from the gyrokinetic code XGC1, comparing and contrasting edge turbulence characteristics from a simulation of the DIII-D tokamak against a simulation of the Alcator C-Mod tokamak. We find that the equilibrium ExB flux across the separatrix has a similar poloidal pattern in both discharges which can be explained by rB-drifts and trapped ion excursions. Furthermore, collisionality is noted to play a major role in the way that it prevents local charge accumulations from having more global effects in the C-Mod case. In both cases, turbulent electron heat flux is observed to be higher than the ion one and is possibly related to the need of electrons to maintain quasineutrality through the only channel available to them for exiting the confinement. By Fourier analysis, we identify turbulent frequencies and growth rates of the dominant mode in both simulations. In the case of C-Mod, these numbers point to the presence of a drift wave. In the DIII-D case, further linear simulations with the Gene code reveal a trapped electron mode. Furthermore, using a blob detection and tracking tool, we present the amplitude and size distributions of the blobs from both simulations. The amplitude distributions are in qualitative agreement with experimental observations while the size distributions are consistent with the fact that most of the blobs are not connecting to the divertor plates and suggest that they are generated by the shearing of the turbulent modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A review of privacy in energy applications

As the distribution system continues to experience an increase in distributed energy resource (DER) and electric vehicle (EV) penetration, so does the need for new solutions that can help grid operators manage and leverage their capabilities. This will undoubtedly lead to new operational schemes and business opportunities that will transform the traditional consumer into a prosumer who will be more actively engaged in grid operations. Although the field is still under active development, many of the potential use cases presented in literature or industry are built upon edge computing, two-way communications, and other innovative computational constructs to attain their goals. However, at their core, many use cases assume a great level of data access to aid with the decision-making process, an assumption that may need to be revised to ensure fair and equitable operational processes are maintained. This may be particularly true as edge resources are predicted to participate in retail-side, many-to-many, or peer-to-peer markets and thus may lead to financial impacts if data access considerations are ignored. The need to revise data access mechanisms can be further justified by the introduction of new participants into the operational process, who do not have the same level of trust, nor the incentives to focus on energy delivery as their primary objective. At the same time, more consumers are becoming aware of their own data, and the potential impacts of its abuse. To help solution developers better understand these risks, this report has been developed to offer an initial introduction to the topic of privacy. This is achieved by 1) Highlighting the need for privacy-aware solutions; 2) Encouraging system designers to be inquisitive about the status quo; 3) Documenting the existing threat space; 4) Presenting and evaluating tools that may be helpful towards enabling better privacy postures; and 5) Making recommendations to encourage the adoption of better practices. From a technical perspective, the report focuses on evaluating two potential techniques by applying them to the Transactive Energy Space. Based on the obtained results, it can be established that differential privacy (DP) methods may have limited applicability when highly correlated, time-series data records need to be protected. However, DP may be a powerful tool when it is used to aggregate and analyze mid-size and large-size data sets in a more traditional statistical environment. The second tool under evaluation is threshold cryptography, which can guarantee complete secrecy (and thus privacy) but requires the establishment of key management procedures and dedicated communication channels for key coordination. Therefore, due to its increased computational overhead, the use of threshold cryptography must be weighted using a cost/benefit analysis on a per-application basis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Mineralogy-dependent sulfide oxidation via polysulfide and thiosulfate pathways during weathering of mixed-sulfide bearing mine waste rock

Tracking the S oxidation pathway in sulfide-bearing mine waste-rock piles is complicated by variations in water content, O 2 and Fe 3+ concentrations, microbial diversity, mineralogy, the occurrence of a variety of S species associated with incomplete oxidation, and nonlinear coupling between physicochemical processes. Synchrotron-based S K-edge X-ray absorption near edge structure (XANES) spectroscopy facilitates the identification and quantification of S species (i.e., S 2– , $S_{2}O_{2}^{2–}$, S 8 , $S_{2}O_{3}^{2–}$, and $S_{2}O_{4}^{2–}$) in a weathered mine waste-rock pile containing pyrite, sphalerite, galena, pyrrhotite, and chalcopyrite. Mineralogy-dependent polysulfide and thiosulfate pathways both affect the S oxidation within the waste-rock pile. Currently, the polysulfide pathway, with S 8 as the intermediate S species, generates high concentrations of dissolved metals (from the rapid oxidation of monosulfides including sphalerite, galena, pyrrhotite, and chalcopyrite). As these monosulfides are depleted, the thiosulfate pathway of pyrite oxidation, including intermediate oxidation products (i.e., S 8 and $S_{2}O_{3}^{2–}$), will become the dominant oxidation pathway, resulting in the potential for generation of additional acidic drainage. This information highlights the importance of identifying the sulfide-mineral oxidation reaction pathways when attempting to estimate acid generation potential, and when developing remediation strategies for the storage and management of sulfide-bearing waste rock.

58 GEOSCIENCES↗
DOE OSTI2023

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Cesium-Coated Halide Perovskites as a Photocathode Material: Modeling Insights

Photocathodes emit electrons when illuminated, a process utilized across many technologies. Cutting-edge applications require a set of operating conditions that are not met with current photocathode materials. Meanwhile, halide perovskites have been studied extensively and have shown a lot of promise for a wide variety of optoelectronic applications. Well-documented halide perovskite properties such as inexpensive growth techniques, improved carrier mobility, low trap density, and tunable direct band gaps make them promising candidates for next-generation photocathode materials. In this study, we use density functional theory to explore the possible application of pure inorganic perovskites (CsPbBr 3 and CsPbI 3 ) as photocathodes. It is determined that the addition of a Cs coating improved the performance by lowering the work function anywhere between 1.5 and 3 eV depending on the material, crystal surface, and surface coverage. A phenomenological model, modified from that developed by Gyftopoulos and Levine, is used to predict the reduction in work function with Cs coverage. The results of this work aim to guide the further experimental development of Cs-coated halide perovskites for photocathode materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Mechanistic understanding of speciated oxide growth in high entropy alloys

Abstract Complex multi-element alloys are gaining prominence for structural applications, supplementing steels, and superalloys. Understanding the impact of each element on alloy surfaces due to oxidation is vital in maintaining material integrity. This study investigates oxidation mechanisms in these alloys using a model five-element equiatomic CoCrFeNiMn alloy, in a controlled oxygen environment. The oxidation-induced surface changes correlate with each element’s interactive tendencies with the environment, guided by thermodynamics. Initial oxidation stages follow atomic size and redox potential, with the latter becoming dominant over time, causing composition inversion. The study employs in-situ atom probe tomography, transmission electron microscopy, and X-ray absorption near-edge structure techniques to elucidate the oxidation process and surface oxide structure evolution. Our findings deconvolute the mechanism for compositional and structural changes in the oxide film and will pave the way for a predictive design of complex alloys with improved resistance to oxidation under extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On the effect of mixing-driven vaporization in a homogeneous relaxation modeling framework

The homogeneous relaxation model (HRM) is one of the most widely used models to describe the liquid–gas phase transition in multiphase flows due to the occurrence of cavitation. However, in its original formulation, the HRM does not account for the presence of ambient gas species, which generally limits its applicability to the injector's internal flow where ambient gases are negligible. In this work, a mixing-driven vaporization (MDV) model was developed to extend the capability of the HRM in handling the mixing effect in the regions external to the nozzle, where vapor–liquid equilibrium for multi-species mixtures of fuel and ambient gas is considered. Herein, to assess the model performance, simulations of the Engine Combustion Network's Spray G injector were performed with the HRM and the MDV model under both flash-boiling and evaporating conditions. It was found that the MDV model led to a better match against x-ray measurements of fuel density in the near-nozzle region. In contrast to the HRM, the MDV model was able to reproduce the vaporization process in the mixing zone at the edge of the fuel jet, which aligns with the expected physics. This resulted in substantial differences in the prediction of other flow characteristics such as mixture temperature and pressure. Furthermore, this work demonstrates that evaporation timescales have a considerable effect on the MDV model's predictions, as shown by a parametric study in which a time factor was introduced to mimic the effect of different timescales due to different phase change mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO 2 emissions generated by each Galaxy job.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Landau Singularities Revisited: Computational Algebraic Geometry for Feynman Integrals

We reformulate the analysis of singularities of Feynman integrals in a way that can be practically applied to perturbative computations in the standard model in dimensional regularization. After highlighting issues in the textbook treatment of Landau singularities, we develop an algorithm for classifying and computing them using techniques from computational algebraic geometry. We introduce an algebraic variety called the principal Landau determinant, which captures the singularities even in the presence of massless particles or UV/IR divergences. We illustrate this for 114 example diagrams, including a cutting-edge 2-loop 5-point nonplanar QCD process with multiple mass scales. Published by the American Physical Society 2024

Physics↗
DOE OSTI2020

Invited: Software defined accelerators from learning tools environment

Next generation systems, such as edge devices, will need to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. In this paper we present the Software Defined Accelerators From Learning Tools Environment (SODALITE), an automated open source high-level ML framework-to-verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets. The SODALITE approach will implement optimal designs by seamlessly combining custom components generated through high-level synthesis (HLS) with templated and fully tunable Intellectural Properties (IPs) and macros, integrated in an extendable resource library. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimensions.

High-Level Synthesis, Accelerators, Hardware-Softw↗
DOE OSTI2020

SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators

Next generation systems, such as edge devices, will have to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. This paper discusses the SODA synthesizer, an automated open source high-level ML framework-to-Verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets based on the LLVM infrastructure. The SODA synthesizers will allow implementing optimal designs by combining templated and fully tunable IPs and macros, and fully custom components generated through high-level synthesis. All these components will be provided through an extendable resource library, characterized with both commercial and open source logic design flows. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimension

Minutoli, Marco↗
DOE OSTI2023

Modeling of the effectiveness of novel edge seal designs for fast, low‐Cap‐Ex manufacturing

Abstract In the manufacturing of photovoltaic (PV) modules, the lamination process can take up to 20 min to complete. In this work, new lamination processes are being developed, and have been prototyped, which hope to be able to cut this time down to as little as 30 s. This could provide significant savings in the cost of lamination equipment, floor space, and energy. PV modules are expected to have a lifespan exceeding 20 to 30 years. For moisture‐sensitive PV technologies, the edge seal between the two layers of glass can be the weakest point of its reliability. There is an inherent challenge when evaluating edge seal materials due to their low permeation rates. As part of Colorado State University's Photovoltaic Research and Development 2, work at the National Renewable Energy Laboratory has developed models to evaluate edge seal configurations in glass‐glass PV modules. Here, this new manufacturing process is evaluated for long‐term moisture durability. Different edge seal design options within glass–glass PV modules are explored. Most of these designs are targeting a superstrate on glass configuration, e.g CdTe, but some designs could be used on conventional crystalline Si cells. Using COMSOL finite element simulation software, we investigated the edge seal and interlayer design configurations containing silicone perimeter edge adhesive, desiccated polyisobutylene‐based edge seal, air, and polyolefin while integrating climate conditions equivalent to a hot and humid environment such as Miami, Florida. We found optimized configurations that will allow the module to prevent moisture ingress over 50 years minimizing the amount of time and material used while utilizing polymers that are easily dispensed.

14 SOLAR ENERGY↗
DOE OSTI2023

Edge-computing with enhanced standoff sensors

A system senses analytes through one or more sensors that detect or measure a physical characteristic. The one or more sensor generate a spectroscopic-data signal corresponding to the detection. An edge device communicatively couples the one or more sensors that communicatively couples a wide-area network coupling a cloud service. The edge device includes a data acquisition device that receives spectroscopic data signals from the one or more sensor and a processor that processes the spectroscopic-data signals to identify an analyte. The edge device also includes a transceiver that transmits data identifying the analytes to the cloud service.

Passian, Ali↗
DOE OSTI2022

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

Abstract The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS

Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Redox Mechanisms and Migration Tendencies in Earth-Abundant 0.7Li 2 MnO 3 ·0.3LiFeO 2 Cathodes: Coupling Spin-Resolved X-ray Absorption Near Edge and X-ray Absorption Fine Structure Spectroscopies

We report the use of iron 1s3p resonant X-ray emission processes to conduct spin-selective, high-energy resolution fluorescence detected X-ray absorption near-edge spectroscopy (HERFD-XANES) on an iron-containing, lithium- and manganese-rich, fully earth-abundant cathode material, Li 1.3 Mn 0.5 Fe 0.2 O 2 (0.7Li 2 MnO 3 ·0.3LiFeO 2 ). Coupling this technique with conventional Mn K-edge XANES and detailed extended X-ray absorption fine structure (EXAFS) analysis from both the Mn and Fe vantage points, we gain fundamental insights into the redox processes and migration tendencies of transition metals in this cathode material at the bulk level. We show that during the first charge, Fe 3+ undergoes oxidation to form Fe 4+ prior to the activation plateau. Toward the end of activation, a significant fraction of the iron is present as tetrahedral Fe 3+ . This observation reveals that iron migration from octahedral to tetrahedral sites and iron reduction are initiated during activation. Upon first discharge from the activated state, a continuous and overlapping reduction of both Fe and Mn is observed, with Fe largely restored back as an octahedrally coordinated Fe 3+ . The manganese local environment gradually changes to a distorted cooperative Jahn–Teller Mn 3+ structure during discharge, with the clear presence of two Mn–O as well as two Mn–Mn correlation distances at 2.0 V. The significant reduction of manganese in the very first discharge is distinctly different from that seen in typical nickel-based lithium-manganese-rich materials but is similar to that observed for pure Li 2 MnO 3 . In conclusion, these findings shed light on key structure–property correlations in the cathode material and point to a causative relationship between the redox mechanisms as well as structural changes endured by the material and relatively poor performance during extended electrochemical cycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

DIMPLES: Distributed Influence Maximization for Pandemic pLanning on Exascale Systems

We study exascale parallel algorithms for the selection of intervention or monitoring strategies in massive realistic socio-technical networks through scalable Influence Maximization (InfMax) algorithms. We employ novel techniques to enable efficient scaling on up to 8k nodes of OLCF Frontier, with 65k AMD GPUs and 458k AMD CPU cores. Current state-of-the-art InfMax tools are limited to networks with only a few million actors (vertices) and a few hundred million interactions (edges). By overcoming these limitations, we show that our approach is capable of processing a realistic social contact network of the United States with 285 million nodes and about 8 billion edges. This two orders-of-magnitude improvement over the previous state-of-the-art is obtained by leveraging algorithmic advancements for the InfMax problem and designing several problem-specific approaches to overlap communication with computation, improve GPU efficiency, and lower the application’s memory requirements. We evaluate strong scaling for computing 10k most influential seeds using up to 8k nodes of an exascale system, and weak scaling from 128 to 8k system nodes for seed sets ranging from 625 to 40k seeds. We achieve the fastest-known runtime of 25 minutes while performing 48 million diffusion simulations totaling 2.31 petabytes to identify 40k influential seeds using 8k nodes, and take 5.75 minutes to identify 10k seeds while using 4k nodes.

Minutoli, Marco [Pacific Northwest National Labora↗
DOE OSTI2020

Pore-Edge Tailoring of Single-Atom Iron–Nitrogen Sites on Graphene for Enhanced CO 2 Reduction

Hosting atomically dispersed nitrogen-coordinated iron sites (Fe-N 4 ) on graphene offers unique opportunities for driving electrochemical CO 2 reduction reaction (CO 2 RR) to CO. However, the strong adsorption of *CO on the Fe-N 4 site embedded in intact graphene limits current density due to slow CO desorption process. Herein, we report how the manipulation of pore edges on graphene alters the local electronic structure of isolated Fe-N 4 sites and improves their intrinsic reactivity for prompting CO generation. We demonstrate that constructing holes on graphene basal plane to support Fe-N 4 can significantly enhance its CO 2 RR compared to the pore-deficient graphene-supported counterpart, exhibiting a CO Faradaic efficiency of 94% and a turnover frequency of 1630 h -1 at 0.58 V vs RHE. Mechanistic studies reveal that the incorporation of pore edges results in the downshifting of the d-band center of Fe sites, which weakens the strength of the Fe-C bond when the *CO intermediate adsorbs on edge-hosted Fe-N 4 , thus boosting the CO desorption and evolution rates. These findings suggest that engineering local support structure renders a way to design high-performance single-atom catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗