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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Generator Interconnection Cost Analysis in the Southwest Power Pool (SPP) Territory

Electric transmission system operators (ISOs, RTOs, or utilities) require new large generators seeking to connect to the grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. Berkeley Lab has collected interconnection cost data for 845 projects from interconnection studies for the Southwest Power Pool (SPP) Territory. The studies were performed between 2002 and 2023 and include all of the most refined cost estimates available. Project-level cost summary data are available for download on this page. We find: -Project-specific interconnection costs can differ widely. -Average interconnection costs are stable for projects that complete all interconnection studies but have escalated for those that withdraw. -Broader network upgrade costs are the primary driver of recent cost increases, especially for withdrawn projects. -Potential interconnection costs of all solar and wind requests have been greater than those of storage and natural gas projects. -Economies of scale exist for completed wind and solar projects but not for other fuel types or withdrawn projects. -Interconnection costs vary by location. Berkeley Lab publishes a series of short analytical papers of generator interconnection costs to the transmission system for MISO, PJM, SPP, ISO-NE and NYISO, which you can find at https://emp.lbl.gov/interconnection_costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

PLC Vulnerabilities and Mitigations

Programmable Logic Controllers (PLCs) are used extensively in many high-importance industrial and nonindustrial settings including controlling elevators, manufacturing machines, and utility facilities such as power and natural gas, however cybersecurity protection for them has been neglected. Within recent years, PLCs have been put under more security scrutiny and experts have advocated for changes from the addition of protocol encryption and network segmentation to intrusion detection systems on the PLCs themselves. While PLC security is critical, a large portion of the PLCs available today will never receive these changes due to being legacy or the difficulty of overhauling the security on existing systems. Due to the infeasibility of applying many recommended security measures towards currently available machines, we aim to provide realistic and affordable best practices for hardening PLCs. We will first conduct security analysis and consider attack vectors within our target PLC. Once we’ve analyzed the device’s security, we will evaluate a variety of mitigation methods and create guidelines to effectively reduce the threat posed by PLC attacks with minimal disruption to operations.

42 ENGINEERING↗
DOE OSTI2021

Bioprospecting Trichoderma: A Systematic Roadmap to Screen Genomes and Natural Products for Biocontrol Applications

Natural products derived from microbes are crucial innovations that would help in reaching sustainability development goals worldwide while achieving bioeconomic growth. Trichoderma species are well-studied model fungal organisms used for their biocontrol properties with great potential to alleviate the use of agrochemicals in agriculture. However, identifying and characterizing effective natural products in novel species or strains as biological control products remains a meticulous process with many known challenges to be navigated. Integration of recent advancements in various “omics” technologies, next generation biodesign, machine learning, and artificial intelligence approaches could greatly advance bioprospecting goals. Herein, we propose a roadmap for assessing the potential impact of already known or newly discovered Trichoderma species for biocontrol applications. By screening publicly available Trichoderma genome sequences, we first highlight the prevalence of putative biosynthetic gene clusters and antimicrobial peptides among genomes as an initial step toward predicting which organisms could increase the diversity of natural products. Next, we discuss high-throughput methods for screening organisms to discover and characterize natural products and how these findings impact both fundamental and applied research fields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Non-Thermal Plasma as a Novel Strategy for Treating or Preventing Viral Infection and Associated Disease

Pathogenic viruses cause many human, animal, and plant diseases that are associated with substantial morbidity, mortality and socio-economic impact. Although effective strategies for combatting virus transmission and associated disease are available, global outbreaks of viral pathogens such as the virus responsible for the COVID-19 pandemic demonstrate that there is still a critical need for new approaches that can be used to interrupt the chain of viral infection and mitigate virus-associated pathogenesis. Recent studies point to non-thermal plasma (NTP), a partly ionized gas comprised of a complex mixture of reactive oxygen and nitrogen species along with physical effectors, as the potential foundation for new antiviral approaches. A more thorough understanding of the antiviral properties and safety of NTP has stimulated explorations of NTP as the basis for treatments of viral diseases. The recently described immunomodulatory properties of NTP are also being evaluated for potential use in immunotherapies of viral diseases as well as in antiviral vaccination strategies. In this review, we present the current state-of-the-art in addition to compelling arguments that NTP merits further exploration for use in the prevention and management of viral infections and associated diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting relatively cold water into subsurface fractures, which are in contact with hot dry rock, and brought back to surface through production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. In this short conference paper, we present a reproducible workflow for modeling EGS. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and high-performance computing. This GTC framework is currently being made open-source, user-friendly, and reproducible through python scripts as well as Google Colab/Jupyter Notebooks. This GTC for EGS modeling scripts are made available at https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS and will constantly be updated to cater for geothermal community. Current GTC framework provides scripts to train deep learning (DL) models for techno-economics and data worth analysis. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. This short paper provides details on the scripts to curate, process, and train DL models. The scripts can easily be modified to train on databases generated by other popular open-source simulators such as PFLOTRAN, STOMP, TOUGH, and GEOSX or commercial software such as ResFrac and COMSOL.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Aqueous organic flow batteries for sustainable energy storage

Aqueous Organic Redox Flow Batteries (RFBs) have the potential to address the large-scale need for storing electrical energy from intermittent sources like solar- and wind-based generation. Unlike metal-based redox systems, small organic molecules present the prospect of achieving sustainability, by being synthesizable from abundantly available carbon dioxide, water, nitrogen, sulfur, and renewable energy. This mini-review focuses on the progress and challenges in designing water-based RFBs based on small organic molecules that can address the requirements of large-scale energy storage. Further, much of the recent research in this area involves discovering new molecular architectures via computational screening, understanding degradation, and cell configurations to address the techno-economic challenges of extraordinary electrochemical durability, recyclability, low cost of precursor materials, and good solubility. Redox materials for the positive side of the cell are notably few. Future research must continue to focus on these techno-economic challenges through rapid materials discovery.

25 ENERGY STORAGE↗
DOE OSTI2023

Advances in multi-dimensional cellulose-based fluorescent carbon dot composites

Recently, fluorescent materials composed of carbon dots (CDs) have attracted increasing attention in diverse fields, including the environment, food, biology, and medicine. Cellulose has emerged as a promising class of materials for carriers due to its low cost, fascinating biodegradability, and various morphologies, especially in available different dimensions. By taking advantage of the respective superiority of fluorescent materials and cellulose carriers, the performance of fluorescence composite materials can be optimized and their application may be extended. Here, in this review, the study emphasizes the synthesis and applications of cellulose-based fluorescent materials with different dimensions. Moreover, some effective strategies and potential challenges for further development of cellulose-based fluorescent materials are discussed. A deeper understanding will provide a guide for the materials with ideal fluorescent performances.

42 ENGINEERING↗
DOE OSTI2022

Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. In this work, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Universal Reversible Hydrogen Potential for Electrocatalytic Ammonia Splitting Reactions in Nonaqueous Solvents from Unified pH Measurements

In this work, we introduce a new approach of using differential potentiometric measurements in four nonaqueous solvents─MeCN, THF, DMF, and PC─to determine the universal pH abs H 2 O values aligned to the aqueous pH scale for dilute NH 4 + /NH 3 solutions. Knowledge of the pH abs H 2 O values allows simple determination of the reversible hydrogen potential in any given solvent relative to the aqueous standard hydrogen electrode (SHE) and, most importantly, ensures comparability across different solvents. As an independent method, Open Circuit Potenial measurements were carried out in the same solvents titrated with NH 4 + /NH 3 to obtain alternative values for the reversible hydrogen potential in these solvents. The close agreement of these two methods, as well as calculated potentials from literature values when available, substantiates the new, simpler, and more robust approach to determine the reversible hydrogen potential introduced here. We further use the reversible hydrogen potential values established here to report, for the first time, the overpotential for ammonia oxidation as a function of solvent, with a recently discovered ruthenium catalyst.

ammonia↗
DOE OSTI2023

Two types of superconducting pairs in stripe-ordered La 2 - x Ba x CuO 4 ( x = 1 / 8 ) : Evidence from resistivity measurements

Recent angle-resolved c-axis resistivity measurements of the stripe-ordered La 2-x Ba x CuO 4 (LBCO) with x = 1/8 revealed an unexpected dependence on the direction of the in-plane magnetic field. We argue that these and other available data for the c-axis transport point to the existence of superconducting pairs of two different types in the x = 1/8 LBCO below the stripe ordering temperature. The pairs of one type carry finite momentum and are confined to the Cu-O planes; the pairs of other type (probably the conventional d wave with zero momentum) propagate along narrow conducting channels traversing the sample in the c-axis direction. The evidence for this comes from the observed exponential temperature dependence of the c-axis resistivity ρ c (T ) which we attribute to the thermally excited slips of the superconducting phase and flux flows. We present a simple theory to fit the observed π/2-periodic dependence of ρ c on the direction of the in-plane magnetic field and the other data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Microscopic calculation of fission product yields with particle-number projection

Fission fragments' charge and mass distribution is an important input to applications ranging from basic science to energy production or nuclear nonproliferation. In simulations of nucleosynthesis or calculations of superheavy elements, these quantities must be computed from models, as they are needed in nuclei where no experimental information is available. Until now, standard techniques to estimate these distributions were not capable of accounting for fine-structure effects, such as the odd-even staggering of the charge distributions. In this work, we combine a fully microscopic collective model of fission dynamics with a recent extension of the particle number projection formalism to provide the highest-fidelity prediction of the primary fission fragment distributions for the neutron-induced fission of 235 U and 239 Pu. Here, we show that particle-number projection is an essential ingredient to reproduce odd-even staggering in the charge yields and benchmark the performance of various empirical probability laws that could simulate its effect. This new approach also enables for the first time the realistic determination of two-dimensional isotopic yields within nuclear density functional theory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Characterizing Microwave Losses in Superconducting Coaxial Cables for Quantum Systems

As superconducting quantum systems scale up to enable applications like large-scale quantum computing, challenges such as fabrication yield, wiring complexity, and microwave crosstalk drive the need for high-fidelity, low-loss, modular interconnects. In recent experiments, the performance of these interconnects is often limited by cable loss, a factor that becomes even more critical as we aim for higher fidelity operations and connect increasingly coherent modules. In this talk, we present a detailed characterization of microwave losses in commercially available superconducting cables and discuss how material characterization techniques can be useful for understanding their microscopic origin.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗
DOE OSTI2024

Numerically Testing Conceptual Models of the Utah FORGE Reservoir Using July 2023 Circulation Test Data

Over the past several years, many new data sets have become available regarding the characterization of the Utah FORGE reservoir. These include, but are not limited to, the stimulation of Well 16A, the drilling and completion of Well 16B, and interwell circulation confirmatory testing. As part of the characterization efforts, conceptual models of the reservoir are re-examined as new data become available. As part of the planning for FORGE activities, numerical models are often used to predict the reservoir response to the planned testing. Stochastic methods are often employed to bound uncertainty and allow for evaluation of comprehensive ranges of key reservoir parameters. For the most recent interwell circulation confirmatory testing (July 2023), a priori numerical model predictions did bound the observed behavior (Xinj et al., 2023), but key deviations from expected behavior prompted the FORGE team to reevaluate our conceptual model of the reservoir. In early October 2023, key members of the development, testing, and monitoring teams met for 2 days to review newly collected data and discuss ‘interesting’ or ‘key’ observations. From these discussions, 15 Key Observations were documented, with several significant ones being that the discrete fracture network developed from the 16A stimulation data may not be appropriate and that the early time pressure data obtained during the summer 2023 reservoir testing were best described using radial solutions. In July 2023, two campaigns of interwell confirmatory testing were conducted, the first set of tests occurred on July 4-5 and the second set on July 18-19. The second set of circulation tests conducted at the Utah FORGE site between the injection well 16A(78)-32 and production well 16B(78)-32 on July 18 and 19, 2023 are used to calibrate material properties in a thermal-hydraulic-mechanical (THM) simulation of the discrete fracture network connecting the wells. The spatially and temporally varying reservoir properties are calibrated to match the time dependent pressure and production profiles from the circulation tests. In future work, this calibrated model will be coupled to the native state THM model of the FORGE reservoir to predict surface deformation and strains resulting from pumping schedules.

58 GEOSCIENCES↗
DOE OSTI2024

Numerically Testing Conceptual Models of the Utah FORGE Reservoir Using July 2024 Circulation Test Data

Over the past several years, many new data sets have become available regarding the characterization of the Utah FORGE reservoir. These include, but are not limited to, the stimulation of Well 16A, the drilling and completion of Well 16B, and interwell circulation confirmatory testing. As part of the characterization efforts, conceptual models of the reservoir are re-examined as new data become available. As part of the planning for FORGE activities, numerical models are often used to predict the reservoir response to the planned testing. Stochastic methods are often employed to bound uncertainty and allow for evaluation of comprehensive ranges of key reservoir parameters. For the most recent interwell circulation confirmatory testing (July 2023), a priori numerical model predictions did bound the observed behavior (Xinj et al., 2023), but key deviations from expected behavior prompted the FORGE team to reevaluate our conceptual model of the reservoir. In early October 2023, key members of the development, testing, and monitoring teams met for 2 days to review newly collected data and discuss ‘interesting’ or ‘key’ observations. From these discussions, 15 Key Observations were documented, with several significant ones being that the discrete fracture network developed from the 16A stimulation data may not be appropriate and that the early time pressure data obtained during the summer 2023 reservoir testing were best described using radial solutions. In July 2023, two campaigns of interwell confirmatory testing were conducted, the first set of tests occurred on July 4-5 and the second set on July 18-19. The second set of circulation tests conducted at the Utah FORGE site between the injection well 16A(78)-32 and production well 16B(78)-32 on July 18 and 19, 2023 are used to calibrate material properties in a thermal-hydraulic-mechanical (THM) simulation of the discrete fracture network connecting the wells. The spatially and temporally varying reservoir properties are calibrated to match the time dependent pressure and production profiles from the circulation tests. In future work, this calibrated model will be coupled to the native state THM model of the FORGE reservoir to predict surface deformation and strains resulting from pumping schedules.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

R&D Effort of Geologic Hydrogen Production at the National Renewable Energy Lab (NREL)

Geologic hydrogen (geoH2) is an emerging technology with massive current market interest and distinct potential to change the paradigm of hydrogen production. Two major subsurface processes influence the amount of geoH2 that are available for energy extraction: 1) geochemical reactions of H2O and Fe2+-bearing rocks which can produce hydrogen in the subsurface environment, where 2) various active microbial communities consume hydrogen as an energy source before the hydrogen reaches the surface. The net gain of hydrogen from these two competing processes dictates the production rate of geoH2. A recent study (Templeton et al., 2024) suggested that for most natural geoH2 systems, five orders of magnitude of production rate enhancement are needed to make geoH2 production economical in the near term. Effective enhancement of the production rate requires an in-depth understanding of the two geoH2 processes, in order to promote the H2-generating geochemical processes and suppress the H2-consuming microbial processes. However, current significant knowledge gaps in these two processes hinders the efforts to formulate strategies to enhance geoH2 production. The National Renewable Energy Laboratory (NREL) is a U.S. Department of Energy National Laboratory with the core mission of leading research, innovation, and strategic partnership to deliver solutions for a clean energy based economy. NREL's extensive research portfolio in hydrogen, bioenergy, geothermal, industrial decarbonization, and energy analysis makes us well positioned to conduct interdisciplinary research and facilitate technology deployment in the geoH2 space. In this presentation, we will discuss ongoing geoH2 research and engagement efforts at NREL, including: 1) geochemical investigation to understand the reaction mechanisms and production rate and potential of different source minerals and rocks, 2) microbiological investigation to understand methanogenesis and acetogenesis in the subsurface geoH2 environment, and identify effective inhibitors for these microbial processes, and 3) preliminary analysis for geoH2 production in the State of Minnesota, where abundant Fe-rich rocks for stimulated geoH2 production and ample opportunity to utilize geoH2 in transforming iron and steel industries are currently available.

08 HYDROGEN↗
DOE OSTI2021

Wave energy resource characterization employing joint distributions in frequency-direction-time domain

Joint and marginal distributions in the frequency, direction, and time domain are employed to demonstrate their value for wave energy resource characterization when full spectra are available. Insights gained through analysis of these distributions support wave energy converter concept design, operation and maintenance. Spatial trends in the wave energy resource and contributing wave energy systems along the continental shelf of the West Coast of the United States are investigated using the most recent two-dimensional wave spectra measurements at four buoys over an eleven year period (2008 to 2018). Resource hot spots and dominant resolved energy resource bands in the frequency-direction-time domain are delineated. Resource attributes, including frequency and directional spreading, and seasonal variability, are characterized using joint distributions and marginal distributions of wave power spectra. North Pacific westerly swells in the winter season, augmented by Aleutian low-pressure southwesterly swells, are the principal suppliers of the dominant resource and main drivers influencing resource attributes. In conclusion, the modification of these systems southward, especially the North Pacific westerly swells, explains the observed spatial resource trends. The dominant resource wave period shifts two seconds to higher wave periods, thirty degrees in the dominant direction band to a more northward orientation, and forward by one month.

16 TIDAL AND WAVE POWER↗