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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Impedance Scan of Inverter-Based Resources and Diesel Generator for Stability Analysis

Impedance-based methods are widely used for power system stability analysis with inverter-based resources (IBRs), e.g., assessing dynamic interactions between the power grid and an IBR, control interactions between multiple IBRs, and the sub-synchronous oscillation and damping phenomenon. Since it is difficult to get a numerical model 100% matching with the hardware IBR, using the hardware inverter directly to obtain its output impedance has become a prominent approach nowadays. Therefore, this article presents the impedance scan using hardware IBRs, and also a hardware diesel generator as it still stays with the grid before the grid completely goes to renewable. The devices under test (DuTs) for the impedance scan includes two 3-..phi.., 480 V, 60 Hz commercial grid-forming IBRs (one of 250 kVA and another of 125 kVA rating) in series with ..delta..-Y transformers, one 3-..phi.., 480 V, 60 Hz commercial grid-following IBR (of 125 kVA rating), and a 3-..phi.., 480 V, 60 Hz commercial diesel generator (of 187.5 kVA rating). Using voltage signals perturbed with sub-, inter-, and higher harmonic components, and measuring the current response, the positive-sequence impedances are computed via an offline-based post-analysis. Moreover, best-fit transfer functions are estimated that closely resemble the measured data points of the positive-sequence impedances. Based on the observations from various outcomes of the hardware experiments, this article also provides some fundamental insights on the equivalent positive-sequence impedance of a combination of multiple hardware components by comparing the estimated and the empirically computed impedances. A comparative insight on the damping capability of the DuTs using the positive-sequence impedances of the hardware is also discussed.

current measurement↗
DOE OSTI2022

Rare Earth Element (REE) and Critical Mineral Fractions of Central Appalachian Coal-Related Strata Determined by 7-Step Sequential Extraction

Rare earth elements (REEs) and critical minerals (CMs) are used in many modern industries, including the automotive sector, generation and storage, clean energy, and defense. The demand for REEs is increasing, and the REE supply chain is unpredictable. The US has driven to assess non-conventional sources of REE (such as coal underclay) to identify domestic resources to stabilize this uncertainty in supply. Knowledge of the minerology, distribution, and modes of occurrence of REEs is integral to the assessment of non-conventional sources. Additionally, extraction techniques can be optimized and targeted when REE distribution in different solid fractions from source material is understood. In this study, four bituminous coal-related samples associated with the Lower and Middle Kittanning coal seams in the Appalachian Basin (US) underwent a seven-step sequential extraction procedure, primarily targeting the water-soluble, exchangeable, acid soluble, mildly reducible, moderately reducible, strongly reducible, and oxidizable fractions. The REE and other elements of interest from each extraction step were analyzed, and the percentages of element extracted from raw solids were calculated. REEs extracted from the total seven steps were reported as the extractable fraction, whereas the fractions in the residual solids were reported as the non-extractable fraction. Less than 6% of REE were extracted from three samples. Twenty-one percent of REE was extracted from the fourth sample, mainly from the steps targeting oxidizable and exchangeable phases. Co-extraction of critical metals (Co, Ni, Cu, and Zn) occurred during the oxidizable, exchangeable, acid soluble, and water-soluble steps for the four samples. In the extracted fractions, the four samples all exhibited a middle and heavy REE enrichment relative to light REE. The mobility of major cation (e.g., Ca, Fe, and P) and REE is associated with exchangeable, oxidizable, and acid soluble fractions. Non-extractable REE is likely held in refractory apatitic phases, and/or primary REE-phosphates (e.g., monazite and xenotime).

58 GEOSCIENCES↗
DOE OSTI2021

Subacute safety assessment of recombinant Lactococcus lactis on the gut microbiota of male Sprague–Dawley rats

Abstract BACKGROUND Lactococcus lactis strain pGSMT/MG1363 is a genetically modified microorganism (GMM) that constitutively expresses human metallothionein‐I fusion protein to combine with intracellular lead. Unlike traditional probiotics, pGSMT/MG1363 lacks a history of safe use in food. Administration of microorganism could influence the gut microbial community and consequently confer health benefits or cause disadvantages to the host. To date, little has been done to assess the influence of recombinant strain pGSMT/MG1363 on the stability of gut microbiota. RESULTS Liver, testis and kidney sections of male Sprague–Dawley rats orally administered pGSMT/MG1363 for 6 weeks showed normal structure and no pathological damage. There were no adverse effects on the analyzed serum biochemical parameters between the pGSMT/MG1363 group and the MG1363 group. Principal coordinate analysis showed that, compared with the MG1363 group, the 6‐week‐old fecal gut microbiota of rats fed with pGSMT/MG1363 was not significantly different (Adonis, P = 0.802). pGSMT/MG1363 treatment for 6 weeks did not significantly change the relative abundance of gut microbiota at the phylum and genus levels in comparison with MG1363 treatment. CONCLUSION Compared to the non‐GM strain MG1363 group, administration of the recombinant strain pGSMT/MG1363 for 6 weeks showed no adverse effects on the analyzed physiological parameters and gut microbial compositions of male Sprague–Dawley rats. The results suggested that, in terms of gut microbiota stability, pGSMT/MG1363 could be considered as safe as MG1363, at least for short‐term intake. Further toxicological evaluations still need to be considered before drawing a definite conclusion concerning the safe use of pGSMT/MG1363. © 2021 Society of Chemical Industry.

Chen, Guowei↗
DOE OSTI2021

Modeling atmospheric data and identifying dynamics Temporal data-driven modeling of air pollutants

Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. Furthermore, with the aid of real-world air quality data collected hourly in different stations throughout Madrid, we present a case study using a series of data-driven techniques with the following goals: (1) Find systems of ordinary differential equations that model the concentration of pollutants and their changes over time; (2) assess the performance and limitations of our model using stability analysis; (3) reconstruct the time series of chemical pollutants not measured in certain stations using delay coordinate embedding results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cyclic Peptides for Lanthanide Binding

Lanthanide ions are difficult to separate from one another due to their similar chemical properties. The discovery of lanthanide-binding peptides and proteins in nature has led to an increased interest in the possibility of utilizing the strong binding of peptides to lanthanide ions for their separations; as such, there has been an effort to identify or design peptides with improved lanthanide binding and selectivity toward particular lanthanide ions. Here, in this study, we designed and characterized lanthanide-binding cyclic peptides (LBCPs) with molecular dynamics simulations, electronic structure calculations, and emission spectroscopy. Luminescent decay measurements were done to determine the number of water molecules coordinated to the Eu 3+ ion in Eu-LBCP complexes and compare to the predicted number of water molecules by computation to assess the lanthanide-binding affinity of LBCPs. Measured stability constants show binding of the LBCPs to the Eu 3+ ion with stronger than micromolar affinity. We were able to identify multiple peptides that selectively bind to middle lanthanides. We describe the structural basis of the lanthanide-binding selectivity trend with strongest binding to the middle lanthanides, followed by the heavier lanthanides, and finally to the lighter ions.

ions↗
DOE OSTI2023

Operational resilience metrics for power systems with penetration of renewable resources

Abstract Modern power grid is evolving towards carbon neutrality by deploying increasing amount of renewable energy resources. However, the impact of renewable generation on power system planning and operation is not sufficiently investigated, especially the capability of renewable penetrated power systems to resist and recover from major disturbances, which is a critical concern for system operators. Novel metrics and evaluation methodologies are needed to depict systems’ ability in response to events caused by natural disasters, and quantitatively evaluate system performance in various time scales. In this paper, operational resilience metrics are proposed for power systems with penetration of renewable energy resources based on transient stability principles. A systematic methodology is proposed to quantitatively assess the evolution of system performance during various stages of the disaster process. Based on the proposed metrics, a resilience‐oriented disaster management strategy is designed and validated using the modified IEEE 39‐bus test system. The simulation results demonstrate the validity of the proposed metrics and strategy, and show that the system resilience is enhanced during the mitigation of fault conditions.

Gui, Jianzhong↗
DOE OSTI2024

Mining experimental magnetized liner inertial fusion data: Trends in stagnation morphology

In magnetized liner inertial fusion (MagLIF), a cylindrical liner filled with fusion fuel is imploded with the goal of producing a one-dimensional plasma column at thermonuclear conditions. However, structures attributed to three-dimensional effects are observed in self-emission x-ray images. Despite this, the impact of many experimental inputs on the column morphology has not been characterized. We demonstrate the use of a linear regression analysis to explore correlations between morphology and a wide variety of experimental inputs across 57 MagLIF experiments. Results indicate the possibility of several unexplored effects. For example, we demonstrate that increasing the initial magnetic field correlates with improved stability. Although intuitively expected, this has never been quantitatively assessed in integrated MagLIF experiments. We also demonstrate that azimuthal drive asymmetries resulting from the geometry of the “current return can” appear to measurably impact the morphology. In conjunction with several counterintuitive null results, we expect the observed correlations will encourage further experimental, theoretical, and simulation-based studies. Finally, we note that the method used in this work is general and may be applied to explore not only correlations between input conditions and morphology but also with other experimentally measured quantities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

NRC Multiphysics Analysis Capability Deployment FY2020: Part 3

This report details progress and activities of Idaho National Laboratory (INL) on the Nuclear Regulatory Commission (NRC) project “Development and Modeling Sup- port for Advanced Non-Light Water Reactors.” The tasks completed for this report are: Task2c: Explicit modeling of pebble transient temperature response. In this simulation, the 400 MWth Pebble-Bed Modular Reactor (PBMR) design, PBMR- 400, experiences a 20-second power ramp from 100% to 150% power. This is followed by a similar reduction in the power back to 100%. Several multiscale pebble coupling approaches are tested with one pebble per mesh element in the active core region. The results show good conservation behavior and the stability of the coupling.; Extended scope part 1: An assessment of the computational efficiency of the Discontinuous Finite Element Method (DFEM) heat transfer solver shows good scalability. The DFEM solver is a factor of 4 more expensive in solution time than the Finite Element Method (FEM) solver for heat transfer problems due to the increased number of degrees of freedom. Nonetheless, the DFEM approach provides the user with the flexibility to model gap heat transfer problems.; Extended scope part 2: The GapHeatTransferInterfaceMaterial was improved to give the user increased flexibility with the modeling of heat transfer through gaps with the DFEM solver. A number of gap parameters can now be coupled both through functions and variables.; Extended scope part 3: Demonstration of how the gap width between hexagonal fuel cells can be calculated during a heat-up transient and used in the GapHeatTransferInterface model. A full-domain DFEM model with gap expansion is coupled to a SubApp that models the thermal expansion of the base plate. The results show the expected physical behavior, although have not been fully bench-marked at this point in time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

3D Printed Chromophoric Sensors

Eight chromophoric indicators are incorporated into Sylgard 184 to develop sensors that are fabricated either by traditional methods such as casting or by more advanced manufacturing techniques such as 3D printing. The sensors exhibit specific color changes when exposed to acidic species, basic species, or elevated temperatures. Additionally, material properties are investigated to assess the chemical structure, Shore A Hardness, and thermal stability. Comparisons between the casted and 3D printed sensors show that the sensing devices fabricated with the advanced manufacturing technique are more efficient because the color changes are more easily detected.

3d printing↗
DOE OSTI2021

Fiber Optic Sensing Technologies for Battery Management Systems and Energy Storage Applications

Applications of fiber optic sensors to battery monitoring have been increasing due to the growing need of enhanced battery management systems with accurate state estimations. The goal of this review is to discuss the advancements enabling the practical implementation of battery internal parameter measurements including local temperature, strain, pressure, and refractive index for general operation, as well as the external measurements such as temperature gradients and vent gas sensing for thermal runaway imminent detection. A reasonable matching is discussed between fiber optic sensors of different range capabilities with battery systems of three levels of scales, namely electric vehicle and heavy-duty electric truck battery packs, and grid-scale battery systems. The advantages of fiber optic sensors over electrical sensors are discussed, while electrochemical stability issues of fiber-implanted batteries are critically assessed. This review also includes the estimated sensing system costs for typical fiber optic sensors and identifies the high interrogation cost as one of the limitations in their practical deployment into batteries. Finally, future perspectives are considered in the implementation of fiber optics into high-value battery applications such as grid-scale energy storage fault detection and prediction systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Impedance Scan of Inverter-Based Resources and Diesel Generator for Stability Analysis: Preprint

Impedance-based methods are widely used for power system stability analysis with inverter-based resources (IBRs), e.g., assessing dynamic interactions between the power grid and an IBR, control interactions between multiple IBRs, and the sub-synchronous oscillation and damping phenomenon. Since it is difficult to get a numerical model 100% matching with the hardware IBR, using the hardware inverter directly to obtain its output impedance has become a prominent approach nowadays. Therefore, this article presents the impedance scan using hardware IBRs, and also a hardware diesel generator as it still stays with the grid before the grid completely goes to renewable. The devices under test (DuTs) for the impedance scan includes two 3-..phi.., 480 V, 60 Hz commercial grid-forming IBRs (one of 250 kVA and another of 125 kVA rating) in series with ..delta..-Y transformers, one 3-..phi.., 480 V, 60 Hz commercial grid-following IBR (of 125 kVA rating), and a 3-..phi.., 480 V, 60 Hz commercial diesel generator (of 187.5 kVA rating). Using voltage signals perturbed with sub-, inter-, and higher harmonic components, and measuring the current response, the positive-sequence impedances are computed via an offline- based post-analysis. Moreover, best-fit transfer functions are estimated that closely resemble the measured data points of the positive-sequence impedances. Based on the observations from various outcomes of the hardware experiments, this article also provides some fundamental insights on the equivalent positive- sequence impedance of a combination of multiple hardware components by comparing the estimated and the empirically computed impedances. A comparative insight on the damping capability of the DuTs using the positive-sequence impedances of the hardware is also discussed.

grid following inverter↗
DOE OSTI2024

Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss

Advanced manufacturing research and development is typically small-scale, owing to costly experiments associated with these novel processes. Deep learning techniques could help accelerate this development cycle but frequently struggle in small-data regimes like the advanced manufacturing space. While prior work has applied deep learning to modeling visually plausible advanced manufacturing microstructures, little work has been done on data-driven modeling of how microstructures are affected by heat treatment, or assessing the degree to which synthetic microstructures are able to support existing workflows. We propose to address this gap by using invertible neural networks (normalizing flows) to model the effects of heat treatment, e.g., tempering. The model is developed using scanning electron microscope imagery from samples produced using shear-assisted processing and extrusion (ShAPE) manufacturing. This approach not only produces visually and topologically plausible samples, but also captures information related to a sample’s material properties or experimental process parameters. We also demonstrate that topological data analysis, used in prior work to characterize microstructures, can also be used to stabilize model training, preserve structure, and improve downstream results. We assess directions for future work and identify our approach as an important step towards end-to-end deep learning system for accelerating advanced manufacturing research and development.

Howland, Sylvia↗
DOE OSTI2021

Fossil energy deployment through midcentury consistent with 2°C climate stabilization

Energy system transformation scenarios satisfying particular climate stabilization objectives, such as 2°C, suggest that a wide range of fossil energy outcomes could be consistent with such objectives. The underlying drivers of variability in these outcomes cannot, however, be easily separated. This paper attempts to shed light on such drivers using a single, state-of-the-art global integrated assessment model (GCAM) to evaluate the energy system implications of climate stabilization near 2°C. We focus specifically on the role of fossil primary energy through midcentury under different assumptions about carbon capture and storage (CCS) and bioenergy supply. In our scenarios, coal and natural gas primary energy are most sensitive to the availability of CCS, with bioenergy supply having a secondary impact on these sources. Looking across primary energy sources, we find that primary energy from coal declines by midcentury relative to 2015 in all GCAM mitigation scenarios considered here, whereas primary energy from natural gas and oil increases by midcentury relative to 2015 in most of the scenarios in which CCS is available. We explain these results in terms of fundamental energy-economic relationships and discuss the implications of these findings for broader energy policy and planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Grid Strength Analysis for Integrating 30 GW of Offshore Wind Generation by 2030 in the U.S. Eastern Interconnection: Preprint

Offshore wind is a key player in the transition to a decarbonized electric gird, and the United States has set ambitious goals of integrating 30 GW of offshore wind capacity by 2030 and 110 GW by 2050. To facilitate this integration, the National Renewable Energy Laboratory and the Pacific Northwest National Laboratory are conducting the Atlantic Offshore Wind Transmission Study to assess transmission solutions. To achieve the 110-GW target by 2050, meticulous planning for network expansion and resource allocation is essential; however, meeting the 2030 goals requires integrating offshore wind power with minimal system upgrades, thus necessitating a careful study of grid strength and stability. The study team developed the Grid Strength Analysis Tool (GSAT) to assess system strength under various operating conditions and contingencies, focusing on the proposed integration of 30 GW of offshore wind power by 2030. In this paper, we provide a summary of key features of the GSAT software and results of the grid strength analysis for integrating 30 GW of offshore wind generation by 2030 in the U.S. Eastern Interconnection.

Automated System-wide Strength Evaluation Tool (AS↗
DOE OSTI2023

Biological toxicity and environmental hazards associated with PLGA nanoparticles

There have been tremendous advances in nanotechnology. More recently, the use of nanoparticles has expanded to applications including materials, packaging, energy, and medical uses such as drug delivery, diagnostics, and therapeutics. The American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) define nanoparticles as particles with at least one dimension measuring between 1 and 100 nm (2), but this view is generally too limited, and recommendations have been made to consider all materials with a dimension measuring under 1,000 nm as nanoparticles. The biomedical uses of nanoparticles are particularly promising because of their ability to reach and target various sites and organs. However, some nanoparticles can be composed of toxic materials or are limited by issues of biodistribution and bioaccumulation, which have hampered their use in biomedicine. The copolymer poly lactic-co-glycolic acid (PLGA) has gained use in biomedical applications as a delivery system because it is considered biocompatible and can be formulated with controlled degradation in physiological environments. PLGA has a history in biomedicine that dates to the 1970s when biodegradable sutures were developed using PLGA. In addition, the United States (US) Food and Drug Administration (FDA) and the European Medicine Agency (EMA) have approved various PLGA particle formulations as therapeutic delivery vehicles. Interest in biocompatible materials to deliver a range of therapeutic drugs, proteins, nucleic acids, and other molecules has risen recently. In addition to drug delivery, sustained drug release can be achieved by tuning the physical properties of PLGA, such as the molecular weight, ratio of lactic to glycolic acid, drug concentration, stabilizing molecules, and particle size. Given the rise of nanoparticles, human and environmental exposure to them is inevitable as more applications use free, unbound, and highly mobile particles. Although PLGA is generally considered safe, a detailed understanding of how PLGA and the other components used to help formulate this copolymer into a nanoparticle delivery vehicle is useful for assessing potential harmful effects. Despite recent advances in PLGA nanoparticle formulations, residual stabilizing molecules, inconsistent preparations, and batch-to-batch variations can lead to toxicity. Poor preparations of PLGA can lead to common mechanisms of toxicity observed with nanoparticles, such as inflammation and oxidative stress. Consistency in PLGA formulations and accurate methods to evaluate nanoparticle toxicity are needed to ensure safety. In order to better assess PLGA nanoparticles as non-toxic delivery systems, it is critical to understand their physical and chemical properties and the formulation protocols that may introduce toxic components into particles.

Biology, nanoparticle, toxicity↗
DOE OSTI2025

Regional and Sexual Dimorphism in Murine Skeletal Responses to Osteocytic HIF Pathway Modulation

Hypoxia-inducible factors (HIFs) are transcription factors stabilized under hypoxia and degraded under normoxia by the E3 ubiquitin ligase Von Hippel-Lindau (Vhl). In osteocytes, the central orchestrators of bone homeostasis, Vhl and HIFs promote an osteoanabolic transcriptional program. In this study, we assessed unique impacts of osteocytic Vhl deletion versus individual HIF-α paralog stabilization on bone structure, mineralization, and mechanics as a function of sex and skeletal region. Microcomputed tomography revealed that osteocytic Vhl deletion robustly increased trabecular bone mass in both sexes and at both axial and appendicular regions, while HIF-2α accumulation increased femoral metaphyseal bone mass in both sexes while enhancing vertebral bone mass only in males. Vhl deletion paradoxically reduced mineral heterogeneity in male vertebrae, despite raising peak and mean calcium content in both sexes. In contrast, HIF-2α stabilization impaired cortical mineralization and mechanics, especially in females. While cortical mineralization was disrupted in both genotypes, Vhl deletion improved whole bone mechanical properties, suggesting that enhanced geometry and mass offset compromised tissue quality. HIF-1α stabilization exerted negligible impacts on any outcome.

Biological and medical sciences↗
DOE OSTI2023

Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2 years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Geochemical data package for performance assessment calculations related to the Savannah River Site

The Savannah River Site (SRS) disposes of low-level radioactive waste (LLW) and stabilizes high-level radioactive waste (HLW) tanks in the subsurface environment. Calculations used to establish the radiological limits of these facilities are referred to as Performance Assessments (PAs), Special Analyses (SAs), and Composite Analyses (CAs). The purpose of this document is to revise the existing geochemical data package used for these calculations (Kaplan, 2016). This work builds on earlier compilations of geochemical data, referred to a geochemical data packages (Kaplan, 2007; Kaplan, 2010; Kaplan, 2016; McDowell-Boyer et al., 2000). This work is being conducted as part of the on-going maintenance program of the SRS PA programs that periodically updates calculations and data packages when new information becomes available.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗