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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 415 records · Page 23

Modeling and Analysis of DC Microgrids as Stochastic Hybrid Systems

This study proposes a method of predicting the influence of random load behavior on the dynamics of dc microgrids and distribution systems. This is accomplished by combining stochastic load models and deterministic microgrid models. Together, these elements constitute a stochastic hybrid system. The resulting model enables straightforward calculation of dynamic state moments, which are used to assess the probability of desirable operating conditions. Specific consideration is given to systems based on the dual active bridge (DAB) topology. Bounds are derived for the probability of zero voltage switching (ZVS) in DAB converters. A simple example is presented to demonstrate how these bounds may be used to improve ZVS performance as an optimization problem. In conclusion, predictions of state moment dynamics and ZVS probability assessments are verified through comparisons to Monte Carlo simulations.

42 ENGINEERING↗

Current-Source Solid-State DC Transformer Integrating LVDC Microgrid, Energy Storage, and Renewable Energy Into MVDC Grid

Solid-state DC transformer to integrate low-voltage DC (LVDC) microgrid, wind turbine (WT) generator, photovoltaic (PV), and energy storage (ES) into medium-voltage (MV) direct-current (MVDC) distribution grids is attractive. This paper proposes current-source DC solid-state transformer (SST) for MVDC collection system in WT, PV, and ES farms or as an interface between the MVDC grid and the LVDC microgrid. Compared to conventional current-source converter (CSC) based SSTs, a switch reduction scheme on reverse-blocking device bridges is proposed to reduce device count and the number of devices on the DC-link current path. Importantly, the proposed switch reduction scheme is generic and can be applied to the DC ports of DC-AC, AC-DC, or DC-DC hard-switching or soft-switching CSC-based SSTs. Based on this scheme, the proposed current-source DC SSTs are derived, which have reduced electrolytic-capacitor-less DC-link. The proposed DC SSTs also achieve single-stage isolated DC-DC or DC-AC conversion, full-range zero-voltage switching (ZVS) for main switches, zero-current switching (ZCS) for resonant switches, and controlled dv/dt. The proposed DC SSTs, operating principles, predictive control method, the ZVS, and the controlled dv/dt under voltage buck-boost ranges are verified with MV simulations and an experimental prototype based on SiC MOSFETs, diodes, and a nanocrystalline transformer.

14 SOLAR ENERGY↗

A Nitroxide Containing Organic Molecule in a Deep Eutectic Solvent for Flow Battery Applications

The nitroxide radical redox organic molecule, 2-phenyl-4,4,5,5-tetrame- thylimidazoline-1-oxyl-3-oxide (PTIO), was investigated for the first time in a deep eutectic solvent (DES)-like system consisting of a 1:4 molar ratio of choline chloride and ethylene glycol(Ch1EG4) as a redox flow battery electrolyte. PTIO is a single molecule with three oxidation states, and can provide both positive and negative redox couples for a flow battery. A flow battery using the PTIO/Ch1EG4 electrolyte demonstrated nearly 50% roundtrip efficiency with an approximately 1 V open circuit potential. Inefficiencies were primarily due to membrane resistance which can be significantly lowered with increased temperature. While PTIO appears stable over short periods (hours), the oxidized form is not stable in the DES-like electrolyte over longer times. Molecular modeling was performed to investigate the relative stability of PTIO in DES as compared to the previously studied 4-hydroxy-TEMPO (4HT). It was found that the oxoammonium cation 4HT + exhibits a noticeably larger nucleophilic reactive cloud as compared to PTIO + , indicating a higher reactivity. This method to predict stability of the oxoammonium cation shows promise to inform the design and synthesis of promising redox systems based on nitroxide radicals in DES electrolytes to identify new chemistries for large scale energy storage.

25 ENERGY STORAGE↗

mvBayes

SAND2026-16980O mvBayes implements multivariate Bayesian regression using MATLAB and decomposes a multivariate or functional response into components based on a user-specified orthogonal basis. This allows for independent modeling of each component with any chosen univariate Bayesian regression model. This tool includes methods for prediction and visualization, facilitating the evaluation of Bayesian surrogate models through the application of Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling techniques. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tucker, J. Derek [Sandia National Lab. (SNL-CA), L↗

Software for High-Resolution Flood Mapping With Sentinel-1 and Sentinel-2 via Misalignment-Robust Cross-Sensor Learning and Generative Despeckling

SF-26-078 This software provides a high-level workflow for satellite-based flood and surface-water mapping using Sentinel-1 SAR and Sentinel-2 multispectral imagery. It supports preparation of remote-sensing data, training and evaluation of deep-learning models, and generation of high-resolution water extent predictions, including methods to improve robustness to SAR speckle noise and cross-sensor image misalignment.

Feinstein, Jeremy [Argonne National Laboratory (AN↗

Persistent memory as an effective alternative to random access memory in metagenome assembly

Abstract Background The assembly of metagenomes decomposes members of complex microbe communities and allows the characterization of these genomes without laborious cultivation or single-cell metagenomics. Metagenome assembly is a process that is memory intensive and time consuming. Multi-terabyte sequences can become too large to be assembled on a single computer node, and there is no reliable method to predict the memory requirement due to data-specific memory consumption pattern. Currently, out-of-memory (OOM) is one of the most prevalent factors that causes metagenome assembly failures. Results In this study, we explored the possibility of using Persistent Memory (PMem) as a less expensive substitute for dynamic random access memory (DRAM) to reduce OOM and increase the scalability of metagenome assemblers. We evaluated the execution time and memory usage of three popular metagenome assemblers (MetaSPAdes, MEGAHIT, and MetaHipMer2) in datasets up to one terabase. We found that PMem can enable metagenome assemblers on terabyte-sized datasets by partially or fully substituting DRAM. Depending on the configured DRAM/PMEM ratio, running metagenome assemblies with PMem can achieve a similar speed as DRAM, while in the worst case it showed a roughly two-fold slowdown. In addition, different assemblers displayed distinct memory/speed trade-offs in the same hardware/software environment. Conclusions We demonstrated that PMem is capable of expanding the capacity of DRAM to allow larger metagenome assembly with a potential tradeoff in speed. Because PMem can be used directly without any application-specific code modification, these findings are likely to be generalized to other memory-intensive bioinformatics applications.

59 BASIC BIOLOGICAL SCIENCES↗

Zika virus dynamics: Effects of inoculum dose, the innate immune response and viral interference

Experimental Zika virus infection in non-human primates results in acute viral load dynamics that can be well-described by mathematical models. The inoculum dose that would be received in a natural infection setting is likely lower than the experimental infections and how this difference affects the viral dynamics and immune response is unclear. Here we study a dataset of experimental infection of non-human primates with a range of doses of Zika virus. We develop new models of infection incorporating both an innate immune response and viral interference with that response. We find that such a model explains the data better than models with no interaction between virus and the immune response. We also find that larger inoculum doses lead to faster dynamics of infection, but approximately the same total amount of viral production.

59 BASIC BIOLOGICAL SCIENCES↗

Mechanistic within-host mathematical model of inhalational anthrax

We present a mathematical model of the dynamics of Bacillus anthracis bacteria within the lymph nodes and blood of a host, following inhalation of an initial dose of spores. We also incorporate the dynamics of protective antigen, which is the binding component of the anthrax toxin produced by the bacteria. The model offers a mechanistic description of the early infection dynamics of inhalational anthrax, while its stochastic nature allows us to study the probabilities of different outcomes (for example, how likely it is that the infection will be cleared for a given inhaled dose of spores) in order to explain dose-response data for inhalational anthrax. The model is calibrated via a Bayesian approach, using in vivo data from New Zealand white rabbit and guinea pig infection studies, enabling within-host parameters to be estimated. We also leverage incubation-period data from the Sverdlovsk 1979 anthrax outbreak to show that the model can accurately describe human time-to-symptoms data under reasonable parameter regimes. Finally, we derive a simple approximate formula for the probability of symptom onset before time t, assuming that the number of inhaled spores has a Poisson distribution.

59 BASIC BIOLOGICAL SCIENCES↗

The effect of enrofloxacin on enteric Escherichia coli : Fitting a mathematical model to in vivo data

Antimicrobial drugs administered systemically may cause the emergence and dissemination of antimicrobial resistance among enteric bacteria. To develop logical, research-based recommendations for food animal veterinarians, we must understand how to maximize antimicrobial drug efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dosing regimens of enrofloxacin (a single high dose or three low doses) on Escherichia coli in cattle. We look specifically at bacteria above and below the epidemiological cutoff (ECOFF), above which the bacteria are likely to have an acquired or mutational resistance to enrofloxacin. We developed a differential equation model for the antimicrobial drug concentrations in plasma and colon, and bacteria populations in the feces. The model was fit to animal data of drug concentrations in the plasma and colon obtained using ultrafiltration probes. Fecal E. coli counts and minimum inhibitory concentrations were measured for the week after receiving the antimicrobial drug. We predict that the antimicrobial susceptibility of the bacteria above the ECOFF pre-treatment strongly affects the composition of the bacteria following treatment. Faster removal of the antimicrobial drugs from the colon throughout the study leads to improved clearance of bacteria above the ECOFF in the low dose regimen. If we assume a fitness cost is associated with bacteria above the ECOFF, the increased fitness costs leads to reduction of bacteria above the ECOFF in the low dose study. These results suggest the initial E. coli susceptibility is a strong indicator of how steers respond to antimicrobial drug treatment.

59 BASIC BIOLOGICAL SCIENCES↗

Brittle failure analysis and modeling of high-burnup PWR fuel cladding alloys

The aim of this research is the development of methods for predicting mechanical behavior and identification of limiting conditions to prevent brittle failure of high-burnup (HBU) pressure water reactor (PWR) fuel cladding alloys. A finite element (FE) model of the ring compression test (RCT) was created to analyze the failure behavior of zirconium-based alloys with radial hydrides during the RCT. An elastic-plastic material model describes the zirconium alloy. The stress-strain curve needed for the elastic-plastic material model was derived by inverse finite element analyses. Cohesive zone modeling is used to reproduce sudden load drops during RCT loading. Based on the failure mechanism in non-irradiated ZIRLO (R) claddings, a micro-mechanical model was developed that distinguishes between brittle failure along hydrides and ductile failure of the zirconium matrix. Two different cohesive laws representing these types of failure are present in the same cohesive interface. The key differences between these constitutive laws are the cohesive strength, the stress at which damage initiates, and the cohesive energy, which is the damage energy dissipated by the cohesive zone. Statistically generated matrix-hydride distributions were mapped onto the cohesive elements and simulations with focus on the first load drop were performed. Computational results are in good agreement with the RCT results conducted on high-burnup M5 (R) samples. It could be shown that crack initiation and propagation strongly depend on the specific configuration of hydrides and matrix material in the fracture area.

Simbruner, Kai↗

Rethinking hysteresis in magnetic materials

Abstract Magnetic materials with zero hysteresis are posed to have a significant impact on sustainable energy conversion, electronics, and communication technologies. As the global market for soft magnetic materials continues to expand, driven by increasing demand in renewable energy, automotive, and power transmission sectors, it is important to design magnets that can be cycled under an external field reversibly and rapidly multiple times without a decay in magnetic response. However, designing magnets with small hysteresis has been a challenge because we do not fully understand its origins. In this article, I outline key research efforts investigating the fundamental mechanisms underpinning hysteresis in soft magnets and, highlight recent developments in the use of nonlinear analysis and nucleation barrier methods to predict coercivity in these materials. Graphical abstract

Renuka Balakrishna, Ananya (ORCID:0000000306015257↗

Tris(isodecyl)guanidine Degradation in the MCU System

The current solvent blend consists of four components; an extractant, the modifier, a suppressor, and the diluent. Of the four components, only the suppressor – tris(isodecyl)guanidine (TiDG) has exhibited an appreciable depletion rate during facility operations. Using data derived from Modular Caustic-Side Solvent Extraction Unit (MCU) process samples, Savannah River National Lab (SRNL) derived a method to predict the TiDG depletion based upon time and volume of feed processed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Exploratory Efforts to Constrain Geologic Material Properties from Remote Sensing Data: Joint Study (FY2020 Final Report)

Identification and characterization of underground events from surface or remote data requires a thorough understanding of the rock material properties. However, material properties usually come from borehole data, which is expensive and not always available. A potential alternative is to use topographic characteristics to approximate the strength, but this has never been done before quantitatively. Here we present the results from the first steps towards this goal. We have found that there are strong correlations between compressive and tensile strengths and slopes, but these correlations vary depending on data analysis details. Rugosity may be better correlated to strength than slope values. More comprehensive analyses are needed to fully understand the best method of predicting strength from topography for this area. We also found that misalignment of multiple GIS datasets can have a large influence on the ability to make interpretations. Lastly, these results will require further study in a variety of climatic conditions before being applicable to other sites.

58 GEOSCIENCES↗

Exploratory Efforts to Constrain Geologic Material Properties from Remote Sensing Data: A Joint Study

Identification and characterization of underground events from surface or remote data requires a thorough understanding of the rock material properties. However, material properties usually come from borehole data, which is expensive and not always available. A potential alternative is to use topographic characteristics to approximate the strength, but this has never been done before quantitatively. Here we present the results from the first steps towards this goal. We have found that there are strong correlations between compressive and tensile strengths and slopes, but these correlations vary depending on data analysis details. Rugosity may be better correlated to strength than slope values. More comprehensive analyses are needed to fully understand the best method of predicting strength from topography for this area. We also found that misalignment of multiple GIS datasets can have a large influence on the ability to make interpretations. Lastly, these results will require further study in a variety of climatic conditions before being applicable to other sites.

42 ENGINEERING↗

PPPL Laboratory Directed Research and Development (Project Final Reports, FY2018 - FY2020)

The U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) is a collaborative national center for fusion energy science, basic sciences, and advanced technology. The Laboratory has three major missions: (1) to develop the scientific knowledge and advanced engineering to enable fusion to power the U.S. and the world; (2) to advance the science of nanoscale fabrication for technologies of tomorrow; and (3) to further the development of the scientific understanding of the plasma universe from laboratory to astrophysical scales. PPPL’s Laboratory Directed Research and Development (LDRD) program supports and encourages creativity and innovation and contributes to its long-term viability. New scientific and technical research areas emerge and are nurtured through the program. Furthermore, new capabilities are developed to enable the Laboratory to meet its and DOE’s missions. The program is used to systematically diversify the Laboratory’s programs and mission. In the last few years, the program has started projects in nanomaterial synthesis, microelectronics, advanced x-ray spectroscopy, high-energy-density physics, superconducting magnet technology, machine learning and artificial intelligence, 3D magnetic fields to optimize fusion plasmas, integration of permanent magnets with simple high-field magnets to reduce the cost of producing complex 3D magnetic fields, advanced computational methods for predictive understanding and control of fusion plasma, development of quantum computing algorithms for plasma physics, liquid metal plasma-facing components for fusion reactors, virtual engineering, and plasma-based space propulsion. The program is also the vehicle to recruit and train talented scientists and engineers with the new skills needed to perform the Laboratory’s mission. Many of the new hires through the program go on to become world-class scientists and engineers in their fields. This report provides descriptions and accomplishments of those LDRD projects that were completed during fiscal years 2018 through 2020.

36 MATERIALS SCIENCE↗

Sea ice evolution along the Northern Sea Route and implications for trans-Arctic shipping from 2021 through 2060

Arctic surface temperatures warmed at twice the global average in the second half of the 20 th century due to Arctic amplification (AA), a phenomenon predominantly caused by regional polar changes, like the melting of perennial sea ice and reduced sea ice extent (leading to more solar radiation being absorbed by the ocean surface as opposed to being reflected back to space by the ice surface). AA is projected to reach a factor of three even if the climate is stabilized by the mid 21 st century by reduced greenhouse gas emissions. In all emissions scenarios, AA is projected to lead to temperature changes at least 2.4 times larger than global mean surface temperature changes occurring between 2070 and 2100. The ice-albedo feedback, which occurs when the polar-marine surface absorbs more radiation as highly reflective sea ice melts, is reversible such that the premise of a runaway process is no longer accepted as a realistic possibility. No matter what actions are taken to reduce CO 2 concentrations in the atmosphere from now on, two different methods of predicting an ice-free Arctic suggest that perennial sea ice will mostly disappear in September by the year 2050. If there is no reduction in anthropogenic CO 2 and methane emissions, that scenario could occur sooner than 2030. Defining Arctic navigability as safe and economic passage of Polar Class 7 cargo ships without need of an escorting icebreaker, no single trans Arctic ship route will be navigable year-round in the first half of the 21 st century, including in the strong emission scenarios. However, seasonal trans-Arctic navigability will increase this century. An estimate on the number of days per year that the Northern See Route (NSR) will be navigable in the future is beyond the scope of this report. Along Northern Sea Routes 5 and 6, which run close to the Russian coast and Yamal LNG plant, an ARC 7 ice class LNG tanker, the equivalent of a Polar Class 3 (PC3) vessel, will be at low risk in December through April at some point during the current decade. May will continue to entail some risk (more than April) along relatively short segments of these routes through the end of the next decade (2030-2039). Come June, snow rapidly melts away, and thereafter the underlying sea ice begins becomes thinner and less concentrated, greatly reducing risk. However, neither path is desirable for ARC 7 tankers due to shallow bathymetry in Sannikov Strait, and a more desirable path for these ships passes to the north of the New Siberian Islands (route 20, discussed further below). Conventional LNG tankers (i.e., non-ice-strengthened vessels according to the IMO classification), without icebreaker escorts, will continue to encounter dangerous or impassable conditions along many sections of the NSR through the end of this decade for many months of the year. Through 2039, these conventional tankers will be able to operate safely along NSR 6 from August through October. By 2040-2049, the span of safe operation increases to August through November, and by 2050-2059 it increases to July through November, assuming a northward deviation to avoid the Sannikov Strait.

54 ENVIRONMENTAL SCIENCES↗

Physical, resource supply, and biological controls on nutrient processing along the river continuum

Nutrient impairment has led to damages to US surface and groundwater systems in excess of 100 billion dollars per year. Therefore, there is a strong need to develop methods to predict the transport, uptake, and export of nutrients along fluvial networks. We present results that are based on a data-driven mechanistic understanding of three factors that largely control nutrient uptake and export: 1) interactions between transport-related processes (mass transfer to metabolically active zones), 2) resource supply dynamics (nutrient concentration, stoichiometric constraints, etc.), and 3) biological controls (microbial community structure and function). Our results were generated from column experiments conducted along the Jemez River-Rio Grande continuum, which spans four orders of magnitude in mean annual discharge, more than 2000 m in altitude, and more than 500 km of stream longitude. Two resource supply injections were performed on each of the columns, i.e., a nitrate only addition, followed by a stoichiometrically ‘balanced’ 106Carbon:16Nitrogen:1Phosphorus addition. We quantified NO3-N uptake kinetics while constraining three variables: stream order, sediment type and type of injection (N vs stoichiometrically ‘balanced’ C:N:P). Following the laboratory nutrient uptake experiments, the columns were destructively sampled and the contents were homogenized to collect subsamples for DNA sequencing. Amplicon analysis was carried out as described by the Earth Microbiome Protocol for 16s and ITS sequencing.

54 ENVIRONMENTAL SCIENCES↗

Novel Phases and Dynamics of Multi-orbital Mott Insulators

The goal of this project is to gain theoretical insight into magnetism in the presence of spin-orbit coupling (SOC) for multi-orbital Mott insulators. Inspired by the exact solution of the Kitaev model that harbors a quantum spin liquid with novel excitations and struck by its relevance for materials with anisotropic orbital interactions, the PI will explore various new classes of 4d and 5d transition metal oxides. Starting with all electron Hamiltonians, the PI will derive minimal magnetic models. The aim will be to understand the role played by orbital frustration, even in the absence of any geometric frustration, in creating orbitally ordered as well as spin-orbital liquid phases. Orbital frustration arises primarily from the directional or anisotropic nature of d-orbitals in contrast to the isotropic nature of the spin degree of freedom. These models will be investigated by a variety of theoretical and numerical methods, including exact diagonalization, mean field theories, density matrix renormalization group and quantum Monte Carlo methods. Testable predictions for three experiments: nuclear magnetic resonance (NMR) and resonant x-ray scattering (RXS) to probe orbital ordering and pump-probe experiments to probe quantum dynamics will test the validity of these models for materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗