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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Design Optimization for Weighted Conduction Loss Minimization in a Dual-Active-Bridge-Based PV Microinverter

This manuscript presents a harmonic-superposition based frequency-domain approach for analysis and optimal design of a single-stage DAB-based DC-AC converter used in low-voltage DC to high voltage AC applications, such as PV microinverter. Specifically, two topological variations and correspondingly different modulation strategies involving up to four control variables (three phase shifts and switching frequency) are analyzed. Optimal trajectories of control variables are determined that result in minimized transformer RMS current at a given average power, while enforcing near-uniform ZVS. To make the design process holistic, an outer optimization loop is used to determine optimal values for the power transfer inductance (integrated as the transformer’s leakage inductance) and turns ratio, which minimize the weighted-average conduction loss contribution to the microinverter’s CEC efficiency. Based on the optimization analyses, a 400 W microinverter prototype is built and experimental results at one operating point are presented, achieving a measured efficiency of 92.8%.

14 SOLAR ENERGY↗
DOE OSTI2022

Nanoscale interplay of native point defects near Sr-deficient SrxTiO 3 /SrTiO 3 interfaces

SrTiO 3 has many applications involving interfaces with other materials, but defects that affect the properties of SrTiO 3 films can also play a significant role at its heterointerfaces and even at junctions with nonstoichiometric SrTiO 3 . Depth-resolved cathodoluminescence spectroscopy (DRCLS) combined with systematic cation Sr content reduction in SrxTiO 3 ultrathin films grown on SrTiO 3 showed an interplay of native point defects and electronic structure within the Sr-deficient film and how interplay extends tens of nanometers into the substrate below. Understanding how defects form and affect interface electronic structure during epitaxial growth is central to improving complex oxide devices. Controlling the balance of oxygen vacancy defects with strontium vacancies and other acceptor-like defects can improve control over free carrier densities. Likewise, control over nanoscale defect charge distributions can advance new device features such as two-dimensional hole gases and the performance of existing devices such as ferroelectric tunnel junctions. This study shows how DRCLS directly measures the relative densities and spatial distributions of multiple native defects within and extending away from nanoscale SrxTiO 3 /SrTiO 3 junctions and how their interplay varies with controlled epitaxial growth. In turn, these growth-dependent defect variations can help guide SrTiO 3 epitaxial growth with other complex oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Smart Manufacturing Pathways for Industrial Decarbonization and Thermal Process Intensification

Rapid decarbonization is fast becoming the primary environmental and sustainability initiative for many economic sectors. Industry consumes more than 30 % of all primary energy in the United States and accounts for nearly 25 % of all greenhouse gas (GHG) emissions. More than 70 % of energy consumed by the industrial sector is related to thermal processes, which are also the largest contributors of carbon emissions, overwhelmingly due to the combustion of fossil fuels. Thermal process intensification (TPI) seeks to dramatically improve the energy performance of thermal systems through technology pillars focusing on alternative energy sources and processes, supplemental technologies, and waste heat management. The impacts of TPI have significant overlap with the goals of industrial decarbonization (ID) that seeks to phase out all GHG emissions from industrial activities. Emerging supplemental technologies such as smart manufacturing (SM) and the industrial internet of things (IoT) enable significant opportunities for the optimization of manufacturing processes. Combining strategies for TPI and ID with SM and IoT can open and enhance existing opportunities for saving time and energy via approaches such as tighter control of temperature zones, better adjustment of thermal systems for variations in production levels and feedstock properties, and increased process throughput. Data collected by smart processes will also enable new advanced solutions such as digital twins and machine learning algorithms to further improve thermal system savings. Herein, this paper examines the individual pathways of TPI, ID, and SM and how the combination of all three can accelerate energy and GHG reductions.

42 ENGINEERING↗
DOE OSTI2022

Microtopographic and Depth Controls on Active Layer Chemistry in Arctic Polygonal Ground: Supporting Data

Polygonal ground is a signature characteristic of the Arctic, and permafrost thaw can potentially generate substantial feedbacks to Arctic ecosystems and climate. This study describes the first comprehensive spatial examination of active layer biogeochemistry that extends across high- and low-centered polygons and their features, including depth. Water chemistry measurements were made on active layer water samples collected near Barrow, Alaska during summer, 2012. Several significant differences in chemistry were observed between high- and low-centered polygons suggesting polygon types may be useful for landscape-scale geochemical classification. However, differences were found for polygon features (centers and troughs) for analytes that were not significant for type, suggesting that finer scale features control biogeochemistry in a different way than polygon type. Depth variations were also significant, demonstrating important multi-dimensional aspects of polygonal ground biogeochemistry. These results have major implications for understanding how polygonal ground ecosystems function, and how they may respond to future change. This data package includes one *.csv data file and one *.pdf user guide. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗
DOE OSTI2020

Geographic variation in the genetic basis of resistance to leaf rust between locally adapted ecotypes of the biofuel crop switchgrass ( Panicum virgatum )

Local adaptation is an important process in plant evolution, which can be impacted by differential pathogen pressures along environmental gradients. However, the degree to which pathogen resistance loci vary in effect across space and time is incompletely described. As such, to understand how the genetic architecture of resistance varies across time and geographic space, we quantified rust ( Puccinia spp.) severity in switchgrass ( Panicum virgatum ) plantings at eight locations across the central USA for 3 yr and conducted quantitative trait locus (QTL) mapping for rust progression. We mapped several variable QTLs, but two large-effect QTLs which we have named Prr1 and Prr2 were consistently associated with rust severity in multiple sites and years, particularly in northern sites. By contrast, there were numerous small-effect QTLs at southern sites, indicating a genotype-by-environment interaction in rust resistance loci. Interestingly, Prr1 and Prr2 had a strong epistatic interaction, which also varied in the strength and direction of effect across space. Our results suggest that abiotic factors covarying with latitude interact with the genetic loci underlying plant resistance to control rust infection severity. Furthermore, our results indicate that segregating genetic variation in epistatically interacting loci may play a key role in determining response to infection across geographic space.

09 BIOMASS FUELS↗
DOE OSTI2022

Manganese Oxidation States in Volcanic Soils across Annual Rainfall Gradients

Manganese (Mn) exists as Mn(II), Mn(III), or Mn(IV) in soils, and the Mn oxidation state controls the roles of Mn in numerous environmental processes. However, the variations of Mn oxidation states with climate remain unknown. Here we determined the Mn oxidation states in highly weathered bulk volcanic soils (primary minerals free) across two rainfall gradients covering mean annual precipitation (MAP) of 0.25–5 m in the Hawaiian Islands. With increasing MAP, the soil redox conditions generally shifted from oxic to suboxic and to anoxic despite fluctuating at each site; concurrently, the proportions of Mn(IV) and Mn(II) decreased and increased, respectively. Mn(III) was low at both low and high MAP, but accumulated substantially, up to 80% of total Mn, in soils with prevalent suboxic conditions at intermediate MAP. Mn(III) was likely hosted in Mn(III,IV) and iron(III) oxides or complexed with organic matter, and its distribution among these hosts varied with soil redox potentials and soil pH. Soil redox conditions and rainfall-driven leaching jointly controlled exchangeable Mn(II) in soils, with its concentration peaking at intermediate MAP. The Mn redox chemistry was at disequilibrium, with the oxidation states correlating with long-term average soil redox potentials better than with soil pH. The soil redox conditions likely fluctuated between oxic and anoxic conditions more frequently at intermediate than at low and high MAP, creating biogeochemical hot spots where Mn, Fe, and other redox-sensitive elements may be actively cycled.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Characterization of MoS 2 films via simultaneous grazing incidence X-ray diffraction and grazing incidence X-ray fluorescence (GIXRD/GIXRF)

Physical vapor deposited (PVD) molybdenum disulfide (nominal composition MoS 2 ) is employed as a thin film solid lubricant for extreme environments where liquid lubricants are not viable. The tribological properties of MoS 2 are highly dependent on morphological attributes such as film thickness, orientation, crystallinity, film density, and stoichiometry. These structural characteristics are controlled by tuning the PVD process parameters, yet undesirable alterations in the structure often occur due to process variations between deposition runs. Nondestructive film diagnostics can enable improved yield and serve as a means of tuning a deposition process, thus enabling quality control and materials exploration. Grazing incidence X-ray diffraction (GIXRD) for MoS 2 film characterization provides valuable information about film density and grain orientation (texture). However, the determination of film stoichiometry can only be indirectly inferred via GIXRD. The combination of density and microstructure via GIXRD with chemical composition via grazing incidence X-ray fluorescence (GIXRF) enables the isolation and decoupling of film density, composition, and microstructure and their ultimate impact on film layer thickness, thereby improving coating thickness predictions via X-ray fluorescence. We have augmented an existing GIXRD instrument with an additional X-ray detector for the simultaneous measurement of energy-dispersive X-ray fluorescence spectra during the GIXRD analysis. This combined GIXRD/GIXRF analysis has proven synergetic for correlating chemical composition to the structural aspects of MoS 2 films provided by GIXRD. We present the usefulness of the combined diagnostic technique via exemplar MoS 2 film samples and provide a discussion regarding data extraction techniques of grazing angle series measurements.

MoS2↗
DOE OSTI2025

Unsteady- and Steady-State Relative Permeability Study with X-ray and Acoustic Monitoring for CO 2 Storage in Deep Saline Aquifers

In this study, we link the multiphase flow measurements with controls of sedimentary structures (e.g., heterogeneity and anisotropy) on relative permeability to variations in ultrasonic velocities for two deep saline aquifer formations (Entrada and Bluff Sandstones) in the San Juan Basin of the Southwestern USA. The rock specimens were extracted from outcrop sites near Durango, CO, USA. They have distinct differences in grain size, cementation composition, and individual chemical amounts, despite both formations being eolian sandstones. We performed a series of unsteady- and steady-state CO 2 -brine relative permeability experiments under capillary-controlled displacement rates. Unsteady-state experiments were conducted at 71 °C and 9.65 MPa; steady-state experiments were conducted at 85 °C and 22.8 MPa and 89 °C and 24.1 MPa for the Bluff and Entrada Sandstones, respectively. During the unsteady experiments, X-ray computed tomography was used to visualize multiphase flow in porous media and quantify saturations during brine drainage under various flow rates. Scan images and saturation profiles indicate that the CO 2 distribution in the pore volume was strongly impacted by the presence of high-angle cross-laminations, heterogeneous rock structure, and direction of bedding orientation. Those factors contribute to dramatic and quick initial breakthroughs and affect the overall saturation dynamics. Steady-state relative permeability tests were conducted at net flow rates of 1 mL/min for both brine drainage and imbibition. During the experimental steps, the CO 2 fractional flow was increased and decreased for both drainage and imbibition scenarios to mimic the front when CO 2 contacts brine and behind the front when brine enters space previously occupied by CO 2 . It was found that compressional velocity decreased, while shear waves slightly increased as brine saturation decreased. The hysteresis effects for the relative permeability and acoustic velocities were distinct. Furthermore, the CO 2 /brine front stability is quantified by applying a mobility ratio approach to spot saturations at which the boundary line between fluids becomes uneven. The results presented in this work can potentially boost the quality and precision of forecasts for the CO 2 storage projects in which the vertical and horizontal core-scale heterogeneity and anisotropy impact the plume migration within host reservoirs.

carbon dioxide (CO2)↗
DOE OSTI2026

Chelation-Driven Chemistry Controls Dissolution Pathways for Facile Critical Mineral Recovery from Ultramafic Resources

The efficient recovery of critical minerals, such as nickel (Ni) and manganese (Mn) from the subsurface is of vital importance, given their role in modern technologies ranging from batteries to advanced alloys. This study explores an approach that employs chelation to enhance critical mineral recovery from ultramafic rocks using chelating ligands under ambient pressures and systematically evaluates the extraction process for the first time. Experimental results demonstrate the superior performance of EDTA and PrDTA in achieving high extraction efficiencies for both Ni and Mn. To further highlight the ability for optimization, the fluid exchange and variation of fluid-to-rock ratio experiments were conducted, revealing tunable controls for engineering recovery. We show that under optimized conditions, a Ni extraction efficiency of ?82% and a Mn extraction efficiency of ~60% is achieved at ambient pressures, which is equivalent to over 23 times the current global Ni production, and 2 million metric tonnes (MMT) greater than the current global Mn production. From both in-situ and ex-situ mining perspectives, the results highlight the potential of chelation to advance sustainable mineral processing. Overall, this work highlights the environmental benefits of innovative ligand-assisted recovery methods, addressing the critical need for the efficient utilization of olivine resources.

Krishnan, Keerthana↗
DOE OSTI2022

Editors’ Choice—Methods—Pressure Control Apparatus for Lithium Metal Batteries

Lithium (Li) metal anodes are essential for developing next-generation high-energy-density batteries. However, Li dendrite/whisker formation caused short-circuiting issue and short cycle life have prevented lithium metal from being viably used in rechargeable batteries. Numerous works have been done to study how to regulate the Li growth in electrochemical cycling by using external stacking forces. While it is widely agreed that stack pressure positively affects the lithium plating/stripping process, the optimized pressure range provided by different works varies greatly because of the difference in the pressure control setup. In this work, a pressure control apparatus is designed for Li metal batteries with liquid and solid-state electrolytes (SSE). With considerations of minimizing cell to cell variation, a reusable split cell and pressure load cell are made for testing electrochemical cells with high precision pressure control. The capability of the designed setup is demonstrated by studying the pressure effect on the Li plating/stripping process.

25 ENERGY STORAGE↗
DOE OSTI2020

Modeling plasma toroidal flow profile control via NTV torque with n = 2 3D fields in MAST-U

Toroidal modeling utilizing the MARS-Q code shows that large (compared to electromagnetic torque) neoclassical toroidal viscous (NTV) torque can be achieved in the plasma core region in reference MAST(-U) L-mode plasmas, by applying the n=2 (n is the toroidal mode number) 3-D fields generated by the magnetic coils used for controlling the edge localized mode and/or by the error field correction coils. Large NTV torque, occurring at relatively slow plasma flow, in combination with strong variation of the torque amplitude versus the coil phasing of the 3-D coils, offers a tool to control the plasma toroidal rotation profile in spherical tokamaks. To effectively employ the toroidal NTV torque, the potentially fast initial flow needs to first be damped by 3-D fields. In conclusion, MARS-Q quasi-linear initial value simulations demonstrate that this is achievable in MAST-U, within the designed 3-D coil current capability for the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Domain wall-magnetic tunnel junction spin–orbit torque devices and circuits for in-memory computing

There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction memory elements can be used for computation by manipulating a domain wall, a transition region between magnetic domains, but the experimental study of such devices has been limited by high current densities and low tunnel magnetoresistance. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy, spin–orbit torque switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 171% and average resistance-area product RA = 29 Ω μm2, close to the RA of the unpatterned film. Device initialization variation in switching voltage is shown to be curtailed to 7%–10% by controlling the domain wall initial position, which we show corresponds to 90%–96% accuracy in a domain wall-magnetic tunnel junction full adder simulation. Repeatability of writing and resetting the device is shown. A circuit shows an inverter operation between two devices, showing that a voltage window is large enough, compared to the variation noise, to repeatably operate a domain wall-magnetic tunnel junction circuit. These results make strides in using magnetic tunnel junctions and domain walls for in-memory and neuromorphic computing applications.

Alamdar, Mahshid (ORCID:0000000221732935)↗
DOE OSTI2023

Simulation of the NuScale SMR and Investigation of the Effect of Load-Following on Component Lifetimes

The NuScale SMR has been modelled using the Virtual Environment for Reactor Applications (VERA) multiphysics environment and the results compared with the publicly reported data in the Design Certification Application (DCA). The results show an excellent agreement for the compared axial and radial power distributions, temperature coefficients of reactivity, boron and control rod worths, and fast neutron flux. This NuScale model is then used to investigate the effect of different operational modes on reactor components to determine how flexible load-following operation may affect control rod and reactor pressure vessel (RPV) lifetimes. The control rod degradation is confirmed to primarily affect the silver-indium-cadmium (AIC) rod tip. The degradation rate is observed to follow a non-linear function of core power level where the increase in degradation decreases with insertion depth. For the variation in core power levels expected with current load-following schemes, the total control rod degradation is found to be mild, at 5-10% of usable life per cycle for a reactor operating at <80% power. Nonetheless, this enables load following strategies to be confirmed and/or modified to ensure that control rods do not need to be replaced during the 60+ year life of the reactor. The RPV degradation was found to be almost directly proportional to the core power level and was not overly sensitive to flux shape perturbations. Future work is planned using these damage functions to optimize operation over multiple NuScale SMR units and develop strategies for prognostics and health management.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Ablating Pellets for Areal Density Symmetry Control in Indirectly Driven Inertial Confinement Fusion Target Designs

Asymmetry of the compressed fusion fuel configuration, as characterized by a variation in areal density, in inertial confinement fusion (ICF) implosions is a known performance limitation. While ignition has been achieved on the National Ignition Facility (NIF), implosion symmetry was not perfect, and subsequent experiments that have extended fusion performance still struggle with symmetry control and would, in principle, perform much better if the areal density uniformity of the fuel was improved by eliminating thin regions in the DT (Deuterium-Tritium) fuel that develop by the time of peak compression. In indirect drive (IDD), altering the time-dependent laser cone fraction and cross-beam energy transfer are the usual tactics for controlling implosion asymmetry in IDD target designs, but it may be that those tools have been pushed to their limits. In this Letter, we present a new tactic for controlling the implosion using satellite ablating pellets to manipulate the time-dependent x-ray field around an ICF capsule. In theory and in simulations the action of these pellets is quite effective at reducing the swing of x-ray drive from waist hot to pole hot, thus achieving a more one-dimensional fuel configuration at peak compression. Simulations show that a factor of ∼2 in yield performance can be obtained via this technique, when applied to past record implosions on the NIF. As a result, some speculative alternate uses of the proposed target concept are also mentioned in the conclusion of this Letter.

Implosion symmetry↗
DOE OSTI2025

Traffic Smoothing Using Explicit Local Controllers: Experimental Evidence for Dissipating Stop-and-go Waves with a Single Automated Vehicle in Dense Traffic

This article presents experimental evidence of the ability of a single automated vehicle acting as a controller to effectively dissipate stop-and-go waves in real traffic. The automated vehicle succeeded in stabilizing the speed profile by reducing oscillations in time and speed variations between vehicles during rush hour on I-24 in the Nashville area. Here, we detail the control design, deployment and results obtained in this experiment, conducted as part of the CIRCLES consortium’s “MegaVanderTest” 2022, which involved a total of 100 automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Adaptive Power System Emergency Control using Deep Reinforcement Learning

Power system emergency control is generally regarded as the final safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenarios or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper proposes a novel adaptive emergency control scheme using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities DRL has for complex systems with high-dimensional variations. Furthermore, an open-source platform named DeepGrid has been designed for the first time to assist the DRL development and benchmarking processes in power system emergency control. Details of the platform, DRL, and emergency control schemes that use dynamic braking or under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.

Deep reinforcement learning, emergency control, lo↗