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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Natural and anthropogenic controls on lake water‐level decline and evaporation‐to‐inflow ratio in the conterminous United States

Abstract Lake water levels are integral to lake function, but hydrologic changes from land and water management may alter lake fluctuations beyond natural ranges. We constructed a conceptual model of multifaceted drivers of lake water levels and evaporation‐to‐inflow ratio (Evap : Inflow). Using a structural equation modeling framework, we tested our model on (1) a national subset of lakes in the conterminous United States with minimal water management to describe natural drivers of lake hydrology and (2) five ecoregional subsets of lakes to explore regional variation in water management effects. Our model fits the national and ecoregional datasets and explained up to 47% of variation in Evap : Inflow, 38% of vertical water level decline, and 79% of horizontal water level decline (littoral exposure). For lakes with minimal water management, Evap : Inflow was related to lake depth ( β = −0.31) and surface inflow ( β = −0.44); vertical decline was related to annual climate (e.g., precipitation β = −0.18) and water management ( β = −0.21); and horizontal decline was largely related to vertical decline ( β = 0.73) and lake morphometry (e.g., depth β = −0.18). Anthropogenic effects varied by ecoregion and likely reflect differences in regional water management and climate. In the West, water management indicators were related to greater vertical decline ( β = 0.38), whereas in the Midwest, these indicators were related to more stable and full lake levels ( β = −0.22) even during drought conditions. National analyses show how human water use interacts with regional climate resulting in contrasting impacts to lake hydrologic variation in the United States.

Fergus, C. Emi↗
DOE OSTI2023

Copper-nickel electroplating of 3D-printed acrylonitrile butadiene styrene for interference and radiation shielding applications

Here, in this study, electromagnetic and radiation shielding structures were fabricated by 3D printing acrylonitrile butadiene styrene (ABS) structures via fused filament fabrication and electroplating with copper (Cu) and nickel (Ni) metals. Prior to electroplating, the printed ABS materials were made conductive by surface electrodeposition of polypyrrole (PPy). ABS adsorbs the pyrrole monomer, which subsequently polymerizes via oxidation forming conductive PPy layer on the printed structures. The successful anchoring of PPy layer can be attributed to its hydrogen bonding and electrostatic interactions with ABS. Thermal analysis, optical, and electron microscopy equipped with energy dispersive X-ray were employed to characterize the printed shielding structures, while the interference and radiation shielding effectiveness were evaluated using a spectrum analyzer and dosimeter, respectively. The interference shielding was found to be effective at 60 ± 5 dB over relatively small frequency range. Shielding was also apparent against gamma and X-ray ionizing rays, but more significantly effective against beta rays. Furthermore, the Cu/Ni coating was able to reduce any heat-induced dimensional changes for the 3D printed ABS substrate without compromising the mechanical integrity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Design and Characterization of the Engineering Model of the Spectrometer Onboard LuSEE‐Night

Abstract The Lunar Surface Electromagnetics Explorer—Night, LuSEE‐Night, is a low‐frequency radio astronomy experiment that will explore the cosmic Dark Ages signal on the radio‐quiet farside of the Moon. The LuSEE‐Night carries a radio frequency spectrometer consisting of a set of antennas, analog and digital processing electronics, and will be launched by NASA's Commercial Lunar Payload Services in 2025. The spectrometer is designed to observe the spectrum of the radio sky in the 0.5−50 MHz band. The engineering model (EM) of the four‐channel spectrometer has been developed. The EM has been characterized for linearity, gain, noise, and their temperature dependence, confirming that the EM meets all the requirements for LuSEE‐Night. Three mitigation techniques have been implemented and verified to suppress self‐induced electromagnetic interference. The flight model of the spectrometer is currently being developed and is scheduled to be shipped to the integration site in early 2024.

Tamura, Emi↗
DOE OSTI2023

Field reversal in low pressure, unmagnetized radio frequency capacitively coupled argon plasma discharges

In general, the radio frequency (rf) electric field within a sheath points toward the metal electrode in low pressure, unmagnetized rf electropositive capacitively coupled plasma (CCP) glow discharges. This is due to the large ratio of electron to ion mobility and the formation of an ion sheath. In this work, we studied, using fully kinetic particle-in-cell simulations, a reversed electric field induced by the strong secondary electron emission during the phase of sheath collapse in a high-voltage rf-driven low pressure CCP glow discharge. We explored the transition behavior of the formation of field reversal as a function of driving voltage amplitude and found that field reversal starts to form at around 750 V, for a discharge with an electrode spacing of 4 cm at 10 mTorr argon pressure driven at 13.56 MHz. Accordingly, the energy distribution function of electrons incident on the electrode shows peaks from around 3 to 10 eV while varying the driving voltage from 150 to 2000 V, showing potentially beneficial effects in plasma material processing where relatively directional electrons are preferred to solely thermal diffusion electrons.

Physics↗
DOE OSTI2023

On the importance of excited state species in low pressure capacitively coupled plasma argon discharges

In the past three decades, first principles-based fully kinetic particle-in-cell Monte Carlo collision (PIC/MCC) simulations have been proven to be an important tool for the understanding of the physics of low pressure capacitive discharges. However, there is a long-standing issue that the plasma density determined by PIC/MCC simulations shows quantitative deviations from experimental measurements, even in argon discharges, indicating that certain physics may be missing in previous modeling of the low pressure radio frequency (rf) driven capacitive discharges. In this work, we report that the energetic electron-induced secondary electron emission (SEE) and excited state atoms play an important role in low pressure rf capacitive argon plasma discharges. The ion-induced secondary electrons are accelerated by the high sheath field to strike the opposite electrode and produce a considerable number of secondary electrons that lead to additional ionizing impacts and further increase of the plasma density. Importantly, the presence of excited state species even further enhances the plasma density via excited state neutral and resonant state photon-induced SEE on the electrode surface. The PIC/MCC simulation results show good agreement with the recent experimental measurements in the low pressure range (1–10 Pa) that is commonly used for etching in the semiconductor industry. At the highest pressure (20 Pa) and driving voltage amplitudes 250 and 350 V explored here, the plasma densities from PIC/MCC simulations considering excited state neutrals and resonant photon-induced SEE are quantitatively higher than observed in the experiments, requiring further investigation on high pressure discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Escherichia coli amino acid auxotrophic expression host strains for investigating protein structure–function relationships

Abstract A set of C43(DE3) and BL21(DE3) Escherichia coli host strains that are auxotrophic for various amino acids is briefly reviewed. These strains require the addition of a defined set of one or more amino acids in the growth medium, and have been specifically designed for overproduction of membrane or water-soluble proteins selectively labelled with stable isotopes, such as 2H, 13C and 15N. The strains described here are available for use and have been deposited into public strain banks. Although they cannot fully eliminate the possibility of isotope dilution and mixing, metabolic scrambling of the different amino acid types can be minimized through a careful consideration of the bacterial metabolic pathways. The use of a suitable auxotrophic expression host strain with an appropriately isotopically labelled growth medium ensures high levels of isotope labelling efficiency as well as selectivity for providing deeper insight into protein structure–function relationships.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Review of Ultrafast Switching Power Modules: Trends, Challenges, and Technical Solutions

Benefiting from the superior properties of wide-bandgap semiconductor materials, wide-bandgap power devices demonstrate exceptional switching performance, enabling more efficient and compact power electronics systems. However, ultrafast switching poses challenges to the reliability of the system in terms of severe oscillations and voltage overshoot, electromagnetic interference, and increased risk of partial discharge. By developing advanced power module packaging for fast-switching power devices, the above-mentioned challenges can be mitigated at the packaging level, enabling the full utilization of the fast-switching capability of wide-bandgap devices. Meanwhile, such technology also lays the groundwork for packaging next-generation power devices with even higher blocking voltage and faster switching speed. In this paper, a comprehensive review of ultrafast switching power modules has been made, including the benefits and status of ultrafast switching power modules, challenges brought by ultrafast switching, and promising technologies to address these challenges. In addition, future development trends and research gaps are also discussed in this paper. Furthermore, this review can serve as a reference for future wide-bandgap power module packaging design.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Solving the sample size problem for resource selection functions

Abstract Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.

Street, Garrett M.↗
DOE OSTI2020

Particle-in-cell simulations and passive bulk model of collisional capacitive discharge

Intermediate pressure (0.2–6.0 Torr), radio frequency capacitive discharges are of increasing importance in the thin film processing industry. However, existing low pressure (<0.1 Torr) discharge models are often used beyond their applicability in this pressure range. In this work, the authors performed one-dimensional particle-in-cell (PIC) simulations of a capacitive 2.5 cm gap argon discharge driven at 13.56 MHz with current density amplitude J 0 50 A/m 2 over a wide range of gas pressures p g = 0.04–20 Torr. For intermediate pressures, the PIC results showed that most of the ionization occurs near the plasma-sheath edges with very little occurring within the central bulk region. This led us to develop a “passive bulk” model of the discharge in which bulk electron heating does not contribute to ionization, and all the ionization is due solely to the sheath-heated electrons. The model assumes a constant ion-neutral mean free path λ i in the plasma bulk and either constant λ i or constant ion mobility μ i in the sheaths. The model is solved using an electron temperature iteration scheme. The PIC scalings of the discharge equilibrium parameters (e.g., electron density and temperature, sheath width, and voltage) with the input parameters p g and J 0 showed good agreement with those of the passive bulk model. A constant μ i sheath model showed the best agreement with the PIC results in the intermediate pressure regime of interest, but a constant λ i model is simpler to implement and showed reasonable agreement over a wide pressure range.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

The plasma structure of the north-east rim of the Cygnus Loop as observed with ASCA

We present the first results from Advanced Satellite for Cosmology and Astrophysics (ASCAS) observations of the Cygnus Loop. A 22 min square of the north-east rim was observed. The imaging capability of ASCA permits clear indentification of the shock front structure. The energy resolving power of ASCA in the energy band between 0.5 keV to 10 keV reveals several emission lines from oxygen to silicon. The intensity ratio between the O VIII and O VII emission lines demonstrates that the plasma has not reached collisional ionization equilibrium. A nonequilibrium ionization model is applied to determine the gradients both in electron temperature T(sub e), and in electron density n(sub e). T(sub e) ranges from 0.22 keV at the shock front to 0.30 keV at 17 min inside the shock front. n(sub e) can be represented as a power law function of radius with an index of about 6 at an ambient gas density of 0.5 cm(exp -3). The plasma parameter n(sub e)t is about 2 x 10(exp 11) cm(exp -3), resulting in an age of about 20000 yr for the Loop.

Miyata, Emi↗