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Results for “abstraction layers”
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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Evaluation of Versalink 143 as an Isolation Layer for High-G Survival.
Abstract not provided.
High-Voltage Polymer Multi-Layer Capacitor Development, Characterization, Environmental Testing.
Abstract not provided.
Integrating process, control-flow, and data resiliency layers - Using a hybrid Fenix/Kokkosa approach.
Abstract not provided.
Aero-Optical Distortions of Turbulent Boundary Layers: DNS up to Mach 8.
Abstract not provided.
Second mode growth in a high-velocity boundary layer using stability theory and DNS.
Abstract not provided.
Wall-Modeled Large-Eddy Simulation of Turbulent Mach 14 Boundary Layer for Aero-Optical Analysis.
Abstract not provided.
The role of RF-induced E×B flows in the mitigation of scrape-off-layer convective transport during ion cyclotron resonance heating
Abstract While multiple experiments have reported a decrease in intermittent fluctuations in the far Scrape-Off-Layer (SOL) during ion cyclotron resonance heating (Antar et al 2010 Phys. Rev. Lett. 105 165001, Li et al 2022 Nucl. Eng. Technol. 54 207–19, Antar et al 2012 Nucl. Fusion 52 103005), the physical mechanism behind this observation has not been fully established yet. In this work, we demonstrate, for the first time, a direct correlation between the amplitude of RF-induced E×B flows and turbulence suppression in the far SOL. Using the Gas Puff Imaging (GPI) diagnostic on Alcator C-Mod, we show again that Ion Cyclotron Range of Frequencies can significantly alter the flow in the SOL and introduce a shear layer in regions magnetically connected close to the antenna (Cziegler et al 2012 Plasma Phys. Control. Fusion 54 105019). With the 4-strap field-aligned antenna operated in dipole phasing, the ratio of the power coupled by the central two straps to the power coupled by the outer two straps was varied. The resulting RF-induced radial electric field magnitude thus varied, and we show that the impact on the far SOL turbulence correlates with the modified E×B velocity. We then apply a newly-developed blob tracking algorithm (Han et al 2022 Sci. Rep. 12 18142) to higher-resolution GPI videos in order to directly observe the process of blob shearing by RF-induced E×B flows. We show that the radially sheared poloidal flows act as a transport barrier by stretching, stopping, and destroying filaments, which is consistent with the observed difference in turbulence statistics.
Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic
Abstract. Over the eastern North Atlantic (ENA) ocean, a total of 20 non-precipitating single-layer marine boundary layer (MBL) stratus and stratocumulus cloud cases are selected to investigate the impacts of the environmental variables on the aerosol–cloud interaction (ACIr) using the ground-based measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility at the ENA site during 2016–2018. The ACIr represents the relative change in cloud droplet effective radius re with respect to the relative change in cloud condensation nuclei (CCN) number concentration at 0.2 % supersaturation (NCCN,0.2 %) in the stratified water vapor environment. The ACIr values vary from −0.01 to 0.22 with increasing sub-cloud boundary layer precipitable water vapor (PWVBL) conditions, indicating that re is more sensitive to the CCN loading under sufficient water vapor supply, owing to the combined effect of enhanced condensational growth and coalescence processes associated with higher Nc and PWVBL. The principal component analysis shows that the most pronounced pattern during the selected cases is the co-variations in the MBL conditions characterized by the vertical component of turbulence kinetic energy (TKEw), the decoupling index (Di), and PWVBL. The environmental effects on ACIr emerge after the data are stratified into different TKEw regimes. The ACIr values, under both lower and higher PWVBL conditions, more than double from the low-TKEw to high-TKEw regime. This can be explained by the fact that stronger boundary layer turbulence maintains a well-mixed MBL, strengthening the connection between cloud microphysical properties and the below-cloud CCN and moisture sources. With sufficient water vapor and low CCN loading, the active coalescence process broadens the cloud droplet size spectra and consequently results in an enlargement of re. The enhanced activation of CCN and the cloud droplet condensational growth induced by the higher below-cloud CCN loading can effectively decrease re, which jointly presents as the increased ACIr. This study examines the importance of environmental effects on the ACIr assessments and provides observational constraints to future model evaluations of aerosol–cloud interactions.
The Microscopic Mechanism of Lithiation and Delithiation in the Ag/C Buffer Layer for Anode‐Free Solid‐State Batteries
Abstract Lithium metal solid‐state batteries (LMSSBs) have demonstrated their high energy density and cycling performance at high current densities in an anode‐free architecture, featuring a thin Ag/C composite buffer layer (BL) between the current collector (CC) and the solid electrolyte (SE). This study explains the microscopic mechanism of the Ag/C BL by using first‐principles atomistic and continuum modeling. It is shown that Ag effectively acts as a homogeneous solid‐solution beyond AgLi 2.32 and maintains a positive potential even at AgLi 25 during lithiation. Key factors underlying the working of the Ag/C BL include lower interfacial resistance at the BL/CC interface than at the BL/SE interface, leading to predominant Li deposition on BL/CC, and substantial Ag–Li volume expansion during lithiation. This, combined with stronger BL/SE adhesion, causes BL/SE separation and Ag–Li extrusion toward the CC side. During delithiation, Ag re‐precipitates as nanoparticles uniformly on the CC, with its positive lithiation potential homogenizing Li currents in subsequent cycles. Other metals are less effective due to their relatively large overpotential, premature lithiation termination, and limited volume expansions hindering movement toward the CC. The study aids the BL design, focusing on metal choice and optimization material and microstructural properties, such as the Li‐ion conductivity and interfacial resistance.
Insights into the Crossover Effects in Cells with High‐Nickel Layered Oxide Cathodes and Silicon/Graphite Composite Anodes
Abstract Silicon anodes are regarded as one of the most promising alternatives to graphite (Gr) anodes due to their ultrahigh capacity, abundance, and low cost. Coupling Si‐based anodes with high‐nickel layered oxide cathodes LiNi x Mn y Co 1− x − y O 2 (NMC, x ≥ 0.8) can enhance the driving range for electric vehicles. Transition‐metal (TM) ion dissolution and deposition has been a long‐known failure mode for Gr‐based lithium‐ion batteries. However, the mechanistic insight associated with TM ion deposition on Si‐based anodes has rarely been reported. Herein, the impact of in situ deposited TM ions on SiO x /Gr composite anodes and the individual influences of Ni, Mn, and Co on the structural and electrochemical stability along with the underlying degradation mechanisms are presented. TM ion dissolution causes a greater deleterious impact on Si than on Gr, with different TM ions exhibiting different influences on anode‐electrolyte interphase formation. Specifically, Ni deposit induces more aggressive salt decomposition; Co deposit has negligible effect on salt decomposition, but significantly accelerates solvent decomposition; and Mn deposit aggravates both salt and solvent decompositions, resulting in worse full cell performance. The extent of degradation decreases in the order Mn 2+ > Ni 2+ > Co 2+ . The systematic comparisons presented can guide further development of high energy systems.
Single Ru Atoms Stabilized by Hybrid Amorphous/Crystalline FeCoNi Layered Double Hydroxide for Ultraefficient Oxygen Evolution
Abstract In view of the sluggish kinetics suppressing the oxygen evolution reaction (OER), developing efficient and robust OER catalysts is urgent and essential for developing efficient energy conversion technologies. Herein, hybrid amorphous/crystalline FeCoNi layered double hydroxide (LDH)‐supported single Ru atoms (Ru SAs/AC‐FeCoNi) are developed for enabling a highly efficient electrocatalytic OER. The amorphous outer layer in Ru SAs/AC‐FeCoNi is composed of abundant defect sites and unsaturated coordination sites, which can serve as anchoring sites to stabilize single Ru atoms. The crystalline inner has a highly symmetric rigid structure, thereby strengthening the stability of support for a long‐lasting OER. The synergistic effects endow this hybrid catalyst with extremely low overpotential (205 mV at 10 mA cm −2 ). Density functional theory calculation indicates that single Ru atoms stabilized by hybrid amorphous/crystalline FeCoNi LDH facilitate the formation of Ru–O* (rate‐determining step), thus accelerating the OER process.
Methodology to determine the coupling of continental clouds with surface and boundary layer height under cloudy conditions from lidar and meteorological data
Abstract. The states of coupling between clouds and surface or boundary layer have been investigated much more extensively for marine stratocumulus clouds than for continental low clouds, partly due to more complex thermodynamic structures over land. A manifestation is a lack of robust remote sensing methods to identify coupled and decoupled clouds over land. Following the idea for determining cloud coupling over the ocean, we have generalized the concept of coupling and decoupling to low clouds over land, based on potential temperature profiles. Furthermore, by using ample measurements from lidar and a suite of surface meteorological instruments at the U.S. Department of Energy's Atmospheric Radiation Measurement Program's Southern Great Plains site from 1998 to 2019, we have developed a method to simultaneously retrieve the planetary boundary layer (PBL) height (PBLH) and coupled states under cloudy conditions during the daytime. The new lidar-based method relies on the PBLH, the lifted condensation level, and the cloud base to diagnose the cloud coupling. The coupled states derived from this method are highly consistent with those derived from radiosondes. Retrieving the PBLH under cloudy conditions, which has been a persistent problem in lidar remote sensing, is resolved in this study. Our method can lead to high-quality retrievals of the PBLH under cloudy conditions and the determination of cloud coupling states. With the new method, we find that coupled clouds are sensitive to changes in the PBL with a strong diurnal cycle, whereas decoupled clouds and the PBL are weakly related. Since coupled and decoupled clouds have distinct features, our new method offers an advanced tool to separately investigate them in climate systems.
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.