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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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Atmospheric electron energy spectrum in range 70-2000 mev measured, using scintillation telescope with gas Cerenkov detector and lead glass total energy spectrometer
Overview presentation to be used in multiple venues to discuss the current state of advanced nuclear energy and integrated energy systems.
Panel presentation and discussion on "Nuclear Energy for Industrial Uses: Bridging the Gap." This presentation will introduce the motivation for extended use of nuclear energy and will provide brief insight into historical experience and current programs on the topic.
Presentation on results of submitted paper "A High Temperature Gas Reactor Integrated Energy System Using Energy Storage as a Buffer for Hydrogen Production."
This fact sheet provides a general overview of the Energy Transitions Initiative Partnership Project (ETIPP). It describes the general purpose of the program, and provides some general examples of technical assistance that could be offered through the program.
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.
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Performing reliable computer simulations of elementary processes occurring at metal–water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Here, due to the large system sizes needed for studying processes at liquid water–metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate–metal interactions. Next, for the water–water and water–adsorbate interactions, we used established classical force fields. Finally, for the water–Ru surface interaction, for which no reliable force fields have been published, we used Behler–Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.
Understanding the driving forces behind the nucleation of different polymorphs is of great importance for material sciences and the pharmaceutical industry. This includes understanding the reaction coordinate that governs the nucleation process and correctly calculating the relative free energies of different polymorphs. Here, we demonstrate, for the prototypical case of urea nucleation from the melt, how one can learn such a one-dimensional reaction coordinate as a function of prespecified order parameters and use it to perform efficient biased all-atom molecular dynamics simulations. The reaction coordinate is learnt as a function of the generic thermodynamic and structural order parameters using the “spectral gap optimization of order parameters (SGOOP)” approach [Tiwary, P. and Berne, B. J. Proc. Natl. Acad. Sci. U.S.A. (2016)] and is biased using well-tempered metadynamics simulations. The reaction coordinate gives insights into the role played by different structural and thermodynamics order parameters, and the biased simulations obtain accurate relative free energies for different polymorphs. This includes an accurate prediction of the approximate pressure at which urea undergoes a phase transition and one of the metastable polymorphs becomes the most stable conformation. Here, we believe the ideas demonstrated in this work will facilitate efficient sampling of nucleation in complex, generic systems.
High-Ni layered oxides with Ni contents greater than 90% are promising cathode candidates for high-energy-density Li-ion batteries. However, drastic electrode-electrolyte reactions and mechanical degradation issues limit their cycle life and practical viability. We demonstrate here that LiNi 0.94 Co 0.04 Zn 0.02 O 1.99 (NCZ), obtained by incorporating 2 mol% Zn 2+ into an ultrahigh-Ni baseline cathode material LiNi 0.94 Co 0.06 O 2 (NC), delivers superior cell performance. NCZ retains 74% of the initial capacity after 500 cycles in full cell assembled with graphite anode, outperforming NC (62% retention). NCZ also possesses a higher average discharge voltage relative to NC with an outstanding average voltage retention of over 99% after 130 cycles in half cells. Bulk structural investigations unveil that Zn doping promotes a smoother phase transition, suppresses anisotropic lattice distortion, and maintains the mechanical integrity of cathode particles. Furthermore, NCZ shows an enhanced interphase stability after long-term cycling, in contrast to the seriously degraded surface chemistry in NC. Finally, this work provides a practically viable approach for designing higher-energy-density high-Ni layered oxide cathodes for lithium-ion batteries.
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Non-natural light-driven enzymatic reactivity was recently developed to perform the highly stereoselective reactions required for pharmaceutical synthesis. However, photoenzymes require high-intensity light to function because of the poor absorption properties of their photoactive intermediates. Inspired by the modular architecture of photosynthesis, we designed a conjugate composed of a covalently linked photoenzyme and a light antenna to separate light capture from catalysis. Spectroscopic characterization of the conjugate showed the presence of efficient energy transfer from the light-harvesting components to the photoenzyme. In the presence of energy transfer, a ~4-fold increase in product yield was observed for intramolecular hydroalkylation of alkenes, and reactivity was enabled for intermolecular hydroalkylation of alkenes. As a result, these improvements establish the power of incorporating nature’s design into non-natural photoenzymatic catalysis.
This work, together with its companion paper, Secco, Samuroff et al., presents the Dark Energy Survey Year 3 cosmic shear measurements and cosmological constraints based on an analysis of over 100 million source galaxies. With the data spanning 4143 deg 2 on the sky, divided into four redshift bins, we produce the highest significance measurement of cosmic shear to date, with a signal-to-noise of 40. We conduct a blind analysis in the context of the ΛCDM model and find a 3% constraint of the clustering amplitude, S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.759$^{+0.025}_{-0.023}$. A ΛCDM-Optimized analysis, which safely includes smaller scale information, yields a 2% precision measurement of S 8 = 0.772$^{+0.018}_{-0.017}$ that is consistent with the fiducial case. The two low-redshift measurements are statistically consistent with the Planck Cosmic Microwave Background result, however, both recovered S 8 values are lower than the high-redshift prediction by 2.3σ and 2.1σ (ρ-values of 0.02 and 0.05), respectively. The measurements are shown to be internally consistent across redshift bins, angular scales and correlation functions. The analysis is demonstrated to be robust to calibration systematics, with the S 8 posterior consistent when varying the choice of redshift calibration sample, the modeling of redshift uncertainty and methodology. Similarly, we find that the corrections included to account for the blending of galaxies shifts our best-fit S 8 by 0.5σ without incurring a substantial increase in uncertainty. We examine the limiting factors for the precision of the cosmological constraints and find observational systematics to be subdominant to the modeling of astrophysics. Specifically, we identify the uncertainties in modeling baryonic effects and intrinsic alignments as the limiting systematics.
In this work, we present a validation of the Dark Energy Survey Year 3 (DES Y3) 3×2-point analysis choices by testing them on Buzzard2.0, a new suite of cosmological simulations that is tailored for the testing and validation of combined galaxy clustering and weak-lensing analyses. We show that the buzzard2.0 simulations accurately reproduce many important aspects of the DES Y3 data, including photometric redshift and magnitude distributions, and the relevant set of two-point clustering and weak-lensing statistics. We then show that our model for the 3×2-point data vector is accurate enough to recover the true cosmology in simulated surveys assuming the true redshift distributions for our source and lens samples, demonstrating robustness to uncertainties in the modeling of the nonlinear matter power spectrum, nonlinear galaxy bias, and higher-order lensing corrections. Additionally, we demonstrate for the first time that our photometric redshift calibration methodology, including information from photometry, spectroscopy, clustering cross-correlations, and galaxy–galaxy lensing ratios, is accurate enough to recover the true cosmology in simulated surveys in the presence of realistic photometric redshift uncertainties.
Beyond-two-point statistics contain additional information on cosmological as well as astrophysical and observational (systematics) parameters. In this methodology paper we provide an end-to-end simulationbased analysis of a set of Gaussian and non-Gaussian weak lensing statistics using detailed mock catalogs of the Dark Energy Survey (DES). Here, we implement: 1) second and third moments; 2) wavelet phase harmonics (WPH); 3) the scattering transform (ST). Our analysis is fully based on simulations, it spans a space of seven $νw$CDM cosmological parameters, and it forward models the most relevant sources of systematics of the data (masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration). We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a likelihood-free-inference approach. We validate the pipeline extensively, and we find that WPH exhibits the strongest performance when combined with second moments, followed by ST, and then by third moments. The combination of all the different statistics further enhances constraints with respect to second moments, up to 25 percent, 15 percent, and 90 percent for S 8 , Ω m , and the figure-of-merit FoM S8;Ωm , respectively. We further find that non-Gaussian statistics improve constraints on w and on the amplitude of intrinsic alignment with respect to second moments constraints. The methodological advances presented here are suitable for application to Stage IV surveys from Euclid, Rubin-LSST, and Roman with additional validation on mock catalogs for each survey. In a companion paper we present an application to DES Year 3 data.
We present the first joint analysis of cluster abundances and auto or cross-correlations of three cosmic tracer fields: galaxy density, weak gravitational lensing shear, and cluster density split by optical richness. From a joint analysis (4×2pt+N) of cluster abundances, three cluster cross-correlations, and the auto correlations of the galaxy density measured from the first year data of the Dark Energy Survey, we obtain Ωm=0.305-0.038+0.055 and σ8=0.783-0.054+0.064. This result is consistent with constraints from the DES-Y1 galaxy clustering and weak lensing two-point correlation functions for the flat νΛCDM model. Consequently, we combine cluster abundances and all two-point correlations from across all three cosmic tracer fields (6×2pt+N) and find improved constraints on cosmological parameters as well as on the cluster observable-mass scaling relation. This analysis is an important advance in both optical cluster cosmology and multiprobe analyses of upcoming wide imaging surveys.
The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.