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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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Relating Solvent Parameters to Electrochemical Properties to Predict the Electrochemical Performance of Vanadium Acetylacetonate for Non-Aqueous Redox Flow Batteries

Non-aqueous redox flow batteries have shown promise for applications in grid energy storage. Increasing the efficiency of these batteries by developing the electrolyte chemistries is needed. Herein, we investigate the correlation between solvent properties and the electrochemical parameters of vanadium acetylacetonate V(acac) 3 . Using cyclic voltammetry (CV) and rotating disk electrode experiments (RDE), we show that trends in the performance of the V(acac) 3 kinetics are directly related to solvent properties. In this study, we found strong relationships between the solvents polarity, viscosity, and donor number with the electrochemical behavior of V(acac) 3 in terms of the electrochemical working widow, electron kinetics and stability towards cycling. Based on these finding, we also demonstrate how solvent selection can be improved with limited a priori knowledge.

25 ENERGY STORAGE↗

The Shape Factor for Pits and Its Impact on Pit Stability

This study employs the finite element method (FEM) to predict the impact of pit shape on pit stability via shape factors of various pit geometries relevant to localized corrosion. Lower values of the shape factor indicate an increased ease in maintaining pit stability. Analyzed geometries include undercut pits, bispherical pit-within-pit structures, and covered pits with perforated (lacy) covers. The effect of the water layer thickness, transport properties of the electrolyte inside and outside the pit, and cathode location on the pit shape factor were also explored. Results show that occluded pits exhibit lower shape factors than open ones, with disk-shaped pits decreasing further as c/r ratio and occlusion angle increase. The findings also suggest minimal influence from a secondary pit if the primary remains active. An equation is presented that quantifies the impact of lacy covers, revealing significant shape factor value reduction. Additionally, high salt concentrations inside pits have a limited stabilizing effect compared to geometry, while thin water layers and adjacent cathodic/sinks reduce pit stability.

Shehi, A. (ORCID:0009000271548041)↗

Platinum and CeO 2-x Occlusion Electrodeposition at Unsupported Vulcan XC-72R for Methanol and Ethanol Oxidation Reactions in Alkaline Media

Abstract Platinum was electrodeposited in a slurry solution of CeO2 and Vulcan XC-72R to produce Pt/CeO2-x/Vulcan XC-72R catalysts by using the Rotating Disk Slurry Electrodeposition (RoDSE) Technique. The activity of the catalysts was measured towards methanol and ethanol oxidation reactions in alkaline conditions by cyclic voltammetry and chronoamperometry. The electrochemical results were compared to those obtained on commercial catalysts. Pt/CeO2-x/Vulcan XC-72R (with ~ 26 wt.% Pt) catalyst was the most active for both alcohol oxidation reactions when compared to commercial 40 wt.% and 20% /Vulcan XC-72R (ETEK) catalysts. The mass activity increases 2.0x and 2.4x for methanol and ethanol oxidation reactions when compare with 40 and 20 Pt wt. % commercial catalysts, respectively. The Pt/CeO2-x/Vulcan XC-72R catalysts contained Pt nanoparticles growth within the cerium oxide through an occlusion electrodeposition method. These effects were systematically investigated using X-ray Diffraction (XRD), High Resolution Transmission Electron Microscopy (HR-TEM), and X-ray Photoelectron Spectroscopy (XPS).

Suleiman Mahmoud, Amal↗

Influence of Proton Activity Gaps between Electrodes on Open-Circuit Potential of H 2 /H 2 and H 2 /Air Cells

Polymer -electrolyte-fuel-cell open-circuit voltages (OCVs) are exactly defined by equation (1), where cathode and anode proton activities [(aH+)cathode and (aH+)anode, respectively] usually are identical, so the third term in the right-hand side of equation (2) is ignored. OCV=E0+RT/2F*ln(a1/2 O2*(a2 H+)cathode/aH2O)-RT/2F*ln((a2 H+)anode/aH2) (1) =E0+RT/2F*ln(a1/2 O2*aH2/aH2O)+RT/2F*ln((a2 H+)cathode/(a2 H+)anode) (2) Water vapor pressure is a colligative property that fundamentally correlates to electrolyte concentrations in aqueous solutions. Proton activity is a function of acid concentration, such as pH, when electrolytes are acids. In polymer-electrolyte membranes, water vapor pressure and acid concentration are understood as relative humidity (RH) and water uptake (λ), respectively, where λ represents number of water molecules per sulfonic acid molecule. Several investigations have reported the relationship between RH and λ, meaning that proton activities and associated water uptakes are intimately related to RH. In actual fuel cell operation, cathode RH is determined by ambient-atmosphere and/or humidifier RH(s), and anode RH depends on hydrogen-circulator RH. Therefore, RH is not always identical at both electrodes, and the difference between electrode RHs is considerable during dry operation of polymer electrolyte fuel cells. Therefore, the third term in the right-hand side of equation (2) may be significant for dry operation. We measured OCVs when hydrogen was supplied to both electrodes at 80°C. One electrode (A) was fixed at 30% RH, while RH at the other electrode (B) was varied (0, 5, 10, 20, and 30%). Measured OCVs varied from 0 to 75 mV. For fuel cell tests, electrode A was supplied with hydrogen at 30% RH; electrode B, oxygen at 0, 5, 10, 20, and 30% RH. OCVs deviated from that measured when RH at electrode B was 30%, increasing from 0 to 60 mV with decreasing RH at electrode B. Results are also shown in Figure 1. Proton activities of both electrodes were thermodynamically calculated. The Gibbs–Duhem relation was applied to obtain molar Gibbs free energies of water and sulfonic acid, and proton activity coefficient was calculated using the Gibbs free energy of sulfonic acid and the relationship between RH and λ1–4, assuming that protons and sulfonic anions show identical ionic-activity coefficients. OCVs were estimated using the third term in the right-hand side of equation (2). Results are shown in Figure 1. Fuel-cell current–voltage performance was poor when RHs at the anode and cathode were 30 and 20%, respectively. To determine kinetic current, we measured the oxygen-reduction reaction (ORR) using a rotating-disk electrode (RDE) in concentrated-acid aqueous solutions, which modeled catalyst-layer ionomers. Kinetic currents decreased with acid concentrations. References T. A. Zawodzinski, Jr., C. Derouin, S. Radzinski, R. J. Sherman, V. T. Smith, T. E. Springer and S. Gottesfeld , J. Electrochem. Soc., 140,1041 (1993) P. K. Das and A. Z. Weber, Proceedings of the ASME 2013 11th Fuel Cell Science, Engineering and Technology Conference, Fuel Cell 18010 (2013) V. A. Sethuraman, J. W. Weidner, A. T. Haug, S. Motupally,b and L. V. Protsailo, J. Electrochem. Soc., 155, B50 (2008) A. Kusoglu and A. Z. Weber, Chem. Rev., 117, 987 (2017) Figure 1

Yoshida, Toshihiko↗

Mathematical Modeling of Hydroxide-Exchange-Membrane Water Electrolyzer

Water electrolyzers can transform intermittent renewable energy like solar energy and wind energy into the chemical energy of hydrogen with zero greenhouse-gas emissions. The hydroxide-exchange membrane electrolyzer (HEME) combines the capability to produce pressurized hydrogen with the advantage of being able to use low or non-platinum group metal (PGM) electrocatalysts in the alkaline environment.1 Hydroxide salts, for example, KOH, are added to the HEME water feed on both anode and cathode to improve its performance. However, the specific mechanism of performance improvement still needs to be further understood. In addition, at high current densities, bubble evolution can result in mass-transport limitations, a less well studied phenomena. Mathematical modeling is ideal to explore these issues as it is cost and time efficient and can deconvolute the physics, processes, and observed phenomena and study the applied-voltage breakdown. In this work, we extend our previously developed 1D two-phase continuum model2 to study the varies processes in the HEME and provide insights on performance optimizations. First, the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) kinetics at different hydroxide concentrations have been studied by rotating disk electrodes (RDE) and implemented in the model. Then, the model is calibrated and validated against experimental HEME polarization curves for different KOH concentrations as a liquid electrolyte. The model clearly shows a performance increase with increasing KOH concentrations, which is consistent with the experimental results. The reduced ohmic resistance and increased electrochemical active surface area (ECSA) are the two main reasons for performance increase. The large amount of hydroxide in the liquid electrolyte not only helps to distribute the reactant hydroxide throughout the catalyst layer (CL), which reduces ohmic loss, but also enables reaction at the interface between the liquid electrolyte and electrocatalyst, which increases the ECSA. Applied-voltage breakdown demonstrates that the electrolyzer performance is dominated by anode kinetics and ohmic loss. A comparison with the DI water feed shows a more uniform current distribution in the anode CL when KOH is added, which indicates a higher utilization of the CL. Second, we present modeling on the effects of bubble coverage. As gas evolves, part of the ECSA is minimized due to bubble coverage. To account for this effect, an empirical relationship between the fractional bubble coverage and the current density is implemented in the model.3 The model shows this bubble coverage effect is more pronounced at large current densities with DI water feed. Acknowledgements This work was funded under the HydroGEN Consortium by the Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office, of the U. S. Department of Energy under contract number DE-AC02-05CH11231. References R. Abbasi, B. P. Setzler, S. Lin, J. Wang, Y. Zhao, H. Xu, B. Pivovar, B. Tian, X. Chen, G. Wu and Y. Yan, 31, 1805876 (2019). L. N. Stanislaw, M. R. Gerhardt and A. Z. Weber, ECS Transactions, 92, 767 (2019). H. Vogt and R. J. Balzer, Electrochimica Acta, 50, 2073 (2005).

Liu, Jiangjin↗

PLEXOSUtils.jl

PLEXOSUtils.jl provides functionality for loading in PLEXOS result zipfiles on disk into in-memory Julia data structures. The data structures mirror the relationships defined in the original PLEXOS result XML and provide a convenient starting point for writing application-specific code.

Stephen, Gordon↗

Yet Another Hauser-Feshbach Code

Code framework to perform simulations of nuclear reactions with incident particles including gammas, protons, neutrons, deuterons, tritons, helium-3, and alphas to develop nuclear data libraries and study nuclear reaction phenomona. The code can model nuclear reactions or initial populations specified by the user. The decays principally follow the compound-nucleus hypothesis and the statistical decay of Hauser and Feshbach. The code models nuclear decays with a Monte Carlo process that tracks the decay of each state. Data libraries are produced that can be translated into GNDS (generalized Nuclear Data Structure). YAHFC can also be used as an event generator, with individual decays written to disk for offline analysis. Both serial and MPI versions are provided. YAHFC does NOT perform transport of nuclear reactions through materials, only the primary reaction with a target material.

Ormand, WilliamE.↗

Bloom Filter framework for Web Archives

The software provides a framework to build a fast look up layer that reflects the holdings of an archive or database and operates between search/discovery system and disk storage. The software utilizes Bloom filter (BF) data structure. The discovery service powered by the Bloom filter layer comes with a very high level of accuracy yet space-saving, since BF compresses data to fit into RAM.

Balakireva, Lyudmila↗

AMR-Wind [SWR-20-85]

AMR-Wind is a massively parallel, block-structured adaptive-mesh, incompressible flow solver for wind turbine and wind farm simulations. The solver is built on top of the AMReX library. AMReX is developed at LBNL , NREL , and ANL as part of the Block-Structured AMR Co-Design Center in DOE's Exascale Computing Project. AMReX library provides the mesh data structures, mesh adaptivity, as well as the linear solvers used for solving the governing equations. The primary applications for AMR-Wind are: performing large-eddy simulations (LES) of atmospheric boundary layer (ABL) flows, simulating wind farm turbine-wake interactions using actuator disk or actuator line models for turbines, and as a background solver when coupled with a near-body solver with overset methodology to perform blade-resolved simulations of multiple wind turbines within a wind farm.

Ananthan, Shreyas↗

Elastic Stacker

Stacker is a library and command-line tool for interacting with components of the Elastic Stack remotely using their APIs. For example, Stacker allows the user to persist API resources to disk and re-create them using the same APIs, potentially allowing configuration to be kept in sync across multiple Elastic Stack instances.

Taliaferro, JamesP.↗

A Data Processing Pipeline To Extract A Knowledge Graph From Sec Documents For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest (disk) expressed as a latitude/longitude point and distance, and a set of SEC form types from which to extract entities and relations. There are three main components to this pipeline as currently implemented: Social Network Extraction, Critical Infrastructure Network Extraction, and Inference and Fusion. First, Social Network Extraction, implemented as the `organizations_sec` component of the workflow graph queries the SEC EDGAR webservice using the list of initial companies from the configuration file. Given this, it extracts metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Second, the Critical Network Extraction component extracts entities and relations for a critical infrastructure sector. Currently, we focus on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Third, the Inference and Fusion component relates the social network graph to the critical infrastructure graph in order to understand the impact of a company within a geographic region. Relations include ownership of the EV Charging Station asset as well as maintenance/ownership of the EV payment networks. The fused network can be represented in many ways and currently we emit a knowledge graph.

Weaver, GabrielA.↗

rmon (Resource monitor) [SWR-24-128]

The resource monitor application provides monitoring, collection, and visualization of resource utilization in compute nodes. This package contains utilities to monitor system resource utilization (CPU, memory, disk, network). Here are the ways you can use it: -Monitor resource utilization for a compute node for a given set of resource types and process IDs. -Start a process and monitor its resource utilization. -Monitor resource utilization for a compute node asynchronously with the ability to dynamically change the resource types and process IDs being monitored. -Produce JSON reports of aggregated metrics. -Produce interactive HTML plots of the statistics.

Thom, Daniel [National Renewable Energy Laboratory↗

NeuroFEM

SAND2025-00525O NeuroFEM is a software tool that demonstrates a neuromorphic algorithm for solving finite element problems. It sets up a 2D finite element problem for the Poisson equation on a disk, constructs synaptic matrices, and simulates neural dynamics to solve the resulting sparse linear system. The software illustrates how the algorithm converges to the solution and plots the results, showcasing a neuromorphic counterpart to traditional methods like Conjugate Gradient or GMRES. This tool is designed to highlight the potential of neuromorphic algorithms for solving sparse linear systems, which are prevalent in various computational applications. 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.

SciDAC↗

kynema-sgf [SWR-20-85]

Kynema-SGF (formerly AMR-Wind), wherein SGF stands for structured-grid fluid dynamics, is a massively parallel, block-structured adaptive-mesh, incompressible flow solver. The codebase was initiated in 2019 from incflo. The solver is built on top of the AMReX library. AMReX library provides the mesh data structures, mesh adaptivity, as well as the linear solvers used for solving the governing equations. Kynema-SGF is actively developed and maintained by a dedicated multi-institutional team from Lawrence Berkeley National Laboratory, National Laboratory of the Rockies, and Sandia National Laboratories. The primary applications for Kynema-SGF are: performing large-eddy simulations (LES) of atmospheric boundary layer (ABL) flows, simulating wind farm turbine-wake interactions using actuator disk or actuator line models for turbines, and as a background solver when coupled with a near-body solver (e.g., Kynema-UGF) with overset methodology to perform blade-resolved simulations of multiple wind turbines within a wind farm. For offshore applications, the ability to model the air-sea interaction effects and its impact on the ABL characteristics is another focus for the code development effort. As with other codes in the Kynema ecosystem, Kynema-SGF shares the following objectives: *an open, well-documented implementation of the state-of-the-art computational models for modeling wind farm flow physics at various fidelities that are backed by a comprehensive verification and validation (V&V) process; *be capable of performing the highest-fidelity simulations of flow fields within wind farms; and *be able to leverage the high-performance leadership class computing facilities available at DOE national laboratories.

Ananthan, Shreyas↗

JGI Archive and Metadata Organizer (JAMO) v2.0.0

JAMO (JGI Archive and Metadata Organizer) helps researchers keep large collections of scientific files organized, findable, and safe. It lets you submit files with consistent, template-driven metadata, bundle related files into sets, and track them as a group instead of one by one. As data ages, JAMO automatically moves it from fast disk to cost-saving tape and can bring it back when needed, keeping storage lean without losing access. A simple web/CLI workflow supports submitting, checking status, retrying, and updating metadata. Compared with generic storage, JAMO's strengths are: clear, searchable metadata tuned for science; set-level organization that mirrors real projects; and built-in lifecycle care (archive, purge, restore) so you don't have to manage those steps yourself.

Cassol, Daniela [Lawrence Berkeley National Labora↗

Energy Exascale Earth System Model v2.0.1

First patch release of v2.0.0 Changes since v2.0.0 [Important change] Fix ocean threading bug seen in debug cases on Chrysalis with Intel 20.0.4. Was introduced around time of v2.0.0 tag. Does not change v2.0.0 answers on Chrysalis because those didn't use threading or debugging. [EAM] Add semi-lagrangian tracer transport for theta-l (F90 and C++), add new algorithm for finding tropopause, add DSCREAM to allow v2 and SCREAM settings in same code such as adjust_ps [EAM-MMF] 60L default, allow C++ back end of RRTMGP (EAM too). [EAMxx] add nu-top functionality, fix forcing functor, add ttype9 and dcmip2012 tests 2.1, 2.2, and 3 HOMME: remove obsolete remap algs, option to specify dynamics alg indep of tracer, new sponge layer, add imex tests [ELM] Add topography-based subgrid (topounits), add FATES-ELM Nitro., Phos. and CH4 coupling, add land-use ts for NARRM, add lulc for SSP3 RCP7, Fix nutrient fertilization exp test and carbon isotope flux, Fix xactive lnd dry deposition, add lake water storage option, fix plant hydraulics 2d params, fix carbon budget calc, fix soil nutrient conc. bug, fix mosart dam bug, add test for new ELM, MOSART features, fix bug in O3 dry dep stomatal resistances, fix plant hydraulics restart BFB error, update mkmapdata. [MOSART] fix bug for reading the latitude from an unstructured input file, fix oversat in bubble test. [MPAS-ocean] Add CFC11, CFC12 tracers, add 2D spherical transport tests, fix del4 tracer mixing, add MARBL ocean tracer mixing, modify harmonic analysis options, add GPU port of vmix routines, fix calc of ML-averaged BV freq. [MPAS-seaice] Change extents of initial polar disks for oRRS18to6v3 grid, fix ice BGC with MARBL, update spherical test cases, fix DON coupling, Remove Cf from sea ice constants. [MPAS-landice] add CRYO1850-4xCO2 compset [CIME] add GCP, ANL GCE, Spock, Perlmutter, deprecate config_compilers.xml, fix and clean-up cmake macros, fix slurm bindings, refactor CIME internal testing, cleanup SCORPIO perf data, allow position independent compset naming, [also] update v2 benchmarking suite, extend e3sm_prod with throughput and memory checks

E3SM Project, DOE↗

Infrared Optical Constants from Pressed Pellets of Powders: I. Improved n and k Values of (NH4)(2)SO(4)from Single-Angle Reflectance

In combination with other parameters, the real, n(?), and imaginary, k(?), components of the complex refractive index, = n + ik, can be used to simulate the optical properties of a material in different forms, e.g. its infrared spectra. But obtaining reliable values of the optical constants n/k for solid materials is challenging due to the lack of optical quality specimens, usually crystals, large enough to measure. An alternative to crystals is to press the powder into a uniform disk. We have produced pellets from ammonium sulfate, (NH4)2SO4, powder and derived the pellets’ n and k values via single-angle reflectance using a specular reflectance device in combination with a Fourier transform infrared spectrometer. The single-angle technique measures amplitude of light reflected from the material as a function of wavelength over a wide spectral domain; the optical constants are determined from the reflectance data using the Kramers-Kronig relationship. We investigate several parameters associated with the pellets and pellet formation and their effects upon delivering the most reliable n/k values. Parameters studied include pellet diameter, mass and density (void space), drying, grinding, sieving and particle size in the pellet formation, as well as pressing pressure and duration. Of these parameters, using size-selected mixtures of dried, small (< 100 µm) particles and pressing at = 10 tons for at least 30 minutes were found key to forming highly reflective samples. Comparison to two sets of previous literature n(?) and k(?) values obtained from crystalline (NH4)2SO4 both as crystal reflectance as well as extinction spectra of aerosols measured in a flow tube show reasonable agreement, but suggests the present values, as confirmed from two independent techniques, represent a substantial improvement for n/k values for (NH4)2SO4, also demonstrating promise to measure the optical constants of other materials.

Optical constants, index of refraction, single-ang↗

HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications

Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).

Asynchronous I/Ol↗