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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Robust Optimal Experimental Design of Infinite-Dimensional Bayesian Nonlinear Inverse Problems

Abstract. We consider robust optimal experimental design (ROED) for nonlinear Bayesian inverse problems governed by partial differential equations (PDEs). An optimal design is one that maximizes some utility quantifying the quality of the solution of an inverse problem. However, the optimal design is dependent on elements of the inverse problem such as the simulation model, the prior, or the measurement error model. ROED aims to produce an optimal design that is aware of the additional uncertainties encoded in the inverse problem and remains optimal even after variations in them. We follow a worst-case scenario approach to develop a new framework for robust optimal design of nonlinear Bayesian inverse problems. The proposed framework (a) is scalable and designed for infinite-dimensional Bayesian nonlinear inverse problems constrained by PDEs; (b) develops efficient approximations of the utility, namely the expected information gain; (c) employs eigenvalue sensitivity techniques to develop analytical forms and efficient evaluation methods of the gradient of the utility with respect to the uncertainties against which we wish to be robust; and (d) employs a probabilistic optimization paradigm that properly defines and efficiently solves the resulting combinatorial max-min optimization problem. The effectiveness of the proposed approach is illustrated for optimal sensor placement problem in an inverse problem governed by an elliptic PDE.

Chowdhary, Abhijit↗
DOE OSTI2021

Charging and ion ejection dynamics of large helium nanodroplets exposed to intense femtosecond soft xray pulses

Ion ejection from charged helium nanodroplets exposed to intense femtosecond soft X-ray pulses is studied by single-pulse ion time-of-flight (TOF) spectroscopy in coincidence with small-angle X-ray scattering. Scattering images encode the droplet size and absolute photon flux incident on each droplet, while ion TOF spectra are used to determine the maximum ion kinetic energy, Ekin, of He+ j fragments (j = 1-4). Measurements span HeN droplet sizes between N similar to 10(7) and similar to 10(10) (radii R-0 = 78-578 nm), and droplet charges between similar to 9x10(-5) and similar to 3x10(-3) e/atom. Conditions encompass a wide range of ionization and expansion regimes, from departure of all photoelectrons from the droplet, leading to pure Coulomb explosion, to substantial electron trapping by the electrostatic potential of the charged droplet, indicating the onset of hydrodynamic expansion. The unique combination of absolute Xray intensities, droplet sizes, and ion Ekin on an event-by-event basis reveals a detailed picture of the correlations between the ionization conditions and the ejection dynamics of the ionic fragments. The maximum E-kin of He+ is found to be governed by Coulomb repulsion from unscreened cations across all expansion regimes. The impact of ion-atom interactions resulting from the relatively low charge densities is increasingly relevant with less electron trapping. The findings are consistent with the emergence of a charged spherical shell around a quasineutral plasma core as the degree of ionization increases. The results demonstrate a complex relationship between measured ion E-kin and droplet ionization conditions that can only be disentangled through the use of coincident single-pulse TOF and scattering data.

Helium↗
DOE OSTI2020

Excited state electron transfer in A 2 and A 2 B 2 functionalized zinc porphyrins carrying rigid and flexible β -pyrrole π-extended substituents

Optical absorption and emission, electrochemical, and photochemical properties of peripherally functionalized with flexible and rigid π-extended substituents on A 2 and A 2 B 2 type zinc porphyrins is investigated. The significance of rigid π-substituents over flexible ones in governing the spectral properties is unraveled. Flexible π-substituents on the porphyrin ring caused appreciable spectral broadening compared to porphyrin carrying rigid π-substituents. Further, supramolecular dyads are formed by coordinating phenyl imidazole functionalized fullerene, C 60 Im. Here, the calculated binding constants for the 1:1 complexes is in the order of 2–7 × 10 5 M −1 suggesting stable complex formation. Free-energy calculations performed according to the Rehm–Weller approach suggested possibility of excited state electron transfer in these dyads. Femtosecond transient absorption studies of the dyads performed in o-dichlorobenzene showed evidence of occurrence of electron transfer from the singlet-excited state that was in competition with the intersystem crossing process to populate the triplet-excited state of porphyrins.

A2 and A2B2 functionalized zinc↗
DOE OSTI2021

Computational Complexity of Neuromorphic Algorithms

Neuromorphic computing has several characteristics that make it an extremely compelling computing paradigm for post Moore computation. Some of these characteristics include intrinsic parallelism, inherent scalability, collocated processing and memory, and event-driven computation. While these characteristics impart energy efficiency to neuromorphic systems, they do come with their own set of challenges. One of the biggest challenges in neuromorphic computing is to establish the theoretical underpinnings of the computational complexity of neuromorphic algorithms. In this paper, we take the first steps towards defining the space and time complexity of neuromorphic algorithms. Specifically, we describe a model of neuromorphic computation and state the assumptions that govern the computational complexity of neuromorphic algorithms. Next, we present a theoretical framework to define the computational complexity of a neuromorphic algorithm. We explicitly define what space and time complexities mean in the context of neuromorphic algorithms based on our model of neuromorphic computation. Finally, we leverage our approach and define the computational complexities of six neuromorphic algorithms: constant function, successor function, predecessor function, projection function, neuromorphic sorting algorithm and neighborhood subgraph extraction algorithm.

Date, Prasanna↗
DOE OSTI2026

SetGo: Metadata Readiness for Scientific AI Datasets

Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for Data Integration (REDI) addresses computational readiness, but no corresponding tool evaluates whether a dataset’s metadata are sufficiently complete, governed, and standards-compliant for publication and agent-based consumption. Existing FAIR assessors operate only on published repository records, and no single system covers FAIR compliance, licensing, provenance, governance, reproducibility, and catalog readiness together. We present SetGo, an open-source Python toolkit that assesses and repairs metadata readiness across these six dimensions before a dataset is published or archived. Applied to four scientific corpora, SetGo surfaces deficiencies that general-purpose tools do not detect: ERA5 climate metadata scores 4% on ACDD 1.3 compliance; materials datasets fail OPTIMADE species-definition requirements; and PDB-derived proteomics data carries licensing terms incompatible with standard SPDX identifiers. Guided enrichment raises overall FAIR scores from 52–57% to 81–91%, and a single setgo publish command pushes to Hugging Face Hub, CKAN, or OpenMetadata with ML Commons Croissant 1.0 metadata sidecars. To support interactive and automated workflows, SetGo integrates with coding agents powered by large language models (LLMs) through a /setgo skill that enables natural-language execution of the full assess–enrich–publish loop, with user involvement limited to supplying missing metadata values.

Wilkinson, Sean [ORNL] (ORCID:0000000214437479)↗
DOE OSTI2020

Effect of Ammonia on the Electrocatalysis of Oxygen Reduction Reaction in Base

We investigated the effect of ammonia on the activity of seven benchmark oxygen reduction reaction (ORR) electrocatalysts, Pt/C, Pd/C, Au/C, Ag/C, commercial Fe–N–C, zeolitic imidazolate framework (ZIF)-based Fe–N–C (Fe-ZIF), and ZIF-based Fe,Co–N–C (Fe,Co-ZIF) in alkaline media and direct ammonia fuel cell (DAFC) relevant conditions using a rotating disk electrode. We found that ammonia exposure causes considerable ORR activity loss for all studied electrocatalysts. Should we rank the electrocatalysts in terms of their ORR activity loss and their irreversible ORR activity loss as the result of exposure to ammonia (from the highest loss to the lowest loss), we get Au/C > Pt/C ≈ Pd/C > Ag/C » commercial Fe–N–C > Fe-ZIF ≈ Fe,Co-ZIF and Pd/C > Au/C > Ag/C > commercial Fe–N–C > Fe-ZIF ≈ Fe,Co-ZIF > Pt/C, respectively. The ammonia oxidation reaction activity and adsorptive properties of the Gerisher-Mauerer reaction intermediates do not govern the trend of the ORR activity loss of Pt/C, Pd/C, Au/C, and Ag/C electrocatalysts. Notably, we found that Pt/C electrocatalyst shows the lowest irreversible ORR activity loss, while Fe-ZIF and Fe,Co-ZIF electrocatalysts show the lowest overall ORR activity loss. Our comprehensive study suggests that Pt-based, Fe-ZIF, and Fe,Co-ZIF electrocatalysts are promising cathode candidates for future DAFCs.

Abbasi, Reza (ORCID:0000000249384151)↗
DOE OSTI2021

An Analysis of the Impact of Particle Growth on Transport Losses in Polymer-Electrolyte Fuel Cells

Voltage cycling causes catalyst nanoparticles in polymer-electrolyte fuel cells to grow. The concomitant loss of interfacial area results in larger kinetic, transport, and possibly ohmic overpotentials. This paper uses recently published experimental data and mathematical models to investigate the evolutions of transport and ohmic resistances to platinum nanoparticles located on the surface and inside the micropores of carbon black supports. Resistance to oxygen transport rises as surface area declines primarily because the flux to each remaining larger platinum particle increases. The path lengths governing oxygen diffusion to surface and buried platinum sites also increase as nanoparticles grow. Platinum nanoparticles on the surface become relatively less favorable as voltage cycling proceeds because they grow faster than platinum in micropores. Because voltage cycling causes total interfacial area to decline, and the fraction located inside micropores to increase, nanoscale ohmic losses increase as a catalyst layer decays. The practical importance of this effect is difficult to discern because proton conductivity in carbon micropores is not well characterized.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Corrosion Electrochemistry of Chromium in Molten FLiNaK Salt at 600 °C

The manuscript revisits the corrosion behavior of pure Cr in molten FLiNaK salt at 600 °C from the perspective of corrosion electrochemistry. In this work, the potential-dependent, rate-limiting charge-transfer, and salt film-mediated mass-transport controlled regimes of Cr corrosion in FLiNaK at 600 °C are investigated. The kinetic and thermodynamic parameters that limit electrodissolution and the consideration of grain orientation on these regimes are elucidated. At low Cr(III) concentrations, the corrosion process is governed by charge transfer control at low overpotentials and is crystal orientation dependent. However, when Cr(III) concentrations are high or when there is a high overpotential, the formation of a metal fluoride salt film on the Cr surface shifts the kinetic behavior to be governed by mass transport control at all anodic potentials with a surface morphology controlled by salt film deposition location and identity. Evan’s diagrams were developed to consolidate and elucidate these observations. These findings were supported by an examination of the post-corrosion microstructure, X-ray diffraction of solidified salts, and thermo-kinetics analysis in each corrosion regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Mathematical Framework Underlying the In Situ Electrochemical Diagnosis of Adsorbed Intermediates Formed during Redox Reactions at Electrode Surfaces

Previously, we have presented an electrochemical technique wherein an electroactive tracer species is employed to probe the rate-limiting factors governing redox reactions at an electrode surface. In this technique, the electrode is first held potentiostatically to facilitate a redox process (step 1), and then the potential is released to open circuit conditions (step 2) so as to monitor the time-dependent re-equilibration of the electrode potential in the presence of the tracer. The time-dependent potential response in step 2 has been shown to contain information about diffusion—limited or desorption—limited steps, enabling in situ probing of the electrochemistry at the electrode surface during step 1. In the present contribution, a theoretical model governing the transient response in step 2 is developed for two limiting cases: diffusion—limited and desorption—limited recovery of the electrode potential. Mathematical modeling shows that, during re-equilibration, the step 2 potential transient corresponding to a case where step 1 involves surface adsorbed species which undergo desorption in step 2 exhibits a much longer time constant than that when re-equilibration occurs under diffusion limitations. The mathematical framework presented herein provides a sound fundamental basis for applying the aforementioned technique to studying adsorption-desorption processes during electrochemistry. Also, technique limitations are presented in light of the modeling findings.

25 ENERGY STORAGE↗
DOE OSTI2024

DQL (Django Query Logger) [SWR-24-96]

The Django Query Logger (DQL) is a tool that allows developers of Django web applications to stream the raw database queries that are being executed in real-time from any Django application for review, profiling, filtering, formatting, and analysis. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or Alliance for Sustainable Energy, LLC. The views and opinions of authors expressed in the available or referenced documents do not necessarily state or reflect those of the United States Government or Alliance.

Swindler, Alexander↗
DOE OSTI2024

Database Manager for use with PLEXOS XML files [SWR-24-90]

This software is an API for create and modify XML that are used for Plexos. DISCLAIMER: PLEXOS is a registered trademark of Energy Exemplar Pty Ltd ("Owner"). Owner has no affiliation to or participation in this software. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or Alliance for Sustainable Energy, LLC ("Alliance"). The views and opinions of authors expressed in the available or referenced documents do not necessarily state or reflect those of the United States Government or Alliance.

Sanchez Perez, Pedro Andres↗
DOE OSTI2025

Datum: A Scientific Metadata Catalog

The data catalog market is currently flooded with a myriad of different products, but none serve the scientific community well. There are cloud-native tools like Databricks, Snowflake,to on-premise solutions like Collibra and Datahub. The common failing of all these tools however, is their inability to serve the scientific data community directly. Most catalogs are targeted towards financial, health, or user data - not sensor or scientific domain data. They also prioritize integrations that often don’t exist or are just starting to be used in the scientific realm - all while ignoring common scientific tools and file types. Datum is a catalog which targets the scientific data directly, including the tools and networks in which those tools are used. We work with the producers and consumers of the data where they are, targeting cloud and on-premise with a focus on classified networks. Datum is an Erlang/Elixir application. Technical Features Note: The features listed below are still under development and may change, slightly, upon final delivery of the product. File Formats - Datum has the ability to read additional metadata and provides processing pipelines for the following file formats: Plain Text, PDF, LaTeX, HTML, Open Document Format (.odt), XML, CSV/TSV (and other standard delimiters), OpenDocument Database and Spreadsheets, Geo-Referenced TIFF, Common Data Format, HDF/HDF5, LabView TDMS, Excel, DeltaTables, Parquet, Apache Iceberg, Apache Hudi and many others. Metadata Collection - Scanners for the local and networked file systems and cloud storage providers. Network integration with common databases such as MSSQL and MySQL. User Plugin System - Users are able to provide either file processing, metadata extraction, or sampling plugins in the programming language of their choice. Authentication/Authorization -: OIDC integration, SCIM provisioning and EntraID integration out of the box. Full user and group management system with a “least privilege” operating mode. Governance - Customizable data governance platform; dictate and enforce required metadata, enforce data embargos, and enforce user agreements and NDAs before data access. Ability to create health checks on data, rejecting abandoned or poorly curated data and automatically removing it from the search index. Ability for users to submit corrections. Search - Semantic search is a first class citizen. No licenses to expensive, external software required. Integrated use of vectors and vector-based search allows for AI agent integration at all levels of operation. Metadata Model - Display and control data’s lineage and connections to other data and data directories. Data is modeled after a filesystem - an organization instantly recognizable and navigable by most any user. CLI and SDK - Ships with a Command Line Interface (CLI) tool and with a fully-featured Python SDK. This allows for rapid and programmatic use of Datum by every level of user. Minimal Infrastructure - Datum ships as a single executable file and can be run on any operating system and most CPU architectures. Datum has no reliance on external databases, search indexing tools, or other outside services - and it runs equally well on edge computing devices, cloud services, or in a clustered HPC environment.

darrington, john↗
DOE OSTI2024

Established Regulatory Limits for Surface Water and Groundwater

The regulatory limits for surface water and groundwater contained in this database comprise chemical-specific preliminary remediation goals (PRGs) based on potential Applicable or Relevant and Appropriate Requirements (ARARs). These values should be used in conjunction with PRGs based on risk assessment to ensure that the PRGs for a site meet the residual risk requirements for protection of human health and the environment in the National Oil and Hazardous Substance Pollution Contingency Plan (NCP). In some cases, the values presented in this database may be To-Be-Considered (TBC) Guidance because these values are superseded by site-specific state requirements. ARARs are available for the Federal government, California, Georgia, Illinois, Kentucky, New Jersey, New Mexico, Nevada, New York, Ohio, South Carolina, Tennessee, Washington, and Wisconsin. The dataset provides maximum contaminant levels for drinking water and water quality criteria for fresh and marine environments based on acute and chronic exposures. Users can select ARAR sources, drinking water standards and water quality standards, individual chemicals, and retrieve results in tabular or spreadsheet formats for analysis. ARARs are derived from authoritative sources, including federal and state government agencies. The dataset supports environmental risk assessments, regulatory decision-making, and environmental planning, with tools for benchmarking against promulgated standards. This structured approach ensures a robust evaluation of environmental risks tailored to regulatory needs.

Stewart, Debra [Oak Ridge National Laboratory (ORN↗
DOE OSTI2022

Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Shortwave Spectral Radiometer for Atmospheric Science: Capabilities and Applications from the ARM User Facility

Industry advances have greatly reduced the cost and size of ground-based shortwave (SW) sensors for the ultraviolet, visible, and near-infrared spectral ranges that make up the solar spectrum, while simultaneously increasing their ruggedness, reliability, and calibration accuracy needed for outdoor operation. These sensors and collocated meteorological equipment are an important part of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility, which has supported parallel integrated measurements of atmospheric and surface properties for more than two decades at fixed and mobile sites around the world. The versatile capability of these ground-based measurements includes 1) rich spectral information required for retrieving cloud and aerosol microphysical properties, such as cloud phase, cloud particle size, and aerosol size distributions, and 2) high temporal resolution needed for capturing fast evolution of cloud microphysical properties in response to rapid changes in meteorological conditions. Here we describe examples of how ARM's spectral radiation measurements are being used to improve understanding of the complex processes governing microphysical, optical, and radiative properties of clouds and aerosol.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simplified Approximations of Direct Cumulus Entrainment and Detrainment

Abstract In recent years, direct calculations of simulated cumulus entrainment and detrainment have facilitated new physical insights into these highly elusive but critically important processes. However, these calculations require substantial computational resources that may limit their widespread usage. To facilitate such calculations, two simplified approximations of direct cumulus entrainment and detrainment are examined herein. The first approximation, termed the “semidirect” method, follows a standard bulk approach but makes more realistic assumptions about the sources of entrained and detrained air near the cloud edges. In contrast, the second approximation (the “projection” method) uses the governing equations of motion to project whether grid points near the cloud edge will entrain or detrain as the mean cloud ascends by one grid point. Verification exercises using large-eddy simulations reveal that both methods generally agree better with corresponding direct entrainment/detrainment estimates than the traditional bulk formulation, with the projection method outperforming the semidirect method. The two methods can be used in a synergistic fashion, with the semidirect method helping to optimize the projection method, to suit a wide range of applications. Because the latter incorporates the essential dynamics of entrainment and detrainment at the local scale, it can be used to gain physical insight into the causal mechanisms regulating these complex processes.

Meteorology & Atmospheric Sciences↗
DOE OSTI2026

The Role of Subcloud Mesoscale Convergence in Sculpting Convective Updraft Width and Depth

The initiation of deep moist convection is governed in part by the horizontal width of updrafts near cloud base, which limits the deleterious effects of entrainment-driven dilution on buoyant thermals ascending through the free troposphere. However, the factors controlling cloud-base updraft width, which in turn dictates cloud depth, are not well understood. We track the evolving three-dimensional structure of the mesoscale subcloud forcing for vertical motion and near-cloud thermodynamic ingredients within a high-resolution ensemble of simulations of seven realistic daytime orographic convection initiation events to determine their relative roles in controlling cloud width and depth. Statistical analysis of approximately 5000 cloudy updraft samples indicates that the most important contributors to the width of cloudy updrafts across the ensemble are the depth and magnitude of the subcloud mesoscale ascent. However, the depth achieved by clouds is more consistently predicted by the near-cloud ambient relative humidity within the lower to middle free troposphere and convective available potential energy. Therefore, although the width of cloudy updrafts may be partly set at low levels by the mesoscale vertical mass and moisture flux, the likelihood of deep moist convection is governed by the generation of positive buoyancy within cumulus thermals and entrainment-driven dilution that reduces it. The persistence of the low-level mesoscale vertical forcing locally consolidates and vertically transports boundary layer moisture, helping to reduce updraft dilution. However, these factors vary in relative impacts on cloudy updrafts across individual cases, indicating multiple pathways for deep convection initiation.

Convective storms↗
DOE OSTI2020

Scalability of high-performance PDE solvers

Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite elements.

97 MATHEMATICS AND COMPUTING↗