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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Expandable Log Analyzing Framework

Prior to my internship, I was informed that a previous intern had built a tool to analyse MongoDB logs and look for invalid access attempts, which served as a great reference point for my project. I was initially tasked with expanding on her prototype and filling in the gaps such as integrating it with the main monitoring tool the lab uses. Eventually, the scope grew, expanding to support other databases and a growing collection of tools. I organized the framework around an observer pattern, meaning one point in the program sending updates to the rest of the framework. Every time a log was read and parsed, it was sent to be processed by the tools, using the type of event as a means to determine which tools should get a chance to act on the log. This decouples the tools from the log reader, making future updates and additions much easier. The framework processes MongoDB logs at ~135,000 entries per second and PostgreSQL logs at ~170,500 entries per second, accurately detecting anomalies such as slow queries and connections from unknown addresses. This framework serves to fill gaps in database monitoring tools currently implemented at the lab, such as tracking failed authentication for PostgreSQL and MongoDB which had very minimal or none before this framework. National labs such as Fermilab hold sensitive data and valuable computing resources, making them attractive targets. Monitoring intrusion attempts on databases is made much easier by this comprehensive monitoring suite.

Clark, Dylan [Unlisted, IL]↗
DOE OSTI2023

Optimized thermodynamic properties of REE aqueous species (REE 3+ and REEOH 2+ ) and experimental database for modeling the solubility of REE phosphate minerals (monazite, xenotime, and rhabdophane) from 25 to 300 °C

Rare earth elements (REE) are critical elements found in monazite, xenotime, and hydrated REE phosphates which typically form in hydrothermal mineral deposits. Accurate predictions of the solubility of these REE phosphates and the speciation of REE in aqueous fluids are both key to understanding the controls on the transport, fractionation, and deposition of REE in natural systems. Previous monazite and xenotime solubility experiments indicate the presence of large discrepancies between experimentally derived solubility constants versus calculated solubilities by combining different data sources for the thermodynamic properties of minerals and aqueous species at hydrothermal conditions. In this study, these discrepancies were resolved by using the program GEMSFITS to optimize the standard partial molal Gibbs energy of formation (Δ f G° 298 ) of REE aqueous species (REE 3+ and REE hydroxyl complexes) at 298.15 K and 1 bar while keeping the thermodynamic properties fixed for the REE phosphates. A comprehensive experimental database was compiled using solubility data available between 25 and 300 °C. The latter permits conducting thermodynamic parameter optimization of Δ f G° 298 for REE aqueous species. Optimal matching of the rhabdophane solubility data between 25 and 100 °C requires modifying the Δ f G° 298 values of REE 3+ by 1–6 kJ/mol, whereas matching of the monazite solubility data between 100 and 300 °C requires modifying the Δ f G° 298 values of both REE 3+ and REEOH 2+ by ~15–31 kJ/mol and ~2–10 kJ/mol, respectively. For xenotime, adjustments of Δ f G° 298 values by 1–26 kJ/mol are only necessary for the REE 3+ species. The optimizations indicate that the solubility of monazite in acidic solutions is controlled by the light (L)REE 3+ species at <150 °C and the LREEOH 2+ species at >150 °C, whereas the solubility of xenotime is controlled by the heavy (H)REE 3+ species between 25 and 300 °C. Based on the optimization results, we conclude that the revised Helgeson-Kirkham-Flowers equation of state does not reliably predict the thermodynamic properties of REE 3+ , REEOH 2+ , and likely other REE hydroxyl species at hydrothermal conditions. We therefore provide an experimental database (ThermoExp_REE) as a basic framework for future updates, extensions with other ligands, and optimizations as new experimental REE data become available. As a result, the optimized thermodynamic properties of aqueous species and minerals are available open access to accurately predict the solubility of REE phosphates in fluid-rock systems.

58 GEOSCIENCES↗
DOE OSTI2024

Carbon Storage Technical Viability Approach (CS TVA): An Integrated Approach for Feasibility and Data Resource Assessment

There is currently a poor understanding and lack of workflow to understand the technical viability of carbon storage spatially. To address this gap, the multi-faceted Carbon Storage Technical Viability Approach (CS TVA) is being developed to incorporate CO2 storage resources, environmental and socio-economic justice (EJ/SJ) factors to enable more comprehensive assessments. The CS TVA includes a (1) matrix framework, (2) an integrated and labeled database, (3) a data availability assessment workflow, and (4) spatial data availability assessment results. This approach leverages spatial and data science analytics to communicate data density, uncertainty, and gaps. The workflow can be applied in whole or in part, based on user needs.

Rodriguez, Neyda Cordero↗
DOE OSTI2022

Evaluation of WRF-Solar Cloud Forecast Using the NSRDB: Preprint

Cloud forecast is a crucial component in predicting solar irradiance from numerical weather prediction (NWP) models. Assessing cloud properties from the NWP models requires significant work due to the need for high-quality data, spatial analysis covering model extent, and detailed analysis of model performance for different types of clouds. This study presents an evaluation of the WRF-Solar cloud forecast using the National Solar Radiation Database (NSRDB). We propose an evaluation framework applied to a single model prediction as well as ensemble-based forecasts. Various cloud detection metrics are calculated when comparing with the satellite-derived dataset. The mismatched clouds from the WRF-Solar model are quantified using nine cloud types classified by cloud top height and cloud optical depth. The results based on the WRF-Solar forecasts covering the entire U.S. for the full year of 2018 shows mismatched cloud frequency in the range of 8% - 46% for thick and high-level (deep convective) to thin and low-level (cumulus) clouds.

cloud mask forecast↗
DOE OSTI2022

GDSA Framework Development and Process Model Integration FY2022

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (Sassani et al. 2021). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2022 advances of the Geologic Disposal Safety Assessment (GDSA) performance assessment (PA) development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to assess probabilistically the performance of generic disposal options and generic sites. The modeling capability under development is called GDSA Framework (pa.sandia.gov). GDSA Framework is a coordinated set of codes and databases designed for probabilistically simulating the release and transport of disposed radionuclides from a repository to the biosphere for post-closure performance assessment. Primary components of GDSA Framework include PFLOTRAN to simulate the major features, events, and processes (FEPs) over time, Dakota to propagate uncertainty and analyze sensitivities, meshing codes to define the domain, and various other software for rendering properties, processing data, and visualizing results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Comparison of zirconium redistribution in BISON EBR-II models using FIPD and IMIS databases with experimental Post Irradiation Examination

The BISON code framework is utilized to model and analyze zirconium redistribution within UPuZr metallic fuels. The simulation results are compared against past experimental metallography data to validate the BISON code models. Various BISON models are implemented and compared to properly reflect the Zr present in past data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

CALPHAD Uncertainty Quantification and TDBX

CALPHAD uncertainty quantification (UQ) is the foundation of materials design with quantified confidence. We report a framework and software packages to enable CALPHAD UQ assessment and calculation using commercial CALPHAD software (Thermo-Calc). This Bayesian inference framework is coupled with a Markov chain Monte Carlo algorithm to establish uncertainty traces with a given thermodynamic database file (TDB) and corresponding experimental data points. This general framework is demonstrated with the Ni–Cr binary system. The algorithm is firstly validated on synthetic data with known ground truth. Then it is applied to real experimental data to generate posterior traces. We develop a file format named TDBX, which provides a single source of truth by combining the original TDB content and the traces for each assessed Gibbs energy parameter. CALPHAD UQ calculations are performed based on the TDBX file, from which uncertainties for phase boundaries, enthalpy curves, and solidification range are collected as examples of basic design parameters. This TDBX file with corresponding scripts are made open-source. Finally, the combination of CALPHAD UQ assessments and calculations connected by TDBX supports uncertainty-assisted modeling, enabling the integrated application of modern design with uncertainty methodologies to computational materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Integrated deep learning framework for unstable event identification and disruption prediction of tokamak plasmas

Abstract The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors like rotating modes, locked modes, H-to-L back transitions and radiative collapses. The first part of our study demonstrates that the DL-based unstable event identifier trained on 160 manually labeled DIII-D shots can achieve, on average, 84% event identification rate of various frequent unstable events (like H-L back transition, locked mode, radiative collapse, rotating MHD mode, large sawtooth crash), and the trained identifier can be adapted to label unseen discharges, thus expanding the original manually labeled database. Based on these results, the integrated DL-based framework is developed using a combined database of manually labeled and automatically labeled DIII-D data, and it shows state-of-the-art (AUC = 0.940) disruption prediction and event identification abilities on DIII-D. Through cross-machine numerical disruption prediction studies using this new integrated model and leveraging the C-Mod, DIII-D, and EAST disruption warning databases, we demonstrate the improved cross-machine disruption prediction ability and extended warning time of the new model compared with a baseline predictor. In addition, the trained integrated model shows qualitatively good cross-machine event identification ability. Given a labeled dataset, the strategy presented in this paper, i.e. one that combines a disruption predictor with an event identifier module, can be applied to upgrade any neural network based disruption predictor. The results presented here inform possible development strategies of machine learning based disruption avoidance algorithms for future tokamaks and highlight the importance of building comprehensive databases with unstable event information on current machines.

plasma instabilities↗
DOE OSTI2022

Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Computed entropy spectra for grain boundary segregation in polycrystals

Abstract Grain boundary solute segregation influences most bulk material properties, and understanding solute thermodynamics at grain boundaries is critical for engineering them. However, the vast grain boundary space in polycrystals is challenging to evaluate due to its size, especially for the intrinsically hard-to-compute segregation excess entropy. Here data science methods are used to generate a database of site-wise grain boundary segregation entropy spectra for 155 dilute binary alloys within the harmonic approximation. The spectral framework allows scale bridging between the calculated atomistic site-wise energy-entropy spectra and macroscopic segregation entropy estimates. The results affirm that macroscopic averaging is not sufficient: a spectral treatment of grain boundary segregation is needed to accurately model bulk temperature dependence of grain boundary solute segregation. The calculated spectral entropy database and thermodynamic framework can be applied for both understanding segregation experiments and alloy design exercises, paving the way to a finite-temperature grain boundary genome.

Chemistry↗
DOE OSTI2024

Database schema design: Energy Flexibility Environmental Tradeoffs Tool

The Energy Flexibility-Environment Tradeoff Toolset is designed using Streamlit framework, with Python as the programming language. Data storage is facilitated through the use of SQLite. Streamlit is an open-source Python framework for machine learning and data science teams, and SQLite is the most used database engine.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Data Qualification Framework

Proposed Data Qualification System Background Originally developed as a data qualification system for the Bioenergy Feedstock Library Provide data users a means of assessing data quality based on user driven quality metrics. Give users information regarding common data quality metrics to determine and assess the data quality. Potential Use Same type of quality assessment framework could be potentially applied to other databases and/or datasets for common assessment. Provide metric for assessment of appropriateness of data for various uses.

09 BIOMASS FUELS↗
DOE OSTI2025

Evaluation of New Additions to OLI Software in Predicting Mercuric and Mercurous Species in Liquid Waste Operations

Speciation of mercury during the pretreatment steps of tank waste processing is critical to successful mercury removal prior to vitrification during Liquid Waste Operations (LWO) at SRS. OLI software has been used to predict mercury speciation and activity throughout LWO. The OLI software operates based on a thermodynamic framework called the Mixed Solvent Electrolyte (MSE) framework. The MSE framework allows prediction in theoretically infinitely dilute to concentrated mixtures (e.g., purely solute solutions). Before modification to the MSE framework databanks, certain critical mercury species were missing in the MSE databank, and some thermodynamic data needed to be updated for the OLI software to accurately predict mercury chemical species in SRS waste tanks. To better reflect streams across LWO, new mercury species were integrated into the MSE database. To evaluate the changes to the OLI MSE framework per the Technical Task Request (TTR) and the Task Technical and Quality Assurance Plan (TTQAP), waste stream compositions from Tanks 38, 43, and Tank 50 decontaminated salt solution (DSS) were used as model inputs. Models were developed and executed using both the old and new databases. Compositional analyses from caustic Tank 50 DSS and caustic Tanks 38 and 43 were used as the input streams. These streams represent the most comprehensive chemical data sets where both mercury and tank constituents were measured together. Results for Tank 50 DSS predict HgO as the predominant species in both databases. Both methyl and dimethyl Hg species are present when the new database is ‘on’ and are not predicted with the new database turned ‘off’. The new database predicts a greater amount of HgO and a greater fraction of it in the solid phase. Pourbaix diagrams (potential vs. pH) generated for each Tank 50 DSS were identical regardless of which database was used. Elemental Hg and HgO were predicted in the water stable region under basic conditions. Tanks 38 and 43 follow similar trends as the Tank 50 DSS models. Unlike Tanks 38 and 50 DSS, the Tank 43 Pourbaix plot shows a region of stability for an aqueous HgOHCO3 - species between approximately pH 7-11. In all streams, when MeHg+ is included in the inputs, the new database predicts aqueous MeHgOH as the dominant species. If elemental or dimethyl mercury is in the waste stream, the new database model predicts they are unchanged and remain in those states and quantities. Additionally, the total mercury values are reported for both the measured input data and the OLI output data for all considered tanks. The summary indicates that the percentage error between the measured and calculated values is less than 1% in all cases The reconciliations and generation of the Pourbaix diagrams for Tank 50 DSS took approximately ten times longer with the new database ‘on’. In addition, over the course of that time, models with the new database ‘on’ were more likely to crash or display an error. Some modest performance improvements were noted when modeling with an i7 processor versus an i5. An example error is found in Appendix A. Furthermore, Appendix B provides V&V for two chemical systems analyzed with the OLI software, results were satisfactory. It is recommended to utilize the new databases (i.e., HCO.ddb and SR-Hg.ddb) in future Savannah River Mission Completion applications of OLI to represent pseudo steady-state. Furthermore, the integration and utilization of the new databases (i.e., HCO.ddb and SR-Hg.ddb) in modeling applications (e.g., Aspen) is also recommended.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Evaluation of the Self Retrieval Augmented Generation Technique on Common Security Advisory Framework Data

This small experimental report evaluates a variation of Retrieval Augmented Generation (RAG), called Self-RAG. This method uses a generative language model that incorporates retrieved facts into its generation and is explicitly trained to be able to determine whether retrieved information is enough to answer the input query, with a user-defined threshold for confidence. We performed an experiment using data from the publicly available CISA Common Security Advisory Framework (CSAF) repository (https://github.com/cisagov/CSAF) as the database of facts to be used in retrieval. Qualitative results from the experiment demonstrate that the Self-RAG method has some ability to provide reasonable answers to queries that are in the dataset and will often ignore irrelevant information when asked outside of domain questions (e.g., general facts). In settings with deliberately confusing questions (the question is within domain, but asks about a fabricated advisory), it was able to refuse 40% of the time without further adjustments to the original framework. While this performance is not sufficient for current practical use, further improvements to data formatting, disambiguating results, and leveraging threshold values could improve performance significantly. However, evaluating this will require more extensive evaluations on larger datasets and potentially better models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra

X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. Here, we address these challenges with OmniXAS, a framework that contains a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on the K-edge spectra database covering eight 3⁢d transition metals (Ti–Cu). The OmniXAS framework is built upon three distinct strategies. First, we use M3GNet [Nat. Comput. Sci. 2, 718 (2022)] to derive latent representations of the local chemical environment of absorption sites as input for XAS prediction, achieving significant improvements over conventional featurization techniques. Second, we employ a hierarchical transfer learning strategy, training a universal multitask model across elements before fine-tuning for element-specific predictions. Models based on this cascaded approach after elementwise fine-tuning outperform element-specific models by up to 69%. Third, we implement cross-fidelity transfer learning, adapting a universal model to predict spectra generated by simulation of a different fidelity with a much higher computational cost. This approach improves prediction accuracy by up to 11% over models trained on the target fidelity alone. Our approach significantly boosts the throughput of XAS modeling by orders of magnitude as compared to first-principles simulations and is extendable to XAS prediction for a broader range of elements. The proposed transfer learning framework is generalizable to enhance deep-learning models that target other properties in materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Framework to select robust energy retrofit measures for residential communities

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.

Shu, Lei↗
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A Comparison of Human Error Probabilities Collected from HuREX and SHEEP Frameworks

This paper discusses how different are the HEPs collected from HuREX and SHEEP frameworks. This study is a preceding research to infer full-scope HRA data based on the data collected from the SHEEP framework. For the comparison, we used HEPs in the HuREX database published in KAERI-TR-6649 [6]. The HEPs have been collected from actual licensed operators when manipulating MCR simulators of Westinghouse type and Optimized Power Reactor (OPR1000) type in South Korea. For the HEPs based on the SHEEP framework, these have been collected from actual licensed operators and students when using a simplified simulator, i.e., Rancor Microworld.

99 GENERAL AND MISCELLANEOUS↗