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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction

Recent progress in the application of machine learning (ML)/artificial intelligence (AI) algorithms to improve the Equilibrium Fitting (EFIT) code equilibrium reconstruction for fusion data analysis applications is presented. A device-independent portable core equilibrium solver capable of computing or reconstructing equilibrium for different tokamaks has been created to facilitate adaptation of ML/AI algorithms. A large EFIT database comprising of DIII-D magnetic, motional Stark effect, and kinetic reconstruction data has been generated for developments of EFIT model-order-reduction (MOR) surrogate models to reconstruct approximate equilibrium solutions. Furthermore, a neural-network MOR surrogate model has been successfully trained and tested using the magnetically reconstructed datasets with encouraging results. Other progress includes developments of a Gaussian process Bayesian framework that can adapt its many hyperparameters to improve processing of experimental input data and a 3D perturbed equilibrium database from toroidal full magnetohydrodynamic linear response modeling using the Magnetohydrodynamic Resistive Spectrum - Feedback (MARS-F) code for developments of 3D-MOR surrogate models.

Gaussian process↗
DOE OSTI2024

Leveraging BERT and Network-Based Attention Analysis for Identifying Treatment Milestones in EHRs

This study introduces a sophisticated data-driven framework for analyzing Electronic Health Records (EHRs) using transformer-based models to identify and disentangle overlapping treatment contexts. The framework leverages a preprocessing pipeline that transforms structured procedural codes into semantically enriched descriptive text, enabling the use of attention mechanisms to cluster medical events into treatment milestones—cohesive and distinct components of care processes. The methodology is rigorously validated using synthetic datasets derived from the MIMIC-III database, designed to simulate the heterogeneity and overlapping procedural contexts characteristic of real-world EHR scenarios. Quantitative evaluation highlights the framework’s robustness in disentangling concurrent care pathways, with attention metrics and unsupervised clustering approaches demonstrating the ability to preserve intra-context relationships while distinguishing inter-context dependencies. By addressing challenges inherent in data heterogeneity, this approach provides a foundation for uncovering complex treatment patterns, advancing clinical decision-making, and optimizing resource allocation in diverse healthcare environments.

Kim, Minsu [ORNL] (ORCID:0000000224185535)↗
DOE OSTI2020

Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

The analysis of connected data is an increasingly important application in high-performance computing. Such analyses can reveal fraudulent patterns in financial transactions, optimize telecommunications networks, predict information flow in social networks, etc. However, the landscape of graph analytics is highly diverse. Graph algorithms stress processor architectures differently, and no one graph can represent all topologies. Consequently, no single approach or framework is expected to be optimal for all graph analytics problems. To help make sense of this diverse landscape, we evaluated four approaches to graph analytics: GraphBLAS, Galois, BGL17, GraphIt; and compare them against hand-tuned implementations that take advantage of hardware features on our test platform. Graph- BLAS formulates graph analytics as sparse linear algebra. Galois provides syntactic constructs for data parallelism over irregular data structures. BGL17 is a generic C++ template library for implementing graph algorithms. GraphIt provides a domain- specific language to describe and optimize graph algorithms. We use the GAP Benchmark Suite to establish baseline performance and guide the side-by-side evaluation of each framework. GAP consists of 30 tests: six graph analytics algorithms (breadth- first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) run on five graphs, each with different topological characteristics (e.g., high diameter, skewed degree distribution, high average degree). High-performance reference implementations are included for each benchmark algorithm. Because a graph can be loaded into memory a number of ways (e.g., flat file on disk, compressed sparse format, data frames, retrieved from SQL or NoSQL databases), our evaluation focused on computational performance rather than I/O. Our results show the relative strengths of each framework.

Graph algorithms, Benchmarking, shared-memory prog↗
DOE OSTI2025

Technical Track on Biomass Carbon Removal and Storage (BiCRS): Mapping bioresources, phase 1 - Consistency check comparing Mission Innovation’s Data Visualization Tool for Bioresources and the Clean Energy Ministerial Biofuture Initiative Global Biomass data accessible via the US Department of Energy’s Bioenergy Knowledge Discovery Framework (KDF)

The Mission Innovation (MI) Carbon Dioxide Removal (CDR) Mission, Technical Track on Biomass Carbon Dioxide Removal and Storage (BiCRS), has produced a biomass resource database for its members. In parallel, Oak Ridge National Laboratory (ORNL) developed the International Feedstock Reporting data portal—herein referred to as the CEM Biofuture-KDF data—on behalf of the Clean Energy Ministerial Biofuture Initiative (CEM Biofuture), as a specific task under Biofuture’s 2024–25 Action Plan. This work was conducted at the request of CEM Biofuture and funded by the U.S. Department of Energy in support of that initiative, and it is hosted within DOE’s Knowledge Discovery Framework (KDF).

09 BIOMASS FUELS↗
DOE OSTI2021

dbprocessing

dbprocessing is a python framework for automating data processing pipelines. The dbprocessing package uses a configuration file to define the input, intermediate, and output data products for a particular data type as well as the processes that connect those data products. In addition, dbprocessing uses python scripts called inspectors to determine if a particular file matches a configured data product. If a particular data product is found, it is ingested into the dbprocessing database (sqlite3 or postgres) which then triggers all of the chained processes to the final data product. While the framework is written in python, it can run software in any language but may require a “wrapper” to translate the dbprocessing command line arguments to the form expected by the software.

Walker, Andrew↗
DOE OSTI2023

Application of community data to surface complexation modeling framework development: Iron oxide protolysis

This study presents a comprehensive community data-driven surface complexation modeling framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources. Using this framework, the impact of surface complexation model type and surface site density (SSD) on the fit quality and protolysis constants can be readily evaluated. For example, the non-electrostatic model yielded a poor data fit compared to diffuse double layer model and constant capacitance models due to the absence of known surface charge effects. Regardless of the choice of iron oxide mineral, pK a1 decreased with increasing SSD while the opposite tendency was observed for pK a2 . This newly developed framework demonstrates a method to reconcile community data-wide potentiometric titration data using Findable, Accessible, Interoperable, Reusable data principles to produce mineral protolysis constants that improve robustness of surface complexation models for applications in metal sorption and reactive transport modeling. The framework is readily expandable (as community data increase) and extensible (as the number of minerals increase). The framework provides a path forward for developing self-consistent, comprehensive, and updateable surface complexation databases for surface complexation and reactive transport modeling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Assessing the economic viability of unconventional rare earth element feedstocks

Sourcing rare earth elements (REEs) from unconventional feedstocks has substantial environmental and societal-economic benefits, but its economic viability remains unclear. Here, we propose a framework for assessing the economic viability of unconventional REE feedstocks. Here, we first develop a database of capital and operating expenses for REE recovery and establish consistent process unit costs for common stages in the conventional supply chain. We then use market prices to develop well-defined methods for determining diverse product values. Finally, we report the effects of economies of scale and feedstock purity on capital and operating costs. These tools can reduce the uncertainty in the assessment of the economic viability of producing REE from unconventional feedstocks, determine the value of innovative pathways for REE production from diverse sources, and inform research priorities across a wide range of REE product development stages, contributing to a sustainable REE supply.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Elevating zero dimensional global scaling predictions to self-consistent theory-based simulations

In this work, we have developed an innovative workflow, Stability, Transport, Equilibrium, and Pedestal (STEP)-zero-dimensional (0D), within the OMFIT integrated modeling framework. Through systematic validation against the International Tokamak Physics Activity global H-mode confinement database, we demonstrated that STEP-0D, on average, predicts the energy confinement time with a mean relative error of less than 19%. Moreover, this workflow showed promising potential in predicting plasmas for proposed fusion reactors such as the affordable, robust, compact (ARC) reactor, the European demonstration power plant (EU-DEMO), and the China fusion engineering test reactor (CFETR) indicating moderate H-factors between 0.9 and 1.2. STEP-0D allows theory-based prediction of tokamak scenarios, beginning with 0D quantities. The workflow initiates with the PRO-create module, generating physically consistent plasma profiles and equilibrium using the same 0D quantities as the IPB98(y,2) confinement scaling. This sets the starting point for the STEP module, which further iterates between theory-based physics models of equilibrium, core transport, and pedestal to yield a self-consistent solution. Given these attributes, STEP-0D not only improves the accuracy of predicting plasma performance but also provides a path toward a novel fusion power plant design workflow. When integrated with engineering and costing models within an optimization, this new approach could eliminate the iterative reconciliation between plasma models of varying fidelity. This potential for a more efficient design process underpins STEP-0D's significant contribution to future fusion power plant development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Review of ECCS Acceptance Criteria and Experimental Basis Evolution Toward Fuel Fragmentation, Relocation, and Dispersal Studies

The U.S. nuclear industry is pursuing extensions of light water reactor (LWR) fuel burnup and enrichment limits to approximately 75 GWd/t and 10 wt.% 235 U to achieve economic and operational benefits. A central safety consideration in this effort is the behavior of high burnup (HBu) fuel during loss-of-coolant accidents (LOCAs), particularly fuel fragmentation, relocation, and dispersal (FFRD). Here, this work provides a historical and technical review of U.S. LOCA regulation and experimentation, clarifying how the evolution of Emergency Core Cooling System (ECCS) acceptance criteria in 10 CFR 50.46 has shaped both testing approaches and interpretations of fuel safety. The study revisits the original intent of the ECCS criteria, showing that the peak cladding temperature and equivalent cladding reacted limits were developed as surrogates to preserve a coolable geometry. The explicit inclusion of the coolable geometry criterion in the regulation was intended to emphasize the underlying safety philosophy and as a safeguard against unforeseen failure modes, an intent that remains directly relevant to modern concerns regarding FFRD. The review traces the lineage of HBu LOCA experiments to the Argonne National Laboratory furnace tests, from which subsequent programs at Studsvik, Halden, and Oak Ridge National Laboratory were derived. These tests employed a 5 °C/s heating rate inherited from early embrittlement studies, a stylized temperature history that does not represent actual LWR LOCA thermal-hydraulics. Comparison of these test conditions to pressurized water reactor large break LOCAs and separate effects data indicates that the existing HBu LOCA database may not be fully applicable to all LWR LOCA scenarios, from which a qualitative framework for applicability is proposed.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

The eco-evolutionary role of fire in shaping terrestrial ecosystems

Fire is an inherently evolutionary process, even though much more emphasis has been given to ecological responses of plants and their associated communities to fire. Here, in this work, we synthesize contributions to a Special Feature entitled ‘Fire as a dynamic ecological and evolutionary force’ and place them in a broader context of fire research. Topics covered in this Special Feature include a perspective on the impacts of novel fire regimes on differential forest mortality, discussions on new approaches to investigate vegetation-fire feedbacks and resulting plant syndromes, synthesis of fire impacts on plant–fungal interactions, and a meta-analysis of arthropod community responses to fire. We conclude by suggesting pathways forward to better understand the ecological and evolutionary consequences of fire. These include developing ecological and evolutionary databases for fire ecology, integrating hierarchical genetic structure or phylogenetic structure, and developing new experimental frameworks that limit context-dependent outcomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Computer Science↗
DOE OSTI2025

User Guide for BLADE_LC_processor.py

BLADE_LC_processor.py is an optional script within the BLADE (Bolide Light-curve Analysis and Discrimination Explorer) open-source software package. It converts per-event light curve CSVs plus a metadata table into maps, plots, a per-sample trajectory file, and an aggregate summary. It anchors each trajectory at peak brightness, assumes constant speed and entry angle across the event, and propagates altitude and ground track relative to that anchor. The script converts the input azimuth internally to a travel bearing for the map and trajectory. Outputs include event folders with figures and a consolidated CSV containing start, peak, and end altitudes and all original metadata, sorted newest to oldest. For further background, users are referred to the foundational publication: Silber, E. A., Sawal, V. (2025), “BLADE: An Automated Framework for Classifying Light Curves from the Center for Near-Earth Object Studies Fireball Database,” The Astronomical Journal, doi: 10.3847/1538-3881/adeb55.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1987

Using bibliographic databases in technology transfer

When technology developed for a specific purpose is used in another application, the process is called technology transfer--the application of an existing technology to a new use or user for purposes other than those for which the technology was originally intended. Using Bibliographical Databases in Technology Transfer deals with demand-pull transfer, technology transfer that arises from need recognition, and is a guide for conducting demand-pull technology transfer studies. It can be used by a researcher as a self-teaching manual or by an instructor as a classroom text. A major problem of technology transfer is finding applicable technology to transfer. Described in detail is the solution to this problem, the use of computerized, bibliographic databases, which currently contain virtually all documented technology of the past 15 years. A general framework for locating technology is described. NASA technology organizations and private technology transfer firms are listed for consultation.

Huffman, G. David↗
NASA NTRS2003

Development of a Dynamically Configurable, Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation

The following reports are presented on this project:A first year progress report on: Development of a Dynamically Configurable,Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation; A second year progress report on: Development of a Dynamically Configurable, Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation; An Extensible, Interchangeable and Sharable Database Model for Improving Multidisciplinary Aircraft Design; Interactive, Secure Web-enabled Aircraft Engine Simulation Using XML Databinding Integration; and Improving the Aircraft Design Process Using Web-based Modeling and Simulation.

Afjeh, Abdollah A.↗
NASA NTRS2013

Space Images for NASA JPL Android Version

This software addresses the demand for easily accessible NASA JPL images and videos by providing a user friendly and simple graphical user interface that can be run via the Android platform from any location where Internet connection is available. This app is complementary to the iPhone version of the application. A backend infrastructure stores, tracks, and retrieves space images from the JPL Photojournal and Institutional Communications Web server, and catalogs the information into a streamlined rating infrastructure. This system consists of four distinguishing components: image repository, database, server-side logic, and Android mobile application. The image repository contains images from various JPL flight projects. The database stores the image information as well as the user rating. The server-side logic retrieves the image information from the database and categorizes each image for display. The Android mobile application is an interfacing delivery system that retrieves the image information from the server for each Android mobile device user. Also created is a reporting and tracking system for charting and monitoring usage. Unlike other Android mobile image applications, this system uses the latest emerging technologies to produce image listings based directly on user input. This allows for countless combinations of images returned. The backend infrastructure uses industry-standard coding and database methods, enabling future software improvement and technology updates. The flexibility of the system design framework permits multiple levels of display possibilities and provides integration capabilities. Unique features of the software include image/video retrieval from a selected set of categories, image Web links that can be shared among e-mail users, sharing to Facebook/Twitter, marking as user's favorites, and image metadata searchable for instant results.

Nelson, Jon D.↗
NASA NTRS2010

Kepler Science Operations Center Pipeline Framework

The Kepler mission is designed to continuously monitor up to 170,000 stars at a 30 minute cadence for 3.5 years searching for Earth-size planets. The data are processed at the Science Operations Center (SOC) at NASA Ames Research Center. Because of the large volume of data and the memory and CPU-intensive nature of the analysis, significant computing hardware is required. We have developed generic pipeline framework software that is used to distribute and synchronize the processing across a cluster of CPUs and to manage the resulting products. The framework is written in Java and is therefore platform-independent, and scales from a single, standalone workstation (for development and research on small data sets) to a full cluster of homogeneous or heterogeneous hardware with minimal configuration changes. A plug-in architecture provides customized control of the unit of work without the need to modify the framework itself. Distributed transaction services provide for atomic storage of pipeline products for a unit of work across a relational database and the custom Kepler DB. Generic parameter management and data accountability services are provided to record the parameter values, software versions, and other meta-data used for each pipeline execution. A graphical console allows for the configuration, execution, and monitoring of pipelines. An alert and metrics subsystem is used to monitor the health and performance of the pipeline. The framework was developed for the Kepler project based on Kepler requirements, but the framework itself is generic and could be used for a variety of applications where these features are needed.

Klaus, Todd C.↗