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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Multi-Mission Automated Task Invocation Subsystem

Multi-Mission Automated Task Invocation Subsystem (MATIS) is software that establishes a distributed data-processing framework for automated generation of instrument data products from a spacecraft mission. Each mission may set up a set of MATIS servers for processing its data products. MATIS embodies lessons learned in experience with prior instrument- data-product-generation software. MATIS is an event-driven workflow manager that interprets project-specific, user-defined rules for managing processes. It executes programs in response to specific events under specific conditions according to the rules. Because requirements of different missions are too diverse to be satisfied by one program, MATIS accommodates plug-in programs. MATIS is flexible in that users can control such processing parameters as how many pipelines to run and on which computing machines to run them. MATIS has a fail-safe capability. At each step, MATIS captures and retains pertinent information needed to complete the step and start the next step. In the event of a restart, this information is retrieved so that processing can be resumed appropriately. At this writing, it is planned to develop a graphical user interface (GUI) for monitoring and controlling a product generation engine in MATIS. The GUI would enable users to schedule multiple processes and manage the data products produced in the processes. Although MATIS was initially designed for instrument data product generation,

Cheng, Cecilia S.↗
DOE OSTI2015

SU-E-J-22: A Feasibility Study On KV-Based Whole Breast Radiation Patient Setup

Purpose: In room kilovoltage x-ray (kV) imaging provides higher contrast than Megavoltage (MV) imaging with faster acquisition time compared with on-board cone-beam computed tomography (CBCT), thus improving patient setup accuracy and efficiency. In this study we evaluated the clinical feasibility of utilizing kV imaging for whole breast radiation patient setup. Methods: For six breast cancer patients with whole breast treatment plans using two opposed tangential fields, MV-based patient setup was conducted by aligning patient markers with in room lasers and MV portal images. Beam-eye viewed kV images were acquired using Varian OBI system after the set up process. In house software was developed to transfer MLC blocks information overlaying onto kV images to demonstrate the field shape for verification. KV-based patient digital shift was derived by performing rigid registration between kV image and the digitally reconstructed radiography (DRR) to align the bony structure. This digital shift between kV-based and MV-based setup was defined as setup deviation. Results: Six sets of kV images were acquired for breast patients. The mean setup deviation was 2.3mm, 2.2mm and 1.8mm for anterior-posterior, superior-inferior and left-right direction respectively. The average setup deviation magnitude was 4.3±1.7mm for six patients. Patient with large breast had a larger setup deviation (4.4–6.2mm). There was no strong correlation between MV-based shift and setup deviation. Conclusion: A preliminary clinical workflow for kV-based whole breast radiation setup was established and tested. We observed setup deviation of the magnitude below than 5mm. With the benefit of providing higher contrast and MLC block overlaid on the images for treatment field verification, it is feasible to use kV imaging for breast patient setup.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

D–MOPH–25: diverse MOF–molecule pairs for Henry’s constants prediction

Computational methods like grand-canonical Monte Carlo simulations and machine learning (ML) have accelerated metal–organic frameworks (MOF) exploration but are typically limited to a narrow range of adsorbates due to data availability and force field constraints. In this study, we introduce a dataset of diverse MOF–molecule pairs for Henry’s constant prediction, D–MOPH–25, which systematically explores a diverse chemical space by combining 113 molecular adsorbates with over 5000 MOF structures through an active learning process. D–MOPH–25 constitutes the most diverse adsorbate dataset used in any ML study of molecular adsorption in MOFs to date. Our workflow builds a benchmark for predicting Henry’s constants at 300 K, leveraging conformal prediction for uncertainty quantification. Assessment through Shannon entropy and uniform manifold approximation and projection confirms the comprehensiveness of D–MOPH–25 while highlighting the importance of robust classification to filter out unphysical data points in regression tasks. Although future enhancements in model architecture and sampling criteria could improve predictive performance, our dataset already spans the target space using only 2.31% of total possibilities. This comprehensive dataset facilitates assessment of model generalizability across adsorbate species and can establish a foundation for high-throughput MOF screening and ML-driven separation processes.

active learning↗
DOE OSTI2025

Soil porous microstructure control over soil organic matter mobility: A multimethod workflow for understanding chemistry-dependent organic matter binding in soil

Soil organic matter (SOM) has attracted a great deal of interest; particularly for its potential to mitigate human derived CO 2 emissions. Studies have demonstrated that SOM plays a critical role in carbon storage and CO 2 sequestration. However, the sorption properties of SOM, which influence its transport in pore water and stabilization within the soil, remain poorly understood. This study develops a workflow to: (1) examine compound-specific advective and diffusive transport and desorption behaviors, (2) quantify desorption rates through stop-flow and continuous-flow column experiments, and (3) evaluate the impact of soil microporosity on SOM mobility using high-resolution imaging and extractions. Intact core column experiments were conducted on Uncultivated (Natural) and Cultivated soil samples, both were arid soils, collected in Washington State. X-ray computed tomography was employed to measure porosity and pore connectivity, while Fourier-transform ion cyclotron resonance mass spectrometry was used to analyze SOM composition. The findings revealed that cultivation increased total carbon and nitrogen levels due to irrigation and fertilization, enhancing carbon capture potential in arid soils. In contrast, the Natural soil, characterized by higher porosity and connectivity, contained more oxidized carbon. Pore network analysis indicated that soil compaction in the Cultivated soil may lead to longer diffusion pathways, significantly influencing SOM transport and stability.

Hydraulic Properties↗
DOE OSTI2025

ATLAS HL-LHC Demonstrators with Data Carousel: Dataon-Demand and Tape Smart Writing

The High Luminosity upgrade to the LHC (HL-LHC) is expected to deliver scientific data at the multi-exabyte scale. To tackle this unprecedented data storage challenge, the ATLAS experiment initiated the Data Carousel project in 2018. Data Carousel is a tape-driven workflow in which bulk production campaigns with input data resident on tape are executed by staging and promptly processing a sliding window to disk buffer such that only a small fraction of inputs are pinned on disk at any one time. Put in ATLAS production before Run3, Data Carousel continues to be our focus for seeking new opportunities in disk space savings, and enhancing tape usage throughout the ATLAS Distributed Computing (ADC) environment. These efforts are highlighted by two recent ATLAS HL-LHC demonstrator projects: data-on-demand and tape smart writing. In this paper, we will discuss the recent studies and outcomes from these projects. The research was conducted together with site experts at CERN and Tier-1 centers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Autonomous x-ray scattering

Autonomous experimentation (AE) is an emerging paradigm that seeks to automate the entire workflow of an experiment, including—crucially—the decision-making step. Beyond mere automation and efficiency, AE aims to liberate scientists to tackle more challenging and complex problems. We describe our recent progress in the application of this concept at synchrotron x-ray scattering beamlines. We automate the measurement instrument, data analysis, and decision-making, and couple them into an autonomous loop. We exploit Gaussian process modeling to compute a surrogate model and associated uncertainty for the experimental problem, and define an objective function exploiting these. We provide example applications of AE to x-ray scattering, including imaging of samples, exploration of physical spaces through combinatorial methods, and coupling to in situ processing platforms These uses demonstrate how autonomous x-ray scattering can enhance efficiency, and discover new materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Curifactory: A research experiment manager

Curifactory is a command line tool and framework for organizing Python experiment code, configuration parameters, and results. It is an opinionated and lightweight approach to workflow management infrastructure and is primarily intended to support researchers conducting experiments on one machine. This software was developed to support the reproducibility of results for several data science projects in the Nuclear Nonproliferation Division at Oak Ridge National Laboratory. Curifactory is intended to be a general framework and is not specific to machine learning or data science. It can aid in any field in which experiments are primarily computation-based studies and can be implemented in Python (e.g., high-energy physics, astronomy, computational chemistry). Here, the design emphasizes the automated caching of intermediate data analysis artifacts to speed up development involving computationally intensive tasks. It also allows for data provenance and experiment reproduction. Individual experiment runs are tracked through logs and their output reports, and entire copies of a run with all cached data and metadata can be exported for others to run using Curifactory on another machine. Curifactory experiments can either be integrated into a project from the beginning or can be written on top of an existing codebase without needing significant modification. A few important views of the Curifactory library can be seen in Figure 1.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Predictive Engineering Tools for Injection-Molded Long-Carbon-Fiber Thermoplastic Composites (FY 2015 Third Quarterly Report)

During the third quarter of FY 2015, the following technical progress has been made toward project milestones: 1) Magna oversaw the tool build and prepared the molding plan for the complex part of Phase II. 2) PlastiComp hosted a visit by Magna and Toyota on April 23rd to finalize the molding scope and schedule. The plan for molding trials including selection of molding parameters for both LFT and D-LFT for the U-shape complex part was established. 3) Toyota shipped the U-shape complex part tool to Magna on May 28th, 2015. 4) Plasticomp provided 30wt% LCF/PP and 30wt% LCF/PA66 compounded pellets to Magna for molding the complex part. 5) Magna performed preliminary molding trials on June 2nd, 2015 to validate wall thickness, fill profile, tool temperature and shot size requirements for the complex part. 6) Magna performed the first complex part run on June 16th and 17th, 2015 at Magna’s Composite Centre of Excellence in Concord, ON, Canada. Dale Roland of Plasticomp, and Umesh Gandhi of Toyota also attended the molding. 7) Magna discussed and finalized the plan with PNNL and the team for cutting samples from molded parts at selected locations for fiber orientation and length measurements. 8) Magna provided the computer-aided design (CAD) files of the complex parts with and without ribs to PNNL and Autodesk to build the corresponding ASMI models for injection molding simulations. Magna also provided the actual parameters used. 9) Plasticomp’s provided knowledge and experience of molding LCF materials essential to the successful molding of the parts including optimization of fill speed, tool temperatures, and plasticizing conditions for the 30wt% LCF/PP and 30wt% LCF/PA66 materials in both rib and non-rib versions. 10) Magna molded additional parts for evaluation of mechanical property testing including torsional stiffness on June 29th and 30th, 2015 at Magna’s Composite Center of Excellence. 11) Toyota began preparation for the torsion test of the specimens. Preparation of a computer-aided engineering (CAE) model to predict the performance is in progress. 12) Autodesk fixed an error in the implementation of the proper orthogonal decomposition (POD) calculation of fiber length that had caused the ASMI solution to crash and provided an updated build of ASMI containing the fix. 13)Autodesk reviewed and provided feedback for the complex part molding and measurement locations. 14) Autodesk provided support to set up the workflow for ASMI-ABAQUS® analysis, and provided a fix and workaround for a bug in the ASMI-ABAQUS® output command. 15) Autodesk helped build ASMI analysis models for the complex parts with and without ribs. 16) Autodesk worked on improving the orientation prediction accuracy in the shearing layer for 3D meshes based on comparison to measured data of the plaque moldings. 17) PNNL installed a new ASMI version received from Autodesk and performed comparative analyses to assess mid-plane versus 3D fiber length predictions using the full fiber length model and the reduced-order model (ROM) using POD. 18) PNNL presented the project scope, accomplishments, significant results and future plans to DOE and the USCAR Materials Tech Team on June 3rd, 2015. 19) PNNL discussed the cutting of samples from molded parts and finalized a plan with Magna and the team suggesting the sample size, locations and number of samples per location. 20) PNNL and Autodesk built ASMI models for the complex parts with and without ribs, and preliminary analyses of the part with ribs were conducted using the actual molding parameters received from Magna. 21) PNNL worked on a procedure to extract fiber orientation and length results from a 3D ASMI analysis to a 3D ABAQUS model. This procedure is essential to import ASMI fiber orientation and length to a 3D ABAQUS model of the part allowing future part structural analysis for weight reduction study.

36 MATERIALS SCIENCE↗
DOE OSTI2022

LANL Activities on Mechanistic Approach to Analyzing and Improving Unconventional Hydrocarbon Production

Hydrocarbon production from shale reservoirs is inherently inefficient and challenging since these are low permeability plays. In addition, there is a limited understanding of the fundamentals and the controlling mechanisms, further complicating how to optimize these plays. Herein, we summarize our experimental and computational efforts fully and partially supported by the fundamental shale portfolio to reveal unconventional shale fundamentals and devise development strategies to enhance extraction efficiency with a minimal environmental footprint. Integrating these fundamentals with machine learning, we outline a pathway to improve the predictive power of our models, which enhances the forecast quality of production, thereby improving the economics of operations in unconventional reservoirs. For instance, we have developed science informed workflows and platforms for optimizing pressure-drawdown at a site, which allow operators to make reservoir-management decisions that optimize recovery in consideration of future production. Recently, our work relies on the hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (combined with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. Consequently, the workflow and platform are readily extendable to operations at other sites, plays, and basins.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

5G Energy FRAME Report on 5G for Grid Use Case (Year 3 Final Report)

This report provides an extensive overview of the interrelationships among energy, communication, and computing—especially in the context of decarbonization goals, challenges, and opportunities. Technical examples enabled by 5G technologies and their performance are presented, discussed, based on the experiment performed at Pacific Northwest National Laboratory. This is the first use case focused on using 5G for the U.S. power grid and will be referred to as the 5G for Grid Use Case from here on. Specifically, this use case looks at the workflow, which integrates the performance data of a real-world 5G communication testbed and a grid transmission and distribution co-simulation platform. The cross-domain information flow and logic design are illustrated with a combination of power grid contingencies and events. Lastly, a summary of the project achievement and outcome is provided, along with a technology roadmap envisioned by the project team.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Reduced Models for Chemical Kinetics derived from Parallel Ensemble Simulations of Stirred Reactors

Detailed chemical kinetic models are essential to improve the predictive capability of computational fluid dynamics (CFD) simulations and to impact the development of next generation combustion technologies. However, such detailed kinetic models are beyond the reach of CFD codes due to the computational cost of transporting additional chemical degrees of freedom. Reduced order models for combustion kinetics exploit the presence of lower dimensional manifolds in the state space and require far fewer number of scalars to be transported as part of CFD simulations. In this paper, we describe the software infrastructure developed to allow large ensembles of stirred reactor configurations with detailed chemical kinetics. The canonical reactor simulations are incorporated in a workflow with sparse grid and data reduction methodologies aimed at development of reduced order models. We present results from analyzing the results of the stirred reactor calculations for syngas mixture using principal component analysis (PCA) and neural network methodologies. The results show that nonlinear autoencoder model can surpass PCA in representing the original dataset with far fewer degrees of freedom.

Zhang, Pei↗
NASA NTRS2019

Modeling of Tunable Elastic Ultralight Aircraft

Aircraft weight is one of the most critical factors in the design and operation of modern vehicles. The ability to integrate ultra-light materials into the primary load bearing structures has the potential to reduce aircraft weight significantly. Ultralight materials tend to be lattice-based meta-materials that are difficult and computationally expensive to model. One of the advantages of meta-materials is to be able to tune or "program" their bulk material properties through the placement of heterogeneous components in the material. A large amount of time devoted to the simulation in the development time for the tuning of the material can be a barrier to the adoption of large scale lattice materials. In this paper, we present a workflow and analysis tool-set to provide first-order estimates for rapid development of engineered lattice materials for aerospace applications. We present results for estimating the displacement and maximum structural stresses.

cellular solids↗
DOE OSTI2024

Dose Consequence and Probabilistic Risk Assessment Integration into Digital Documented Safety Analysis

This is an intern poster presentation. The current reactor authorization process is complex and error prone. The development of a digital Documented Safety Analysis has been proposed to provide an automated and integrated solution to enhance the design and authorization process. The digital DSA will consist of interlinked models, analyses, and reports, all of which will be updated using automated workflows when design changes are made. This poster examines the integration of the probabilistic risk assessment (PRA) with the transient and dose consequence analyses. A PRA for a generic high temperature gas-cooled reactor (HTGR) has been constructed which will drive the input parameters for a transient analysis model currently being constructed. Dose consequence will be determined using the results of the transient analysis and the Radiation Safety Analysis Computer (RSAC) code. Dose consequence data will then be input back into the PRA to drive design parameters.

42 ENGINEERING↗
DOE OSTI2018

Accelerated Climate Modeling For Energy

This is the final report on the work of the New York University team to the DOE Accelerated Climate Modeling For Energy (ACME) project. NYU was one of the original developers of the UV-CDAT tool for climate data analysis, which is being used in ACME. NYU primarily focused on the analysis package graphical user interface, workflow and provenance functionality. The most significant activities in the last year of the grant were the development of the new plotting system in UVCMetrics package and the package EzLinePlot for 1D plots which are described in detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Chimbuko: A Workflow-Level Scalable Performance Trace Analysis Tool

ABSTRACT Due to the sheer volume of data it is typically impractical to analyze the detailed performance of an HPC application running at-scale. While conventional small-scale benchmarking and scaling studies are often sufficient for simple applications, many modern workflow-based applications couple multiple elements with competing resource demands and complex inter-communication patterns for which performance cannot easily be studied in isolation and at small scale. This work discusses Chimbuko, a performance analysis framework that provides real-time, in situ anomaly detection. By focusing specifically on performance anomalies and their origin (aka provenance), data volumes are dramatically reduced without losing necessary details. To the best of our knowledge, Chimbuko is the first online, distributed, and scalable workflow-level performance trace analysis framework. We demonstrate the tool's usefulness on Oak Ridge National Laboratory's Summit system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy

Abstract Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe 2 film. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. FAST is easy to adapt for any scanning microscope; its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Quantitative interpretation of time-lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time-lapse velocity responses

Here, this study investigates the contribution of fluid saturation variation to the time-lapse velocity response by performing fluid substitution modeling. The methodology is exemplified by the time-lapse seismic monitoring of carbon dioxide at Farnsworth field unit (FWU). In order to evaluate the fluid distribution in a matured oil reservoir, the Southwest Regional Partnership (SWP) acquired multiple vertical seismic profile (VSP) surveys at different times during the CO 2 –water alternatinggas (WAG) injection period. In this work, we present a thorough methodology for computing the elastic response of the saturated rock for different fluid saturations using a site-specific petro-elastic model (PEM). The output from the PEM was combined with results from a fluid compositional model to compute the seismic velocities at times corresponding to each VSP survey. To produce a calibrated simulated response, the measured time-lapse seismic velocities were integrated into the numerical simulation model. The mismatches between the predicted and measured time-lapse velocities were minimized through an iterative calibration process using a trained artificial neural network proxy (ANN) coupled with a particle swarm optimizer (PSO). Our study indicates that the hybrid optimization workflow can effectively perform the history matching. With an accurate prediction of the hydrodynamic properties, the migration of CO 2 within the subsurface was modeled by predicting the spatial velocity distribution for a radius of 305 m around the injection well. The technology demonstrated and the expertise gained from this study can guide similar CO 2 -WAG projects.

58 GEOSCIENCES↗
DOE OSTI2022

A workflow for automatic generation and efficient refinement of individual pressure-dependent networks

Manual chemical kinetic model construction often requires modelists to at least implicitly guess all possible decay pathways and their relative fluxes for all important species. We present a workflow and associated tool for automatic generation and re finement of pressure-dependent networks that should enable modelists to efficiently and comprehensively identify decay pathways and estimate respective parameters for chemical species. This tool combines the capabilities of the Reaction Mechanism Generator (RMG) software to generate possible reaction paths, determine thermochemistry, approximate rate coefficients and estimate frequencies with the capabilities of the Arkane software to use quantum chemical parameters to compute thermochemistry and pressure dependent rates. A flux-based algorithm is used to decide which isomers to add to the network. Isomers added to the network are reacted to form other channels. When enough of the flux is accounted for by the isomers and bimolecular product channels, the generation process terminates to yield a comprehensive network. Network sensitivity analysis is applied to the network to identify the most important wells and barriers that can be refined using quantum chemistry calculations. Iterative refinement can then be used to achieve accuracy. The comprehensive network can be easily reduced using energy and flux-based algorithms to the most important channels and isomers in the network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗