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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

Fluid structure interaction of a subaqueous pendulum: Analyzing the effect of wake correction via large eddy simulations

The dynamic behavior of a subaqueous cylindrical pendulum and corresponding flow dynamics are investigated. The objectives were twofold: (i) to examine whether the two-dimensional model equations sufficiently capture the three-dimensional dynamics and (ii) to investigate the emerging three-dimensional vortical flow structures. Large eddy simulations with two-way coupling fluid structure interaction were carried out using the immersed boundary method to simulate the motion of the pendulum and its interactions with the initially stagnant water. The resulting pendulum motion is compared against measured data obtained in a series of experimental tests to validate the simulation results and the model equations with and without wake corrections. An analysis of the flow vorticity revealed the development of a vortex ring during the first swing and the formation of tip vortices. The evolution of the vortex rings emerging from the motion of the subaqueous cylindrical pendulum was visualized using Q-criteria showing a reasonable agreement with vortical structures observed in the experiment using particle imaging velocimetry. The hydrodynamic moments acting on the simulated pendulum and the moments calculated from the model equations are analyzed. Using the insights from these numerical simulations, a modification of the wake correction is proposed to enhance the accuracy of the rate of decay and period. The transient effect of coherent flow on pendulum dynamics, especially the added mass effect, is discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

(Project 14-6770) An Investigation to Establish Multiphysical Property Dataset of Nuclear Materials Based on in-situ Observations and Measurements

In-core nuclear materials including fuel pins and cladding materials fail due to issues including corrosion, mechanical wear, and pellet cladding interaction. In most such scenario microstructure dependent and corrosioninduced chemistry dependent property changes significantly affect performance of cladding, pellet, and housing. Emphasis of this work was on replace conventional pellet-cladding material models with a new straingradient viscoplasticity model that is informed by transmission electron microscopy (TEM) based measurements and by nanomechanical Raman spectroscopy (NMRS) based measurements. The TEM measurements are quantitative in nature and therefore reveal stress-strain relations with simultaneous insights into mechanisms of deformation at nanoscale. The NMRS measurements reveal the similar information at mesoscale along with additional information on relating local microstructural stresses with applied stresses. The resulting information is used to fit constants in the strain gradient viscoplasticity model as well as to validate one. During TEM measurements, a micro-electro-mechanical system based setup was developed with mechanical actuation, sensing, heating, and electrical loading. Contrary to post-mortem analysis or qualitative visualization, this setup combines direct visualization of the mechanisms behind deformation with measurement of stress, strain, thermal and electrical properties. The unique research philosophy of visualizing the microstructure at high resolution while measuring the properties led to fundamental understanding in grain size and temperature effects on measured mechanical properties such as fracture toughness. A key contribution is the role of mechanical loading boundary conditions to deconvolute the insitu TEM based nanoscale and NMRS based mesoscale data to bulk behavior. First the literature based pellet cladding mechanical interaction model based on the work of Retel’s and Williamson’s in literature work to predict tempurature and stress distribution in cladding and pellet at normal operating condition was analyzed. Later the data was fitted to find constants for a viscoplastic strain gradient model. The developed model still needs to be refined and calibrated using various experimental results. That remains the focus of future work. Overall, a major thrust of the work was therefore on active control of the microstructure (grain size, defect density and types) exploiting the multi-physics coupling in materials. In particular, using experiments the synergy of current density, mechanical stress and temperature were studied to annihilate defects and recrystallize grains. The developed model is being examined for implementation in BISON. Multiple invited talks, international journal publications, and conference publications were performed by students supported on this work. Another output is support multiple PhD and masters thesis students who will be an important asset for future basic nuclear research. Future Work Recommendations: A nuclear reactor operates under significant variations of thermal loads due to energy cycling and mechanical loads due to constraint effects. Significant thermal and chemical diffusion takes place at the pallet-cladding level. While the proposed work established new experimental approach and new dataset for Zircaloy-4, the irradiation level was in the range of 1-2 dpa. Samples with higher dpa need to be examined. Therefore, a continual of support of the performed work is essential. Currently, these are the only experiments that can measure the produced data. The work also needs to be extended to different fuel types and cladding types such as SiC and FeCrAl based claddings. A combination of datasets for these materials can then be used to analyze accurately predict behavior of critical pellet cladding systems in accident scenario with high heat flux and high thermal loads. This is a BIG unknown as if now.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models

We describe a new framework that allows users to explore and steer ensembles of energy systems simulations by coupling multiple energy models and interactive visualization through a dataflow API. Through the visual interface, users can interactively explore complex parameter spaces populated by hundreds, or thousands, of simulation runs and interactively spawn new simulations to 'fill in' regions of interest in the parameter space. The computational and visualization capabilities reside within a general-purpose dataflow architecture for connecting producers of multidimensional timeseries data, such as energy simulations, with consumers of that data, whether they be visualizations, statistical analyses, or datastores. Fast computation and agile dataflow can enhance the engagement with energy simulations, allowing users to populate the parameter space in real time. However, many energy simulations are far too slow to provide an interactive response. To support interactive feedback, we are creating reduced-form simulations developed through machine learning techniques, which provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost. These reduced-form simulations have response times on the order of seconds, suitable for real-time human-in-the-loop design and analysis. The approximation methods apply to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. Such reduced-form representations do not replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and exploration for large ensembles of simulations. The improved understanding, facilitated by the reduced-form models, dataflow API, and visualization tools, allows researchers to better allocate computational resources to capture informative relationships within the system as well as provide a low-cost method for validating and quality-checking large-scale modeling efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Public Health Analysis Transport Optimization Model v. 1.0

PHANTOM models logistic functions of national public health systems. The system enables public health officials to visualize and coordinate options for public health surveillance, diagnosis, response and administration in an integrated analytical environment. Users may simulate and analyze system performance applying scenarios that represent current conditions or future contingencies what-if analyses of potential systemic improvements. Public health networks are visualized as interactive maps, with graphical displays of relevant system performance metrics as calculated by the simulation modeling components.

Beyeler, Walt↗
DOE OSTI2023

PathTrace and MPVEASI: A Path Analysis Comparative Validation Study

Developed in 2018, PathTrace is a software package built with the intention of making path analysis simple and intuitive. PathTrace is a top-down pathway analysis software where a user is able to explore vulnerable pathways into a facility. The intention of utilizing a software tool like PathTrace is to characterize an existing physical protection system (PPS) and to upgrade the system to achieve a high level of response interruption, or probability of interruption (P I ) of the adversary. There are four steps for conducting path analysis using PathTrace. The first step is to identify an image to use to build the model and scale the model within PathTrace using a section of known distance (wall or fence perimeter, for example). The scaling process will produce a grid of cells through which the user is able to build a model. The second step is to fill out the grid of cells with four categories of materials: Barriers, Detection Areas, Jumps, and Targets. These materials apply associated delay and detection values to the cells in which they are applied. The third step is to represent the adversary and response forces. The adversaries are represented by their capabilities in interacting with the materials identified in step two, and the response is represented by how quickly they will be able to respond to an adversary attack. Finally, the user is able to take all of the information from the previous three steps and perform a Most Vulnerable Path (MVP) analysis. In this stage, the user is able to visualize vulnerable adversary pathways and reason about how to upgrade these pathways to provide a high level of P I .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

HERMES: A cyber-energy SAAS platform to provide real-time 2d and 3d interactive visualizations [SWR-23-15]

HERMES is a novel visualization SAAS platform for achieving real-time visualization of large-scale environments involving cyber-energy devices. The platform receives data from emulated environments which include physical hardware and network devices that represent a complex IT / OT system. The platform is capable of streaming, filtering, storing, and visualizing all data within the environment and scaling vertically and horizontally. This capability enables high fidelity visual analysis of events to be performed in real time as well as the collection and storage of historical data for forensic analysis.

Van Natta, Joshua↗
DOE OSTI2018

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.

3D crop modeling↗
DOE OSTI2017

New Models and Methods for the Electroweak Scale

This is the Final Technical Report to the US Department of Energy for grant DE-SC0013529, New Models and Methods for the Electroweak Scale, covering the time period April 1, 2015 to March 31, 2017. The goal of this project was to maximize the understanding of fundamental weak scale physics in light of current experiments, mainly the ongoing run of the Large Hadron Collider and the space based satellite experiements searching for signals Dark Matter annihilation or decay. This research program focused on the phenomenology of supersymmetry, Higgs physics, and Dark Matter. The properties of the Higgs boson are currently being measured by the Large Hadron collider, and could be a sensitive window into new physics at the weak scale. Supersymmetry is the leading theoretical candidate to explain the natural nessof the electroweak theory, however new model space must be explored as the Large Hadron collider has disfavored much minimal model parameter space. In addition the nature of Dark Matter, the mysterious particle that makes up 25% of the mass of the universe is still unknown. This project sought to address measurements of the Higgs boson couplings to the Standard Model particles, new LHC discovery scenarios for supersymmetric particles, and new measurements of Dark Matter interactions with the Standard Model both in collider production and annihilation in space. Accomplishments include new creating tools for analyses of Dark Matter models in Dark Matter which annihilates into multiple Standard Model particles, including new visualizations of bounds for models with various Dark Matter branching ratios; benchmark studies for new discovery scenarios of Dark Matter at the Large Hardon Collider for Higgs-Dark Matter and gauge boson-Dark Matter interactions; New target analyses to detect direct decays of the Higgs boson into challenging final states like pairs of light jets, and new phenomenological analysis of non-minimal supersymmetric models, namely the set of Dirac Gaugino Models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

l3d2vtk: An MCNPTools Utility to Enable LNK3DNT File Visualization & Post-processing [Slides]

This presentation discusses the objective of the project which is to provide a utility (among other utilities) that improves MCNP analysis post-processing and visualization capabilities. Much recent work has been done to improve MCNP post-processing and visualization. It also discusses the goals of the project, which are to create a product that is portable, open, easily interrogated by a user, no dependencies, including a mesh tally-to-VTK conversion, an EEOUT-to-VTK conversion, and an interactive, 3-D, visualization environments (e.g., ParaView). The presentation also discusses the reasons regarding why this is being done, which are to provide physical insight into behaviors and trends, to facilitate error mitigation (easier to “see” errors than “read” them), and to improve communication with collaborators. It also discusses why this is being done now as analyses are getting ever-more complicated and are more flexible in terms of build, test, and deployment capabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

ICAT: The Interactive Corpus Analysis Tool

The Interactive Corpus Analysis Tool (ICAT) is a Python library for creating dashboards to explore textual datasets and build simple binary classification models to help filter through them and focus on entries of interest. This tool uses a form of interactive machine learning (IML), a paradigm of “machine teaching” (Simard et al., 2017) that sits at the intersection of the fields of human computer interaction (HCI), visual analytics, and machine learning. The intent of ICAT is to allow subject matter experts (SME) with limited to no experience in machine learning to benefit from an iterative human-in-the-loop (HITL) approach to building their own model without needing to understand the details of the underlying algorithm. This interactivity is achieved by allowing the user to create features, label data points, and visually manipulate a representation of the features to manually cluster and investigate data, while a model is trained on the fly based on these actions. ICAT is built on top of the Panel (Holoviz, 2018) library, using a combination of Vega, a custom IPyWidget using D3, and ipyvuetify, and is intended to be used inside of a Jupyter environment.

Martindale, Nathan [Oak Ridge National Laboratory ↗
DOE OSTI2017

IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets

IslandViewer is a widely-used webserver for the prediction and interactive visualization of genomic islands (GIs, regions of probable horizontal origin) in bacterial and archaeal genomes. GIs disproportionately encode factors that enhance the adaptability and competitiveness of the microbe within a niche, including virulence factors and other medically or environmentally important adaptations. We report here the release of IslandViewer 4, with novel features to accommodate the needs of larger-scale microbial genomics analysis, while expanding GI predictions and improving its flexible visualization interface. A user management web interface as well as an HTTP API for batch analyses are now provided with a secured authentication to facilitate the submission of larger numbers of genomes and the retrieval of results. In addition, IslandViewer’s integrated GI predictions from multiple methods have been improved and expanded by integrating the precise Islander method for pre-computed genomes, as well as an updated IslandPath-DIMOB for both pre-computed and user-supplied custom genome analysis. Finally, pre-computed predictions including virulence factors and antimicrobial resistance are now available for 6193 complete bacterial and archaeal strains publicly available in RefSeq. IslandViewer 4 provides key enhancements to facilitate the analysis of GIs and better understand their role in the evolution of successful environmental microbes and pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

TX$^2$: Transformer eXplainability and eXploration

The Transformer eXplainability and eXploration (Martindale & Stewart, 2021), or TX 2 software package, is a library designed for artificial intelligence researchers to better understand the performance of transformer models (Vaswani et al., 2017) used for sequence classification. The tool is capable of integrating with a trained transformer model and a dataset split into training and testing populations to produce an ipywidget (Project Jupyter Contributors, 2021) dashboard with a number of visualizations to understand model performance with an emphasis on explainability and interpretability. The TX 2 package is primarily intended to integrate into a workflow centered around Jupyter Notebooks (Kluyver et al., 2016), and currently assumes the use of PyTorch (Paszke et al., 2019) and Hugging Face transformers library (Wolf et al., 2020). The dashboard includes visualization and data exploration features to aid researchers, including an interactive UMAP embedding graph (McInnes et al., 2018) to understand classification clusters, a word salience map that can be updated as researchers alter textual entries in near real time, a set of tools to understand word frequency and importance based on the clusters in the UMAP embedding graph, and a set of traditional confusion matrix analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CyanoCyc cyanobacterial web portal

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists’ work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗
DOE OSTI2017

FULCRUM user interface for SCALE 6.2

SCALE 6.2 consolidated the multiple special-purpose graphical user interfaces (GUI) of SCALE 6.1 into a completely new and modern interface called FULCRUM. FULCRUM is a cross-platform GUI designed to create, edit, validate, and visualize SCALE input, output, and data. It is intended to be usable by both novice and experienced SCALE users with an unobtrusive design. This paper presents a summary of FULCRUM capabilities. Specifically, interactive input validation, context-aware auto-completion, navigation, interactive geometry visualization with mesh results overlay, job execution, data plotting, and highly configurable workspace layouts will be discussed. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Microsegregation Measurement: Methods and Applications

Microsegregation is chemical variation at the microstructural level in a solidified ingot, which can cause undesirable effects on material properties. Its formation is driven by different levels of solute elemental solubility between solid and liquid phases, resulting in solute buildup or depletion in the liquid phase during solidification. Characterization of microsegregation can be essential for understanding its formation and removing it through heat treatment. Measurement is frequently carried out via scanning electron microscope (SEM), though metallographic analysis, mechanical testing and optical radiography have been used. SEM analysis can consist of point measurements on single dendrites, line scans across or along dendrites, elemental maps, and extended line-based or grid-based analyses extending over many dendrites, which can generate large amounts of data. Line-based or grid-based data can be analyzed using scatterplots of element concentrations to visualize segregation behavior and sorting/ranking of data by a single element or a combination of elements, to give compositional profiles as a function of extent of solidification. For both qualitative and quantitative analyses, effects of phase transformations and secondary phases need to be considered as well as sorting of measurement errors when generating compositional profiles. Applications of analysis include determination of partition ratios, examination of solute–element interactions, determination of effects of cooling rate and back diffusion, applications to freckling studies, validations of modeling, and development of heat-treatment profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ElasTool v3.0: Efficient computational and visualization toolkit for elastic and mechanical properties of materials

Efficient computation and visualization of elastic and mechanical properties are crucial in the selection of materials and the design of new materials. Here, the ElasTool v3.0 toolkit marks a significant advancement in the computational analysis and visualization of elastic and mechanical properties of materials, essential in material selection and design. This enhanced version extends beyond standard calculations like elastic tensor, Young's modulus, bulk modulus, and Poisson's ratio. It introduces capabilities for computing minimum thermal conductivity, linear compressibility, rendering the Christoffel equation, and elastic energy density. Notably, it integrates advanced visualization tools, including compatibility with Plotly and Elate web platforms for interactive web-based property exploration. A key feature of ElasTool v3.0 is the implementation of second-order elastic constants (SOECs) for tubular 2D-based nanostructures and nanotubes. Leveraging high-efficiency strain-matrix sets (OHESS), the toolkit now facilitates efficient computation of elastic constants and mechanical properties at both zero and finite temperatures for 1D, 2D, and 3D dimensions. ElasTool is openly accessible on GitHub: https://github.com/gmp007/elastool.

1D, 2D, 3D, and tubular 2D nanostructure and nanot↗