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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

l3d2vtk: An MCNPTools Utility to Enable LNK3DNT File Visualization & Post-Processing

This paper describes recent work to develop and incorporate a utility program into the MCNPTools package that enables LNK3DNT file visualization & post-processing. A LNK3DNT file is a binary Cartesian, cylindrical, or spherical structured mesh file that describes material and material relative density distributions by mesh voxel. This mesh file format is principally used by the PARTISN discrete-ordinates radiation-transport code. However, the MCNP ® Monte Carlo radiation-transport code can use LNK3DNT files to specify geometry, and the MCNP code can produce LNK3DNT files representing a user-defined region of the MCNP constructive solid geometry (CSG). Because of the use of LNK3DNT files as MCNP input and output and with other computer codes, improving the ability to visualize and/or quantitatively post-process LNK3DNT files benefits end users. In particular, it is helpful to visually verify that the material and density distributions are as expected. Also, an analyst can orient viewers with respect to other in formation, e.g., by co-plotting a voxelized representation of MCNP CSG with mesh tally and/or particle track results. However, until now, the only capability to interactively interrogate LNK3DNT files is through a 2-D slice plotter provided by the Monte Carlo Application ToolKit (MCATK), which is not currently distributed outside of Los Alamos National Laboratory. The utility created as part of this work converts LNK3DNT binary files to an ASCII XML-based StructuredMesh VTK (.vts) file. The geometries supported are 1-D spherical and cylindrical ( r ), 1-D Cartesian ( x ), 2-D cylindrical ( r-z and r-θ ) and Cartesian ( x-y ), and 3-D cylindrical ( r-z-θ ) and Cartesian ( x-y-z ). This file format is chosen to minimize external dependencies (e.g., on an HDF5 library) and to provide a file that can be interrogated by the user in a text editor or standard visualization applications (e.g., ParaView or VisIt). However, other file formats may provide more utility such as XDMF, which can use HDF5 and is discussed further as something to explore as future work. This paper is organized as follows: a brief introduc tion to the MCNPTools library and utility programs is given, the LNK3DNT-to-VTK (l3d2vtk) utility functionality is de scribed, and then an example post-processing workflow using the ParaView application is given.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Visualization of flow interaction in a channel flow produced by a pair of helically coiled rods

Helically coiled tubes have been considered in the design of steam generators or heat exchangers of next generation nuclear power plants. Its compactness, efficiency, and the capability to absorb thermal expansion are desirable for nuclear reactor designs. However, the complex flow generated by the helically coiled geometry can result in flow induced vibration (FIV). In order to predict the FIV, there have been efforts using Computational Fluid dynamics (CFD)4, 5 to be coupled with multi-mechanics simulations. However, it is required to validate CFD to have confidence in capturing the flow structure characteristics of the transient complex flow behavior and fluctuation. In the present study, a simplified helically coiled steam generator (HCSG) design experiment was built acquiring data for CFD validation purpose. It is not perfect representation of the multiple rods, however, the current test facility produced transient data of the flow interaction between the rods and flow fluctuations near the rods. Particle Image Velocimetry (PIV) was applied to visualize the turbulent flow in a channel with slant rods. The two dimensional turbulent flow fields were analyzed. The velocity components were defined as below. u = U + u' (1) v = V + v' (2) where u is the lateral velocity on x-axis, v is the vertical velocity on y-axis, U and V are the mean velocities, and u' and v' are the fluctuation. Reynolds number, Re, was calculated using the rod diameter, d = 15.9 mm, and the upstream approach velocity, V{sub 0} = 0.5 m/s. Re = V{sub 0} d/η (3) where η is the kinematic viscosity of water. The experiment was conducted at Re ≅ 9,000 considering the currently reasonable computing power. Flow visualization in a flow channel produced by helically coiled rods was performed using PIV. The mean velocity field and streamlines were presented in this paper. The vortical flow structures in the cavity between rods were observed. Because of the asymmetric geometry, the flow profile was skewed. Additional measurements and transient analysis will be conducted to produce benchmark data for FIV study using CFD. Also, a test facility with a 5-rod heat exchanger design is operating to produce more data as a further work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
NASA NTRS1985

Research in computer science

Synopses are given for NASA supported work in computer science at the University of Virginia. Some areas of research include: error seeding as a testing method; knowledge representation for engineering design; analysis of faults in a multi-version software experiment; implementation of a parallel programming environment; two computer graphics systems for visualization of pressure distribution and convective density particles; task decomposition for multiple robot arms; vectorized incomplete conjugate gradient; and iterative methods for solving linear equations on the Flex/32.

Ortega, J. M.↗
DOE OSTI2023

Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions: Preprint

With the growing size and complexity of turbulent flow models, data compression approaches are of the utmost importance to analyze, visualize, or restart the simulations. Recently, in-situ autoencoder-based compression approaches have been proposed and shown to be effective at producing reduced representations of turbulent flow data. However, these approaches focus solely on training the model using point-wise sample reconstruction losses that do not take advantage of the physical properties of turbulent flows. In this paper, we show that training autoencoders with additional physics-informed regularizations, e.g., enforcing incompressibility and preserving enstrophy, improves the compression model in three ways: (i) the compressed data better conform to known physics for homogeneous isotropic turbulence without negatively impacting point-wise reconstruction quality, (ii) inspection of the gradients of the trained model uncovers changes to the learned compression mapping that can facilitate the use of explainability techniques, and (iii) as a performance byproduct, training losses are shown to converge up to 12x faster than the baseline model.

auto-encoders↗
DOE OSTI2016

Using Python For 3D Visualizations of MCNP output

MCNP is ubiquitously used within industries, universities and national laboratories as a tool for accurately modeling radiation. With so much focus on the details of transport, attention is seldom given to the visualizations of these meticulously constructed outputs. Python is a programing language designed to promote code readability and has steadily grown in popularity since the 1990's. Python is also a heavily crowd-sourced programing language which generally means that the problems that you are trying to solve with computer codes are already solved; The user is often left with the now-simplified task of cobbling together a good and useful compilation of these tools and adapting them to solve the problem at hand. I have taken advantage of this situation and constructed some visualization tools in python that can be used to duplicate some of the 2D radiation fields that MCNP produces and also produce a meaningful 3D representation of a radiation field. (author)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

SyReNN: A tool for analyzing deep neural networks

Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Unfortunately, modern DNNs have been shown to be vulnerable to a variety of attacks and buggy behavior. This has motivated recent work in formally analyzing the properties of such DNNs. Furthermore, this paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation. The key insight is to decompose the DNN into linear functions. Our tool is designed for analyses using low-dimensional subsets of the input space, a unique design point in the space of DNN analysis tools. We describe the tool and the underlying theory, then evaluate its use and performance on three case studies: computing Integrated Gradients, visualizing a DNN’s decision boundaries, and repairing buggy DNNs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Simulation and flight evaluation of a head-up landing aid for general aviation

A head-up general aviation landing aid called a landing site indicator (LASI) was tested in a fixed-base, visual simulator and in an airplane to determine the effectiveness of the LASI. The display, which had a simplified format and method of implementation, presented to the pilot in his line of sight through the windshield a graphic representation of the airplane's velocity vector. In each testing model (simulation of flight), each of 4 pilots made 20 landing approaches with the LASI and 20 approaches without it. The standard deviations of approach and touchdown parameters were considered an indication of pilot consistency. Use of the LASI improved consistency and also reduced elevator, aileron, and rudder control activity. Pilots' comments indicated that the LASI reduced work load. An appendix is included with a discussion of the simulator effectiveness for visual flight tasks.

Harris, R. L., Sr.↗
DOE OSTI2022

Stochastic Neutronics Primer (Volume I)

The purpose of this document is to introduce the elementary concepts and to build a concrete understanding of the stochastic theory of neutron transport to the motivated undergraduate student, the Ph.D. engineer/physicist/mathematician, and the staff scientist or professor learning yet another new skill. The authors understand that there are many learning types- from visual to analytical to repetitive to word-based to analogy-based (and combinations thereof)- and so we have tasked ourselves with providing as many representations as possible within to ensure every reader a fruitful endeavor. This document is written as a set of chapters that continually build upon the previous chapter. In this chapter, we provide the discussion, motivation, and background topics for the remainder of the text.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2003

Flux-Based Deadbeat Control of Induction-Motor Torque

An improved method and prior methods of deadbeat direct torque control involve the use of pulse-width modulation (PWM) of applied voltages. The prior methods are based on the use of stator flux and stator current as state variables, leading to mathematical solutions of control equations in forms that do not lend themselves to clear visualization of solution spaces. In contrast, the use of rotor and stator fluxes as the state variables in the present improved method lends itself to graphical representations that aid in understanding possible solutions under various operating conditions. In addition, the present improved method incorporates the superposition of high-frequency carrier signals for use in a motor-self-sensing technique for estimating the rotor shaft angle at any speed (including low or even zero speed) without need for additional shaft-angle-measuring sensors.

Kenny, Barbara H.↗
DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interactive visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contexualized representation is a valid approach and encourages contextual data management.

17 WIND ENERGY↗
DOE OSTI2017

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. Here in this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics.

Science & Technology - Other Topics↗
DOE OSTI2022

Demonstrating the viability of Lagrangian in situ reduction on supercomputers

Performing exploratory analysis and visualization of large-scale time-varying computational science applications is challenging due to inaccuracies that arise from under-resolved data. In recent years, Lagrangian representations of the vector field computed using in situ processing are being increasingly researched and have emerged as a potential solution to enable exploration. However, prior works have offered limited estimates of the encumbrance on the simulation code as they consider “theoretical” in situ environments. Further, the effectiveness of this approach varies based on the nature of the vector field, benefitting from an in-depth investigation for each application area. With this study, an extended version of Sane et al. (2021), we contribute an evaluation of Lagrangian analysis viability and efficacy for simulation codes executing at scale on a supercomputer. We investigated previously unexplored cosmology and seismology applications as well as conducted a performance benchmarking study by using a hydrodynamics mini-application targeting exascale computing. Here, to inform encumbrance, we integrated in situ infrastructure with simulation codes, and evaluated Lagrangian in situ reduction in representative homogeneous and heterogeneous HPC environments. To inform post hoc accuracy, we conducted a statistical analysis across a range of spatiotemporal configurations as well as a qualitative evaluation. Additionally, our study contributes cost estimates for distributed-memory post hoc reconstruction. In all, we demonstrate viability for each application — data reduction to less than 1% of the total data via Lagrangian representations, while maintaining accurate reconstruction and requiring under 10% of total execution time in over 90% of our experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

Abstract We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.

36 MATERIALS SCIENCE↗
NASA NTRS2016

Distributed Visualization Project

Distributed Visualization allows anyone, anywhere to see any simulation at any time. Development focuses on algorithms, software, data formats, data systems and processes to enable sharing simulation-based information across temporal and spatial boundaries without requiring stakeholders to possess highly-specialized and very expensive display systems. It also introduces abstraction between the native and shared data, which allows teams to share results without giving away proprietary or sensitive data. The initial implementation of this capability is the Distributed Observer Network (DON) version 3.1. DON 3.1 is available for public release in the NASA Software Store (https://software.nasa.gov/software/KSC-13775) and works with version 3.0 of the Model Process Control specification (an XML Simulation Data Representation and Communication Language) to display complex graphical information and associated Meta-Data.

Tech Port↗
NASA NTRS1986

Computational approaches to vision

Vision is examined in terms of a computational process, and the competence, structure, and control of computer vision systems are analyzed. Theoretical and experimental data on the formation of a computer vision system are discussed. Consideration is given to early vision, the recovery of intrinsic surface characteristics, higher levels of interpretation, and system integration and control. A computational visual processing model is proposed and its architecture and operation are described. Examples of state-of-the-art vision systems, which include some of the levels of representation and processing mechanisms, are presented.

Barrow, H. G.↗
DOE OSTI2020

Database development and exploration of process–microstructure relationships using variational autoencoders

The paper demonstrates graphical representation of a large database containing process–microstructure relationships using an unsupervised machine learning algorithm. Correlating microstructural features to processing is an essential first step to answer the difficult problem of process sequence design. Here, a large database of 346,200 orientation distribution functions resulting from a variety of process sequences is constructed, where each sequence comprises up to four stages of tension, compression and rolling along different directions in various permutations. This open-source database is constructed for collaborative development of process design algorithms. The paper demonstrates a novel application of the large database: graphical representation of texture–process relationships. A variational autoencoder is used to reduce the entire database to a two dimensional latent space where variations in processes and properties can be visualized. Using proximity analysis in this latent space, we can quickly unearth multiple process solutions to the problem of texture or property design.

36 MATERIALS SCIENCE↗