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Results for “Graph processing”

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 397 records · Page 22

All sources NASA NTRS DOE OSTI

NASA NTRS1974

Study of multilayered insulation pipe penetration. Thermal acoustic oscillation

Tests were conducted to determine the net heat leak to a source of liquid nitrogen caused by a metal penetration through the blanket of multilayer insulation. The conditions under which the tests were conducted are described. A graph of the theoretical and experimental temperature distribution is developed for comparison. The variables involved in the computer program to process the data are defined. A study was conducted to develop analytical methods for predicting the effect and magnitudes of thermoacoustic oscillations on the penetration heat leak to cryogens. The oscillations develop as a result of large thermal gradients imposed on a compressible fluid. The predominant amplitudes and frequencies of the thermal acoustic oscillations were investigated.

Lovin, J. K.↗

NASA NTRS1988

Design and dynamic testing of an instrumented spacecraft component

Details relating to the design and subsequent vibration and shock tests of a spacecraft component for a severe vibration and shock environment are presented. The design process and analysis method involve the use of finite element analysis coupled with the Modal Strain Energy method with risk graphs to determine the adequacy of the design. The vibration levels experienced by box parts are reduced by the application of passive constrained layer viscoelastic treatments that significantly improve component reliability. All significant internal components were fully instrumented in both the random vibration and shock tests, the latter being done on a mechanical impact pyro simulator. Correlation between analysis and test data is good, validating the modeling and analysis techniques.

Dwyer, Thomas J.↗

NASA NTRS1993

Computerized atmospheric trace contaminant control simulation for manned spacecraft

Buildup of atmospheric trace contaminants in enclosed volumes such as a spacecraft may lead to potentially serious health problems for the crew members. For this reason, active control methods must be implemented to minimize the concentration of atmospheric contaminants to levels that are considered safe for prolonged, continuous exposure. Designing hardware to accomplish this has traditionally required extensive testing to characterize and select appropriate control technologies. Data collected since the Apollo project can now be used in a computerized performance simulation to predict the performance and life of contamination control hardware to allow for initial technology screening, performance prediction, and operations and contingency studies to determine the most suitable hardware approach before specific design and testing activities begin. The program, written in FORTRAN 77, provides contaminant removal rate, total mass removed, and per pass efficiency for each control device for discrete time intervals. In addition, projected cabin concentration is provided. Input and output data are manipulated using commercial spreadsheet and data graphing software. These results can then be used in analyzing hardware design parameters such as sizing and flow rate, overall process performance and program economics. Test performance may also be predicted to aid test design.

Perry, J. L.↗

NASA NTRS1999

A Web Based Collaborative Design Environment for Spacecraft

In this era of shrinking federal budgets in the USA we need to dramatically improve our efficiency in the spacecraft engineering design process. We have come up with a method which captures much of the experts's expertise in a dataflow design graph.

collaborative↗

DOE OSTI2023

Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer

Trigger (interesting events) detection is crucial to high-energy and nuclear physics experiments because it improves data acquisition efficiency. It also plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes involving charm and beauty quarks. Furthermore, these particles are produced in collisions involving two proton beams, two gold nuclei beams, or a combination of the two and give critical insights into the formation of the early universe. This paper presents a model architecture for trigger detection with geometric information from two fast silicon detectors. Transverse momentum is introduced as an intermediate feature from physics heuristics. We also prove its importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our model is parsimonious and increases the accuracy and the AUC score by more than 15%.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Reference-free structural variant detection in microbiomes via long-read co-assembly graphs

Motivation: The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining. Results: We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Fast shared-memory streaming multilevel graph partitioning

In this report we show that a fast parallel graph partitioner can benefit many applications by reducing data transfers. The online methods for partitioning graphs have to be fast and they often rely on simple one-pass streaming algorithms, while the offline methods for partitioning graphs contain more involved algorithms and the most successful methods in this category belong to the multilevel approaches. In this work, we assess the feasibility of using streaming graph partitioning algorithms within the multilevel framework. Our end goal is to come up with a fast parallel offline multilevel partitioner that can produce competitive cutsize quality. We rely on a simple but fast and flexible streaming algorithm throughout the entire multilevel framework. This streaming algorithm serves multiple purposes in the partitioning process: a clustering algorithm in the coarsening, an effective algorithm for the initial partitioning, and a fast refinement algorithm in the uncoarsening. Its simple nature also lends itself easily for parallelization. The experiments on various graphs show that our approach is on the average up to 5.1x faster than the multi-threaded MeTiS, which comes at the expense of only 2x worse cutsize.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Continuous integration data-driven platform of industrial-scale subsurface storage for real-time analytics

This project helped address the growing need for efficient and scalable models to support geological carbon and energy storage, which are crucial for achieving net-zero emissions. Traditionally accurate high-fidelity numerical models have been used to simulate relevant storage processes under a handful of processes, however such models are computationally demanding, making uncertainty quantification impractical. Consequently, we first developed a machine learning framework, based on Graph Neural Operators (GNOs), to improving the accuracy of model predictions for a fixed computational budget. We then developed an Ensemble of Improved Neural Operators (ENO), which uses bagging and Monte Carlo dropout techniques, to further improve prediction accuracy. Lastly, we developed the way to explain progressive transfer learning methods to reduce the amount of training data and computational cost of training (i.e., reduce trainable parameters) when using our models for multiple storage sites. Our numerical investigation, which used real-world case studies, demonstrated that our framework can significantly improve the safety and efficiency of geological storage operations, with potential applications in other domains such as geothermal reservoirs and climate modeling.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

Figueroa, Jorge F.↗

DOE OSTI2023

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function

Abstract Motivation Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently. Results We developed TransFun—a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy. Availability and implementation The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2008

Quantifying VOC-Reaction Tracers, Ozone Production, and Continuing Aerosol Production Rates in Urban and Far-Downwind Atmospheres

We have found a surprisingly informative decomposition of the complex question of smoggy ozone production (basically, [HO2] in a more locally determined field of [NO]) in the process of linked investigations of modestly smoggy Eastern North America (by NASA aircraft, July 2004) and rather polluted Flushing, NYC (Queens College, July, 2001). In both rural and very polluted situations, we find that a simple contour graph parameterization of the local principal ozone production rate can be estimated using only the variables [NO] and j(sub rads) [HCHO]: Po(O3) = c (j(sub rads) [HCHO])(sup a) [HCHO](sup b). Here j(sub rads) is the photolysis of HCHO to radicals, presumably capturing many harder-UV photolytic processes and the principle ozone production is that due to HO2; mechanisms suggest that ozone production due to RO2 is closely correlated, often suggesting a limited range of different proportionality factors. The method immediately suggests a local interpretation for concepts of VOC limitation and NOx limitation. We believe that the product j(sub rads) [HCHO] guages the oxidation rate of observed VOC mixtures in a way that also provides [HO2] useful for the principle ozone production rate k [HO2] [NO], and indeed, all ozone chemical production. The success of the method suggests that dominant urban primary-HCHO sources may transition to secondary plume-HCHO sources in a convenient way. Are there other, simple, near-terminal oxidized VOC's which help guage ozone production and aerosol particle formation? Regarding particles, we report on, to the extent NASA Research resources allow, on appealing relationships between far-downwind (Atlantic PBL) HCHO and very fine aerosol (including sulfate. Since j(sub rads) [HCHO] provides a time-scale, we may understand distant-plume particle production in a more quantitative manner. Additionally we report on a statistical search in the nearer field for relationships between glyoxals (important near-terminal aromatic and isoprene reaction products) and aerosol production, looking for VOC's that might be most implicated. All three variables j(sub rads), [HCHO], and [NO] are relatively easily measured in widespread air pollution monitoring networks, and all are deducible form space-borne observations, though estimation of [NO] from [NO2] (the species observable from space) may require care. We report also on airborne and surface observations of HCHO, suggesting that concentrated (urban) and more diffuse (forest) sources may be distinguishable from space. The use of the 3.58 micron microwindow for HCHO remote sensing should allow much sharper resolution of HCHO than the UV. UV sensing requires large and expensive instruments, but even these seem justified since formaldehyde is so informative.

Chatfield, Robert↗

NASA NTRS2018

More Efficient Precipitation Rate Data Acquisition with the STORM Swath-Based Analysis Tool

A multitude of locations exist for acquiring satellite-derived precipitation rate data. Because the volume of data exceeds what can reasonably be analyzed manually, online tools have been developed to help researchers make more informed data acquisition decisions. Some interfaces provide sample images generated from prioritized variables to enable efficient visual inspection, while others include the ability to subset geographically to reduce the volume of the data to the area of interest. Building upon such value-added services is the STORM (the NASA Precipitation Processing System (PPS) data product ordering system) Swath-Based Analysis Tool (https://storm.pps.eosdis.nasa.gov/storm/Analysis.jsp). With it, users can compare overflights of multiple precipitation satellites simultaneously and from this make determinations about which swaths will best serve their research. They can preview these overflights with two distinct visualization tools. Finally, they can then download geographically subset swaths that have precipitation rate characteristics matching their research needs. A user starts by making up to six selections. They then select their date range of interest going back to 1997. Finally, they select a geographical region either by inputting latitude-longitude corners of a rectangle or drawing that rectangle on an interactive map. The system processes each file, determining aggregate statistics about the precipitation rates within the selected region as observed by each instrument. These statistical values are added to a time series graph as a "time remaining" counter is continuously updated. The chart is fully interactive, with detailed values displayed upon mouseover, and numerous options available upon clicking each point. Since the user is likely to need the relevant files for their own research, they can generate an order from their request. All files are geographically subset to the region of interest, and the user can use the statistical values to limit which files are added to the order. This flexibility helps to ensure researchers are only downloading the files they need for their work, making for a more efficient process both from a time-consumption and a storage perspective. This paper will introduce the Swath-Based Analysis Tool and explore its numerous features. It will discuss several use cases for precipitation scientists.

remote sensing↗

NASA NTRS1993

Knowledge engineering for temporal dependency networks as operations procedures

This paper presents a case study of the knowledge engineering process employed to support the Link Monitor and Control Operator Assistant (LMCOA). The LMCOA is a prototype system which automates the configuration, calibration, test, and operation (referred to as precalibration) of the communications, data processing, metric data, antenna, and other equipment used to support space-ground communications with deep space spacecraft in NASA's Deep Space Network (DSN). The primary knowledge base in the LMCOA is the Temporal Dependency Network (TDN), a directed graph which provides a procedural representation of the precalibration operation. The TDN incorporates precedence, temporal, and state constraints and uses several supporting knowledge bases and data bases. The paper provides a brief background on the DSN, and describes the evolution of the TDN and supporting knowledge bases, the process used for knowledge engineering, and an analysis of the successes and problems of the knowledge engineering effort.

Fayyad, Kristina E.↗

NASA NTRS2002

An Efficient Scheme for Updating Sparse Cholesky Factors

Raghavan had earlier developed the software package DCSPACK which can be used for solving sparse linear systems where the coefficient matrix is symmetric and positive definite (this project was not funded by NASA but by agencies such as NSF). DSCPACK-S is the serial code and DSCPACK-P is a parallel implementation suitable for multiprocessors or networks-of-workstations with message passing using MCI. The main algorithm used is the Cholesky factorization of a sparse symmetric positive positive definite matrix A = LL(T). The code can also compute the factorization A = LDL(T). The complexity of the software arises from several factors relating to the sparsity of the matrix A. A sparse N x N matrix A has typically less that cN nonzeroes where c is a small constant. If the matrix were dense, it would have O(N2) nonzeroes. The most complicated part of such sparse Cholesky factorization relates to fill-in, i.e., zeroes in the original matrix that become nonzeroes in the factor L. An efficient implementation depends to a large extent on complex data structures and on techniques from graph theory to reduce, identify, and manage fill. DSCPACK is based on an efficient multifrontal implementation with fill-managing algorithms and implementation arising from earlier research by Raghavan and others. Sparse Cholesky factorization is typically a four step process: (1) ordering to compute a fill-reducing numbering, (2) symbolic factorization to determine the nonzero structure of L, (3) numeric factorization to compute L, and, (4) triangular solution to solve L(T)x = y and Ly = b. The first two steps are symbolic and are performed using the graph of the matrix. The numeric factorization step is of dominant cost and there are several schemes for improving performance by exploiting the nested and dense structure of groups of columns in the factor. The latter are aimed at better utilization of the cache-memory hierarchy on modem processors to prevent cache-misses and provide execution rates (operations/second) that are close to the peak rates for dense matrix computations. Currently, EPISCOPACY is being used in an application at NASA directed by J. Newman and M. James. We propose the implementation of efficient schemes for updating the LL(T) or LDL(T) factors computed in DSCPACK-S to meet the computational requirements of their project. A brief description is provided in the next section.

Raghavan, Padma↗

NASA NTRS1991

Space Station Freedom science utilization: 2000 and beyond

The Office of Space Science and Applications (OSSA) Goals are: (1) to advance scientific knowledge of the Earth, the Solar System, and the Universe; (2) to understand the effects of the space environment on biological and physical processes; and (3) to expand the human presence beyond the Earth into the Solar System. The topics covered are presented in view graph form and include the following: (1) a strategic plan; (2) evolving U.S. space science capabilities; (3) life science goals; (4) OSSA Space Station Payload Traffic Model May 1991; and (5) microgravity science and applications goals.

Source record↗

NASA NTRS1995

Parametric design and gridding through relational geometry

Relational Geometric Synthesis (RGS) is a new logical framework for building up precise definitions of complex geometric models from points, curves, surfaces and solids. RGS achieves unprecedented design flexibility by supporting a rich variety of useful curve and surface entities. During the design process, many qualitative and quantitative relationships between elementary objects may be captured and retained in a data structure equivalent to a directed graph, such that they can be utilized for automatically updating the complete model geometry following changes in the shape or location of an underlying object. Capture of relationships enables many new possibilities for parametric variations and optimization. Examples are given of panelization applications for submarines, sailing yachts, offshore structures, and propellers.

Letcher, John S., Jr.↗

NASA NTRS2002

Support of Data Access for the IMP-8 UMD Experiment

This grant report provides information on data from the Interplanetary Monitoring Platform-8 (IMP-8). Topics covered include: (1) the science involved in the project; (2) the collection of data; (3) the processing of data; (4) the submission of data to other facilities; (5) the availability of data on the world wide web (WWW). Graphs are also included of data on the interstellar medium.

Ipavich, F. M.↗

DOE OSTI2023

Resilience Assessment Framework For Electric Distribution Systems Performance Under Extreme Conditions

The devastating impact of extreme weather-related events is increasingly evident on power grids, especially on distribution grids. The severity of their potential impact calls for 1) developing a suitable resilience assessment framework to capture the system performance and 2) assessing relevant mitigative strategies to lessen the impact of such events. This paper proposes a framework to identify grid vulnerabilities using the energy-at-risk concept to select, disconnect and isolate grid portions due to a resilience event. The proposed framework mainly consists of two steps; i) processing the utility's available infrastructure, i.e., a network model, possible switching combinations, and outage information for those combinations, as a graph-based database, and ii) implementing a novel optimal switching algorithm leveraging database and grid simulated metrics. These switching actions are generated to implement load curtailment in a rolling manner during anticipated grid scarcity conditions. In this study, a test case is created using two taxonomy feeders and is simulated against an extreme temperature event, e.g., a long, relatively cold, and prolonged freeze peak, thereby creating stress on the grid. It is demonstrated that the proposed framework allows utilities to predict the energy-at-risk during such resilience events and design suitable outage management strategies.

Poudel, Shiva↗