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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Valleti, Sai Mani Prudhvi↗
DOE OSTI2024

Event-to-Video Conversion for Overhead Object Detection

Collecting overhead imagery using an event camera is desirable due to the energy efficiency of the image sensor compared to standard cameras. However, event cameras complicate downstream image processing, especially for complex tasks such as object detection. In this paper, we investigate the viability of event streams for overhead object detection. We demonstrate that across a number of standard modeling approaches, there is a significant gap in performance between dense event representations and corresponding RGB frames. We establish that this gap is, in part, due to a lack of overlap between the event representations and the pre-training data that the object detectors were initially trained on through a number of experiments. Then, apply an off-the-shelf event-to-video conversion tool that converts event streams into gray-scale video to close this gap. We demonstrate that this approach results in a large performance increase, outperforming even event-specific object detection techniques on our overhead target task. These results suggest that better aligning event representations with existing large pre-trained models may result in greater short-term performance gains compared to end-to-end event-specific architectural improvements.

machine learning (ML), computer vision, Neuromorph↗
NASA NTRS1991

Theoretical foundations for information representation and constraint specification

Research accomplished at the Knowledge Based Systems Laboratory of the Department of Industrial Engineering at Texas A&M University is described. Outlined here are the theoretical foundations necessary to construct a Neutral Information Representation Scheme (NIRS), which will allow for automated data transfer and translation between model languages, procedural programming languages, database languages, transaction and process languages, and knowledge representation and reasoning control languages for information system specification.

Menzel, Christopher P.↗
NASA NTRS1976

Correlation of AH-1G airframe test data with a NASTRAN mathematical model

Test data was provided for evaluating a mathematical vibration model of the Bell AH-1G helicopter airframe. The math model was developed and analyzed using the NASTRAN structural analysis computer program. Data from static and dynamic tests were used for comparison with the math model. Static tests of the fuselage and tailboom were conducted to verify the stiffness representation of the NASTRAN model. Dynamic test data were obtained from shake tests of the airframe and were used to evaluate the NASTRAN model for representing the low frequency (below 30 Hz) vibration response of the airframe.

Cronkhite, J. D.↗
NASA NTRS1992

Artificial intelligence approach to planning the robotic assembly of large tetrahedral truss structures

An assembly planner for tetrahedral truss structures is presented. To overcome the difficulties due to the large number of parts, the planner exploits the simplicity and uniformity of the shapes of the parts and the regularity of their interconnection. The planning automation is based on the computational formalism known as production system. The global data base consists of a hexagonal grid representation of the truss structure. This representation captures the regularity of tetrahedral truss structures and their multiple hierarchies. It maps into quadratic grids and can be implemented in a computer by using a two-dimensional array data structure. By maintaining the multiple hierarchies explicitly in the model, the choice of a particular hierarchy is only made when needed, thus allowing a more informed decision. Furthermore, testing the preconditions of the production rules is simple because the patterned way in which the struts are interconnected is incorporated into the topology of the hexagonal grid. A directed graph representation of assembly sequences allows the use of both graph search and backtracking control strategies.

Homemdemello, Luiz S.↗
DOE OSTI2023

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences

Abstract Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data—A Simulation Study

We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.

42 ENGINEERING↗
NASA NTRS1992

High resolution gravity models combining terrestrial and satellite data

Spherical harmonic expansions to degree 360 have been developed that combine satellite potential coefficient information, terrestrial gravity data, satellite altimeter information as a direct tracking data type and topographic information. These models define improved representations of the Earth's gravitational potential beyond that available from just satellite or terrestrial data. The development of the degree 360 models, however, does not imply a uniform accuracy in the determination of the gravity field as numerous geographic areas are devoid of terrestrial data or the resolution of such data is limited to, for example, 100 km. This paper will consider theoretical and numerical questions related to the combination of the various data types. Various models of the combination process are discussed with a discussion of various correction terms for the different models. Various sources of gravity data will be described. The new OSU91 360 model will be discussed with comparisons made to previous 360 models and to other potential coefficient models that are complete to degree 50. Future directions in high degree potential coefficient models will be discussed.

Rapp, Richard H.↗
DOE OSTI2018

CPU Isosurface Ray Tracing of Adaptive Mesh Refinement Data

Adaptive mesh refinement (AMR) is a key technology for large-scale simulations that allows for adaptively changing the simulation mesh resolution, resulting in significant computational and storage savings. However, visualizing such AMR data poses a significant challenge due to the difficulties introduced by the hierarchical representation when reconstructing continuous field values. Here, we detail a comprehensive solution for interactive isosurface rendering of block-structured AMR data. We contribute a novel reconstruction strategy—the octant method—which is continuous, adaptive and simple to implement. Furthermore, we present a generally applicable hybrid implicit isosurface ray-tracing method, which provides better rendering quality and performance than the built-in sampling-based approach in OSPRay. Finally, we integrate our octant method and hybrid isosurface geometry into OSPRay as a module, providing the ability to create high-quality interactive visualizations combining volume and isosurface representations of BS-AMR data. Lastly, we evaluate the rendering performance, memory consumption and quality of our method on two gigascale block-structured AMR datasets.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

BCAUS Project description and consideration of separation of data and control

The commonly stated truths that data may be segregated from program control in generic expert system shells and that such tools support straightforward knowledge representation were examined. The ideal of separation of data from program control in expert systems is difficult to realize for a variety of reasons. One approach to achieving this goal is to integrate hybrid collections of specialized shells and tools instead of producing custom systems built with a single all purpose expert system tool. Aspects of these issues are examined in the context of a specific diagnostic expert system application, the Backup Control Mode Analysis and Utility System (BCAUS), being developed for the Gamma Ray Observatory (GRO) spacecraft. The project and the knowledge gained in working on the project are described.

Bush, Joy L.↗
NASA NTRS1990

Recognition of simple visual images using a sparse distributed memory: Some implementations and experiments

Previously, a method was described of representing a class of simple visual images so that they could be used with a Sparse Distributed Memory (SDM). Herein, two possible implementations are described of a SDM, for which these images, suitably encoded, will serve both as addresses to the memory and as data to be stored in the memory. A key feature of both implementations is that a pattern that is represented as an unordered set with a variable number of members can be used as an address to the memory. In the 1st model, an image is encoded as a 9072 bit string to be used as a read or write address; the bit string may also be used as data to be stored in the memory. Another representation, in which an image is encoded as a 256 bit string, may be used with either model as data to be stored in the memory, but not as an address. In the 2nd model, an image is not represented as a vector of fixed length to be used as an address. Instead, a rule is given for determining which memory locations are to be activated in response to an encoded image. This activation rule treats the pieces of an image as an unordered set. With this model, the memory can be simulated, based on a method of computing the approximate result of a read operation.

Jaeckel, Louis A.↗
NASA NTRS2005

An XML Representation for Crew Procedures

NASA ensures safe operation of complex systems through the use of formally-documented procedures, which encode the operational knowledge of the system as derived from system experts. Crew members use procedure documentation on the ground for training purposes and on-board space shuttle and space station to guide their activities. Investigators at JSC are developing a new representation for procedures that is content-based (as opposed to display-based). Instead of specifying how a procedure should look on the printed page, the content-based representation will identify the components of a procedure and (more importantly) how the components are related (e.g., how the activities within a procedure are sequenced; what resources need to be available for each activity). This approach will allow different sets of rules to be created for displaying procedures on a computer screen, on a hand-held personal digital assistant (PDA), verbally, or on a printed page, and will also allow intelligent reasoning processes to automatically interpret and use procedure definitions. During his NASA fellowship, Dr. Simpson examined how various industries represent procedures (also called business processes or workflows), in areas such as manufacturing, accounting, shipping, or customer service. A useful method for designing and evaluating workflow representation languages is by determining their ability to encode various workflow patterns, which depict abstract relationships between the components of a procedure removed from the context of a specific procedure or industry. Investigators have used this type of analysis to evaluate how well-suited existing workflow representation languages are for various industries based on the workflow patterns that commonly arise across industry-specific procedures. Based on this type of analysis, it is already clear that existing workflow representations capture discrete flow of control (i.e., when one activity should start and stop based on when other activities start and stop), but do not capture the flow of data, materials, resources or priorities. Existing workflow representation languages are also limited to representing sequences of discrete activities, and cannot encode procedures involving continuous flow of information or materials between activities.

Simpson, Richard C.↗
DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Lalor, Peter W.↗
NASA NTRS2012

Onboard Sensor Data Qualification in Human-Rated Launch Vehicles

The avionics system software for human-rated launch vehicles requires an implementation approach that is robust to failures, especially the failure of sensors used to monitor vehicle conditions that might result in an abort determination. Sensor measurements provide the basis for operational decisions on human-rated launch vehicles. This data is often used to assess the health of system or subsystem components, to identify failures, and to take corrective action. An incorrect conclusion and/or response may result if the sensor itself provides faulty data, or if the data provided by the sensor has been corrupted. Operational decisions based on faulty sensor data have the potential to be catastrophic, resulting in loss of mission or loss of crew. To prevent these later situations from occurring, a Modular Architecture and Generalized Methodology for Sensor Data Qualification in Human-rated Launch Vehicles has been developed. Sensor Data Qualification (SDQ) is a set of algorithms that can be implemented in onboard flight software, and can be used to qualify data obtained from flight-critical sensors prior to the data being used by other flight software algorithms. Qualified data has been analyzed by SDQ and is determined to be a true representation of the sensed system state; that is, the sensor data is determined not to be corrupted by sensor faults or signal transmission faults. Sensor data can become corrupted by faults at any point in the signal path between the sensor and the flight computer. Qualifying the sensor data has the benefit of ensuring that erroneous data is identified and flagged before otherwise being used for operational decisions, thus increasing confidence in the response of the other flight software processes using the qualified data, and decreasing the probability of false alarms or missed detections.

Wong, Edmond↗
DOE OSTI2022

Ten simple rules for getting and giving credit for data

This article attempts to summarize current best practices that support the movement towards enabling researchers to cite and receive credit for their data. The authors are a small representation of the people and organizations trying to make this happen, and we acknowledge that it is not possible to capture all efforts behind this endeavor in 10 Simple Rules. We encourage interested readers to dive deeper by providing related resources along the way.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Modeling Complex Relationships in Large-Scale Data using Hypergraphs (LDRD Final Report)

This SAND report documents the findings of the LDRD project, "Modeling Complex Relationships in Large-Scale Data using Hypergraphs". The project ran from October 2017 through September 2019. The focus of the project was the development and application of hypergraph data analytics to Sandia relational data applications. In this project, we attempted to apply a hypergraph data analysis method—specifically, hypergraph eigenvector centrality—to Sandia mission problems to identify influential entities (people, location, times, etc.) in the data. Unfortunately, the application data led to graph and hypergraph representations containing disconnected components. To date, there are no well-established techniques for applying eigenvector centrality to such graphs and hypergraphs. In this report, we present several heuristics for computing eigenvector centrality for disconnected graphs. We believe this is an important start to understanding how to approach the similar problem for hypergraphs, but this project concluded before we made progress on that problem. The ideas, methods, and suggestions presented here can be used for further research into this challenging problem. We also present our ideas for generating graphs with known degree and centrality distributions. The goal in presenting this work is to identify a procedure for analyzing such graphs once the problem of addressing disconnected components has been addressed. When working with a single data set, this generator can be used to create many instances of graphs that can be used to analyze the robustness of the centrality computations for the original data set. Although the results did not match perfectly in the case of the Facebook Ego dataset used in the experiments presented here, this again represents a good start in the direction of a graph generator for such problems. We note that there are potential trade-offs between how the degree and centrality distributions are fit to the original data and suggested several possible avenues for follow-on research efforts.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Compendium of NASA data base for the Global Tropospheric Experiment's Arctic Boundary Layer Experiments ABLE-3A and ABLE-3B

The report provides a compendium of NASA aircraft data that are available from NASA's Global Tropospheric Experiment's (GTE) Arctic Boundary Layer Experiments (ABLE) conducted in July and August of 1988 (ABLE-3A) and 1990 (ABLE-3B). ABLE-3A flight experiments were based at Barrow and Bethel, Alaska, and included survey/transit flights to Thule, Greenland. ABLE-3B flight experiments were based at North Bay (Ontario) and Goose Bay, Canada, and included flights northward to Frobisher Bay, Canada. The primary purposes of the experiments were (1) the measurement of the flux of various trace gases from high-arctic ecosystems, (2) the elucidation of factors important to the production and destruction of ozone, and (3) the documentation of source and chemical signature of air common to and transported into the regions. The report provides a representation, in the form of selected data plots, of aircraft data that are available in archived format via NASA Langley's Distributed Active Archive Center. The archived data bases include data for other species measured on the aircraft as well as numerous supporting data, including meteorological observations/products, results from surface studies, satellite observations, and sondes releases.

Gregory, Gerald L.↗
NASA NTRS1994

Compressible Turbulent Channel Flows: DNS Results and Modeling

The present paper addresses some topical issues in modeling compressible turbulent shear flows. The work is based on direct numerical simulation of two supersonic fully developed channel flows between very cold isothermal walls. Detailed decomposition and analysis of terms appearing in the momentum and energy equations are presented. The simulation results are used to provide insights into differences between conventional time-and Favre-averaging of the mean-flow and turbulent quantities. Study of the turbulence energy budget for the two cases shows that the compressibility effects due to turbulent density and pressure fluctuations are insignificant. In particular, the dilatational dissipation and the mean product of the pressure and dilatation fluctuations are very small, contrary to the results of simulations for sheared homogeneous compressible turbulence and to recent proposals for models for general compressible turbulent flows. This provides a possible explanation of why the Van Driest density-weighted transformation is so successful in correlating compressible boundary layer data. Finally, it is found that the DNS data do not support the strong Reynolds analogy. A more general representation of the analogy is analysed and shown to match the DNS data very well.

Huang, P. G.↗