SEARCH · Search NASA

Results for “knowledge extraction”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Method for Extracting Aerodynamic Coefficients and Winds On-the-Fly Using Real-Time GPS Data

The landing accuracy of an autonomous guided parafoil is influenced by its ability to predict the trajectory, which is shaped by the parafoil’s aerodynamic characteristics and atmospheric winds. The parafoil is subjective to imperfections from manufacturing, damage during deployment, lateral asymmetry, and so forth, leading to discrepancies between the actual aerodynamic properties and the onboard aerodynamic model. Thus, by acquiring an accurate knowledge of the aerodynamic properties and real-time winds, the onboard simulation can reduce the undertainties in the dynamics modeling for trajectory propagation to improve the landing accuracy. For that purpose, a non-linear least-squares method is developed to calculate differential corrections for optimizing and extracting the parafoil aerodynamic coefficients and winds on-the-fly using real-time data from the Global Positioning System (GPS). Post-flight analyses from two test flights have shown that the method provides improved simulations that closely match the GPS trajectories.

Lisa Ling↗

NASA NTRS1989

Venus surface properties deduced from radar and radiometry

The brightness of surface features on side looking radar images of Venus is determined by many factors: the angles of incidence and reflection, polarization, surface geometry and composition, and so forth. The contribution from surface properties themselves can only be deduced by combining several types of measurement. For instance, without additional information, it is impossible to distinguish the effects of changes in surface roughness from those in dielectric constant. In common with the Moon and Mars, the surface of Venus appears to scatter radar waves in two ways: small-scale surface inhomogeneities, i.e., those smaller than the incident wavelength, depolarize and scatter the energy over a wide range of angles. The Pioneer Venus radar mapper experiment made three overlapping sets of measurements of the equatorial region of Venus from 15 deg S latitude to 45 deg N; the backscatter cross section at a range of incidence angles, the shape and intensity of radar echoes from the nadir, and the microwave brightness temperature of the surface. These techniques developed during the analysis of Pioneer Venus data will be used during the Magellan mission to extract measurements of surface slopes and dielectric constants over all areas covered by the SAR and altimeter antennae, with a resolution of about 10 km. A knowledge of the mechanisms that govern surface scattering will also be useful in the analysis of higher resolution side looking radar images, particularly in distinguishing the effects of changing roughness from those caused by a long range surface tilt or changing dielectric constant.

Ford, P. G.↗

NASA NTRS1994

Ceramic material life prediction: A program to translate ANSYS results to CARES/LIFE reliability analysis

This manual describes the use of the ANSCARES program to prepare a neutral file of FEM stress results taken from ANSYS Release 5.0, in the format needed by CARES/LIFE ceramics reliability program. It is intended for use by experienced users of ANSYS and CARES. Knowledge of compiling and linking FORTRAN programs is also required. Maximum use is made of existing routines (from other CARES interface programs and ANSYS routines) to extract the finite element results and prepare the neutral file for input to the reliability analysis. FORTRAN and machine language routines as described are used to read the ANSYS results file. Sub-element stresses are computed and written to a neutral file using FORTRAN subroutines which are nearly identical to those used in the NASCARES (MSC/NASTRAN to CARES) interface.

Vonhermann, Pieter↗

NASA NTRS1999

Steady-State Characterization of Bacteriorhodopsin-D85N Photocycle

An operational characterization of the photocycle of the genetic mutant D85N of bacteriorhodopsin, BR-D85N, is presented. Steady-state bleach spectra and pump-probe absorbance data are obtained with thick hydrated films containing BR-D85N embedded in a gelatin host. Simple two- and three-state models are used to analyze the photocycle dynamics and extract relevant information such as pure-state absorption spectra, photochemical-transition quantum efficiencies, and thermal lifetimes of dominant states appearing in the photocycle, the knowledge of which should aid in the analysis of optical recording and retrieval of data in films incorporating this photochromic material. The remarkable characteristics of this material and their implications from the viewpoint of optical data storage and processing are discussed.

Timucin, Dogan A.↗

NASA NTRS2005

Spherical Harmonic Decomposition of Gravitational Waves Across Mesh Refinement Boundaries

We evolve a linearized (Teukolsky) solution of the Einstein equations with a non-linear Einstein solver. Using this testbed, we are able to show that such gravitational waves, defined by the Weyl scalars in the Newman-Penrose formalism, propagate faithfully across mesh refinement boundaries, and use, for the first time to our knowledge, a novel algorithm due to Misner to compute spherical harmonic components of our waveforms. We show that the algorithm performs extremely well, even when the extraction sphere intersects refinement boundaries.

Fiske, David R.↗

NASA NTRS2012

Purifying, Separating, and Concentrating Cells From a Sample Low in Biomass

Frequently there is an inability to process and analyze samples of low biomass due to limiting amounts of relevant biomaterial in the sample. Furthermore, molecular biological protocols geared towards increasing the density of recovered cells and biomolecules of interest, by their very nature, also concentrate unwanted inhibitory humic acids and other particulates that have an adversarial effect on downstream analysis. A novel and robust fluorescence-activated cell-sorting (FACS)-based technology has been developed for purifying (removing cells from sampling matrices), separating (based on size, density, morphology), and concentrating cells (spores, prokaryotic, eukaryotic) from a sample low in biomass. The technology capitalizes on fluorescent cell-sorting technologies to purify and concentrate bacterial cells from a low-biomass, high-volume sample. Over the past decade, cell-sorting detection systems have undergone enhancements and increased sensitivity, making bacterial cell sorting a feasible concept. Although there are many unknown limitations with regard to the applicability of this technology to environmental samples (smaller cells, few cells, mixed populations), dogmatic principles support the theoretical effectiveness of this technique upon thorough testing and proper optimization. Furthermore, the pilot study from which this report is based proved effective and demonstrated this technology capable of sorting and concentrating bacterial endospore and bacterial cells of varying size and morphology. Two commercial off-the-shelf bacterial counting kits were used to optimize a bacterial stain/dye FACS protocol. A LIVE/DEAD BacLight Viability and Counting Kit was used to distinguish between the live and dead cells. A Bacterial Counting Kit comprising SYTO BC (mixture of SYTO dyes) was employed as a broad-spectrum bacterial counting agent. Optimization using epifluorescence microscopy was performed with these two dye/stains. This refined protocol was further validated using varying ratios and mixtures of cells to ensure homogenous staining compared to that of individual cells, and were utilized for flow analyzer and FACS labeling. This technology focuses on the purification and concentration of cells from low-biomass spacecraft assembly facility samples. Currently, purification and concentration of low-biomass samples plague planetary protection downstream analyses. Having a capability to use flow cytometry to concentrate cells out of low-biomass, high-volume spacecraft/ facility sample extracts will be of extreme benefit to the fields of planetary protection and astrobiology. Successful research and development of this novel methodology will significantly increase the knowledge base for designing more effective cleaning protocols, and ultimately lead to a more empirical and true account of the microbial diversity present on spacecraft surfaces. Refined cleaning and an enhanced ability to resolve microbial diversity may decrease the overall cost of spacecraft assembly and/or provide a means to begin to assess challenging planetary protection missions.

Benardini, James N.↗

NASA NTRS2010

Detection of Natural Oil Seeps in the Atlantic Ocean Using MODIS

Natural oil seepage is the release of crude oil into the ocean from fissures in the seabed. Oil seepage is a major contributor to the total amount of oil entering the world s oceans. According to a 2003 study by the National Academy of Sciences (NAS), 47 percent of oil entering the world s oceans is from natural seeps, and 53 percent is from human sources (extraction, transportation, and consumption). Oil seeps cause smooth oil slicks to form on the water s surface. Oil seeps can indicate the location of stores of fossil fuel beneath the ocean floor. Knowledge of the effect of oil seepage on marine life and marine ecosystems remains limited. In the past, remote sensing has been used to detect oil seeps in the Gulf of Mexico and off of the coast of southern California. This project utilized sun glint MODIS imagery to locate oil slicks off of the Atlantic coast, an area that had not previously been surveyed for natural oil seeps using remote sensing. Since 1982, the Atlantic Ocean has been closed to any oil and gas drilling. Recently, however, the U.S. Minerals Management Services (MMS) has proposed a lease for oil and gas drilling off the coasts of Virginia and North Carolina. Determining the location of seepage sites in the Atlantic Ocean will help MMS locate potential deposits of oil and natural gas, thereby reducing the risk of leasing areas for petroleum extraction that do not contain these natural resources.

Reahard, Ross↗

NASA NTRS1990

Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

Szu, Harold H.↗

NASA NTRS1998

Experiment 5: Science and Technology of Surface Controlled Oscillations: Report on USML-2 Results

Minuscule amounts (e.g., 1 part in 10,000) of a surface-active material in a liquid can drastically affect the surface behavior of the liquid, influencing how the material flows and mixes with other liquid and solid materials. In many respects, the science of surfactants has been empirical, with trial and error dominating over the ability to predict how surfactant type and concentration influence surface behavior. A program for the modeling of surfactant behavior has been established at Yale. This program combines experimental work performed both on the ground and in space, and theoretical and numerical modeling. By levitating a drop of liquid in air, away from solid container surfaces, and by manipulating the drop with acoustic radiation forces, we have been able to establish idealized conditions for surface behavior studies. The primary experiments involve the study of the free oscillations of initially deformed drops. In STS-73, the USML-2 mission of the Space Shuttle, we performed the following measurements: 1) the oscillation of a spherical drop in its quadrupole mode; 2) the oscillation of a drop about a deformed (oblate) shape; 3) the slow static squeezing of the drop from spherical to nearly flat; and 4) the superoscillations of drops when the radiation forces maintaining the drop in a flattened state are suddenly reduced. Analytic and numerical studies have enabled us to understand the physics of these oscillations and to extract material properties such as the dynamic surface tension and the surface viscosities (shear and dilatational). The relation to ground-based studies is essential, because the knowledge and understanding gleaned from our space studies enable us to interpret ground-based data.

Apfel, Robert E.↗

NASA NTRS2004

Inductive System Health Monitoring

The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.

Iverson, David L.↗

NASA NTRS2005

INDUCTIVE SYSTEM HEALTH MONITORING WITH STATISTICAL METRICS

Model-based reasoning is a powerful method for performing system monitoring and diagnosis. Building models for model-based reasoning is often a difficult and time consuming process. The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS processes nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. In particular, a clustering algorithm forms groups of nominal values for sets of related parameters. This establishes constraints on those parameter values that should hold during nominal operation. During monitoring, IMS provides a statistically weighted measure of the deviation of current system behavior from the established normal baseline. If the deviation increases beyond the expected level, an anomaly is suspected, prompting further investigation by an operator or automated system. IMS has shown potential to be an effective, low cost technique to produce system monitoring capability for a variety of applications. We describe the training and system health monitoring techniques of IMS. We also present the application of IMS to a data set from the Space Shuttle Columbia STS-107 flight. IMS was able to detect an anomaly in the launch telemetry shortly after a foam impact damaged Columbia's thermal protection system.

Iverson, David L.↗

NASA NTRS2013

Data Mining for Understanding and Improving Decision-making Affecting Ground Delay Programs

Weather↗

NASA NTRS2013

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

data mining↗

NASA NTRS2015

Numerical Analysis of Incipient Separation on 53 Deg Swept Diamond Wing

A systematic analysis of incipient separation and subsequent vortex formation from moderately swept blunt leading edges is presented for a 53 deg swept diamond wing. This work contributes to a collective body of knowledge generated within the NATO/STO AVT-183 Task Group titled 'Reliable Prediction of Separated Flow Onset and Progression for Air and Sea Vehicles'. The objective is to extract insights from the experimentally measured and numerically computed flow fields that might enable turbulence experts to further improve their models for predicting swept blunt leading-edge flow separation. Details of vortex formation are inferred from numerical solutions after establishing a good correlation of the global flow field and surface pressure distributions between wind tunnel measurements and computed flow solutions. From this, significant and sometimes surprising insights into the nature of incipient separation and part-span vortex formation are derived from the wealth of information available in the computational solutions.

Frink, Neal T.↗

NASA NTRS2002

A Framework for Creating a Function-based Design Tool for Failure Mode Identification

Knowledge of potential failure modes during design is critical for prevention of failures. Currently industries use procedures such as Failure Modes and Effects Analysis (FMEA), Fault Tree analysis, or Failure Modes, Effects and Criticality analysis (FMECA), as well as knowledge and experience, to determine potential failure modes. When new products are being developed there is often a lack of sufficient knowledge of potential failure mode and/or a lack of sufficient experience to identify all failure modes. This gives rise to a situation in which engineers are unable to extract maximum benefits from the above procedures. This work describes a function-based failure identification methodology, which would act as a storehouse of information and experience, providing useful information about the potential failure modes for the design under consideration, as well as enhancing the usefulness of procedures like FMEA. As an example, the method is applied to fifteen products and the benefits are illustrated.

Arunajadai, Srikesh G.↗

NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗

NASA NTRS1988

Knowledge-based network operations

An expert system for enhancing the operability of the ground communication element of the Jet Propulsion Laboratory's Deep Space Network is described. The system performs network fault management, configuration management, and performance management in real time. Extracted management information serves as input to the expert system and is used to update a management information data base. The monitor and control activities involve dividing software for each processor into layers which are each modeled as a finite state machine.

Wu, Chuan-Lin↗

NASA NTRS1992

Microgravity fluid physics research in the Space Station Freedom era

Microgravity fluid physics covers an exciting range of established and potential fields of scientific research. Areas in which the Microgravity Science and Applications Division of NASA's Office of Space Science and Applications is currently supporting research include: multiphase flow and phase change heat transfer, behavior of granular media and colloids; and interface dynamics, morphological stability, and contact line phenomena. As they contribute to our knowledge of fluid behavior, advances in these areas will enhance our understanding of materials processing on Earth and in space, and will contribute to technologies as diverse as chemical extraction, the prediction of soil behavior in earthquakes, and the production of oil reservoirs. NASA' s primary platform for research in microgravity fluid physics will soon be the Fluid Physics/Dynamics Facility on Space Station Freedom. This facility shares a rack for control and utilities with the Modular Combustion Facility, and has one rack for experiment-unique instruments. It is planned to change out the content of the experiment-unique rack at intervals on the order of one year. In order to obtain a maximum return on the operation of the facility during these intervals, the research community must carefully plan and coordinate an effort that brings the efforts of many investigators to bear on problems of particular importance. NASA is currently working with the community to identify research areas in which microgravity can make a unique and valuable contribution, and to build a balanced program of research around these areas or thrusts. Selections will soon be made from our first solicitation for research in fluid dynamics and transport phenomena. These solicitations will build the research community that will make Space Station Freedom a catalyst for scientific and technological discovery, and offer U.S. scientists in many disciplines a unique opportunity to participate in space science.

Carpenter, Bradley M.↗