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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Remote Sensing and Problems of the Hydrosphere

A discussion of freshwater and marine systems is presented including areas of the classification of lakes, identification and quantification of major functional groups of phytoplankton, sources and sinks of biochemical factors, and temporal and regional variability of surface features. Atmospheric processes linked to hydrospheric process through the transfer of matter via aerosols and gases are discussed. Particle fluxes to the aquatic environment and global geochemical problems are examined.

Goldberg, E. D.↗
NASA NTRS1975

The integration of commercial payloads into Spacelab

Results of a three-year study for identification of beneficial uses of space are used to project potential problems in integrating commercial payloads into Spacelab. Specific examples from this NASA-funded study are given to illustrate these problems, and to suggest some potential approaches for arriving at solutions.

Bloom, H. L.↗
NASA NTRS1991

Major wildfires at the Cretaceous-Tertiary boundary

The current status of the reconstruction of major biomass fire events at the Cretaceous-Tertiary boundary is discussed. Attention is given to the sources of charcoal and soot, the identification of biomass and fossil carbon, and such ignition-related problems as delated fires, high atmospheric O2 content, ignition mechanisms, and the greenhouse-effect consequences of fire on the scale envisioned. Consequences of these factors for species extinction patterns are noted.

Anders, Edward↗
NASA NTRS1998

WSR-88D Cell Trends

This report documents the Applied Meteorology Unit's evaluation of the Cell Trends display as a tool for radar operators to use in their evaluation of storm cell strength. The objective of the evaluation is to assess the utility of the WSR-88D graphical Cell Trends display for local radar cell interpretation in support of the 45th Weather Squadron (45 WS), Spaceflight Meteorology Group (SMG), and National Weather Service (NWS) Melbourne (MLB) operational requirements. The analysis procedure was to identify each cell and track the maximum reflectivity, height of maximum reflectivity, storm top, storm base, hail and severe hail probability, cell-based Vertically Integrated Liquid (VIL) and core aspect ratio using WATADS Build 9.0 cell trends information. One problem noted in the analysis phase was that the Storm Cell Identification and Tracking (SCIT) algorithm had a difficult time tracking the small cells associated with the Florida weather regimes. The analysis indicated numerous occasions when a cell track would end or an existing cell would be give a new ID in the middle of its life cycle. This investigation has found that most cells, which produce hail or microburst events, have discernable Cell Trends signatures. Forecasters should monitor the PUP's Cell Trends display for cells that show rapid (1 scan) changes in both the heights of maximum reflectivity and cell-based VIEL. It is important to note that this a very limited data set (four case days). Fifty-two storm cells were analyzed during those four days. The above mentioned t=ds, increase in the two cell attributes for hail events and decrease in the two cell attributes for wind events were noted in most of the cells. The probability of detection was 88% for both events. The False Alarm Rate (FAR) was a 36% for hail events and a respectable 25% for microburst events. In addition the Heidke Skill Score (HSS) is 0.65 for hail events and 0.67 for microburst events. For random forecast the HSS is 0 and that a perfect score is 1.

Wheeler, Mark M.↗
NASA NTRS1995

Modes in Supervisory Control Systems: Structure and Transitions

Mode confusion is becoming a major drawback in operator interaction with systems that allow for multiple levels of automation. This drawback has manifested itself in several mode related accidents and incidents in commercial aviation, military control systems, as well as high technology medical systems. In the domain of commercial aviation, there have been four recent airline accidents, all involving highly automated aircraft, in which mode related problems were present. Mode problems, we argue, stem from three principal factors: (1) mis-identification of the current mode, (2) difficulty in apprehending current mode behavior; and (3) difficulty in predicting the consequences of the next mode transition. This combination of factors may lead to mode confusion and possibly unwanted results. We define in this paper a mode as the system's manner of behavior. Any given system or machine, may have several ways of behaving. A controller, either human or machine, provides the input that triggers the transition from one mode of behavior to another. At any point in time, a machine may be in one mode or another modes are mutually exclusive. Complex systems are typically comprised of several subsystems, or components, each one with its own set of modes. Therefore, the status of the system at any point in time can be described as a vector of all the active modes.

Degani, Asaf↗
NASA NTRS2008

Kodiak Star: A Success in Partnership

The Kodiak Star Mission was very challenging and offers significant lesson for future missions. A multinational fully integrated team had the opportunity to perform a truly first of a kind mission from a new launch complex with a unique manifest of experimental spacecraft. The integration goal of 10-months was met utilizing quick identification of the issues, and determining innovative ways to solve the problems

Skrobot, Garrett L.↗
NASA NTRS1977

Analysis and application of minimum variance discrete linear system identification

An on-line minimum variance (MV) parameter identifier is developed which embodies both accuracy and computational efficiency. The formulation results in a linear estimation problem with both additive and multiplicative noise (AMN). The resulting filter which utilizes both the covariance of the parameter vector itself and the covariance of the error in identification is proven to be mean-square convergent and mean-square consistent. The MV parameter identification scheme is then used to construct a stable state and parameter estimation algorithm.

Kotob, S.↗
NASA NTRS1976

Analysis and application of minimum variance discrete time system identification

An on-line minimum variance parameter identifier was developed which embodies both accuracy and computational efficiency. The new formulation resulted in a linear estimation problem with both additive and multiplicative noise. The resulting filter is shown to utilize both the covariance of the parameter vector itself and the covariance of the error in identification. It is proven that the identification filter is mean square covergent and mean square consistent. The MV parameter identification scheme is then used to construct a stable state and parameter estimation algorithm.

Kotob, S.↗
NASA NTRS1987

Comparison of two numerical techniques for aerodynamic model identification

A new algorithm, called the Minimal Residual QR algorithm, is presented to solve subset regression problems. It is shown that this new scheme can be used as a numerically reliable implementation of the stepwise regression technique, which is widely used to identify an aerodynamic model from flight test data. This capability as well as the numerical superiority of this scheme over the stepwise regression technique is demonstrated in an experimental simulation study.

Verhaegen, M. H.↗
NASA NTRS2006

Multiple concurrent recursive least squares identification with application to on-line spacecraft mass-property identification

The present invention is a method for identifying unknown parameters in a system having a set of governing equations describing its behavior that cannot be put into regression form with the unknown parameters linearly represented. In this method, the vector of unknown parameters is segmented into a plurality of groups where each individual group of unknown parameters may be isolated linearly by manipulation of said equations. Multiple concurrent and independent recursive least squares identification of each said group run, treating other unknown parameters appearing in their regression equation as if they were known perfectly, with said values provided by recursive least squares estimation from the other groups, thereby enabling the use of fast, compact, efficient linear algorithms to solve problems that would otherwise require nonlinear solution approaches. This invention is presented with application to identification of mass and thruster properties for a thruster-controlled spacecraft.

Wilson, Edward↗
NASA NTRS1976

Rotor Dynamic State and Parameter Identification from Hovering Transients

State and parameter identifications based on a form of the maximum likelihood method are applied to the problem of extracting linear perturbation models, including rotor dynamic inflow effects, from transient blade flapping measurements. The estimation method is first studied in computer simulations and then applied to cyclic pitch stirring transients generated with a four-bladed rotor model operating in hovering trim conditions. The analytical perturbation models extracted from the transient test results are compared with transient and frequency response tests not used in the state and parameter identification. The identified analytical perturbation model is also compared with a simple theory. The method that is applicable both to small scale and full scale dynamic rotor testing is being extended to perturbations from forward flight trim conditions.

Hohenemser, K. H.↗
NASA NTRS1974

A square root formulation for the combined state-parameter estimator with application to the identification of sailplane performance

A square root formulation is presented for the discrete combined state parameter estimation problem with linear plant dynamics, Gaussian random disturbances, and constant but uncertain parameters. The estimator is a combination of the classical Kalman filter and a maximum likelihood algorithm which maximizes the parameter log-likelihood function using a first order search routine.

Froidevaux, M. R.↗
NASA NTRS1971

A program for identification of linear systems

A program has been written for the identification of parameters in certain linear systems. These systems appear in biomedical problems, particularly in compartmental models of pharmacokinetics. The method presented here assumes that some of the state variables are regularly modified by jump conditions. This simulates administration of drugs following some prescribed drug regime. Parameters are identified by a least-square fit of the linear differential system to a set of experimental observations. The method is especially suited when the interval of observation of the system is very long.

Buell, J.↗
NASA NTRS1988

The IUE data base: Homogenizing the IUE object nomenclature

The IUE project started to homogenize the object nomenclature in the IUE data base. Due to the absence of an official IAU nomenclature hierarchy and in view of the increasing confusion in IUE (and, in general, astronomical) object identifications, the IUE project adopted its own nomenclature hierarchy. The scheme and problems encountered in establishing it are described.

Barylak, Michael↗
NASA NTRS1992

Control design for the SERC experimental testbeds

Viewgraphs on control design for the Space Engineering Research Center experimental testbeds are presented. Topics covered include: SISO control design and results; sensor and actuator location; model identification; control design; experimental results; preliminary LAC experimental results; active vibration isolation problem statement; base flexibility coupling into isolation feedback loop; cantilever beam testbed; and closed loop results.

Jacques, Robert↗
NASA NTRS1994

Parameter identification for X-31A at high angles of attack

The U.S./German experimental aircraft X-31A was designed and constructed to demonstrate enhanced fighter maneuverability. Post-stall maneuvering is enabled by applying new technologies such as high angle of attack aerodynamics and flight control system integrated thrust vectoring. Two demonstrator aircraft have been built by the main contractors, Rockwell International and Deutsche Aerospace (formerly MBB). Flight testing started in October 1990 and before the end of 1992 both aircraft had accomplished a significant number of flights covering the entire AoA regime from about -5 to 70 deg. Throughout the envelope expansion, DLR Institute of Flight Mechanics conducted parameter identification (PID) to determine the aerodynamic parameters of the aircraft from flight test data and to compare the results to the predictions from the aerodynamic dataset (ADS). The application of system identification to high AoA / post-stall flight data raises some major problems, which are discussed in this paper. Results from both longitudinal and lateral-directional motion will be presented.

S Weiss↗
NASA NTRS1998

Smart Optical RAM for Fast Information Management and Analysis

Statement of Problem Instruments for high speed and high capacity in-situ data identification, classification and storage capabilities are needed by NASA for the information management and analysis of extremely large volume of data sets in future space exploration, space habitation and utilization, in addition to the various missions to planet-earth programs. Parameters such as communication delays, limited resources, and inaccessibility of human manipulation require more intelligent, compact, low power, and light weight information management and data storage techniques. New and innovative algorithms and architecture using photonics will enable us to meet these challenges. The technology has applications for other government and public agencies.

Liu, Hua-Kuang↗
NASA NTRS2021

A Machine Learning Approach to Objective Identification of Dust in Satellite Imagery

Airborne dust has broad adverse effects on human activity, including aviation, human health, and agriculture. Remote sensing observations are used to detect dust and aerosols in the atmosphere using long established techniques. False color Red-Green-Blue (RGB) imagery using band differences sensitive to dust absorption (Dust RGB) is currently used operationally to assist forecasters and decision-makers in identifying dust at night, but there are still limitations, subjectivity, and nuances to image interpretation making night-time dust identification difficult even for experts. This study applies machine learning to the problem of night-time dust detection with a simple random forest (RF) model using Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) infrared imagery, band differences sensitive to dust absorption, and Dust RGB color components as inputs to the model. The RF model achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels and 99.96% of no-dust pixels for all dust images in the validation data set. The addition of a single null case to the training data set drastically reduces error in labeling no-dust pixels as dust from 45% to 14.5%. Application of the machine learning model to the April 13–14, 2019 dust event demonstrates the ability of the model to identify dust during night-time hours when visual dust detection is limited by the cooling ground surface characteristics.

dust↗