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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Mapping Environmental Contaminants at Ray Mine, AZ

Airborne Visible and InfraRed Imaging Spectrometer (AVIRIS) data was collected over Ray Mine as part of a demonstration project for the Environmental Protection Agency (EPA) through the Advanced Measurement Initiative (AMI). The overall goal of AMI is to accelerate adoption and application of advanced measurement technologies for cost effective environmental monitoring. The site was selected to demonstrate the benefit to EPA in using advanced remote sensing technologies for the detection of environmental contaminants due to the mineral extraction industry. The role of the Jet Propulsion Laboratory in this pilot study is to provide data as well as performing calibration, data analysis, and validation of the AVIRIS results. EPA is also interested in developing protocols that use commercial software to perform such work on other high priority EPA sites. Reflectance retrieval was performed using outputs generated by the MODTRAN radiative transfer model and field spectra collected for the purpose of calibration. We are presenting advanced applications of the ENVI software package using n-Dimensional Partial Unmixing to identify image-derived endmembers that best match target materials reference spectra from multiple spectral libraries. Upon identification of the image endmembers the Mixture Tuned Match Filter algorithm was applied to map the endmembers within each scene. Using this technique it was possible to map four different mineral classes that are associated with mine generated acid waste.

McCubbin, Ian↗
NASA NTRS2012

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Zaychik, Kirill B.↗
NASA NTRS2005

High Temperature Performance of a SiC MESFET Based Oscillator

A hybrid, UHF-Band differential oscillator based on 10 w SiC RF Power Metal Semiconductor Field Effect Transistor (MESFET) has been designed, fabricated and characterized through 475 C. Circuit is fabricated on an alumina substrate with thin film spiral inductors, chip capacitors, chip resistors, and wire bonds for all crossovers and interconnectors. The oscillator delivers 15.7 dBm at 515 MHz into a 50 Ohm load at 125 C with a DC to RF conversion efficiency of 2,8%. After tuning the load impedance, the oscillator delivers 18.8 dBm at 610 MHz at 200 C with a DC to RF conversion efficiency of 5.8%. Finally, by tuning the load and bias conditions, the oscillator delivers 4.9 dBm at 453 MHz at 475 C.

Schwartz, Zachary D.↗
NASA NTRS2000

Discriminator Stabilized Superconductor/Ferroelectric Thin Film Local Oscillator

A tunable local oscillator with a tunable circuit that includes a resonator and a transistor as an active element for oscillation. Tuning of the circuit is achieved with an externally applied dc bias across coupled lines on the resonator. Preferably the resonator is a high temperature superconductor microstrip ring resonator with integral coupled lines formed over a thin film ferroelectric material. A directional coupler samples the output of the oscillator which is fed into a diplexer for determining whether the oscillator is performing at a desired frequency. The high-pass and lowpass outputs of the diplexer are connected to diodes respectively for inputting the sampled signals into a differential operational amplifier. The amplifier compares the sampled signals and emits an output signal if there is a difference between the resonant and crossover frequencies. Based on the sampled signal, a bias supplied to the ring resonator is either increased or decreased for raising or lowering the resonant frequency by decreasing or increasing, respectively, the dielectric constant of the ferroelectric.

Romanofsky, Robert R.↗
NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗
NASA NTRSDate not supplied

Generalized Augmented-State Covariance Analysis for Spaceflight

The use of linear covariance analysis techniques, also known as LinCov, has been used extensively for more than a half century for spaceflight applications. Originally, its primary purpose was to facilitate navigation analysis. For many past and current applications, the specific implementations only support navigation studies still. When the concept of an augmented-state linear covariance analysis approach was initially introduced that allowed for both navigation and trajectory dispersion analysis, the enhancement was motivated and primarily utilized to support navigation filter tuning and error budget analysis. Relatively few utilize this alternate augmented-state formulation of LinCov due to its additional complexity. The untapped potential of the augmented-state linear covariance analysis technique slowly unfolded in the past two-decades as its capability to rapidly and reliably capture the integrated closed-loop guidance, navigation, and control (GN&C) system performance became more apparent. Even with this dual purpose of generating insights to both navigation errors along with trajectory and delta-v dispersions, the core theoretical development had a heavy emphasis on the impacts of the navigation system and largely neglected the details of the actual guidance, targeting, and control systems. This paper extends the navigation-centric theoretical development by formulating a generalized augmented-state covariance analysis (GAUSCOV) technique that allows for the intricacies of a variety of targeting and control strategies along with ground planning and mission operations to be more formally included in assessing the impacts to spaceflight GN&C system performance.

Linear Covariance Analysis↗
NASA NTRS1983

Solid-state laser sources for remote sensing

Recent progress in slab-geometry and conventional rod Nd:YAG solid-state lasers for applications in remote sensing is presented. Developments in slab geometry lasers, which were aimed at improving pulse energy and tuning range, have been based on the use of a Nd:glass substrate with a zig-zag optical path, with selective Raman shifting in gases and harmonic generation in LiNbO3 and KDP to extend the tuning range into the UV and visible regions. The theoretically predicted advantages of the elimination of birefringence and thermal and stress-induced focusing in the slab-geometry laser have been confirmed in measurements on a test-bed Nd:glass system, and a CW lamp pumped Nd:YAG oscillator, which have also demonstrated an order of magnitude improvement in laser performance. A single axial mode Nd:YAG oscillator has also been designed which, operating in a 3-msec quasi-CW mode, has a chirp rate of 30 kHz/microsec and a free-running stability of + or - 20 MHz. With chirp compensation, this stability is adequate for wind velocity measurements by coherent lidar.

Byer, R. L.↗
NASA NTRS1982

Robustness of adaptive control algorithms in the presence of unmodeled dynamics

This paper reports the outcome of an exhaustive analytical and numerical investigation of stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances. The class of adaptive algorithms considered are those commonly referred to as model-reference adaptive control algorithms, self-tuning controllers, and dead-beat adaptive controllers; they have been developed for both continuous-time systems and discrete-time systems. The existing adaptive control algorithms have been proven to be globally asymptotically stable under certain assumptions, the key ones being (1) that the number of poles and zeroes of the unknown plant are known, and (2) that the primary performance criterion is related to good command following. These theoretical assumptions are too restrictive from an engineering point of view. Real plants always contain unmodeled high-frequency dynamics and small delays, and hence no upper bound on the number of the plant poles and zeroes exists. Also real plants are always subject to unmeasurable output additive disturbances, although these may be guide small. Hence, it is important to critically examine the stability robustness properties of the existing adaptive algorithms when some of the theoretical assumptions are removed; in particular, their stability and performance properties in the presence of unmodeled dynamics and output disturbances. Previously announced in STAR as N83-16061

Rohrs, C. E.↗
NASA NTRS1988

Defining and measuring pilot mental workload

A theory is sought that is general enough to help the researcher deal with a wide range of situations involving pilot mental stress. A limited capacity theory of attention forms the basis for the theory. Mental workload is then defined as an intervening variable, similar to attention, that modulates or indexes the tuning between the demands of the environment and the capacity of the organism. Two methods for measuring pilot mental workload are endorsed: (1) objective measures based on secondary tasks; and (2) psychophysiological measures, which have not yet been perfected but which will become more useful as theoretical models are refined. Secondary-task research is illustrated by simulator studies in which flying performance has been shown not to be adversely affected by adding a complex choice-reaction secondary task.

Kantowitz, Barry H.↗
NASA NTRS1990

An electronically tuned, stable 8415 MHz dielectric resonator FET oscillator for space applications

A voltage-controlled 8415-MHz FET oscillator stabilized by a dielectric resonator is described. The oscillator provides over 3.2-MHz linear electronic tuning range with a flat power output equal to +1.8 dBm (27 C, nominal), a single-sideband noise-to-carrier ratio of -68 dBc/Hz at 1 kHz off carrier, and a frequency-temperature coefficient of 0.54 parts per million/C over a -24 C to 75 C range. The oscillator withstood 150 Krads (Si) of gamma radiation with no significant performance degradation. The overall performance of the FET oscillator is far superior in many ways to that of an equivalent bipolar oscillator for space applications. For space applications, the FET dielectric resonator oscillator (DRO) is preferred over bipolar DRO because of its lower DC power consumption, better thermal frequency stability, linear electronic tunability, and higher RF output power capability.

Mysoor, Narayan R.↗
NASA NTRS2005

Development and Testing of Control Laws for the Active Aeroelastic Wing Program

The Active Aeroelastic Wing research program was a joint program between the U.S. Air Force Research Laboratory and NASA established to investigate the characteristics of an aeroelastic wing and the technique of using wing twist for roll control. The flight test program employed the use of an F/A-18 aircraft modified by reducing the wing torsional stiffness and adding a custom research flight control system. The research flight control system was optimized to maximize roll rate using only wing surfaces to twist the wing while simultaneously maintaining design load limits, stability margins, and handling qualities. NASA Dryden Flight Research Center developed control laws using the software design tool called CONDUIT, which employs a multi-objective function optimization to tune selected control system design parameters. Modifications were made to the Active Aeroelastic Wing implementation in this new software design tool to incorporate the NASA Dryden Flight Research Center nonlinear F/A-18 simulation for time history analysis. This paper describes the design process, including how the control law requirements were incorporated into constraints for the optimization of this specific software design tool. Predicted performance is also compared to results from flight.

Dibley, Ryan P.↗
NASA NTRS2011

Simultaneous Retrieval of Aerosol and Cloud Properties During the MILAGRO Field Campaign

Estimation of Direct Climate Forcing (DCF) due to aerosols in cloudy areas has historically been a difficult task, mainly because of a lack of appropriate measurements. Recently, passive remote sensing instruments have been developed that have the potential to retrieve both cloud and aerosol properties using polarimetric, multiple view angle, and multi spectral observations, and therefore determine DCF from aerosols above clouds. One such instrument is the Research Scanning Polarimeter (RSP), an airborne prototype of a sensor on the NASA Glory satellite, which unfortunately failed to reach orbit during its launch in March of 2011. In the spring of 2006, the RSP was deployed on an aircraft based in Veracruz, Mexico, as part of the Megacity Initiative: Local and Global Research Observations (MILAGRO) field campaign. On 13 March, the RSP over flew an aerosol layer lofted above a low altitude marine stratocumulus cloud close to shore in the Gulf of Mexico. We investigate the feasibility of retrieving aerosol properties over clouds using these data. Our approach is to first determine cloud droplet size distribution using the angular location of the cloud bow and other features in the polarized reflectance. The selected cloud was then used in a multiple scattering radiative transfer model optimization to determine the aerosol optical properties and fine tune the cloud size distribution. In this scene, we were able to retrieve aerosol optical depth, the fine mode aerosol size distribution parameters and the cloud droplet size distribution parameters to a degree of accuracy required for climate modeling. This required assumptions about the aerosol vertical distribution and the optical properties of the coarse aerosol size mode. A sensitivity study was also performed to place this study in the context of future systematic scanning polarimeter observations, which found that the aerosol complex refractive index can also be observed accurately if the aerosol optical depth is larger than roughly 0.8 at a wavelength of (0.555 m).

Knobelspiesse, K.↗
NASA NTRSDate not supplied

Durable Anti-Reflective and Anti-Fog Coatings Produced by Aerosol Impact Driven Assembly

The Exploration Extravehicular Mobility Unit’s (xEMU) helmet is a complex assembly designed to accomplish several tasks to protect and enable astronauts. In addition to maintaining a suitable environment for the wearer, it must also allow for appropriate mobility and provide a wide and undistorted view of the surroundings. A critical component of the helmet’s optical system is the anti-fog coating. While previous versions of the anti-fog coating have provided suitable anti-fog performance, they have been difficult to apply, lacked mechanical or chemical durability, or resulted in unanticipated failures (e.g., outgassing of eye-irritating materials during use). This work describes the use of a new coating technology, aerosol impact-driven assembly (AIDA), to develop a next-generation permanent anti-fog coating for the xEMU helmet. AIDA’s unique ability to tune both the refractive index and surface roughness of films was used to deposit a thin, transparent (>85% transmittance of visible light), and hydrophilic (contact angle <10°) anti-fog coating. The coating’s abrasion resistance and chemical resistance was evaluated and it was found that the coating maintained both its anti-fog and anti-reflective functionally. Finally, the scalability of the process was demonstrated by successfully coating the polycarbonate blanks used to form the helmet’s bubble.

Spacesuit↗
NASA NTRS1982

Vibration analysis of three guyed tower designs for intermediate size wind turbines

Three guyed tower designs were analyzed for intermediate size wind turbines. The four lowest natural frequencies of vibration of the three towers concepts were estimated. A parametric study was performed on each tower to determine the effect of varying such tower properties as the inertia and stiffness of the tower and guys, the inertia values of the nacelle and rotor, and the rotational speed of the rotor. Only the two lowest frequencies were in a range where they could be excited by the rotor blade passing frequencies. There two frequencies could be tuned by varying the guy stiffness, the guy attachment point on the tower, the tower and mass stiffness, and the nacelle/rotor/power train masses.

Christie, R. J.↗
NASA NTRS1986

Spectroscopic and laser characteristics of Ti:Al2O3

Spectroscopic measurements and laser performance of T:Al2O3 are discussed in detail. Data on absorption and fluorescence spectra and fluorescence lifetime as a function of temperature are presented. Laser characteristics observed with pulsed-dye-laser, frequency-doubled Nd:YAG-laser, and argon-ion-laser pumping are covered and show that nearly quantum-limited conversion of pump radiation cna be achieved, along with tuning over the wavelength range 660-986 nm.

Moulton, P. F.↗
NASA NTRS1988

An electronically tuned, stable 8415-MHz dielectric resonator FET oscillator for space applications

A voltage-controlled 8415 MHz FET oscillator stabilized by a dielectric resonator is described. The oscillator provides a linear electronic tuning range of over 3.2 MHz with a flat power output equal to +1.8 dBm (27 C nominal), a single-sideband noise to carrier ratio of -68 dBc/Hz at 1 kHz off carrier, and a frequency temperature coefficient of 0.54 part per million/C over a -24 to 75 C range. The oscillator withstood 150 krads(Si) of gamma radiation with no significant performance degradation. The overall performance of the oscillator is in many ways far superior to that of an equivalent bipolar oscillator for space applications.

Lakshminarayana, M.↗
NASA NTRSDate not supplied

Modeling and Analysis of Stirling Power Convertors

Modeling and Analysis of Stirling Power Convertors Luis A. Rodriguez1 Steven M. Geng, Terry V. Reid, Scott D. Wilson NASA Glenn Research Center, Cleveland, OH, 44135, USA NASA Glenn’s Thermal Energy Conversion Branch is supporting the development of the next generation free-piston Stirling power convertors. American Superconductor (AMSC) and Sunpower Inc. are the two firms under contract to develop the Flexure Isotope Stirling Convertor (FISC) and the Sunpower Robust Stirling Convertor (SRSC), respectively. To comprehend and forecast convertor performance, Sage, ANSYS® Maxwell, and ANSYS® Fluent were used to model the Stirling thermodynamic cycle, alternator electromagnetics, and piston and displacer dynamics. I. Introduction Stirling convertors are being developed by NASA as a potential steady source of electrical power for NASA’s future scientific space missions. Currently, NASA Glenn Research Center has two corporations under contract, American Superconductor (AMSC) and Sunpower Inc., for the development of the next generation of free-piston Stirling convertors for dynamic radioisotope power systems. AMSC is developing the Flexure Isotope Stirling Convertor (FISC), which uses flexures to prevent side motion and rubbing of the piston. Similarly, Sunpower Inc, is developing the Sunpower Robust Stirling Convertor (SRSC). The SRSC uses gas bearings to prevent radial contact of the moving piston. As convertor development continues, it is increasingly important to understand and predict the interactions of components in the system, how they respond to one another, and how they perform as a response to changes in operating conditions. A suitable and enlightening way to demonstrate and foresee these interactions is with the use of accurate modeling software. Sage, ANSYS® Maxwell, and ANSYS® Fluent are the current modeling tools used by NASA to analytically determine convertor performance. Sage is a one-dimensional object-oriented commercial software package used for modeling and optimizing Stirling convertors for Dynamic Radioisotope Power Systems (DRPS) and it is one of the most accurate Stirling convertor codes in use by NASA. This code is the successor to GLIMPS (Globally-Implicit Stirling Cycle Simulation) and GLOP (GLIMPS Optimization) software created by Gedeon Associates [1]. Model input parameters are typically material/gas type, component physical dimensions, temperatures, frequency, charge pressure, and number of time/space nodes. Sage is used to model both the FISC’s and SRSC’s Stirling cycle thermodynamics and piston/displacer dynamics. Performance maps were created and analyzed for both power systems to better understand the relationship between the following conditions: cold-end temperature, hot-end temperature, piston/displacer amplitudes, pressure drop, and thermal input power. The synergy between these conditions will help determine parameter sensitivity. ANSYS® Maxwell was used to create a three-dimensional (3-D) axisymmetric model for both FISC and SRSC alternators. The significant physical components included in each model are the magnets, magnet carrier, outer/inner laminations, and the coil. Inputs to the model are piston amplitude, piston frequency, alternator load, coil resistance, tuning capacitance, and specific material properties. The alternator models calculate terminal voltage, current, piston/current phase, voltage/current phase, coil inductance, terminal power and efficiency. The RI2 losses, core (hysteresis and eddy) losses, and magnet/can eddy losses are also a part of the final results. ANSYS® Fluent is used to build 3-D computational fluid dynamic (CFD) models to examine the Stirling cycle thermodynamics for both the FISC and SRSC systems. Three-dimensional Computer Aided Design (CAD) models were used to create the physical components of each convertor. Steady-state simulations were conducted for hardware testing, prediction of environmental losses during testing, and generation of radiation look-up tables. The model inputs to the aforementioned analysis are the material properties and boundary thermal conditions. The steady-state model calculates temperature and heat flow distributions. Transient 3-D calculations were also part of the CFD analysis. In this study a physically reduced version of the FISC is used to obtain a prediction of available engine power. For the gas bearing SRSC, the transient effort is used to obtain a prediction of bearing pad performance and its sensitivity to micro-channel geometric variation. The model inputs to the transient simulations are the piston amplitude, displacer amplitude, frequency, displacer/piston phase angle, dynamic deforming CFD grid, temperature boundary conditions, and user defined files describing motion profile of piston/displacer. The results of the model are temperature distributions, heat distributions, and PV power produced at pre-determined conditions.

Luis A Rodriguez↗
NASA NTRS2014

Evolution of the JPSS Ground Project Calibration and Validation System

The Joint Polar Satellite System (JPSS) is the National Oceanic and Atmospheric Administrations (NOAA) next-generation operational Earth observation Program that acquires and distributes global environmental data from multiple polar-orbiting satellites. The JPSS Program plays a critical role to NOAAs mission to understand and predict changes in weather, climate, oceans, coasts, and space environments, which supports the Nation’s economy and protection of lives and property. The National Aerospace and Atmospheric Administration (NASA) is acquiring and implementing the JPSS, comprised of flight and ground systems on behalf of NOAA. The JPSS satellites are planned to fly in the afternoon orbit and will provide operational continuity of satellite-based observations and products for NOAA Polar-orbiting Operational Environmental Satellites (POES) and the Suomi National Polar-orbiting Partnership (SNPP) satellite. To support the JPSS Calibration and Validation (CalVal) node Government Resource for Algorithm Verification, Independent Test, and Evaluation (GRAVITE) services facilitate: Algorithm Integration and Checkout, Algorithm and Product Operational Tuning, Instrument Calibration, Product Validation, Algorithm Investigation, and Data Quality Support and Monitoring. GRAVITE is a mature, deployed system that currently supports the SNPP Mission and has been in operations since SNPP launch. This paper discusses the major re-architecture for Block 2.0 that incorporates SNPP lessons learned, architecture of the system, and demonstrates how GRAVITE has evolved as a system with increased performance. It is now a robust, stable, reliable, maintainable, scalable, and secure system that supports development, test, and production strings, replaces proprietary and custom software, uses open source software, and is compliant with NASA and NOAA standards.

Data Processing↗