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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

An MME-based attitude estimator using vector observations

In this paper, an optimal batch estimator and filter based on the Minimum Model Error (MME) approach is developed for three-axis stabilized spacecraft. Three different MME algorithms are developed. The first algorithm estimates the attitude of a spacecraft using rate measurements. The second algorithm estimates the attitude without using rate measurements. The absence of rate data may be a result of intentional design or from unexpected failure of existing gyros. The third algorithm determines input-torque modeling error trajectories. All of the algorithms developed in this paper use attitude sensors (e.g., three-axis magnetometers, sun sensors, star trackers, etc.). Results using these new algorithms indicate that an MME-based approach accurately estimates the attitude, rate, and input torque trajectories of an actual spacecraft.

Crassidis, John L.↗
NASA NTRS1984

A radiometric Bode's Law: Predictions for Uranus

The magnetospheres of three planets, Earth, Jupiter, and Saturn, are known to be sources of intense, nonthermal radio bursts. The emissions from these sources undergo pronounced long term intensity fluctuations that are caused by the solar wind interaction with the magnetosphere of each planet. Determinations by spacecraft of the low frequency radio spectra and radiation beam geometry now permit a reliable assessment of the overall efficiency of the solar wind in stimulating these emissions. Earlier estimates of how magnetospheric radio output scales with the solar wind energy input must be revised greatly, with the result that, while the efficiency is much lower than previously thought, it is remarkably uniform from planet to planet. The formulation of a radiometric Bode's Law from which a planet's magnetic moment is estimated from its radio emission output is presented. Applying the radiometric scaling law to Uranus, the low-frequency radio power is likely to be measured by the Voyager 2 spacecraft as it approaches this planet.

Desch, M. D.↗
NASA NTRS1984

Predictions for Uranus from a radiometric Bode's law

Determinations by spacecraft of the low-frequency radio spectra and radiation beam geometry of the magnetospheres of earth, Jupiter, and Saturn now permit a reliable assessment of the overall efficiency of the solar wind in stimulating intense, nonthermal radio bursts from these magnetospheres. It is found that earlier estimates of how magnetospheric radio output scales with the solar wind energy input must be greatly revised, with the result that, while the efficiency is much lower than previously thought, it is remarkably uniform from planet to planet. A 'radimetric Bode's law' is formulated from which a planet's magnetic moment can be estimated from its radio emission output. This law is applied to estimate the low-frequency radio power likely to be measured for Uranus by Voyager 2. It is shown how measurements of Uranus's radio flux can be used to estimate the planetary magnetic moment and solar wind stand-off distance before the in situ measurements.

Desch, M. D.↗
NASA NTRS1981

Evaluation of the procedure 1A component of the 1980 US/Canada wheat and barley exploratory experiment

Several techniques which use clusters generated by a new clustering algorithm, CLASSY, are proposed as alternatives to random sampling to obtain greater precision in crop proportion estimation: (1) Proportional Allocation/relative count estimator (PA/RCE) uses proportional allocation of dots to clusters on the basis of cluster size and a relative count cluster level estimate; (2) Proportional Allocation/Bayes Estimator (PA/BE) uses proportional allocation of dots to clusters and a Bayesian cluster-level estimate; and (3) Bayes Sequential Allocation/Bayesian Estimator (BSA/BE) uses sequential allocation of dots to clusters and a Bayesian cluster level estimate. Clustering in an effective method in making proportion estimates. It is estimated that, to obtain the same precision with random sampling as obtained by the proportional sampling of 50 dots with an unbiased estimator, samples of 85 or 166 would need to be taken if dot sets with AI labels (integrated procedure) or ground truth labels, respectively were input. Dot reallocation provides dot sets that are unbiased. It is recommended that these proportion estimation techniques are maintained, particularly the PA/BE because it provides the greatest precision.

Chapman, G. M.↗
NASA NTRS1972

A slope reversal video processor having amplitude and pulse width discrimination.

A timing discriminator whose output is relatively insensitive to input pulse width and amplitude is described. The video-handling technique is known as slope reversal processing and is based on tapped delay line estimation methods. The concept, design, breadboard construction, and preliminary laboratory test results are reviewed. The processor system features (1) a peak input voltage dynamic range of 80x starting at 100 microvolts; (2) a four stage preamplifier/line-driver having a voltage gain of 2400x; (3) a tapped delay-line estimator implemented for slope reversal triggering by differential comparison; (4) a nominal plus or minus 2.5 nsec shift of the output trigger leading edge over the entire input voltage range, (5) up to 7 dB discrimination against pulse widths less than 35 nsec; and (6) up to one order of magnitude reduction in the false alarm rate compared to a constant fraction system.

Hoge, F. E.↗
NASA NTRS1986

Robust detection, isolation and accommodation for sensor failures

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

Emami-Naeini, A.↗
NASA NTRS1992

Satellite remote sensing of surface energy balance - Success, failures, and unresolved issues in FIFE

Utilizing single-view-angle visible and NIR satellite observations, it was possible to make the necessary radiometric corrections to produce reliable estimates of the simple ratio and the normalized difference vegetation index as inputs to surface evaporation models. Agreement was achieved between helicopter and satellite measures of surface reflectance to within +/-1 percent absolute for both the visible and the NIR bands. A theoretical calculation utilizing radiative transfer models indicates that this translates into a +/-10 percent error in the estimation in Fpar.

Hall, Forrest G.↗
NASA NTRS2010

Model Effects on GLAS-Based Regional Estimates of Forest Biomass and Carbon

Ice, Cloud, and land Elevation Satellite (ICESat) / Geosciences Laser Altimeter System (GLAS) waveform data are used to estimate biomass and carbon on a 1.27 X 10(exp 6) square km study area in the Province of Quebec, Canada, below the tree line. The same input datasets and sampling design are used in conjunction with four different predictive models to estimate total aboveground dry forest biomass and forest carbon. The four models include non-stratified and stratified versions of a multiple linear model where either biomass or (biomass)(exp 0.5) serves as the dependent variable. The use of different models in Quebec introduces differences in Provincial dry biomass estimates of up to 0.35 G, with a range of 4.94 +/- 0.28 Gt to 5.29 +/-0.36 Gt. The differences among model estimates are statistically non-significant, however, and the results demonstrate the degree to which carbon estimates vary strictly as a function of the model used to estimate regional biomass. Results also indicate that GLAS measurements become problematic with respect to height and biomass retrievals in the boreal forest when biomass values fall below 20 t/ha and when GLAS 75th percentile heights fall below 7 m.

Nelson, Ross F.↗
NASA NTRS2011

Estimation of Surface Air Temperature Over Central and Eastern Eurasia from MODIS Land Surface Temperature

Surface air temperature (T(sub a)) is a critical variable in the energy and water cycle of the Earth.atmosphere system and is a key input element for hydrology and land surface models. This is a preliminary study to evaluate estimation of T(sub a) from satellite remotely sensed land surface temperature (T(sub s)) by using MODIS-Terra data over two Eurasia regions: northern China and fUSSR. High correlations are observed in both regions between station-measured T(sub a) and MODIS T(sub s). The relationships between the maximum T(sub a) and daytime T(sub s) depend significantly on land cover types, but the minimum T(sub a) and nighttime T(sub s) have little dependence on the land cover types. The largest difference between maximum T(sub a) and daytime T(sub s) appears over the barren and sparsely vegetated area during the summer time. Using a linear regression method, the daily maximum T(sub a) were estimated from 1 km resolution MODIS T(sub s) under clear-sky conditions with coefficients calculated based on land cover types, while the minimum T(sub a) were estimated without considering land cover types. The uncertainty, mean absolute error (MAE), of the estimated maximum T(sub a) varies from 2.4 C over closed shrublands to 3.2 C over grasslands, and the MAE of the estimated minimum Ta is about 3.0 C.

Shen, Suhung↗
NASA NTRS2005

Model Structures and Algorithms for Identification of Aerodynamic Models for Flight Dynamics Applications

This paper describes model structures and parameter estimation algorithms suitable for the identification of unsteady aerodynamic models from input-output data. The model structures presented are state space models and include linear time-invariant (LTI) models and linear parameter-varying (LPV) models. They cover a wide range of local and parameter dependent identification problems arising in unsteady aerodynamics and nonlinear flight dynamics. We present a residue algorithm for estimating model parameters from data. The algorithm can incorporate apriori information and is described in detail. The algorithms are evaluated on the F-16XL wind-tunnel test data from NAS Langley Research Center. Results of numerical evaluation are presented. The paper concludes with a discussion major issues and directions for future work.

Prasanth, Ravi K.↗
NASA NTRS1975

A Monte Carlo analysis of the effects of instrumentation errors on aircraft parameter identification

An output error estimation algorithm was used to evaluate the effects of both static and dynamic instrumentation errors on the estimation of aircraft stability and control parameters. A Monte Carlo analysis, using simulated cruise flight data, was performed for a high performance military aircraft, a large commercial transport, and a small general-aviation aircraft. The effects of variations in the information content of the flight data, resulting from two different choices of control input maneuvers, were also determined. The results indicate that unmodeled instrumentation errors can cause inaccuracies in the estimated parameters which are comparable to their nominal values. Control input errors and angular accelerometer lags were found to be most significant of the instrumentation errors evaluated, and the perturbations they produce are much larger than those arising from the combined effects of static errors and white noise in the output response measurements.

Bryant, W. H.↗
NASA NTRS1994

Parameter estimation in a structural acoustic system with fully nonlinear coupling conditions

A methodology for estimating physical parameters in a class of structural acoustic systems is presented. The general model under consideration consists of an interior cavity which is separated from an exterior noise source by an enclosing elastic structure. Piezoceramic patches are bonded to or embedded in the structure; these can be used both as actuators and sensors in applications ranging from the control of interior noise levels to the determination of structural flaws through nondestructive evaluation techniques. The presence and excitation of patches, however, changes the geometry and material properties of the structure as well as involves unknown patch parameters, thus necessitating the development of parameter estimation techniques which are applicable in this coupled setting. In developing a framework for approximation, parameter estimation and implementation, strong consideration is given to the fact that the input operator is unbonded due to the discrete nature of the patches. Moreover, the model is weakly nonlinear. As a result of the coupling mechanism between the structural vibrations and the interior acoustic dynamics. Within this context, an illustrating model is given, well-posedness and approximations results are discussed and an applicable parameter estimation methodology is presented. The scheme is then illustrated through several numerical examples with simulations modeling a variety of commonly used structural acoustic techniques for systems excitations and data collection.

Banks, H. T.↗
NASA NTRS1976

Possibility of detecting magnetospheric radio bursts from Uranus and Neptune

The intensity of magnetospheric radio bursts (MRBs) is scaled to solar-wind input into planetary magnetospheres and the frequency of emission is scaled to polar surface magnetic-field strength in order to estimate the possibility of detecting MRBs from Uranus and Neptune. A scaling law is derived which relates the ratio of power radiated in MRBs to the solar-wind input for earth, Jupiter, and Saturn. Power-flux spectra of MRBs from these three planets are plotted, and it is shown that Jupiter and Saturn may radiate 1% to 5% of the solar-wind energy input into their magnetospheres. The properties of MRBs from Uranus and Neptune are estimated by assuming a conversion efficiency of 1% to 5%, a bandwidth of half the peak frequency, and conformity of Uranus' and Neptune's dipole moments with the magnetic Bode's law. Based on the results, it is suggested that detection of MRBs from these two planets may be a reasonable cruise-mode radio-astronomy objective on future missions to the outer solar system.

Kennel, C. F.↗
NASA NTRSDate not supplied

Estimating Future Changes of Energy Demand for Heating and Cooling Buildings at NASA Centers GC23J-1198

With its unique and trusted earth observations, NASA is a critical source in informing decisions that will help achieve the U.S. goal of Net-Zero Greenhouse Gas (GHG) Emissions by 2050. NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). As part of the development of new data products to support the energy and building heating/cooling design communities, we estimate the changes in energy required to heat and cool buildings in the future climate at 14 different NASA site locations spread throughout the continental United States, as projected by CMIP6 climate models under different emissions scenarios. Bias-corrected downscaled time series of meteorological variables are taken from NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP-CMIP6) downscaled climate model data. The spread between the different model projections is accounted for by analyzing both the ensemble average of 22 CMIP6 models and 6 representative models with different climate sensitivities and different interannual variability. Changes in energy use are estimated in two ways. First, changes in the total annual heating and cooling degree days (HDD and CDD, respectively) are calculated relative to the current climate. This is done at all 14 sites. Second, the downscaled time series are used as inputs into RETScreen(R), a clean energy management decision tool, to give an estimate of heating/cooling energy use for a typical office building. We use this estimation method with model data at Langley Research Center. In the next 50 years, the annual total of HDD (CDD) is projected to decrease by 8-38% (increase by 5-28%) at all sites, with the increase in CDD typically a larger magnitude the decrease in HDD. For a typical small office building at Langley Research Center, the amount of energy needed to cool increases by 33-54% and the amount of energy to heat decreases by 29-40%. POWER is working to develop long term climate data services based on these results to include in future data products to provide to users.

Bradley M. Hegyi↗
NASA NTRS1992

An experimental methodology for a fuzzy set preference model

A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate models and vague linguistic preferences has greatly limited the usefulness and predictive validity of existing preference models. A fuzzy set preference model that uses linguistic variables and a fully interactive implementation should be able to simultaneously address these issues and substantially improve the accuracy of demand estimates. The parallel implementation of crisp and fuzzy conjoint models using identical data not only validates the fuzzy set model but also provides an opportunity to assess the impact of fuzzy set definitions and individual attribute choices implemented in the interactive methodology developed in this research. The generalized experimental tools needed for conjoint models can also be applied to many other types of intelligent systems.

Turksen, I. B.↗
NASA NTRS1981

NASA/BLM Applications Pilot Test (APT), phase 2. Volume 1: Executive summary

Data from LANDSAT, low altitude color aerial photography, and ground visits were combined and used to produce vegetation cover maps and to estimate productivity of range, woodland, and forest resources in northwestern Arizona. A planning session, two workshops, and four status reviews were held to assist technology transfer from NASA. Computer aided digital classification of LANDSAT data was selected as a major source of input data. An overview is presented of the data processing, data collection, productivity estimation, and map verification techniques used. Cost analysis and digital LANDSAT digital products are also considered.

Source record↗
NASA NTRS2016

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

model structure uncertainty↗