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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 325 records · Page 18
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NASA NTRS1978

The thermal structure of Saturn: Inferences from ground-based and airborne infrared observations

Spectroscopic and photometric infrared observations of Saturn are reviewed and compared to the expected flux from thermal structure models. Large uncertainties exist in the far-infrared measurements, but the available data indicate that the effective temperature of the disk of Saturn is 90 + or - 5 K. The thermal structure models proposed by Tokunaga and Cess and by Gautier et al. (model 'N') agree best with the observations. North-South limb scans of Saturn at 10 and 20 micrometers show that the temperature inversion is much stronger at the South polar region than at the equator.

Tokunaga, A.↗
NASA NTRS2013

The Ocean Colour Climate Change Initiative: III. A Round-Robin Comparison on In-Water Bio-Optical Algorithms

Satellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situ Rrs as input to the models, the performance of eleven semianalytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.

Phytoplankton↗
NASA NTRS2018

Reliability Analysis in the Presence of Aleatory Uncertainty

This paper proposes a method for modeling a system’s response using data. In contrast to approaches that identify a limit state function, we focus on the case in which not all uncertain parameters affecting the response are observable and the measured response is corrupted by noise. To this end, the system response is not characterized by a limit state function but instead by a Random Predictor Model (RPM) having a nonparametric structure. Consequently, the resulting failure probability is not a scalar but a random variable. This variable accounts for the aleatory contributions of the model-form uncertainty and the measurement noise into the response. Furthermore, we propose a framework that enables trading off the predicted range of failure probabilities resulting from such an analysis with a measure of risk. In this context, risk is the percentage of all predicted outcomes the analyst is willing to ignore. The reliability analysis of an aeroelastic structure subject to flutter is used to illustrate the ideas proposed.

Crespo, L. G.↗
NASA NTRS2002

Problems in Assessment of the UV Penetration into Natural Waters from Space-based Measurements

Satellite instruments currently provide global maps of surface UV (ultraviolet) irradiance by combining backscattered radiance data with radiative transfer models. The models are often limited by uncertainties in physical input parameters of the atmosphere and surface. Global mapping of the underwater UV irradiance creates further challenges for the models. The uncertainties in physical input parameters become more serious because of the presence of absorbing and scattering quantities caused by biological processes within the oceans. In this paper we summarize the problems encountered in the assessment of the underwater UV irradiance from space-based measurements, and propose approaches to resolve the problems. We have developed a radiative transfer scheme for computation of the UV irradiance in the atmosphere-ocean system. The scheme makes use of input parameters derived from satellite instruments such as TOMS (Total Ozone Mapping Spectrometer) and SeaWiFS (Sea-viewing Wide Field-of-view Sensor). The major problem in assessment of the surface UV irradiance is to accurately quantify the effects of clouds. Unlike the standard TOMS UV algorithm, we use the cloud fraction products available from SeaWiFS and MODIS (Moderate Resolution Imaging Spectrometer) to calculate instantaneous surface flux at the ocean surface. Daily UV doses can be calculated by assuming a model of constant cloudiness throughout the day. Both SeaWiFS and MODIS provide some estimates of seawater optical properties in the visible. To calculate the underwater UV flux the seawater optical properties must be extrapolated down to shorter wavelengths. Currently, the problem of accurate extrapolation of visible data down to the UV spectral range is not solved completely, and there are few available measurements. The major difficulty is insufficient correlation between photosynthetic and photoprotective pigments of phytoplankton absorbing in the visible and UV respectively. We propose to empirically parameterize seawater absorption in the UV on a basis of available data sets of bio-optical measurements from a variety of ocean waters. Another problem is the lack of reliable data on pure seawater absorption in the UV. Laboratory measurements of the UV absorption of both pure water and pure seawater are required.

Vasilkov, Alexander P.↗
NASA NTRSDate not supplied

Use of three-cornered hat error estimates in MERRA-2 to guide an improved reanalysis-Part 1

The three-cornered hat (3CH) method estimates the uncertainties of three different co-located model or observational data sets (Anthes and Rieckh, 2018; Sjoberg et al., 2021). Rieckh et al. (2021) used the 3CH method to compare the random error statistics of different global forecast and reanalysis models, as well as radio occultation (RO) and radiosonde observations. That study showed that the MERRA-2 reanalysis, while having smaller errors in the stratosphere than its predecessor MERRA, had larger errors in the troposphere than many of the other data sets analyzed. The MERRA-2 errors were particularly large in the tropics. In a collaborative effort between UCAR’s COSMIC (Constellation Observing System for Meteorology, Ionosphere and Meteorology) program and NASA’s Global Modeling and Assimilation Office (GMAO), we carried out further 3CH error diagnostics to help isolate the causes of these larger errors and help guide the development of an improved reanalysis. This presentation summarizes random error statistics associated with MERRA-2, ECMWF’s ERA5 reanalysis, and COSMIC-2 (C2) RO observations. We compute 3CH error variance estimates of refractivity, as well as temperature and specific humidity using UCAR’s COSMIC Data Analysis and Archive Center (CDAAC) improved 1D-variational (1D-Var) retrieval (wetPf2) over 15 latitude bands from 45S to 45N. The 1D-Var retrievals of specific humidity and temperature for C2 use NCEP’s Global Forecast System (GFS) as the background. Anthes et al. (2021) showed that it gives accurate estimates of temperature and specific humidity in the tropics and subtropics, even in the challenging environment of intense Hurricane Dorian (2019). This presentation confirms the previous results that MERRA-2 has significantly larger errors in the tropics and subtropics than either C2 or ERA5. Its errors are larger between 30S and 30N compared to 30-45 N-S latitudes, and are also larger over land compared to oceans. Most of the MERRA-2 refractivity errors come from specific humidity, except over land below 3 km where temperature errors are large. These results suggest that moist convection and atmospheric boundary layer physics in MERRA-2 may be responsible for a significant part of the higher uncertainties. These results are being used to guide GMAO in developing an improved next-generation reanalysis, as shown in a companion presentation submitted to this conference (El Akkraoui et al., 2021), which extends this study and describes improvements to MERRA-2 leading to the next GMAO reanalysis.

Jeremiah Sjoberg↗
NASA NTRS1990

Methods for evaluating the predictive accuracy of structural dynamic models

Uncertainty of frequency response using the fuzzy set method and on-orbit response prediction using laboratory test data to refine an analytical model are emphasized with respect to large space structures. Two aspects of the fuzzy set approach were investigated relative to its application to large structural dynamics problems: (1) minimizing the number of parameters involved in computing possible intervals; and (2) the treatment of extrema which may occur in the parameter space enclosed by all possible combinations of the important parameters of the model. Extensive printer graphics were added to the SSID code to help facilitate model verification, and an application of this code to the LaRC Ten Bay Truss is included in the appendix to illustrate this graphics capability.

Hasselman, T. K.↗
NASA NTRS1987

L-band radar backscatter modeling of forest stands

An L-band HH radar backscatter model of a coniferous forest stand is described and compared with SIR-B L-band image data of the Mount Shasta region of northern California. Being based upon an identification and implementation of the expected major components of forest backscattering, the model is simple in form and thus fast computationally, making possible extensive simulations of forest stands. A particularly important component in the model relates to representing the specular reflections expected from tree trunks to the ground and then back to the sensor. These are strong returns and are seen to be necessary to explain both the forest measurements made by the authors and the observations of others. Although the experimental data is limited in quantity and quality, agreement between available experimental and simulated values of forest backscatter is better than the residual uncertainty and relative calibration error of the experimental data, provided the model and experiment are matched initially at one set of parameter values.

Richards, John A.↗
NASA NTRS2013

Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions

A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.

Morelli, Eugene A.↗
NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗
NASA NTRSDate not supplied

Low-cost Quantification of Fluid Flow Parameter Sensitivity using Reduced-order Modeling

Uncertainty quantification of computational fluid dynamics (CFD) simulations is a complicated procedure which still relies in many cases on engineering judgment and factors of safety. This is in part because the computational cost of measuring the simulation's sensitivity to all meaningful parameters (e.g., body surface roughness) and hyperparameters (e.g., subiteration convergence criterion) is intractable for even a single simulation. Reduced-order modeling dramatically lowers this computational cost of simulating fluid flows, but usually only where similar data is already available. In this work, fluid reduced-order models are utilized to quantify a flow's sensitivity to certain physical parameters for the purposes of improved uncertainty quantification. Characteristic observability and sensitivity are both explored. The resulting sensitivity quantifications enable more informed CFD frameworks and more rigorous uncertainty bounds on the resulting data.

uncertainty quantification↗
NASA NTRSDate not supplied

Low-cost Quantification of Fluid Flow Parameter Sensitivity using Reduced-order Modeling

Uncertainty quantification of computational fluid dynamics (CFD) simulations is a complicated procedure which still relies in many cases on engineering judgment and factors of safety. This is in part because the computational cost of measuring the simulation's sensitivity to all meaningful parameters (e.g., body surface roughness) and hyperparameters (e.g., subiteration convergence criterion) is intractable for even a single simulation. Reduced-order modeling dramatically lowers this computational cost of simulating fluid flows, but usually only where similar data is already available. In this work, fluid reduced-order models are utilized to quantify a flow's sensitivity to certain physical parameters for the purposes of improved uncertainty quantification. Characteristic observability and sensitivity are both explored. The resulting sensitivity quantifications enable more informed CFD frameworks and more rigorous uncertainty bounds on the resulting data.

uncertainty quantification↗
NASA NTRS2010

Calibration for Thrust and Airflow Measurements in the CE-22 Advanced Nozzle Test Facility

CE-22 facility procedures and measurements for thrust and airflow calibration obtained with choked-flow ASME nozzles are presented. Six calibration nozzles are used at an inlet total pressure from 20 to 48 psia. Throat areas are from 9.9986 to 39.986 sq. in.. Throat Reynolds number varies from 1.8 to 7.9 million. Nozzle gross thrust coefficient (CFG) uncertainty is 0.25 to 0.75 percent, with smaller uncertainly generally for larger nozzles and higher inlet total pressure. Nozzle discharge coefficient (CDN) uncertainty is 0.15 percent or less for all the data. ASME nozzle calibrations need to be done before and after research model testing to achieve these uncertainties. In addition, facility capability in terms of nozzle pressure ratio (NPR) and nozzle airflow are determined. Nozzle pressure ratio of 50 or more is obtainable at 40 psia for throat areas between 20 and 30 sq. in.. Also presented are results for two of the ASME nozzles vectored at 10deg, a dead-weight check of the vertical (perpendicular to the jet axis) force measurement, a calibration of load cell forces for the effects of facility tank deflection with tank pressure, and the calibration of the metric-break labyrinth seal.

Werner, Roger A.↗
NASA NTRS1993

Experimental modification and identification of the DSS-13 antenna control system model

This work is the first step toward increasing the bandwidth of the DSS-13 antenna position loop controller. A wider bandwidth of the controller allows for faster tracking rates and better pointing performance under windy conditions. To achieve this goal, the antenna control system model has to be improved, such that it will accurately reflect the dynamic properties of the antenna. The existing analytical model, due to its many uncertainties, could not be used in the design of the controller. However, by using experimental data, the analytical model is modified and improved, and a new model is obtained through system identification techniques.

Racho, C. S.↗
NASA NTRS1981

Discrepancies between theoretical and empirical models of the flaring solar chromosphere and their possible resolution

Possible sources of pronounced discrepancy between empirical and theoretical models of the solar chromosphere during flares are discussed. It is noted that a principal source of uncertainty in empirical models is the inhomogeneity of the spectral data on which they are based. With theoretical models, probably the most important source of error is neglect of the radiative coupling of upper and lower chromospheric regions. A new procedure for studying flare energy input is suggested wherein the required input is derived from the empirical model chromosphere. This procedure is applied to the electron-heated case, and it is found that the integral equation defining the flare energy deposition rate can be inverted analytically to yield the injected electron flux energy spectrum from knowledge of the energy balance in the empirical atmosphere. Recent empirical model results are analyzed in this manner, and the calculated injected electron flux spectrum is compared with that needed for hard X-ray bursts in moderately large flares.

Emslie, A. G.↗
NASA NTRS1987

Identification of large space structures: A state-of-practice report

An outline of this work is presented. It begins with a schematic flow diagram and a logical flow diagram of the identification process for large space structures (LSS). Next, the task is defined by a structure model definition. A matrix polynomial formulation with a node displacement equation and a state variable formulation with node displacement and velocities are outlined. Further outlined is the identification of LSS on orbit; modeling errors and uncertainties; verification and validation of model; and noise, computations, and data collection.

Source record↗
NASA NTRS1992

A probabilistic fracture mechanics approach for structural reliability assessment of space flight systems

A probabilistic fracture mechanics approach for predicting the failure life distribution due to subcritical crack growth is presented. A state-of-the-art crack propagation method is used in a Monte Carlo simulation to generate a distribution of failure lives. The crack growth failure model expresses failure life as a function of stochastic parameters including environment, loads, material properties, geometry, and model specification errors. A stochastic crack growth rate model that considers the uncertainties due to scatter in the data and mode misspecification is proposed. The rationale for choosing a particular type of probability distribution for each stochastic input parameter and for specifying the distribution parameters is presented. The approach is demonstrated through a probabilistic crack growth failure analysis of a welded tube in the Space Shuttle Main Engine. A discussion of the results from this application of the methodology is given.

Sutharshana, S.↗
NASA NTRS1993

The laboratory spectrum of acetaldehyde at 1 millimeter (230-325 GHz)

The rotational spectrum of acetaldehyde (CH3CHO) in the frequency range 230-325 GHz has been measured in the laboratory using millimeter/submillimeter direct absorption spectroscopy. Over 250 transition frequencies are presented for this molecule for both A and E symmetry species in its ground (upsilon(sub t) = 0) and first excited (upsilon(sub t) = 1) torsional state, with experimental uncertainties of +/- 50 kHz. The data were fitted with a model involving an internal rotation potential function, which typically reproduces the measured frequencies to nu(sub obs) - nu(sub calc) less than or approximately 50 kHz for both ground and upsilon(sub t) = 1 state. These newly measured rest frequencies should aid in the identification of interstellar CH3CHO and in spectral line assignments for millimeter-band scans.

Barclay, W. L., Jr.↗
NASA NTRS2011

Frost Growth and Densification in Laminar Flow Over Flat Surfaces

One-dimensional frost growth and densification in laminar flow over flat surfaces has been theoretically investigated. Improved representations of frost density and effective thermal conductivity applicable to a wide range of frost circumstances have been incorporated. The validity of the proposed model considering heat and mass diffusion in the frost layer is tested by a comparison of the predictions with data from various investigators for frost parameters including frost thickness, frost surface temperature, frost density and heat flux. The test conditions cover a range of wall temperature, air humidity ratio, air velocity, and air temperature, and the effect of these variables on the frost parameters has been exemplified. Satisfactory agreement is achieved between the model predictions and the various test data considered. The prevailing uncertainties concerning the role air velocity and air temperature on frost development have been elucidated. It is concluded that that for flat surfaces increases in air velocity have no appreciable effect on frost thickness but contribute to significant frost densification, while increase in air temperatures results in a slight increase the frost thickness and appreciable frost densification.

Kandula, Max↗