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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 19 records

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NASA NTRS1985

Utilization of satellite-derived cloud cover to improve the estimation of latent heat release in a winter extratropical cyclone

A simple scheme for utilizing satellite cloud pattern information to improve the estimation of parameterized stable and convective latent heat release is proposed and tested for an extratropical cyclone occurring over the United States during January 9-11, 1975. Calculations are done over the standard North American rawinsonde data region and over a sparse data region simulated by ignoring one-third of the original rawinsonde stations. Results indicate 15 percent and 30 percent improvements in latent heating estimates derived from the total and sparse data sets, respectively. A larger improvement was found using the sparse data set because the parameterized latent heat release estimates obtained using these data were poorer than those obtained using the total data set.

Lin, S.-J.↗

NASA NTRS1987

The terminal area simulation system. Volume 2: Verification cases

The numerical simulation of five case studies are presented and are compared with available data in order to verify the three-dimensional version of the Terminal Area Simulation System (TASS). A spectrum of convective storm types are selected for the case studies. Included are: a High-Plains supercell hailstorm, a small and relatively short-lived High-Plains cumulonimbus, a convective storm which produced the 2 August 1985 DFW microburst, a South Florida convective complex, and a tornadic Oklahoma thunderstorm. For each of the cases the model results compared reasonably well with observed data. In the simulations of the supercell storms many of their characteristic features were modeled, such as the hook echo, BWER, mesocyclone, gust fronts, giant persistent updraft, wall cloud, flanking-line towers, anvil and radar reflectivity overhang, and rightward veering in the storm propagation. In the simulation of the tornadic storm a horseshoe-shaped updraft configuration and cyclic changes in storm intensity and structure were noted. The simulation of the DFW microburst agreed remarkably well with sparse observed data. The simulated outflow rapidly expanded in a nearly symmetrical pattern and was associated with a ringvortex. A South Florida convective complex was simulated and contained updrafts and downdrafts in the form of discrete bubbles. The numerical simulations, in all cases, always remained stable and bounded with no anomalous trends.

Proctor, F. H.↗

NASA NTRS2018

Cross-Cutting Computational Modeling Project: Integrative Modeling Approach

A wide range of computational models and analyses have been applied to spaceflight risk assessment and countermeasure development. The benefits of using computational modeling to enhance Human Research Program (HRP) goals include the ability to mathematically represent physiological systems, integrate multiple, discrete experimental measures, span multiple temporal and spatial scales, determine important factors within the system and provide estimates of unmeasurable quantities. In the area of application, computational models provide a means of developing simulations to test hypotheses, determining key factors of the system to aid experimental design and bridging gaps in sparse data by mathematically simulating large populations. Specifically, computational models and their supporting analysis tools have the proven potential to integrate analyses of risk factors to enhance mission planning and preparation capabilities and to inform spacecraft design and countermeasure development. Appropriately applied, computational models may allow intelligent, unbiased physiological parameter assessment to enable hypothesis testing and model based design of experiments. HRP recently formed the Computational Modeling Project (CMP), managed out of Glenn Research Center, as a cross-cutting activity aimed at leveraging the growing power and acceptance of computational modeling in informing clinical, physiological, and biological studies. This presentation will provide an overview of the challenges and opportunities in implementing various forms of computational models in support of the HRPs path to risk reduction.

biomechanics↗

NASA NTRS2009

Winter QPF Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

Steady increases in computing power have allowed for numerical weather prediction models to be initialized and run at high spatial resolution, permitting a transition from larger scale parameterizations of the effects of clouds and precipitation to the simulation of specific microphysical processes and hydrometeor size distributions. Although still relatively coarse in comparison to true cloud resolving models, these high resolution forecasts (on the order of 4 km or less) have demonstrated value in the prediction of severe storm mode and evolution and are being explored for use in winter weather events . Several single-moment bulk water microphysics schemes are available within the latest release of the Weather Research and Forecast (WRF) model suite, including the NASA Goddard Cumulus Ensemble, which incorporate some assumptions in the size distribution of a small number of hydrometeor classes in order to predict their evolution, advection and precipitation within the forecast domain. Although many of these schemes produce similar forecasts of events on the synoptic scale, there are often significant details regarding precipitation and cloud cover, as well as the distribution of water mass among the constituent hydrometeor classes. Unfortunately, validating data for cloud resolving model simulations are sparse. Field campaigns require in-cloud measurements of hydrometeors from aircraft in coordination with extensive and coincident ground based measurements. Radar remote sensing is utilized to detect the spatial coverage and structure of precipitation. Here, two radar systems characterize the structure of winter precipitation for comparison to equivalent features within a forecast model: a 3 GHz, Weather Surveillance Radar-1988 Doppler (WSR-88D) based in Omaha, Nebraska, and the 94 GHz NASA CloudSat Cloud Profiling Radar, a spaceborne instrument and member of the afternoon or "A-Train" of polar orbiting satellites tasked with cataloguing global cloud characteristics. Each system provides a unique perspective. The WSR-88D operates in a surveillance mode, sampling cloud volumes of Rayleigh scatterers where reflectivity is proportional to the sixth moment of the size distribution of equivalent spheres. The CloudSat radar provides enhanced sensitivity to smaller cloud ice crystals aloft, as well as consistent vertical profiles along each orbit. However, CloudSat reflectivity signatures are complicated somewhat by resonant Mie scattering effects and significant attenuation in the presence of cloud or rain water. Here, both radar systems are applied to a case of light to moderate snowfall within the warm frontal zone of a cold season, synoptic scale storm. Radars allow for an evaluation of the accuracy of a single-moment scheme in replicating precipitation structures, based on the bulk statistical properties of precipitation as suggested by reflectivity signatures.

Molthan, Andrew↗

NASA NTRS2019

A Global Assessment of Added Value in the SMAP Level-4 Soil Moisture Product Relative to Its Baseline Land Surface Model

The Soil Moisture Active Passive (SMAP) Level-4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, an unbiased qualitative estimate of the relative skill of SMAP Level-4 and model-only surface soil moisture (versus true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high-quality, ground-based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data-rich areas like the United States and Europe. In contrast, much larger improvement is observed in data-sparse regions, including much of Africa and central Australia, where model-only simulations are disproportionately impacted by low-quality model forcing. Therefore, ground validation conducted in data-rich areas does not adequately sample the added value of SMAP data assimilation for data-sparse regions and substantially underestimates the added skill provided by the SMAP Level-4 system.

SMAP L4↗

NASA NTRS2002

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Rajkumar, T.↗

NASA NTRS1981

On the sensitivity of numerical weather prediction to remotely sensed marine surface wind data - A simulation study

The reported investigation has the objective to assess the potential impact on numerical weather prediction (NWP) of remotely sensed surface wind data. Other investigations conducted with similar objectives have not been satisfactory in connection with a use of procedures providing an unrealistic distribution of initial errors. In the current study, care has been taken to duplicate the actual distribution of information in the conventional observing system, thus shifting the emphasis from accuracy of the data to the data coverage. It is pointed out that this is an important consideration in assessing satellite observing systems since experience with sounder data has shown that improvements in forecasts due to satellite-derived information is due less to a general error reduction than to the ability to fill data-sparse regions. The reported study concentrates on the evaluation of the observing system simulation experimental design and on the assessment of the potential of remotely sensed marine surface wind data.

Cane, M. A.↗

NASA NTRS1994

Simulated ASTER data for Geologic Studies

The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a high spatial resolution imaging instrument, due to be launched on NASA's Earth Observing System AM-1 satellite platform in 1998. ASTER acquires data in 14 bands, spanning the wavelength region from the visible, near infrared, short wavelength infrared, and thermal infrared, with spatial resolution varying from 15 m to 90 m, depending on wavelength region. In order to evaluate our ability to use ASTER data for geological mapping, we used aircraft data over Cuprite, Nevada to create a simulated 14-band ASTER data set. The study site has sparse vegetation, and exposes a wide range of unaltered and hydrothermally altered volcanic rocks. The wide range of wavelengths covered by ASTER allowed us to recognize or separate iron oxide minerals, clay-bearing minerals, sulfate minerals, ammonia minerals, siliceous rocks, and carbonates. Combined with laboratory spectral measurements, we were able to identify these constituents. Based on both sets of information, we produced an alteration map showing the distribution of argillized rocks, opalized rocks with alunite, silicified rocks, and areas dominated by kaolinite and buddingtonite. The map was as accurate as published maps made by traditional field methods. ASTER promises to be a major improvement over existing satellite systems for geologic mapping.

Advanced↗

NASA NTRSDate not supplied

Development of Aerodynamic Loads Databases for the Space Launch System Booster Separation Event

Booster separation is a mission-critical event within the orbital ascent of the Space Launch System (SLS). The complexity of the engine plume-affected, multibody, supersonic aerodynamics is compounded by the large span of the likely trajectory space. Characterization of the multiple input, multiple output system requires a combination of wind tunnel testing and computational simulation, but additional data processing is also required before the sparse, high-fidelity data can be fused into a continuous database with acceptable uncertainty quantification. This paper outlines the state of this approach as it has been applied to the most recent SLS booster separation aerodynamic loads database: that of the Artemis II launch vehicle.

Michael W Lee↗

NASA NTRSDate not supplied

Development of Aerodynamic Loads Databases for the Space Launch System Booster Separation Event

Michael Lee↗

NASA NTRS1992

Modelling TOVS radiances of synoptic systems

Two operational analysis models, produced by ECMWF and NMC, are compared against observed satellite radiances by converting the analyzed variables into synthetic radiances using a radiative transfer model (RTM). Observed TOVS satellite radiances are used as a ground truth, since they are the only source of continuous, synoptic-scale resolution observations available over the data-sparse tropical Pacific Ocean. It is assumed that if the analysis models correctly simulate atmospheric conditions, the observed and modelled radiance fields will have similar patterns and magnitudes. In each of seven TOVS channels examined, the mean temperature difference between the models was small, but the differences between the models and the observations were large. The mean temperature difference within each channel was larger than the standard deviation in most cases. The moisture channels of the RTM were sensitive to changes in moisture content, but none of the channels were significantly affected by changes in temperature. Single-day modelled radiance analyses contain synoptic information similar to the satellite observations. ECMWF is often too noisy, producing features not verified by the satellite; NMC is often too smooth. NMC more closely resembles the TOVS contours. Modelled water vapor channel analyses resemble observed monthly and composited observations well, but do not accurately reproduce the highly-variable single-day fields. ECMWF generally has more accurate gradients. Neither model simulates the tropics well. The water vapor channels, especially channel eleven (weighted at 700 mb), most closely resembled the tropical plume composite described by McGuirk and Ulsh. The only plume evidence found in the thermal or microwave channels, in either the observations or the models, was a composite trough situated in the correct location, extending from the mid-latitudes into the subtropics. This feature was not readily discernable until the definition and mature stages. Additional information besides the modelled temperatures would be required to produce an accurate forecast for the tropics. Model patterns more closely resembled the observations in the quiescent atmosphere, though the magnitudes were not any closer. ECMWF simulates the TOVS quiescent state better than NMC. The moisture channels continue to present the most realistic patterns. The models contained more detail in the tropics than was verified by the satellite observations, apparently trying to force an active ITCZ. Lag correlations showed TOVS to be more persistent than the models. TOVS also showed a smoother lag trend, but all products appeared to have adequate ability to react to changes in the atmosphere over time.

Coe, Thomas Eddy↗

NASA NTRSDate not supplied

Development of the Ames Global Hyperspectral Synthetic Dataset

This study develops the surface BRDF (bidirectional reflectance distribution function) product of the Ames Global Hyperspectral Synthetic Dataset (AGHSD), based on the corresponding MODIS products, to support the NASA Surface Biology and Geology mission development. A main challenge in deriving a hyperspectral dataset from the multi-band satellite products is how to identify a succinct yet robust algorithm that allow us to infer BRDF at unobserved wavelengths based on the few observed bands. Using the theories of radiative transfer in vegetation canopies, we arrive at a simple equation that accurately approximates hyperspectral surface BRDF as the weighted sum of components from the soil and the vegetation. Each of the components is modeled by the product of the spectrally-dependent optical properties of a surface element (the spectra of the soil surface reflectance, the leaf single albedo, or the canopy scattering coefficient) and a spectrally-independent bidirectional scattering function. The optical properties of the soil and the vegetation can be obtained from existing spectral libraries or model simulations. The bidirectional scattering functions are represented by the Ross-Thick-Li-Sparse BRDF model, where the linear coefficients are estimated with regression analysis from the multi-band MODIS data. We validate the algorithm with simulations by Monte Carlo Ray Tracing model experiments, and the results are highly consistent with the theoretic derivation. We apply the algorithm to generate the AGHSD BRDF product at 1km and 8-day resolutions for the year of 2019. The results are biogeochemically and physically coherent and consistent, and thus serve the goal to support the science and application development of the SBG community.

Hyperspectral↗

NASA NTRS2004

Testing the PV-Theta Mapping Technique in a 3-D CTM Model Simulation

Mapping lower stratospheric ozone into potential vorticity (PV)- potential temperature (Theta) coordinates is a common technique employed to analyze sparse data sets. Ozone transformed into a flow-following dynamical coordinate system is insensitive to meteorological variations. Therefore data from a wide range of times/locations can be compared, so long as the measurements were made in the same airmass (as defined by PV). Moreover, once a relationship between ozone and PV/Theta is established, a full 3D ozone field can be estimated from this relationship and the 3D analyzed PV field. However, ozone data mapped in this fashion can be hampered by noisy PV fields, or "mis-matches" in the resolution and/or exact location of the ozone and PV measurements. In this study, we investigate the PV-ozone relationship using output from a recent 50-year run of the Goddard 3D chemical transport model (CTM). Model constituents are transported using off-line dynamics from the finite volume general circulation model (FVGCM). By using the internally consistent model PV and ozone fields, we minimize noise due to mis-matching and resolution issues. We calculate correlations between model ozone and PV throughout the stratosphere, and test the sensitivity of the technique to initial data resolution. To do this we degrade the model data to that of various satellite instruments, then compare the mapped fields derived from the sub-sampled data to the full resolution model data. With these studies we can determine appropriate limits for the PV-theta mapping technique in latitude, altitude, and as a function of original data resolution.

Frith, Stacey M.↗

NASA NTRS1985

A Structural Dynamics Approach to the Simulation of Spacecraft Control/Structure Interaction

A relatively simple approach to the analysis of linear spacecraft control/structure interaction problems is presented. The approach uses a commercially available structural system dynamic analysis package for both controller and plant dynamics, thus obviating the need to transfer data between separate programs. The unilateral coupling between components in the control system block diagram is simulated using sparse matrix stiffness and damping elements available in the structural dynamic code. The approach is illustrated with a series of simple tutorial examples of a rigid spacecraft core with flexible appendages.

Young, J. W.↗

NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗

NASA NTRS1992

A summary of the computational fluid dynamics analysis of the hypersonic flights of Pegasus

A computational fluid dynamics (CFD) analysis of the hypersonic flights of the NASA's Pegasus launch vehicle was carried out using the PARC code. The flight data included heat transfer rates and sparse pressure coefficients primarily in the fillet region of the vehicle. Since the CFD simulations did not account for ablation at the vehicle surface, the test heat transfer results were obtained for the low Mach number simulations; as the Mach number was increased, the ablative effects at the vehicle surface became more apparent and contributed to the error.

Fricker, Darren↗

NASA NTRS2003

Adjoint-Based Methods for Estimating CO2 Sources and Sinks from Atmospheric Concentration Data

Work to develop adjoint-based methods for estimating CO2 sources and sinks from atmospheric concentration data was initiated in preparation for last year's summer institute on Carbon Data Assimilation (CDAS) at the National Center for Atmospheric Research in Boulder, CO. The workshop exercises used the GSFC Parameterized Chemistry and Transport Model and its adjoint. Since the workshop, a number of simulations have been run to evaluate the performance of the model adjoint. Results from these simulations will be presented, along with an outline of challenges associated with incorporating a variety of disparate data sources, from sparse, but highly precise, surface in situ observations to less accurate, global future satellite observations.

Andrews, Arlyn E.↗

NASA NTRS2016

Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

We estimated global fine particulate matter (PM(sub 2.5)) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically-based satellite-derived PM(sub 2.5) estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998−2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM(sub 2.5) estimates were highly consistent (R(sup 2) equals 0.81) with out-of-sample cross-validated PM(sub 2.5) concentrations from monitors. The global population-weighted annual average PM(sub 2.5) concentrations were 3-fold higher than the 10 micrograms per cubic meter WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM(sub 2.5) data sources can yield valuable improvements to PM(sub 2.5) characterization on a global scale.

aerosols↗