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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Computational Hemodynamic Simulation of Human Circulatory System under Altered Gravity

A computational hemodynamics approach is presented to simulate the blood flow through the human circulatory system under altered gravity conditions. Numerical techniques relevant to hemodynamics issues are introduced to non-Newtonian modeling for flow characteristics governed by red blood cells, distensible wall motion due to the heart pulse, and capillary bed modeling for outflow boundary conditions. Gravitational body force terms are added to the Navier-Stokes equations to study the effects of gravity on internal flows. Six-type gravity benchmark problems are originally presented to provide the fundamental understanding of gravitational effects on the human circulatory system. For code validation, computed results are compared with steady and unsteady experimental data for non-Newtonian flows in a carotid bifurcation model and a curved circular tube, respectively. This computational approach is then applied to the blood circulation in the human brain as a target problem. A three-dimensional, idealized Circle of Willis configuration is developed with minor arteries truncated based on anatomical data. Demonstrated is not only the mechanism of the collateral circulation but also the effects of gravity on the distensible wall motion and resultant flow patterns.

Kim. Chang Sung↗
NASA NTRS2007

Assessment of the National Combustion Code

The advancements made during the last decade in the areas of combustion modeling, numerical simulation, and computing platform have greatly facilitated the use of CFD based tools in the development of combustion technology. Further development of verification, validation and uncertainty quantification will have profound impact on the reliability and utility of these CFD based tools. The objectives of the present effort are to establish baseline for the National Combustion Code (NCC) and experimental data, as well as to document current capabilities and identify gaps for further improvements.

Liu, nan-Suey↗
NASA NTRS2014

Force Limiting Vibration Tests Evaluated from both Ground Acoustic Tests and FEM Simulations of a Flight Like Vehicle System Assembly

Marshall Space Flight Center has conducted a series of ground acoustic tests with the dual goals of informing analytical judgment, and validating analytical methods when estimating vibroacoustic responses of launch vehicle subsystems. The process of repeatedly correlating finite element-simulated responses with test-measured responses has assisted in the development of best practices for modeling and post-processing. In recent work, force transducers were integrated to measure interface forces at the base of avionics box equipment. Other force data was indirectly measured using strain gauges. The combination of these direct and indirect force measurements has been used to support and illustrate the advantages of implementing the Force Limiting approach for equipment qualification tests. The comparison of force response from integrated system level tests to measurements at the same locations during component level vibration tests provides an excellent illustration. A second comparison of the measured response cases from the system level acoustic tests to finite element simulations has also produced some principles for assessing the suitability of Finite Element Models (FEMs) for making vibroacoustics estimates. The results indicate that when FEM models are employed to guide force limiting choices, they should include sufficient detail to represent the apparent mass of the system in the frequency range of interest.

Smith, Andrew↗
NASA NTRS2017

Biomass Retrieval from L-Band Polarimetric UAVSAR Backscatter and PRISM Stereo Imagery

The forest above-ground biomass (AGB) and spatial distribution of vegetation elements have profound effects on the productivity and biodiversity of terrestrial ecosystems. In this paper, we evaluated biomass estimation from L-band Synthetic Aperture Radar (SAR) data acquired by National Aeronautics and Space Administration (NASA) Uninhabited Aerial Vehicle SAR (UAVSAR) and the improvement of accuracy by adding canopy height information derived from stereo imagery acquired by Japan Aerospace Exploration Agency (JAXA) Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) on-board the Advanced Land Observing Satellite (ALOS). Various models for prediction of forest biomass from UAVSAR data were investigated at pixel sizes of 1/4 ha (50 m x 50 m) and 1 ha. The variance inflation factor (VIF) was calculated for each of the explanatory variables in multivariable regression models to assess the multi-collinearity between explanatory variables. In addition, the t-and p-values were used to interpret the significance of the coefficients of each explanatory variables. The R(exp. 2), Root Mean Square Error (RMSE), bias and Akaike information criterion (AIC), and leave-one-out cross-validation (LOOCV) and bootstrapping were used to validate models. At 1/4-ha scale, the R(exp. 2) and RMSE of biomass estimation from a model using a single track of polarimetric UAVSAR data were 0.59 and 52.08 Mg/ha. With canopy height from PRISM as additional independent variable, R(exp. 2) increased to 0.76 and RMSE decreased to 39.74 Mg/ha (28.24%). At 1-ha scale, the RMSE of biomass estimation based on UAVSAR data of a single track was 39.42 Mg/ha with a R(exp. 2) of 0.77. With the canopy height from PRISM, R(exp. 2) increased to 0.86 and RMSE decreased to 29.47 Mg/ha (20.18%). The models using UAVSAR data alone underestimated biomass at levels above approximately 150 Mg/ha showing the saturation phenomenon. Adding canopy height from PRISM stereo imagery significantly improved the biomass estimation and elevated the saturation level in estimating biomass. Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation.Combined use of UAVSAR data acquired from opposite directions (odd and even tracks) slightly improved the biomass estimation at 1/4-ha scale, R(exp. 2) increased from 0.59 to 0.66 and RMSE reduced from 52.08 to 48.57 Mg/ha. Averaging multiple acquisitions of UAVSAR data from the same look azimuth direction did not improve biomass estimation. A biomass map derived from NASA's LVIS (Laser Vegetation Imaging System) wave-form data was used as a reference for evaluation of the biomass maps from these models. The study has also shown that the errors decreased when deciduous, evergreen, and mixed forests were modeled separately but the improvement was not significant

Zhang, Zhiyu↗
NASA NTRS2020

Metrics for Improved Reanalyses in Polar Regions

Atmospheric reanalyses are widely used for a variety of scientific endeavors in the Arctic and Antarctic. Reanalyses are used as boundary conditions for a regional and process-based models, for climate model validation, and for diagnostic analysis of physical processes, weather and climatic events. However, reanalyses are typically global and often do not account for specific, regional considerations, such as for polar regions. In this work, we provide a brief evaluation of a prototype for a new GMAO reanalysis, which incorporates higher spatial resolution, an updated approach for data assimilation, and a revised atmospheric model. We identify differences in the representation of the Arctic atmosphere in comparison to recent reanalyses. Furthermore, we provide a forum for Arctic scientists to consider the future improvements for reanalyses, and seek feedback for the following questions: 1) What are important performance factors to consider in evaluating new reanalyses? 2) What physical processes should be incorporated into new reanalyses? 3) What spatio-temporal scales should be considered?

Parker, Chelsea L.↗
NASA NTRS1998

Model-Based Design of Air Traffic Controller-Automation Interaction

A model of controller and automation activities was used to design the controller-automation interactions necessary to implement a new terminal area air traffic management concept. The model was then used to design a controller interface that provides the requisite information and functionality. Using data from a preliminary study, the Crew Activity Tracking System (CATS) was used to help validate the model as a computational tool for describing controller performance.

Romahn, Stephan↗
NASA NTRS2022

Global Landslide Forecasting System for Hazard Assessment and Situational Awareness

Landslides triggered by extreme rainfall can be devastating, resulting in loss of life, property, and infrastructure. Landslide forecasting systems provide an opportunity to build awareness of potential hazards and ultimately take preemptive measures. There is currently a dearth of forecasting systems that provide regional or global coverage, but these systems can offer important situational awareness in data-sparse, ungauged, or large-scale catchments. A near global, primarily satellite-based system calledthe Landslide Hazard Assessment for Situational Awareness (LHASA) provides near real-time estimates of potential landslide hazard and exposure around the world. In this work, a precipitation forecast module is introduced into LHASA to complement the existing LHASA framework and provide an estimate of landslide hazard up to three days in advanceat 1km resolution. The model-based Goddard Earth Observing System-Forward Processing (GEOS-FP) precipitation forecast product is used as the forcing input for the model in place of the satellite-based Integrated Multi-satellitE Retrievals for Global Precipitation Mission product. Soil moisture and snow depth from the GEOS-FP assimilated product are also incorporated. The study period January 2020–January 2021 is usedto test the model performance against the LHASA near real-time estimates at multiple spatiotemporal scales. Validation of the model is carried out using a collection of rainfall-triggered landslide inventories from around the world as case studies to demonstrate the potential utility and limitations of this system.The rescaling of the GEOS-FP precipitation product is a critical step in incorporating the forecasted precipitation data within LHASA-Forecast (LHASA-F). Combining different streams of forecasted data within the LHASA-F framework shows promise, particularly for larger events at the 1-and 2-day lead time for events. Results indicate that for the case studies evaluated, the LHASA-F is generally able to resolve major landslide events triggered by extreme rainfall, such as from tropical cyclones. The analysis shows that landslide forecast outputs may be represented differently depending on the user’s needs. This framework serves as a first milestone in providing a global predictive view of landslide hazard.

Landslide forecasting system↗
NASA NTRS2007

Differential Radiometers Using Fabry-Perot Interferometric Technique for Remote Sensing Determination of Various Atmospheric Trace Gases

New type of remote sensing instrument based upon the Fabry-Perot inte rferometric technique has been developed at NASA's Goddard Space Flight Center. Fabry-Perot interferometry (FPI) is a well known, powerful spectroscopic technique and one of its many applications is to be use d to measure greenhouse gases and also some harmful species in the at mosphere. With this technique, absorption of particular species is me asured and related to its concentration. A solid Fabry-Perot etalon is used as a frequency filter to restrict the measurement to particular absorption bands of the gas of interest. With adjusting the thicknes s of the etalon that separation (in frequency) of the transmitted fri nges can be made equal to the almost constant separation of the gas a bsorption lines. By adjusting the temperature of the etalon, which changes the index of refi-action of its material, the transmission fring es can be brought into nearly exact correspondence with absorption li nes of the particular species. With this alignment between absorption lines and fringes, changes in the amount of a species in the atmosph ere strongly affect the amount of light transmitted by the etalon and can be related to gas concentration. The instrument that we have dev eloped detects the absorption of various atmospheric trace gases in d irect or reflected sunlight. Our instrument employing Fabry-Perot interferometer makes use of two features to achieve high sensitivity. The first is high spectral resolution enabling one to match the width of an atmospheric absorption feature by the instrumental band pass. The second is high optical throughput enabled by using multiple spectral lines simultaneously. For any species that one wishes to measure, thi s first feature is available while the use of multiple spectral features can be employed only for species with suitable spectra and freedom from interfering species in the same wavelength region. We have deve loped an instrument for use as ground based, airborne and satellite s ensor for gases such as carbon dioxide (1570 nm), oxygen (762 nm and 768 nm lines sensitive to changes in oxygen pressure and oxygen temper ature) and water vapor (940 nm). Our current goal is to develop an ul tra precise, inexpensive, ground based device suitable for wide deplo yment as a validation instrument for the Orbiting Carbon Observatory (OCO) satellite. We show sensitivity measurements for CO2, 02, and H2 O, compare our measurements to those obtained using other types of sensors and discuss some of the peculiarities that must be addressed in order to provide the very high quality column detection required for solving problems about global distribution of greenhouse gases and cl imatological models. In another area of research we are interested in developing a small-size channel for CO2 capable of doing simultaneous measurements with the AERONET (Aerosol Robotic Network) at NASA, God dard to study the hypothesis that atmospheric aerosols affect the reg ional terrestrial carbon cycle. We present recent data from our groun d based measurements of O2, CO2, H2O and (13)CO2 and discuss extensio n of the technique to new species and applications.

Georgieva, E. M.↗
NASA NTRS1985

He II lambda 1640/lambda 4686 and Ly-alpha/H-beta ratios in the extraordinary Seyfert galaxy Markarian 359

The exceptionally narrow-lined Seyfert 1 galaxy Mrk 359 is ideally suited for determining reddening via the He II lambda 1640/lambda 4686 emission-line ratio. Here the validity of He II lines as a reddening indicator for quasars and Seyfert galaxies is reexamined and confirmed in the context of the most complete photoionization models available. In addition, lambda 1640 equivalent width measurements are shown to support previous contentions of substantial differences between observed and intrinsic quasar far-ultraviolet continua. Then IUE satellite and ground-based spectral data are presented, with the surprising result that both the lambda 1640/lambda 4686 and Ly-alpha/H-beta ratios do not differ greatly from case B recombination values in Mrk 359. This object is only slightly reddened, and it shows no evidence for extended, high-optical-depth H(+)/H(0) transition regions in the line-emitting gas. The very narrow lines and lack of deep transition regions and/or reddening may be related. In addition, Mrk 359 has low luminosity compared with most other Seyfert 1 galaxies, measured emission lines suggest an unusually high ionization parameter, and there is a hint of low nitrogen abundance. Further work is needed to examine possible relationships among all these peculiar properties.

Macalpine, G. M.↗
NASA NTRS1987

Remote sensing of earth terrain

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow covered fields is developed using the optimum polarimetric classifier. The covariance matrices for the various terrain cover are computed from theoretical models of random medium by evaluating the full polarimetric scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Theoretical probability of classification error using the full polarimetric matrix are compared with classification based on single features including the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements.

Kong, J. A.↗
NASA NTRS1988

Remote sensing of earth terrain

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow covered fields is developed using the optimum polarimetric classifier. The covariance matrices for the various terrain covers are computed from the theoretical models of random medium by evaluating the full polarimetric scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Theoretical probability of classification error using the full polarimetric matrix are compared with classification based on single features including the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements. A systematic approach is presented for obtaining the optimal polarimetric matched filter which produces maximum contrast between two scattering classes, each represented by its respective covariance matrix.

Kong, J. A.↗
NASA NTRS1990

NASA/Army rotor system flight research leading to the UH-60 airloads program

A review is presented of some of the early rotor systems flight research leading to the present comprehensive NASA/Army rotor system airloads program with the UH-60 helicopter. The experimental and analytical plans and progress for this program are described, including the design and development of a rotor blade which incorporated 242 pressure transducers buried in the surface of the blade, and also the development of calibration hardware for regular calibration and testing of the transducers. Supporting analytical developments based on the comprehensive analytical model of rotorcraft aerodynamics and dynamics (CAMRAD) and various CFD codes are discussed. The highly instrumented UH-60 as well as companion programs of full-scale and model wind tunnel tests of the UH-60 rotor with identical instrumentation will provide the opportunity to explore a full range of rotor experiments and the data necessary to validate the advanced methodologies under development.

Snyder, W. J.↗
NASA NTRS2001

Convective Systems Observed and Simulated During TRMM Field Campaigns

Recently completed TRMM field campaigns (TEFLUN1998, SCSMEX-1998, TRMM.LBA-1999, and KWAJEX 1999) have obtained direct measurements of microphysical data associated with convective systems from various geographical locations. These TRMM field experiments were designed to contribute to fundamental understanding of cloud dynamics and microphysics, as well as for validation,, testing assumptions and error estimates of cloud-resolving models, forward radiative transfer models, algorithms used to estimate rainfall statistics and vertical structure of precipitation and latent heating from both surface-based radar and satellites.

Tao, Wei-Kuo↗
NASA NTRS2010

A Comprehensive Validation Methodology for Sparse Experimental Data

A comprehensive program of verification and validation has been undertaken to assess the applicability of models to space radiation shielding applications and to track progress as models are developed over time. The models are placed under configuration control, and automated validation tests are used so that comparisons can readily be made as models are improved. Though direct comparisons between theoretical results and experimental data are desired for validation purposes, such comparisons are not always possible due to lack of data. In this work, two uncertainty metrics are introduced that are suitable for validating theoretical models against sparse experimental databases. The nuclear physics models, NUCFRG2 and QMSFRG, are compared to an experimental database consisting of over 3600 experimental cross sections to demonstrate the applicability of the metrics. A cumulative uncertainty metric is applied to the question of overall model accuracy, while a metric based on the median uncertainty is used to analyze the models from the perspective of model development by analyzing subsets of the model parameter space.

Norman, Ryan B.↗
NASA NTRS2011

Prognostics of Power MOSFET

This paper demonstrates how to apply prognostics to power MOSFETs (metal oxide field effect transistor). The methodology uses thermal cycling to age devices and Gaussian process regression to perform prognostics. The approach is validated with experiments on 100V power MOSFETs. The failure mechanism for the stress conditions is determined to be die-attachment degradation. Change in ON-state resistance is used as a precursor of failure due to its dependence on junction temperature. The experimental data is augmented with a finite element analysis simulation that is based on a two-transistor model. The simulation assists in the interpretation of the degradation phenomena and SOA (safe operation area) change.

Celaya, Jose Ramon↗
NASA NTRS2017

Online Multi-Modal Learning and Adaptive Information Trajectory Planning for Autonomous Exploration

In robotic information gathering missions, scientists are typically interested in understanding variables which require proxy measurements from specialized sensor suites to estimate. However, energy and time constraints limit how often these sensors can be used in a mission. Robots are also equipped with cheaper to use navigation sensors such as cameras. In this paper, we explore a challenging planning problem in which a robot is required to learn about a scientific variable of interest in an initially unknown environment by planning informative paths and deciding when and where to use its sensors. To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints. Our approach does not grow in complexity with data and is anytime making it highly applicable to field robotics. We tested our approach extensively in simulation and validated it with real data collected during the 2014 Mojave Volatiles Prospector Mission. Our planning algorithm performs statistically significantly better than myopic approaches and at least as well as a coverage-based algorithm in an initially unknown environment while having added advantages of being able to exploit prior knowledge and handle other intricacies of the real world without further algorithmic modifications.

learning↗
NASA NTRS2022

Optimal Estimation Framework for Ocean Color Atmospheric Correction and Pixel-level Uncertainty Quantification

Ocean color remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance ( R rs )(λ) ; sr 1 ) at the ocean surface from the top-of-atmosphere reflectance. Often, the uncertainty of this process is not fully explored. Bayesian inference techniques provide a simultaneous AC and uncertainty assessment via a full posterior distribution of the relevant variables, given the prior distribution of those variables and the radiative transfer (RT) likelihood function. Given uncertainties in the algorithm inputs, the Bayesian framework enables better constraints on the AC process by using the complete spectral information compared to traditional approaches that use only a subset of bands for AC. This paper investigates a Bayesian inference research method (Optimal Estimation, OE) for ocean color AC by simultaneously retrieving atmospheric and ocean properties using all visible and near-infrared spectral bands. The OE algorithm analytically approximates the posterior distribution of parameters based on normality assumptions and provides a potentially viable operational algorithm with a reduced computational expense. We developed a Neural Network (NN) RT forward model look-up-table-based emulator to increase algorithm efficiency further and thus speed up the likelihood computations. We then applied the OE algorithm to synthetic data and observations from the MODerate resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua spacecraft. We compared the R rs )(λ) retrieval and its uncertainty estimates from the OE method with in-situ validation data from the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and Aerosol Robotic Network Ocean Color (AERONET-OC) datasets. The OE algorithm improved R rs )(λ) estimates relative to the NASA standard operational algorithm by improving all statistical metrics at 443, 555, and 667 nm. Unphysical negative R rs )(λ) , which often appear in complex water conditions, was reduced by a factor of 3. The OE-derived pixel-level R rs )(λ) uncertainty estimates were also assessed relative to in-situ data and were shown to have skill.

Atmospheric correction↗
NASA NTRSDate not supplied

Development and Application of NASA SPoRT’s DustTracker-AI Model for Real-Time Identification and Tracking of Dust in Geostationary Satellite Imagery

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed the DustTracker-AI model for identifying and tracking dust in NASA/NOAA Geostationary Operational Environmental Satellite (GOES) imagery in a real-time framework. A training dataset consisting of day and night dust cases was gathered over the southwestern consisting of 115 distinct images and over a million dust pixels and 256 million no dust pixels. The dataset was separated into training (60%), testing (20%), and validation (20%). A simple random forest machine learning model was developed originally to overcome the problem of night-time dust detection and has been expanded to a comprehensive day/night model for dust identification and tracking. This physically-based machine-learning approach uses NASA/NOAA GOES-16 Advanced Baseline Imager infrared imagery as inputs to the model. The model probability of dust output achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels for all dust images in the validation data set. In conjunction with developing the machine-learning model, the NASA Short-term Prediction Research and Transition Center (SPoRT) partnered with NOAA National Weather Service forecast offices to evaluate the model for utility in weather forecasting operations during the 2021 and 2023 late winter-spring seasons. Preliminary evaluation has indicated the majority of forecasters described the DustTracker-AI probabilities as having added confidence to interpreting the Dust RGB and other satellite products to objectively assess the dust extent and trends and increased the amount of time the dust plume could be tracked into the night as compared to use of the Dust RGB. More recently, SPoRT tested small scale events associated with thunderstorm outflow and burn scars to determine the model’s ability to capture local events. This presentation highlights design of the model, validation/evaluation of model performance, and example cases collected during end user product assessments.

Connor H Welch↗