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At least 235 records · Page 13

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NASA NTRS2004

Flow of Fluid and Particle Assemblages in Rotating Systems

NASA-designed bioreactors have been highly successful in growing three-dimensional tissue structures in a low shear environment both on earth and in space. The goal of the present study is to characterize the fluid flow environment within the HFB-S bioreactor and determine the spatial distribution of particles that mimic cellular tissue structures. The results will be used to obtain optimal operating conditions of rotation rates and media perfusehnfuse rates which are required for cell culture growth protocols. Two types of experiments have been performed so far. First, we have performed laser florescent dye visualization of the perfusion loop to determine the mixing times within the chamber. The second type of experiments involved particles which represent cellular tissue to determine the spatial distribution with the chamber. From these experiments we established that mixing times were largely dependant on the speed ratio and sign of the difference between the spinner and the dome. The shortest mixing times occurred when the spinner rotates faster than the dome and longest mixing times occurs with no relative motion between the dome and spinner. Also, we have determined the spatial and temporal distribution of particle assemblages within the chamber.

Kizito, John↗

NASA NTRS2013

Future Extreme Heat Scenarios to Enable the Assessment of Climate Impacts on Public Health over the Coterminous U.S.

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km), to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wideranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S

Quattrochi, Dale A.↗

NASA NTRS2014

Linking Excessive Heat with Daily Heat-Related Mortality over the Coterminous United States

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km) much finer than that of GCMs, to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices, and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.

Quattrochi, Dale A.↗

NASA NTRS1994

Spatial Attention Enhances Perceptual Processing of Single-Element Displays

Shiu and Pashler (1993) reported that precueing masked, single-element displays had negligible effects on identification accuracy. They argued that spatial attention does not actually enhance stimulus perceptibility, but only reduces decision noise. Alternatively, such negative results may arise if cues are sub-optimal, or if masks place an insufficient premium on timely deployment of attention. We report results showing that valid cueing enhances processing of even single-element displays. Spatial attention does indeed enhance perceptual processes.

Bacon, William↗

NASA NTRS1972

A symmetrical laser Doppler velocity meter and its application to turbulence characterization

A symmetrical method of optical heterodyning of the Doppler shifted scattered laser radiation developed for velocity measurements with a minimal instrumental spectral broadening and a high signal-to-noise ratio. The method employs two laser beams incident on the moving scatterer and does not use any reference beam for heterodyning. The Doppler signal frequency is independent of the scattering angle and the signal possesses no receiving aperture broadening. Optical alignment is simple. Typical values of the instrumental spectral broadening were approximately 0.8 percent of the center frequency of the Doppler signal, and the signal-to-noise ratio was approximately 25 dB, obtained from an air flow system using submicron dioctylphthalate scattering aerosol. Experimental and theoretical studies were made on the characteristics of the Doppler signal and the effect of system parameters in turbulent flow measurement. The optimization process involved in the beam optics and in the use of a spatial filter is described. For localized flow measurement in any direction of the three-dimensional orthogonal coordinates, the system, using uncorrected optical components, had a sensing volume which can be described by a sensitive length of 600 microns and a diameter of 100 microns.

Mazumder, M. K.↗

NASA NTRS1973

Phase 1 of the earth resources data analysis program

Research completed in the Earth Resources Data Analysis Program is discussed along with recommendations for future study. Projects discussed include use of the Cholesky decomposition in feature selection and classification algorithms; optimal feature selection and extraction, probability density estimation and nonparametric classifiers; use of spatial information in classification; and model for crop row reflectance. The installation of LARSYS on the ICSA's IBM 370/155 is discussed, and a list of technical reports is included.

Source record↗

NASA NTRS1983

Autoregressive Models for Use in Scene Segmentation

A scene segmentation approach is presented which is based on generating autoregressive field models for each scene component (class) from its a priori spatial statistics. A methodology is also described for using these models in achieving optimal segmentation of a scene. The derivations are presented for the case of single band imagery, however, the method is believed to be extendable to multispectral data.

Naraghi, M.↗

NASA NTRS1988

A numerical study of mixing enhancement in supersonic reacting flow fields

NASA Langley has intensively investigated the components of ramjet and scramjet systems for endoatmospheric, airbreathing hypersonic propulsion; attention is presently given to the optimization of scramjet combustor fuel-air mixing and reaction characteristics. A supersonic, spatially developing and reacting mixing layer has been found to serve as an excellent physical model for the mixing and reaction process. Attention is presently given to techniques that have been applied to the enhancement of the mixing processes and the overall combustion efficiency of the mixing layer. A fuel injector configuration has been computationally designed which significantly increases mixing and reaction rates.

Drummond, J. Philip↗

NASA NTRS1990

Tailoring explicit time-marching schemes to improve convergence characteristics

Multi-stage time-stepping schemes, tailored to chosen spatial-differencing operators, are derived and tested. The schemes are constructed to give optimal damping of the high-frequency waves. They are ideal for use with multi-grid acceleration. The concept of characteristic time-stepping, necessary for the extension of the scalar analysis to systems of equations, is presented. The schemes show a marked improvement over Runge-Kutta schemes.

Powell, Kenneth G.↗

NASA NTRS1991

Design of optimal binary phase and amplitude filters for maximization of correlation peak sharpness

Current binary-phase filters used for optical correlation are usually assumed to have uniform amplitude transmission. Here, a new type of filter is studied, the binary-phase-and-amplitude filter. If binary phase values of 0 and pi are assumed, the amplitude transmittance values of this type of filter can be optimized to maximize the peak sharpness. For a polarization-encoded binary-phase filter this can be translated into optimization of the rotation angle of the output polarizer following the filter-spatial-light modulator. An analytic expression is presented for the optimum polarizer angle and thus for the optimum binary-phase-and-amplitude filter design.

Downie, John D.↗

NASA NTRS2009

Sensorimotor Adaptations Following Exposure to Ambiguous Inertial Motion Cues

The central nervous system must resolve the ambiguity of inertial motion sensory cues in order to derive accurate spatial orientation awareness. We hypothesize that multi-sensory integration will be adaptively optimized in altered gravity environments based on the dynamics of other sensory information available, with greater changes in otolith-mediated responses in the mid-frequency range where there is a crossover of tilt and translation responses. The primary goals of this ground-based research investigation are to explore physiological mechanisms and operational implications of tilt-translation disturbances during and following re-entry, and to evaluate a tactile prosthesis as a countermeasure for improving control of whole-body orientation.

Wood, S. J.↗

NASA NTRS2012

Physics Mining of Multi-Source Data Sets

Powerful new parallel data mining algorithms can produce diagnostic and prognostic numerical models and analyses from observational data. These techniques yield higher-resolution measures than ever before of environmental parameters by fusing synoptic imagery and time-series measurements. These techniques are general and relevant to observational data, including raster, vector, and scalar, and can be applied in all Earth- and environmental science domains. Because they can be highly automated and are parallel, they scale to large spatial domains and are well suited to change and gap detection. This makes it possible to analyze spatial and temporal gaps in information, and facilitates within-mission replanning to optimize the allocation of observational resources. The basis of the innovation is the extension of a recently developed set of algorithms packaged into MineTool to multi-variate time-series data. MineTool is unique in that it automates the various steps of the data mining process, thus making it amenable to autonomous analysis of large data sets. Unlike techniques such as Artificial Neural Nets, which yield a blackbox solution, MineTool's outcome is always an analytical model in parametric form that expresses the output in terms of the input variables. This has the advantage that the derived equation can then be used to gain insight into the physical relevance and relative importance of the parameters and coefficients in the model. This is referred to as physics-mining of data. The capabilities of MineTool are extended to include both supervised and unsupervised algorithms, handle multi-type data sets, and parallelize it.

Helly, John↗

NASA NTRS2013

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Temporal Queries↗

NASA NTRS2015

Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

water cycle extreme events↗

NASA NTRS2019

Using Computational Flow Imaging to Optimize Filtered Rayleigh Scattering Measurements of an Isolator Shock Train

Filtered Rayleigh scattering (FRS) is a laser diagnostic where the intensity of elastically scattered light is measured after it passes through a molecular absorption filter. The filter removes background interference that overwhelms the relatively weak scattering from the gas molecules. However, with a filter, the measured light intensity depends on many of the scattering gas properties, including pressure, density, temperature, and velocity. In this work, CFD simulations of an isolator shock train flow field are input into a physics-based model to predict the values of FRS intensity measurements in a proposed experiment. The goal is to evaluate if a simplistic FRS setup (that utilizes one camera, laser, and absorption filter) can be used to accurately quantify number density despite the fact that the scattered light intensity also depends on other gas properties. It is found that the experimental setup can be optimized such that a linear relationship describes the number density with an average prediction error of 2%. The vast majority of the flow exhibits prediction errors of less than 3%, but small regions of the flow reach up to 11% error. A sensitivity analysis shows that the prediction error increases with the central wavelength of the incident laser light and decreases with the angle between the camera and laser propagation directions. The optimal experimental parameters are chosen based on a compromise between the prediction error, spatial resolution, and the amount of unfiltered light. In the future, the proposed FRS setup will be implemented to acquire new and valuable information on an isolator shock train, a flow field that has been traditionally studied using wall static pressure measurements and path-integrated visualization techniques, such as schlieren and shadowgraphy.

Hunt, Robin L.↗

NASA NTRSDate not supplied

The WFIRST CGI Integral Field Spectrograph: Requirements and Performance Predictions

The WFIRST coronagraphic instrument (CGI) will demonstrate exoplanet spectroscopy using an integral field spectrograph (IFS). The CGI IFS, being designed and built at Goddard Space Flight Center, has a spectral resolution of R50 and is designed to accommodate a 20% bandpass spanning 600- 970 nm. The IFS is principally targeting the abundance of Methane features, with the primary coronagraph band being centered around 770nm. Key to the performance estimates are the achievable signal-to-noise (SNR) ratios and the stability of the microspectra over the course of tens and hundreds of hours. As a technology demonstration for CGI, the ability to close a wavefront control loop around the IFS, maintain a stable dark hole, and provide time resolved data that simultaneously spans spatial and spectral dimensions are crucial demonstrations for future observatories. The IFS is optimized both for coronagraphs and science observations with a potential future starshade. We highlight how the long duration observations, and requirements for both starshades and coronagraphs drive the IFS requirements and the calibrations required both onorbit and on the ground. We also provide further detail on the optomechanical design, its stability based on thermal and structural predictions, anticipated performance, and operations concept of the CGI IFS. The impact of these performance metrics are projected into simulated data products, demonstrating cube extraction of noisy images and the subsequent planet spectrum that can be extracted from them. These demonstrations and performance predictions are key to future missions such as LUVOIR and HabEx, whose principal science case relies on efficient spectroscopy of exoplanets.

Tyler Groff↗

NASA NTRS2023

Optimizing the Resolution of Hydrodynamic Simulations for MCRaT Radiative Transfer Calculations

Despite their discovery about half a century ago, the Gamma-ray burst (GRB) prompt emission mechanism is still not well understood. Theoretical modeling of the prompt emission has advanced considerably due to new computational tools and techniques. One such tool is the PLUTO hydro dynamics code, which is used to numerically simulate GRB outflows. PLUTO uses Adaptive Mesh Refinement to focus computational efforts on the portion of the grid that contains the simulated jet. Another tool is the Monte Carlo Radiation Transfer (MCRaT) code, which predicts electromagnetic signatures of GRBs by conducting photon scatterings within a jet using PLUTO. The effects of the underlying resolution of a PLUTO simulation with respect to MCRaT post-processing radiative transfer results have not yet been quantified. We analyze an analytic spherical outflow and a hydrodynamically simulated GRB jet with MCRaT at varying spatial and temporal resolutions and quantify how decreasing both resolutions affect the resulting mock observations. We find that changing the spatial resolution changes the hydrodynamic properties of the jet, which directly affect the MCRaT mock observable peak energies. We also find that decreasing the temporal resolution artificially decreases the high energy slope of the mock observed spectrum, which increases both the spectral peak energy and the luminosity. We show that the effects are additive when both spatial and temporal resolutions are modified. Our results allow us to understand how decreased hydrodynamic temporal and spatial resolutions affect the results of post-processing radiative transfer calculations, allowing for the optimization of hydrodynamic simulations for radiative transfer codes.

Jose Arita-Escalante↗

NASA NTRS1995

Reduction, analysis, and properties of electric current systems in solar active regions

The specific attraction and, in large part, the significance of solar magnetograms lie in the fact that they give the most important data on the electric currents and the nonpotentiality of active regions. Using the vector magnetograms from the Marshall Space Flight Center (MSFC), we employ a unique technique in the area of data analysis for resolving the 180 deg ambiguity in order to calculate the spatial structure of the vertical electric current density. The 180 deg ambiguity is resolved by applying concepts from the nonlinear multivariable optimization theory. The technique is shown to be of particular importance in very nonpotential active regions. The characterization of the vertical electric current density for a set of vector magnetograms using this method then gives the spatial scale, locations, and magnitude of these current systems. The method, which employs an intermediate parametric function which covers the magnetogram and which defines the local `preferred' direction, minimizes a specific functional of the observed transverse magnetic field. The specific functional that is successful is the integral of the square of the vertical current density. We find that the vertical electric current densities have common characteristics for the extended bipolar (beta) (gamma) (delta)-regions studied. The largest current systems have j(sub z)'s which maximizes around 30 mA/sq m and have a linear decreasing distribution to a diameter of 30 Mn.

Gary, G. Allen↗