Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model Counting”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Statistical Models of Areal Distribution of Fragmented Land Cover Types

Imagery of coarse resolution, such weather satellite imagery with 1 square kilometer pixels, is increasingly used to monitor dynamic and fragmented types of land surface types, such as scars from recent fires and ponds in wetlands. Accurate estimates of these land cover types at regional to global scales are required to assess the roles of fires and wetlands in global warming, yet difficult to compute when much of the area is accounted for by fragments about the same size as the pixels. In previous research, we found that size distribution of the fragments in several example scenes fit simple two-parameter models and related effects of coarse resolution to errors in area estimates based on pixel counts. We summarize our model based approach to improved area estimations and report on progress to develop accurate areas estimates based on modeling the size distribution of the fragments, including analysis of size distributions on an expanded set of maps developed from digital imagery.

Hlavka, C.↗
NASA NTRS2008

The Effect of a Potentially Low Solar Cycle #24 on Orbital Lifetimes of Fengyun 1-C Debris

The magnitude of Solar Cycle #24 will have a non-trivial impact on the lifetimes of debris pieces that resulted from the intentional hypervelocity impact of the Fengyun 1-C satellite in January 2007. Recent solar flux measurements indicate Solar Cycle #24 has begun in the last few months, and will continue until approximately 2019. While there have been differing opinions on whether the intensity of this solar cycle will be higher or lower than usual, the Space Weather Prediction Center within the National Oceanic Atmospheric Administration (NOAA/SWPC) has recently forecast unusually low solar activity, which would result in longer orbital lifetimes. Using models for both the breakup of Fengyun 1-C and the propagation of the resultant debris cloud, the Orbital Debris Program Office at NASA Johnson Space Center conducted a study to better understand the impact of the solar cycle on lifetimes for pieces as small as 1 mm. Using a modified collision breakup model and PROP3D propagation software, the orbits of nearly 2 million objects 1 mm and larger were propagated for up to 200 years. By comparing a normal solar cycle with that of the NOAA/SWPC forecast low cycle, the effect of the solar flux on the lifetimes of the debris pieces is evaluated. The modeling of the low solar cycle shows an additional debris count of 12% for pieces larger than 10 cm by 2019 when compared to the resultant debris count using a normal cycle. The difference becomes more exaggerated (over 15%) for debris count in the smaller size regimes. However, in 50 years, the models predict the differences in debris count from differing models of Solar Cycle #24 to be less than 10% for all size regimes, with less variance in the smaller sizes. Understanding the longevity of the debris cloud will affect collision probabilities for both operational spacecraft and large derelict objects over the next century and beyond.

Whitlock, David↗
NASA NTRS1999

XTE Observations of PSR 1259-63 and a Test of Spin Orbit Coupling in the 4U0115+63 System

During this report period, Mallory Roberts went to GSFC to analyze the data from two minor outbursts, which occurred from 4UO115+63. Unfortunately, the outbursts were not of sufficient duration to do a unique orbital determination (which was the scientific goal of the experiment). As this report is being written, 4UO115+63 is undergoing its first major outburst in four years. We are planning on adding our RXTE PCA data to any public ASM or PCA data that is obtained through the duration of this outburst, and combining it with our BATSE data from 1994 and 1995 outbursts in order to learn something about the orbital evolution in this system. We have formed a collaboration with colleagues at MIT who are working on the ASM data for this outburst. Thus, work on the original data will continue, with no further funding, and we are hopeful that some important questions with regard to the orbital timing will finally be resolved. The PSR 1259-63 data were originally analyzed by Barry Giles, who reported that no pulsations or flux were seen from this source near apastron. Recently, a new background model for low-count rate sources has been developed for the PCA. We intend to use this new background model to reanalyze these data to see if we can improve the upper limit to the flux. This work will also continue with no further funding.

Cominsky, Lynn R.↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRSDate not supplied

Impact Real World System Validation

Introduction NASA has developed a new evidence-based data-driven probabilistic risk assessment and tradespace analysis tool as a successor to the Integrated Medical Model. This updated decision support tool is known as IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). IMPACT estimates the frequency and consequences of medical conditions that might arise during exploration missions. A validation analysis of IMPACT was performed with respect to a set of International Space Station (ISS) and Shuttle Transportation System (STS) real world system (RWS) referent data due to the limited referent data available from exploration missions. Methods Observed mission and crew characteristics from STS and ISS missions were used as model inputs within MEDPRAT (Medical Extensible Dynamic Probabilistic Risk Assessment Tool). For each mission, two hundred thousand simulations were generated. For each mission, model outputs included occurrence counts for each condition, total medical events (TME), and the probability of loss of crew life (LOCL). These simulated model outputs were compared to the RWS referent data. Results The predicted number of total medical events exceeded the total RWS medical events for ISS missions and combined ISS and STS missions and fell within the 90% confidence interval for STS missions. For the 32 ISS missions simulated by IMPACT, the number of total medical events was overpredicted for 19 missions and fell within the 90% confidence interval for 13 missions. For the 21 STS missions, the total number of medical events was overpredicted for 3 missions, fell within the 90% confidence interval for 16 missions, and was underpredicted for 2 missions. Combined, 29 missions were in range, 22 were overpredicted, and 2 were underpredicted. The predicted LOCL probability for the 32 ISS missions, the 21 STS missions, and the combined ISS and STS missions was consistent with the zero LOCL events observed in the RWS referent data. The validation analysis included a comparison of the number of medical events predicted by IMPACT and the number of medical events observed in the RWS data on a condition-by-condition basis. For ISS missions, 50 conditions were in range, 52 conditions were statistically underpowered (not enough observed sample to draw any conclusions on precision), 8 conditions were overpredicted, and 9 conditions were underpredicted. Overall, only 14% (17/119) of conditions were out of range for STS missions, 40 conditions were in range, 59 conditions were statistically underpowered, 10 conditions were overpredicted, and 10 conditions were underpredicted. Overall, only 17% (20/119) of conditions were out of range. For combined ISS and STS missions, 11 conditions were overpredicted, and 11 conditions were underpredicted. Overall, only 18% (22/119) of conditions were out of range. For combined ISS and STS missions, 49 conditions were in range, 46 conditions were statistically underpowered, 18 conditions were overpredicted, and 8 conditions were underpredicted. Overall, 21% (26/121) of conditions were out of range. Conclusion The results of this validation analysis should not be interpreted as a pass/fail test of the validity of IMPACT. Instead, this validation analysis should be used to assess some of the IMPACT outcomes in terms of consistencies and inconsistencies with the ISS and STS RWS referent data.

L. Boley↗
NASA NTRS1997

Contemporaneous IUE, EUVE, and High-Energy Observations of 3C 273

We present the results of our 1994 January and 1995 January observations of the quasar 3C 273 obtained with the International Ultraviolet Explorer (IUE) and the Extreme-Ultraviolet Explorer (EUVE). These observations were part of a large multiwavelength campaign to observe 3C 273 from radio through gamma-rays. Our 1995 January photometric observations with the EUVE Lexan/B Deep Survey (DS) instrument indicate strong evidence for variability, at a 99% confidence level, during the 12 day observing period. We have utilized ROSAT PSPC soft X-ray power-law models to correlate with EUVE count rates. Besides variations in the normalization level between both observations, our EUV count rates are consistent with a simple power-law model with spectral index alpha approx. 1.77 (F(sub upsilon) proportional to upsilon(sup -alpha) that can be extrapolated from the soft X-rays to the EUV range. The active galactic nucleus 3C 273 is an important blazar to study because in our picture it reveals the presence of both disk and relativistic beam spectral contributions.

Ramos, E.↗
NASA NTRS1993

Star counts from the Hubble Space Telescope Snapshot Survey. I - Galactic models

We report a photometric study of stars from 450 fields at high Galactic latitudes that were observed in the Hubble Space Telescope Snapshot Survey to an average limiting apparent magnitude of V = 21.4. There are 166 fields that contain quasars selected in radio, X-ray, and color-excess surveys. This sample of 273 stars is free of selection bias with respect to the density of stars. To within the Poisson errors, the total counts and magnitude distribution of this unbiased sample are in agreement with the Bahcall-Soneira model (Bahcall, 1986). The angular distribution of the faint stars favors, at the 2 sigma level, a somewhat steeper disk luminosity function and a smaller spheroid main-sequence normalization than given by the model. The sample does not have enough statistical power to distinguish between the two-component Bahcall-Soneira model and the three-component model first proposed by Gilmore and Reid (1983), which contains a thick disk. The statistical power of the survey would increase about 15-fold if colors were obtained for the stars: the data probe the main sequences of the disk, thick disk, and spheroid. Models with and without a thick disk could then be distinguished at the 6 sigma level. The HST Snapshot Survey includes an additional 284 fields, 279 of which are centered on quasars that were selected by objective-prism surveys. These 279 fields are expected to show and do exhibit bias against bright stars, making them unsuitable for testing Galactic models.

Gould, A.↗
NASA NTRS2010

The Herschel-ATLAS: Extragalatic Number Counts from 250 to 500 Microns

Aims.The Herschel-ATLAS survey (H-ATLAS) will be the largest area survey to be undertaken by the Herschel Space Observatory. It will cover 550 sq. deg. of extragalactic sky at wavelengths of 100, 160, 250, 350 and 500 microns when completed, reaching flux limits (50-) from 32 to 145mJy. We here present galaxy number counts obtained for SPIRE observations of the first -14 sq. deg. observed at 250, 350 and 500 m. Methods. Number counts are a fundamental tool in constraining models of galaxy evolution. We use source catalogs extracted from the H-ATLAS maps as the basis for such an analysis. Correction factors for completeness and flux boosting are derived by applying our extraction method to model catalogs and then applied to the raw observational counts. Results. We find a steep rise in the number counts at flux levels of 100-200mJy in all three SPIRE bands, consistent with results from BLAST. The counts are compared to a range of galaxy evolution models. None of the current models is an ideal fit to the data but all ascribe the steep rise to a population of luminous, rapidly evolving dusty galaxies at moderate to high redshift.

Clements, D. L.↗
NASA NTRS1979

Population statistics of faint stellar and non-stellar objects

A disc and halo population model is constructed to fit star counts and color data down to V approximately 23 at absolute value of b = 90 deg. This model is used to predict star counts and colors down to V approximately 30. Deviations from these extrapolated relationships provide constraints on the number of faint quasars and black dwarf stars. It is shown that extra-galactic globular clusters start contributing significantly to star counts at V approximately 25 and are more numerous than stars for V 31. Morphological studies of galaxies with approximately 0.5, were made with the space telescope. Significant constraints on theoretical models that describe the evolution of clusters of galaxies are provided.

Vandenbergh, S.↗
NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗
NASA NTRS1994

Statistical association of QSO's with foreground galaxy clusters

We report a statistically significant overdensity of high redshift quasi-stellar objects (QSO's) in the directions of foreground galaxy clusters. QSO's are taken from the Large Bright QSO Survey (LBQS) between 1.4 less than or equal z less than or equal 2.2 with a limiting magnitude of m(sub B) = 18.5. Foreground clusters are regions within 6 Zwicky radii of small Zwicky clusters at a characteristic redshift of about z approximately = 0.2, covering about 40% of the total area surveyed (304 sq. deg). The overdensity, defined as the ratio of the number density of QSO's in the directions of clusters ('association QSO's) to that in the remainder of the fields ('background QSO's), is equal to 1.7, and formally differs from unity at 4.7 sigma significance. The observed overdensity probably is not due to statistical variation in QSO density, intrinsic QSO-QSO and/or cluster-cluster autocorrelations, or patchy Galactic obscuration. We thus interpret this observation as being due to statistical gravitational lensing of background QSO's by galaxy clusters. However, this amplitude of overdensity behind clusters cannot be accounted for in any cluster lensing model if the background QSO number-magnitude counts are similar to the intrinsic (unlensed) counts, and is implausible in any conventional model of cosmic mass distribution.

Rodrigues-Williams, Liliya L.↗
NASA NTRS2001

Computation of Flow Over a Drag Prediction Workshop Wing/Body Transport Configuration Using CFL3D

A Drag Prediction Workshop was held in conjunction with the 19th AIAA Applied Aerodynamics Conference in June 2001. The purpose of the workshop was to assess the prediction of drag by computational methods for a wing/body configuration (DLR-F4) representative of subsonic transport aircraft. This report details computed results submitted to this workshop using the Reynolds-averaged Navier-Stokes code CFL3D. Two supplied grids were used: a point-matched 1-to-1 multi-block grid, and an overset multi-block grid. The 1-to-1 grid, generally of much poorer quality and with less streamwise resolution than the overset grid, is found to be too coarse to adequately resolve the surface pressures. However, the global forces and moments are nonetheless similar to those computed using the overset grid. The effect of three different turbulence models is assessed using the 1-to-1 grid. Surface pressures are very similar overall, and the drag variation due to turbulence model is 18 drag counts. Most of this drag variation is in the friction component, and is attributed in part to insufficient grid resolution of the 1-to-1 grid. The misnomer of 'fully turbulent' computations is discussed; comparisons are made using different transition locations and their effects on the global forces and moments are quantified. Finally, the effect of two different versions of a widely used one-equation turbulence model is explored.

Rumsey, Christopher L.↗
NASA NTRS1991

Galaxy formation and evolution

The presence of high-z quasars and radio galaxies indicates that galaxy formation began at z greater than 5, but leaves unanswered the question of when the bulk of galaxies formed. Recent near-infrared number counts of galaxies strongly favor a cosmological geometry with q0 = 0.5 and Lambda = 0. Such a model grossly underpredicts blue galaxy counts. Spectroscopy shows that the excess blue galaxies at B = 24 are dwarfs at z = 0.4, which are no longer seen at the present time. These dwarfs must contain a large amount of baryonic matter which is not included in current estimates of baryonic Omega.

Cowie, Lennox L.↗
NASA NTRS1991

Galaxy formation and evolution

The presence of high z quasars and radio galaxies tells us that galaxy formation began at z greater than 5, but leaves unanswered the question of when the bulk of galaxies formed. Recent near infrared number counts of galaxies strongly favor a cosmological geometry with q(sub 0) = 0.5 and lambda = 0. Such a model grossly underpredicts blue galaxy counts. Spectroscopy shows that the excess blue galaxies at B = 24 are dwarfs at z approximately equals 0.4 which are no longer seen at the present time. These dwarfs must contain a large amount of baryonic matter which is not included in current estimates of baryonic omega .

Cowie, Lennox L.↗
NASA NTRS2008

Solar Wind Change Exchange from the Magnetosheath

We report the results of a long (approximately 100 ks) XMM-Newton observation designed to observe solar wind charge exchange emission (SWCX) from Earth's magnetosheath. By luck, the observation took place during a period of minimal solar wind flux so the SWCX emission was also minimal. Never-the-less, there is a significant if not stunning correlation between the observed O VIII count rate and our model for magnetosheath emission. We also report on the observed O VII and O VII emission.

Snowden, Steve↗