SEARCH · Engineering Papers

Results for “statistical”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,297 records · Page 72

All sources NASA NTRS DOE OSTI

NASA NTRS2019

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

In order to ensure a sustainable use of low earth orbit in particular and near Earth space in general, reliable and effective close approach prediction be-tween space objects is key. Only this allows for efficient and timely colli-sion avoidance. Space Situational Awareness (SSA) for commercial and government missions will be facing the rapidly growing amount of small and potentially less agile satellites as well as debris in the near earth realm, such as the increase in CubeSat launches and upcoming large constellations. At the same time, space object detection capabilities are expected to increase significantly, allowing for the reliable detection of smaller objects, e.g. when the Air Force Space Fence radar becomes operational. In combination, the space object catalog is expected to increase tremendously in size. In this paper, we introduce an investigative approach based on the latest capabili-ties in artificial intelligence in fostering the potential for fast and accurate close approach predictions. We consider the study of statistical and infor-mation theory parameters in contrast and complementary to the classical probability of collision computation alone, in order to determine the feasi-bility of reliably predicting close approaches.

Mashiku, A.↗

NASA NTRS2019

Predicting Satellite Close Approaches Using Statistical Parameters in the Context of Artificial Intelligence

Mashiku, Alinda↗

NASA NTRS2019

Statistical Analysis of Aquarius Radiometer Radio Frequency Interference (RFI) and Implications on RFI Detection and Mitigation

The Aquarius/SAC-D mission operated between August 2011 and June 2015 with the main goal of providing global estimates of sea surface salinity (SSS). It comprised both active and passive microwave sensors operating at L-band to observe the same surface area almost simultaneously. Measurements from both instruments underwent subsequent filtering to mitigate the effect of Radio Frequency Interference (RFI). This report describes the analysis of statistics of RFI in samples acquired by the Aquarius radiometers, and its results could be used to improve the performance of the interference detection algorithm.

De Matthaeis, Paolo↗

NASA NTRS2019

Statistical Modeling of Quiet Sonic Boom Community Response Survey Data

The existing ban on commercial supersonic flight overland is largely due to the effects of loud and startling sonic booms on communities. NASA is planning a nationwide campaign of community response surveys using the experimental X-59 Quiet SuperSonic Technology (X-59 QueSST) aircraft to understand how communities perceive the sounds of quiet supersonic flight. The X-59 community response survey data will be presented to noise regulators, who are considering replacing the ban with a noise-based certification limit so quiet supersonic vehicles can fly over land. In this document, we use pilot community response survey data to explore and assess multiple approaches to statistically model the dose-response relationship between single-event sonic boom sound exposure and human annoyance. The models have two primary functions—estimating two types of quantities that support setting regulations and experimental design of future surveys.

Jasme Lee↗

NASA NTRS2019

Statistical Study on Multispacecraft Widespread Solar Energetic Particle Events During Solar Cycle 24

We conduct a statistical study on the large three-spacecraft widespread solar energetic particle (SEP) events. Longitudinal distributions of the peak intensities, onset delays, and relation between the SEP intensity, coronal mass ejection (CME) shock speed, width, and the kinetic energy of the CME have been investigated. We apply a Gaussian fit to obtain the SEP intensity Io and distribution width and a forward-modeling fit to determine the true shock speed and true CME width. We found a good correction between and connection angel to the flare site and Io and the kinetic energy of the CME. By including the true chock speed and true CME widths, we reduce root-mean-square errors on the predicted SEP intensity by ~41% for protons compared to Richards et al.'s (2014, https://doi.org/10.1007/s11207-014-0524-8) prediction. The improved correction between the CME kinetic energy and SEP intensity provides strong evidence for the CME-shock acceleration theory of SEPs. In addition, we found that electron and proton release time delays (DTs) relative to Type II radio bursts increase with connection angles. The average electron (proton) DT is ~14 (32) min for strongly anisotropic events and ~2.5 (4.4) hr for weakly anisotropic events. Poor magnetic connectivity and large scattering effects are two main reasons to cause large delays.

Xie, H.↗

NASA NTRS2020

A Statistical Model to Predict the Extratropical Transition of Tropical Cyclones

This paper introduces a logistic regression model for the extratropical transition(ET) of tropical cyclones in the North Atlantic and the Western North Pacific, using elastic net regularization to select predictors and estimate coefficients.Predictors are chosen from the 1979-2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to two days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the Western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic, and 92% of those in the Western North Pacific. 90% of transition time errors are less than 24 h. Select examples of the model's performance on individual storms illustrate its strengths and weaknesses.Two versions of the model are presented: an "operational model" that may provide baseline guidance for operational forecasts, and a "hazard model"that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models' zero lead time predictions perform about as well as the widely used Cyclone Phase Space (CPS) in the Western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.

Melanie Bieli↗

NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term. The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗

NASA NTRSDate not supplied

A Statistical Analysis of Impact Ice Adhesion Strength Data Acquired with a Modified Lap Joint Test

Numerous methodologies have been utilized to measure the adhesion strength of impact ice, and the data reported in the literature varies significantly from method to method. In order to initiate an investigation to determine the cause of this disparity, a lap-joint shear test methodology that was recently developed was utilized in the Icing Research Tunnel at the NASA Glenn Research Center. Data was obtained while varying the temperature, test section velocity, liquid water content, and cloud droplet median volumetric diameter, among other parameters, over five campaigns. A new data set acquired using this new test method is presented. The results are analyzed with standard statistical methodologies and demonstrate a strong correlation between the apparent adhesion strength of ice and temperature, annealing time, and heating time. Observations during the test and analysis of the results suggest the presence of large residual stresses in the samples, which is in agreement with prior work. Due to the nature of the test methodology, and all known ice adhesion test methodologies, the measured, or apparent, adhesion strength is geometry dependent, a fact emphasized by the results presented here. The data was organized both cumulatively and independently by result code. Trends in the data are discussed in the context of causal physical mechanisms. The data shows the apparent adhesion strength was not linear with temperature, liquid water content, annealing time, and other variables. Short-term heating of the samples was shown to have negligible effect, and the apparent adhesion strength had a flat trend with velocity.

Ice Adhesion↗

NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗

NASA NTRS1995

Statistical Performance of TOPEX/POSEIDON Prime Mission Ground and On-Board Ephemerides and Consequences for the Extended Mission

The current statistical performances are shown for the TOPEX/POSEIDON operational orbit ephemeris (OOE) and ephemeris load for the onboard computer. Consequences of this performance for the extended mission are also discussed.

ephemerides↗

NASA NTRS2018

Improved Pyrotechnic Shock Scaling Method Based on Shock Response Spectrum and Statistical Energy Analysis

It has been more than forty years since the pyrotechnic shock scaling method was introduced. The scaling method estimates the attenuation of the Shock Response Spectrum (SRS) based on the distance from the source, structural configurations, types of structural joints and interfaces, and intervening structure [1]. The method has been successfully used in the spacecraft community countless times and still is frequently used to develop pyrotechnic shock requirements at various levels of assembly of a spacecraft. However, since the method was derived empirically from a limited set of shock test data [2], the aerospace community has been looking for an alternative approach. In this paper, a computational scaling method based on Statistical Energy Analysis (SEA) is re-visited. SEA is traditionally used as a method for investigating the diffusion of acoustic and vibratory energies of a system at steady-state [3]. Previously, an approach using the SRS as an acceleration constraint condition to the SEA model was introduced to estimate the attenuation from a shock source [4,5]. In the current investigation, additional examples are provided to further validate the approach. In addition, responses in the time domain are produced by the Local Modal Phase Reconstruction (LMPR) approach which was recently introduced to the community [6].

Fernandez, Juan P.↗

NASA NTRS2020

The NANOGrav 11 yr data set: Evolution of Gravitational-wave Background Statistics

An ensemble of inspiraling supermassive black hole binaries should produce a stochastic background of very low frequency gravitational waves. This stochastic background is predicted to be a power law, with a gravitational-wave strain spectral index of −2/3, and it should be detectable by a network of precisely timed millisecond pulsars, widely distributed on the sky. This paper reports a new “time slicing” analysis of the 11 yr data release from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) using 34 millisecond pulsars. Methods to flag potential “false-positive” signatures are developed, including techniques to identify responsible pulsars. Mitigation strategies are then presented. We demonstrate how an incorrect noise model can lead to spurious signals, and we show how independently modeling noise across 30 Fourier components, spanning NANOGrav’s frequency range, effectively diagnoses and absorbs the excess power in gravitational-wave searches. This results in a nominal, and expected, progression of our gravitational-wave statistics. Additionally, we show that the first interstellar medium event in PSR J1713+0747 pollutes the common red-noise process with low spectral index noise, and we use a tailored noise model to remove these effects.

J S Hazboun↗

NASA NTRS2020

Statistical Characterization of Hot Jupiter Atmospheres Using Spitzer's Secondary Eclipses

We report 78 secondary eclipse depths for a sample of 36 transiting hot Jupiters observed at 3.6 and 4.5 μm using the Spitzer Space Telescope. Our eclipse results for 27 of these planets are new, and include highly irradiated worlds such as KELT-7b, WASP-87b, WASP-76b, and WASP-64b, and important targets for James Webb Space Telescope such as WASP-62b. We find that WASP-62b has a slightly eccentric orbit (e cos w = 0.00614 ± 0.00064), and we confirm the eccentricity of HAT-P-13b and WASP-14b. The remainder are individually consistent with circular orbits, but we find statistical evidence for eccentricity increasing with orbital period in our range from 1 to 5 days. Our day-side brightness temperatures for the planets yield information on albedo and heat redistribution, following Cowan & Agol (2011). Planets having maximum day-side temperatures exceeding ∼2200 K are consistent with having zero albedo and a distribution of stellar irradiance uniformly over the day-side hemisphere. Our most intriguing result is that we detect a systematic difference between the emergent spectra of these hot Jupiters as compared to blackbodies. The ratio of observed brightness temperatures, Tb(4.5)/Tb(3.6), increases with equilibrium temperature by 100 ± 24 parts-per-million per Kelvin, over the entire temperature range in our sample (800–2500 K). No existing model predicts this trend over such a large range of temperature. We suggest that this may be due to a structural difference in the atmospheric temperature profiles of real planetary atmospheres as compared to models.

Exoplanet astronomy↗

NASA NTRSDate not supplied

Comparing Two Statistical Models for Low Boom Dose-response Relationships with Correlated Responses

This study compares two statistical modeling approaches to construct summary dose-response curves for low boom community noise surveys. NASA field survey data were used that consist of multiple responses from a survey participant. These data require an approach that accounts for the correlation among repeated annoyance observations from the same participant. A multilevel model accounts for the correlation by allowing estimated parameters to vary with each survey participant. On the other hand, a population average model utilizes generalized estimating equations and accounts for the correlation via a user-specified within-subject correlation structure. A visual comparison of the dose-response curves for these two methods reveals similar results. When comparing specific points along the summary curves, the multilevel model yields more precise confidence bounds than the population average model. The similarity between the summary curves derived from each model lends validity to both approaches for approximating a population representative summary curve.

X-59↗

NASA NTRS2021

A Rapid Method for Orbital Coverage Statistics with J2 Using Ergodic Theory

Quantifying long-term statistical properties of satellite trajectories typically entails time-consuming trajectory propagation. We present a fast, ergodic1 method of an- alytically estimating these for J2− perturbed elliptical orbits, broadly agreeing with trajectory propagation-based values. We extend the approach in Graven and Lo (2019)2 to estimate: (1) Satellite-ground station coverage with limited satellite field of view and ground station elevation angle with numerically optimized for- mulae, and (2) long-term averages of general functions of satellite position. This method is fast enough to facilitate real-time, interactive tools for satellite constel- lation and network design, with an approximate 1000× GPU speedup.

Lo, Martin W↗

NASA NTRS2022

Comparison of Two Statistical Models for Low Boom Dose-response Relationships with Correlated Responses

This study compares two statistical models to construct summary dose-response curves for low boom community noise surveys. Data from two NASA field surveys are used that consist of multiple responses per survey participant. These data require an approach that accounts for the correlation among repeated annoyance observations from the same participant. A multilevel model accounts for the correlation by allowing estimated parameters to vary with each survey participant. On the other hand, a population average model utilizes generalized estimating equations and accounts for the correlation via a userspecified within-subject correlation structure. A visual comparison of the dose-response curves for these two methods reveals similar results. When comparing specific points along the summary curves, the multilevel model yields more precise confidence bounds than the population average model. The similarity between the summary curves derived from each model lends validity to both approaches for approximating a population representative summary curve, though modeling assumptions may lend favor to the multilevel logistic modeling approach over the population average model.

dose-response↗

NASA NTRS2021

I’ll have the Porter: Interactively Visualizing the Results of Statistical Maneuver Analysis

Mission design and navigation relies on statistical maneuver analysis and Monte Carlo simulations when evaluating candidate mission trajectories. Engineers must analyze large quantities of data to optimize mission safety and propellant margins, but currently rely on static text files and dense PDF slide decks to review simulation results. This approach is time-intensive, non-interactive, and difficult to share or coordinate with other mission designers. To improve this process, we present Porter: a web-based interactive mission analysis tool. This work describes Porter’s user-centered design process, its processing pipeline for importing and processing LAMBIC simulation data, core interactive features, and preliminary user feedback.

Ramaswamy, Basak Alper↗

NASA NTRS2022

File Specification for MERRA-2 Climate Statistics Products

The Modern Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) contains a wealth of information that can be used for weather and climate studies. By combining the assimilation of observations with a frozen version of the Goddard Earth Observing System (GEOS), a global analysis is produced at an hourly temporal resolution spanning from January 1980 through present (Gelaro et al., 2017). It can be difficult to parse through a multidecadal dataset such as MERRA-2 to evaluate the interannual variability of weather that occurs on a daily timescale, let alone determine the occurrence of an extreme weather event. Furthermore, it was recognized that standard metrics were needed to evaluate climate change among climate models and international research efforts. As a result of these concerns, the Expert Team on Climate Change Detection and Indices (ETCCDI) developed a set of indices that represent the frequency and intensity of extreme weather events using a daily time series of 2-m air temperature (T2m) and precipitation (Alexander et al., 2016). These indices were used as a basis to comprise a list of fields that represent daily extreme temperature and precipitation events, heatwaves, multi-day precipitation, as well monthly percentile statistics from the MERRA-2 dataset. Also included in this data product is a climatological long term mean and standard deviation representing the interannual variability on a monthly timescale.

MERRA-2↗