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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Updated Trends of the Stratospheric Ozone Vertical Distribution in the 60°S-60°N Latitude Range Based on the LOTUS Regression Model

This study presents an updated evaluation of stratospheric ozone profile trends in the 60°S - 60°N latitude range over the 2000 - 2020 period using an updated version of the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) regression model that was used to evaluate such trends up to 2016 for the last WMO Ozone Assessment (2018). In addition to the derivation of detailed trends as a function of latitude and vertical coordinates, the regressions are performed with the data sets averaged over broad latitude bands, i.e., 60°S–35°S, 20°S–20°N and 35°N–60°N. The same methodology as in the last Assessment is applied to combine trends in these broad latitude bands in order to compare the results with the previous studies. Longitudinally resolved merged satellite records are also considered in order to provide a better comparison with trends retrieved from ground-based records, e.g., lidar, ozone sondes, Umkehr, microwave and Fourier Transform Infrared (FTIR) spectrometers at selected stations where long-term time series are available. The study includes a comparison with trends derived from the REF-C2 simulations of the Chemistry Climate Model Initiative (CCMI-1). This work confirms past results showing an ozone increase in the upper stratosphere, which is now significant in the three broad latitude bands. The increase is largest in the northern and southern hemisphere midlatitudes, with ~2.2%/decade at ~2.1 hPa, and ~2.1%/decade at ~3.2 hPa respectively, compared to ~1.6%/decade at ~2.6 hPa in the tropics. New trend signals have emerged from the records, such as a significant decrease of ozone in the tropics around 35 hPa and a non-significant increase of ozone in the southern midlatitudes at about 20 hPa. Non-significant negative ozone trends are derived in the lowermost stratosphere, with the most pronounced trends in the tropics. While a very good agreement is obtained between trends from merged satellite records and the CCMI-1 REF-C2 simulation in the upper stratosphere, observed negative trends in the lower stratosphere are not reproduced by models at southern and, in particular, at northern midlatitudes, where models report an ozone increase. However, the lower stratospheric trend uncertainties are quite large, for both measured and modelled trends. Finally, 2000-2020 stratospheric ozone trends derived from the ground-based and longitudinally resolved satellite records are in reasonable agreement over the European Alpine and tropical regions, while at the Lauder station in the southern hemisphere mid-latitudes they show some differences.

Stratospheric ozone trends↗
NASA NTRS1976

LACIE: Yield-weather regression models for the Canadian prairies

Most of the variability in wheat production is due to weather fluctuations. Climatic differences within the region account for a large portion of the variability in yields for different parts of the region. Separate regression models were developed for each of the areas indicated.

Source record↗
NASA NTRS2007

Dynamic and Regression Modeling of Ocean Variability in the Tide-Gauge Record at Seasonal and Longer Periods

Comparison of monthly mean tide-gauge time series to corresponding model time series based on a static inverted barometer (IB) for pressure-driven fluctuations and a ocean general circulation model (OM) reveals that the combined model successfully reproduces seasonal and interannual changes in relative sea level at many stations. Removal of the OM and IB from the tide-gauge record produces residual time series with a mean global variance reduction of 53%. The OM is mis-scaled for certain regions, and 68% of the residual time series contain a significant seasonal variability after removal of the OM and IB from the tide-gauge data. Including OM admittance parameters and seasonal coefficients in a regression model for each station, with IB also removed, produces residual time series with mean global variance reduction of 71%. Examination of the regional improvement in variance caused by scaling the OM, including seasonal terms, or both, indicates weakness in the model at predicting sea-level variation for constricted ocean regions. The model is particularly effective at reproducing sea-level variation for stations in North America, Europe, and Japan. The RMS residual for many stations in these areas is 25-35 mm. The production of "cleaner" tide-gauge time series, with oceanographic variability removed, is important for future analysis of nonsecular and regionally differing sea-level variations. Understanding the ocean model's strengths and weaknesses will allow for future improvements of the model.

Hill, Emma M.↗
NASA NTRS1984

A regression model for the temporal development of soil pipes and associated gullies in the alluvial-fill valley of the Rio Puerco, central New Mexico

On Mars, the association of gullied escarpments and chaotic terrain is evidence for failure and scarp retreat of poorly consolidated materials. Some martian gullies have no surface outlets and may have drained through subterranean channels. Similar features, though on a much smaller scale, can be seen in alluvium along terrestrial river banks in semiarid regions, such as the Rio Puerco Valley of central New Mexico. Many of the escarpments along the Rio Puerco are developing through formation of collapse gullies, which drain through soil pipes. Gully development can be monitored on aerial photographs taken in 1935, 1962, and 1980. A regression model was developed to quantify gully evolution over a known time span. Soil pipes and their associated collapse gullies make recognizable signatures on the air photos. The areal extent of this signature can be normalized to the scarp length of each pipe-gully system, which makes comparisons between systems possible.

Condit, C. D.↗
NASA NTRSDate not supplied

Simulation and Regression Modeling of X-59 Low-Boom Carpets Across America

The NASA X-59 aircraft is predicted to produce a significantly quieter cruise sonic boom than traditional N-wave-producing aircraft. A propagation simulation study was undertaken to quantify loudness levels, exposure size, and variability of the X-59 low-boom carpet using realistic atmospheric profiles across the contiguous United States of America (CONUS). Near-field pressure data of the X-59 in supersonic cruise from NASA’s fully unstructured Navier–Stokes three-dimensional (known as FUN3D) computational fluid dynamics code were propagated using NASA’s PCBoom code, which solves an enhanced Burgers equation along acoustic rays. Atmospheric profiles from the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 database were used for propagation at 138 locations across the CONUS. Carpets at each location were generated for aircraft headings in the four cardinal directions. Over one million X-59 carpets were generated in total. The effects of the heading, season, geography, and climate zone on boom levels and exposure size are presented. Multiple linear regression models were developed to estimate carpet width and loudness metrics across the CONUS. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

X-59↗
NASA NTRS2022

Simulation and Regression Modeling of Nasa'S X-59 Low-Boom Carpets Across America

NASA’s X-59 aircraft is predicted to produce a significantly quieter cruise sonic boom than traditional N-wave-producing aircraft. A propagation simulation study was undertaken to quantify loudness levels, exposure size, and variability of the X-59’s low-boom carpet using realistic atmospheric profiles across the contiguous United States of America (CONUS). Near-field pressure data of the X-59 in supersonic cruise from NASA’s fully unstructured Navier–Stokes three-dimensional (known as FUN3D) computational fluid dynamics code were propagated using NASA’s PCBoom code, which solves an enhanced Burgers equation along acoustic rays. Atmospheric profiles from the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 database were used for propagation at 138 locations across the CONUS. Carpets at each location were generated for aircraft headings in the four cardinal directions. Over one million X-59 carpets were generated in total. The effects of the heading, season, geography, and climate zone on boom levels and exposure size are presented. Multiple linear regression models were developed to estimate carpet width and loudness metrics across the CONUS. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

X-59↗
NASA NTRS1986

Regression models for vegetation radar-backscattering and radiometric emission

Simple regression estimation of radar backscatter and radiometric emission from vegetative terrain is proposed, based on the exact radiative transfer models. A vegetative canopy is modeled as a Rayleigh scattering layer above an irregular Kirchhoff surface. The rms errors between the exact and the estimated ones are found to be less than 5 percent for emission, and 1 dB for the backscattering case, in most practical uses. The proposed formulas are useful in quickly estimating backscattering and emission from the vegetative terrain.

Eom, H. J.↗
NASA NTRS1983

Comparison of regression modeling techniques for resource estimation

The development and validation of resource utilization models is an active area of software engineering research. Regression analysis is the principal tool employed in these studies. However, little attention was given to determining which of the various regression methods available is the most appropriate. The objective of the study is to compare three alternative regession procedures by examining the results of their application to one commonly accepted equations for resource estimation. The data studied was summarized, the resource estimation equation was described, the regression procedures were explained, and the results obtained from the proceures were compared.

Card, D. N.↗
NASA NTRS2005

Design Sensitivity for a Subsonic Aircraft Predicted by Neural Network and Regression Models

A preliminary methodology was obtained for the design optimization of a subsonic aircraft by coupling NASA Langley Research Center s Flight Optimization System (FLOPS) with NASA Glenn Research Center s design optimization testbed (COMETBOARDS with regression and neural network analysis approximators). The aircraft modeled can carry 200 passengers at a cruise speed of Mach 0.85 over a range of 2500 n mi and can operate on standard 6000-ft takeoff and landing runways. The design simulation was extended to evaluate the optimal airframe and engine parameters for the subsonic aircraft to operate on nonstandard runways. Regression and neural network approximators were used to examine aircraft operation on runways ranging in length from 4500 to 7500 ft.

Hopkins, Dale A.↗
NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗
NASA NTRS1985

Tolerance bounds for log gamma regression models

The present procedure for finding lower confidence bounds for the quantiles of Weibull populations, on the basis of the solution of a quadratic equation, is more accurate than current Monte Carlo tables and extends to any location-scale family. It is shown that this method is accurate for all members of the log gamma(K) family, where K = 1/2 to infinity, and works well for censored data, while also extending to regression data. An even more accurate procedure involving an approximation to the Lawless (1982) conditional procedure, with numerical integrations whose tables are independent of the data, is also presented. These methods are applied to the case of failure strengths of ceramic specimens from each of three billets of Si3N4, which have undergone flexural strength testing.

Jones, R. A.↗
NASA NTRSDate not supplied

Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction

All-solid-state structural lithium-ion batteries are sought to enable all-electric propulsion in next generation aerospace concepts through improved safety and systems level weight savings. In this work, the influence of processing conditions on microstructural evolution was evaluated for anode composites of strain-free Li4Ti5O12 and metallic nickel current collector. Beyond size distributions, this study explored methods of quantifying microstructural features that describe changes in the spatial distribution and coalescence of nickel particles as a function of sample composition and sintering conditions. Processing-microstructure-property relationships were described by microstructure quantifiers including nickel particle count per area, nearest neighbor distance distribution, and edge-to-edge distance distribution. Machine learning methods were applied to compare the relative influence of processing conditions and microstructural features on electrical conductivity and mechanical strength to optimize for simultaneous energy storage and load bearing performance. Insights gained from this work inform future evaluation of alternative energy storage materials and microstructures for multifunctional performance, and generation of microstructural descriptors strengthens modeling across length scales.

anode↗
NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗
NASA NTRS2019

Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.

Terrestrial water storage↗
NASA NTRS1979

Relationship of physiography and snow area to stream discharge

The author has identified the following significant results. A comparison of snowmelt runoff models shows that the accuracy of the Tangborn model and regression models is greater if the test data falls within the range of calibration than if the test data lies outside the range of calibration data. The regression models are significantly more accurate for forecasts of 60 days or more than for shorter prediction periods. The Tangborn model is more accurate for forecasts of 90 days or more than for shorter prediction periods. The Martinec model is more accurate for forecasts of one or two days than for periods of 3,5,10, or 15 days. Accuracy of the long-term models seems to be independent of forecast data. The sufficiency of the calibration data base is a function not only of the number of years of record but also of the accuracy with which the calibration years represent the total population of data years. Twelve years appears to be a sufficient length of record for each of the models considered, as long as the twelve years are representative of the population.

Mccuen, R. H.↗