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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Toward Real Time Neural Net Flight Controllers

NASA Ames Research Center has an ongoing program in neural network control technology targeted toward real time flight demonstrations using a modified F-15 which permits direct inner loop control of actuators, rapid switching between alternative control designs, and substitutable processors. An important part of this program is the ACTIVE flight project which is examining the feasibility of using neural networks in the design, control, and system identification of new aircraft prototypes. This paper discusses two research applications initiated with this objective in mind: utilization of neural networks for wind tunnel aircraft model identification and rapid learning algorithms for on line reconfiguration and control. The first application involves the identification of aerodynamic flight characteristics from analysis of wind tunnel test data. This identification is important in the early stages of aircraft design because complete specification of control architecture's may not be possible even though concept models at varying scales are available for aerodynamic wind tunnel testing. Testing of this type is often a long and expensive process involving measurement of aircraft lift, drag, and moment of inertia at varying angles of attack and control surface configurations. This information in turn can be used in the design of the flight control systems by applying the derived lookup tables to generate piece wise linearized controllers. Thus, reduced costs in tunnel test times and the rapid transfer of wind tunnel insights into prototype controllers becomes an important factor in more efficient generation and testing of new flight systems. NASA Ames Research Center is successfully applying modular neural networks as one way of anticipating small scale aircraft model performances prior to testing, thus reducing the number of in tunnel test hours and potentially, the number of intermediate scaled models required for estimation of surface flow effects.

Jorgensen, C. C.↗
NASA NTRS1994

Image Discrimination Models Predict Object Detection in Natural Backgrounds

Object detection involves looking for one of a large set of object sub-images in a large set of background images. Image discrimination models only predict the probability that an observer will detect a difference between two images. In a recent study based on only six different images, we found that discrimination models can predict the relative detectability of objects in those images, suggesting that these simpler models may be useful in some object detection applications. Here we replicate this result using a new, larger set of images. Fifteen images of a vehicle in an other-wise natural setting were altered to remove the vehicle and mixed with the original image in a proportion chosen to make the target neither perfectly recognizable nor unrecognizable. The target was also rotated about a vertical axis through its center and mixed with the background. Sixteen observers rated these 30 target images and the 15 background-only images for the presence of a vehicle. The likelihoods of the observer responses were computed from a Thurstone scaling model with the assumption that the detectabilities are proportional to the predictions of an image discrimination model. Three image discrimination models were used: a cortex transform model, a single channel model with a contrast sensitivity function filter, and the Root-Mean-Square (RMS) difference of the digital target and background-only images. As in the previous study, the cortex transform model performed best; the RMS difference predictor was second best; and last, but still a reasonable predictor, was the single channel model. Image discrimination models can predict the relative detectabilities of objects in natural backgrounds.

Ahumada, Albert J., Jr.↗
NASA NTRS2006

Problem Formulation for Optimal Array Modeling and Planning

In this paper we describe an optimal modeling and planning framework for the future large array of DSN antennas. This framework takes into account the array link performance models, reliability models, constrain models, and objective functions, and determines the optimal sub-array clusters configuration that will support the maximum number of concurrent missions based on mission link properties, antenna element reliabilities, mission requests, and array operation constraints. ..

modeling↗
NASA NTRSDate not supplied

Improving Drought Monitoring for the Middle East and North Africa Region via Data Assimilation Using the NASA Land Information System (LIS)

Food and water security become an increasingly pressing concern for countries within the Middle East and North Africa (MENA) region under climate change. Droughts are among the most important issues facing this region in both economic and social terms, which have become much more severe in recent decades. An effective drought monitoring and early warning system is, therefore, critical to support drought impact assessment and risk management. This presentation describes the effort to improve the simulation of hydrological conditions for such semi-arid environment by Noah Multi-parameterization Land Surface Model within the NASA Land Information System (LIS) via assimilating remotely sensed leaf area index and soil moisture in the presence of irrigation. Multi-source satellite evapotranspiration products are used to evaluate the model performance under the impact of the choice of met-forcings, the presence of irrigation and data assimilation. The improved model configuration is aimed to deliver a better soil moisture estimation for the composite drought indicator (CDI) for drought monitoring and to provide a better initial condition in support for hydrological drought forecasting.

Wanshu Nie↗
NASA NTRS2020

Electric Propulsion for the Psyche Mission: Development Activities and Status

NASA’s Psyche mission will launch in 2022 and begin a 3.6-year cruise to the metallic asteroid Psyche, where it will examine this unique body. The baseline spacecraft design is a hybrid of JPL’s deep-space heritage subsystems with commercial partner Maxar’s electric propulsion, power, and structure subsystems. All primary propulsion will be done with SPT-140 thrusters, which will be the first use of Hall thrusters for a NASA mission. The electric propulsion subsystem and its implementation for the Psyche mission are described here. Major testing activities have included the successful completion of subsystem integrated testing with the design modifications required for Psyche, and a series of low-power thrust repeatability tests that were performed in support of navigation analyses. Thruster performance models have been further validated with new SPT-140 flight data, and new analyses of thruster swirl torque have been performed that result in much higher values than previously estimated. Analysis of recent Maxar flight data has also provided a new understanding of in-flight propellant usage uncertainties. Subsystem integration and test activities are now underway and the status and plans are discussed.

Johnson, Ian↗
NASA NTRSDate not supplied

Statistical Classification of Biosignature Information using Multiple Instrument Observations

The accurate identification of biosignatures (indications of life) from data taken from remote or in situ planetary exploration is one of the most important challenges in astrobiology, the interdisciplinary field examining habitability and the potential for extraterrestrial life. This study employs machine learning algorithms to optimize the identification of biosignatures, with an emphasis on those which are agnostic to a specific biochemical basis. We exploit the wealth of terrestrial data available from biogenic and abiogenic systems to enhance efficient feature prioritization. Our dataset, pulled from public databases and laboratory recorded measurements, includes elemental abundance, isotopic fractionation, and VNIR/Raman spectra The data curation process included standardization for detection limits and ranges. Subsequent feature extraction yielded detailed inputs for machine learning, including combinations of elemental content, isotopic ratios, and parameters of spectral peaks and troughs. Feature significance was evaluated across diverse machine learning methodologies, such as k-nearest neighbors, logistic regression, Random Forest, support vector machines, and Gaussian Naïve Bayes, along with a combined voting classifier. We utilized Receiver Operating Characteristic Area Under the Curve (ROC AUC) across 2,000 50% test-train splits as a robust metric of model performance. Results revealed a promising ROC AUC of 0.853 for the combined voting classifier. Removing elemental abundance data notably reduced model accuracy (13% decrease in AUC), highlighting its critical role in biosignature detection. Several other individual data features exhibited significance within their respective data types, offering additional granularity. This research fortifies the relevance of machine learning to astrobiology, potentially enhancing life detection missions by allowing algorithmic prioritization of high-interest samples for further investigation. Future work will refine data standardization, expand the dataset to include more terrestrial systems, and incorporate convolutional neural networks for spectral feature extraction. The potential for public data sharing is also under exploration, reinforcing our commitment to collective scientific advancement.

Statistical↗
NASA NTRS2023

A Single-Point Modeling Approach for the Intercomparison and Evaluation of Ozone Dry Deposition Across Chemical Transport Models (Activity 2 of AQMEII4)

A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes.

Dry deposition↗
NASA NTRSDate not supplied

Leveraging the Roman Coronagraph Approach for the HWO Error Budget

The Habitable Worlds Observatory (HWO) is currently being considered as a future, coronagraph-equipped space telescope that would fulfill the top priority of Astro-2020. The top priority stated is the building of a telescope capable of detecting and characterizing exoplanets with sensitivity down to Earth-like planets. In a coronagraph approach, the methodology for setting up the error budget and performance model can benefit directly from the Nancy Grace Roman Space Telescope (RST) experience. RST’s Coronagraph Instrument (CGI) is a direct precursor to the HWO coronagraph, with performance reaching below 10 −7 in contrast demonstrated in very recent thermal-vac testing, and the possibility that it will perform better with more time on orbit. The RST CGI development benefited significantly from an error budget approach and analytical model focused on the noise incurred in measuring a planet’s flux ratio. In this brief paper we outline the current state of the application of the Roman approach to an HWO flux ratio noise error budget, including reasonable allocations informed by the Roman experience, and recent studies of narrow angle scatter done independently.

Bijan Nemati↗
NASA NTRS2024

Evaluation of CMIP6 Model Simulations of PM 2.5 and its Components Over China

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM 2.5 ) as major climate forcers. Yet the model performance for PM 2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM 2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM 2.5 components. Seven models output total PM 2.5 concentrations, and they all underestimate the observed total PM 2.5 over eastern China, with GFDL-ESM4 (−1.5 %) and MPI-ESM-1-2-HAM (−1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM 2.5 from the available component output, underestimate the total PM 2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by −71.2 % to −37.8 % across the 14 models) and the smallest for BC (−47.9 % to −12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.74) fairly well, yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM 2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing.

CMIP6↗
NASA NTRS1982

Optimal short-range trajectories for helicopters

An optimal flight path algorithm using a simplified altitude state model and a priori climb cruise descent flight profile was developed and applied to determine minimum fuel and minimum cost trajectories for a helicopter flying a fixed range trajectory. In addition, a method was developed for obtaining a performance model in simplified form which is based on standard flight manual data and which is applicable to the computation of optimal trajectories. The entire performance optimization algorithm is simple enough that on line trajectory optimization is feasible with a relatively small computer. The helicopter model used is the Silorsky S-61N. The results show that for this vehicle the optimal flight path and optimal cruise altitude can represent a 10% fuel saving on a minimum fuel trajectory. The optimal trajectories show considerable variability because of helicopter weight, ambient winds, and the relative cost trade off between time and fuel. In general, reasonable variations from the optimal velocities and cruise altitudes do not significantly degrade the optimal cost. For fuel optimal trajectories, the optimum cruise altitude varies from the maximum (12,000 ft) to the minimum (0 ft) depending on helicopter weight.

Slater, G. L.↗
NASA NTRS1994

Data driven propulsion system weight prediction model

The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

Gerth, Richard J.↗
NASA NTRS1994

Review of solar cell temperature coefficients for space

Energy conversion efficiency is an important parameter for solar cells, and well reported in the literature. However, solar cells heat up in sunlight, and the efficiency decreases. The temperature coefficient of the conversion efficiency is thus also extremely important, especially in mission modeling, but is much less well reported. It is of value to have a table which compiles into a single document values of temperature coefficients reported in the literature. In addition to modeling performance of solar cells in Earth orbit, where operating temperatures may range from about 20 C to as high as 85 C, it is of interest to model solar cells for several other recently proposed missions. These include use for the surface of Mars, for solar electric propulsion missions that may range from Venus to the Asteroid belt, and for laser-photovoltaic power that may involve laser intensities equivalent several suns. For all of these applications, variations in operating temperature away from the nominal test conditions result in significant changes in operating performance. In general the efficiency change with temperature is non-linear, however, in the range from negative 100 C through room temperature to a few hundred degrees C, efficiency is usually quite well modeled as a linear function of temperature (except for a few unusual cell types, such as amorphous silicon, and for extremely low bandgap cells, such as InGaAs).

Landis, Geoffrey A.↗
NASA NTRS1985

A detailed analysis of the erythropoietic control system in the human, squirrel, monkey, rat and mouse

The erythropoiesis modeling performed in support of the Body Fluid and Blood Volume Regulation tasks is described. The mathematical formulation of the species independent model, the solutions to the steady state and dynamic versions of the model, and the individual species specific models for the human, squirrel monkey, rat and mouse are outlined. A detailed sensitivity analysis of the species independent model response to parameter changes and how those responses change from species to species is presented. The species to species response to a series of simulated stresses directly related to blood volume regulation during space flight is analyzed.

Nordheim, A. W.↗
NASA NTRS2007

A Comparison of Land Surface Model Soil Hydraulic Properties Estimated by Inverse Modeling and Pedotransfer Functions

Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.

Gutmann, Ethan D.↗
NASA NTRS2009

Representations of the Stratospheric Polar Vortices in Versions 1 and 2 of the Goddard Earth Observing System Chemistry-Climate Model (GEOS CCM)

This study will document the behavior of the polar vortices in two versions of the GEOS CCM. Both versions of the model include the same stratospheric chemistry, They differ in the underlying circulation model. Version 1 of the GEOS CCM is based on the Goddard Earth Observing System, Version 4, general circulation model which includes the finite-volume (Lin-Rood) dynamical core and physical parameterizations from Community Climate Model, Version 3. GEOS CCM Version 2 is based on the GEOS-5 GCM that includes a different tropospheric physics package. Baseline simulations of both models, performed at two-degree spatial resolution, show some improvements in Version 2, but also some degradation, In the Antarctic, both models show an over-persistent stratospheric polar vortex with late breakdown, but the year-to-year variations that are overestimated in Version I are more realistic in Version 2. The implications of this for the interactions with tropospheric climate, the Southern Annular Mode, will be discussed. In the Arctic both model versions show a dominant dynamically forced variabi;ity, but Version 2 has a persistent warm bias in the low stratosphere and there are seasonal differences in the simulations. These differences will be quantified in terms of climate change and ozone loss. Impacts of model resolution, using simulations at one-degree and half-degree, and changes in physical parameterizations (especially the gravity wave drag) will be discussed.

Pawson, S.↗
NASA NTRS2012

Near-Nadiral Normalized Radar Cross Section of the SEA Surface at Ku, Ka, and W-Bands: Comparison of Measurements and Models

Understanding the relationship between wind speed and direction and the near-nadiral normalized radar cross section (NRCS) of the sea surface is important in many oceanographic and atmospheric remote sensing applications: (1) wind speed retrievals in traditional altimeter systems (2) assistance in calibration and path integrated attenuation processing for atmospheric profiling radars The desired wind speed (and direction in some cases) retrieval requires a clear understanding of the relationship between the relevant geophysical quantities and the observed NRCS Such understanding is available from existing electromagnetic models, but the presence of many such models, as well as implicit descriptions of the sea surface, motivates continued evaluation of model performance.

CloudSat↗
NASA NTRS2016

Evaluating Secondary Inorganic Aerosols in Three Dimensions

The spatial distribution of aerosols and their chemical composition dictates whether aerosols have a cooling or a warming effect on the climate system. Hence, properly modeling the three-dimensional distribution of aerosols is a crucial step for coherent climate simulations. Since surface measurement networks only give 2-D data, and most satellites supply integrated column information, it is thus important to integrate aircraft measurements in climate model evaluations. In this study, the vertical distribution of secondary inorganic aerosol (i.e., sulfate, ammonium, and nitrate) is evaluated against a collection of 14 AMS flight campaigns and surface measurements from 2000 to 2010 in the USA and Europe. GISS ModelE2 is used with multiple aerosol microphysics (MATRIX, OMA) and thermodynamic (ISORROPIA II, EQSAM) configurations. Our results show that the MATRIX microphysical scheme improves the model performance for sulfate, but that there is a systematic underestimation of ammonium and nitrate over the USA and Europe in all model configurations. In terms of gaseous precursors, nitric acid concentrations are largely underestimated at the surface while overestimated in the higher levels of the model. Heterogeneous reactions on dust surfaces are an important sink for nitric acid, even high in the troposphere. At high altitudes, nitrate formation is calculated to be ammonia limited. The underestimation of ammonium and nitrate in polluted regions is most likely caused by a too simplified treatment of the NH3/NH4(+) partitioning which affects the HNO3/NO3(-) partitioning.

aerosols↗
NASA NTRS2017

Reduced ENSO Variability at the LGM Revealed by an Isotope-Enabled Earth System Model

Studying the El Nino Southern Oscillation (ENSO) in the past can help us better understand its dynamics and improve its future projections. However, both paleoclimate reconstructions and model simulations of ENSO strength at the Last Glacial Maximum (LGM; 21 ka B.P.) have led to contradicting results. Here we perform model simulations using the recently developed water isotope-enabled Community Earth System Model (iCESM). For the first time, model-simulated oxygen isotopes are directly compared with those from ENSO reconstructions using the individual foraminifera analysis (IFA). We find that the LGM ENSO is most likely weaker comparing with the preindustrial. The iCESM suggests that total variance of the IFA records may only reflect changes in the annual cycle instead of ENSO variability as previously assumed. Furthermore, the interpretation of subsurface IFA records can be substantially complicated by the habitat depth of thermocline-dwelling foraminifera and their vertical migration with a temporally varying thermocline.

El Niño–Southern Oscillation;isotope-enabled si↗