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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
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 NTRS1982

Prospects for prediction of zonal wind oscillations

The existence of an index cycle, a quasi-periodic oscillation in the strength of the zonal wind intensity, would offer the possibility of predicting an important atmospheric feature over the period of at least one oscillation. To determine whether such a cycle exists, daily time series of several different atmospheric variables for the period November 1-April 30 for 1974-1977 were assembled. Here, the height of the 500 mb surface at latitudes 60 N and 30 N is considered. It is shown that (1) there is evidence that an index cycle exists, (2) there is theoretical reason to expect multiply periodic atmospheric flow patterns, and (3) a relatively simple, dynamically sound prediction scheme can be proposed.

Dutton, J. A.↗
NASA NTRS2012

The Influence of Pickup Protons, from Interstellar Neutral Hydrogen, on the Propagation of Interplanetary Shocks from the Halloween 2003 Solar Events to ACE and Ulysses: A 3-D MHD Modeling Study

We describe our 3-D, time ]dependent, MHD solar wind model that we recently modified to include the physics of pickup protons from interstellar neutral hydrogen. The model has a time-dependent lower boundary condition, at 0.1 AU, that is driven by source surface map files through an empirical interface module. We describe the empirical interface and its parameter tuning to maximize model agreement with background (quiet) solar wind observations at ACE. We then give results of a simulation study of the famous Halloween 2003 series of solar events. We began with shock inputs from the Fearless Forecast real ]time shock arrival prediction study, and then we iteratively adjusted input shock speeds to obtain agreement between observed and simulated shock arrival times at ACE. We then extended the model grid to 5.5 AU and compared those simulation results with Ulysses observations at 5.2 AU. Next we undertook the more difficult tuning of shock speeds and locations to get matching shock arrival times at both ACE and Ulysses. Then we ran this last case again with neutral hydrogen density set to zero, to identify the effect of pickup ions. We show that the speed of interplanetary shocks propagating from the Sun to Ulysses is reduced by the effects of pickup protons. We plan to make further improvements to the model as we continue our benchmarking process to 10 AU, comparing our results with Cassini observations, and eventually on to 100 AU, comparing our results with Voyager 1 and 2 observations.

Detman, T. R.↗
NASA NTRS2018

Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.

aviation safet↗
NASA NTRSDate not supplied

Interpretable Machine Learning Models for Autonomous Characterization of Analogue Ocean World Seawater Chemistry and Biosignature Potential Using Isotope Ratio Data

Background: Future missions to ocean worlds, such as Enceladus and Europa, will attempt to characterize the subsurface seawater chemistry and assess the potential for life. Such missions will be equipped with capabilities to precisely measure volatile isotopes in plumes, atmospheres, and exospheres. Motivation: While large isotopic fractionations can indicate a biological source, there are signatures resulting from abiotic geochemical processes that mimic isotopic biosignatures. While machine learning (ML) has the potential to disentangle competing effects and biotic mimicry, high-dimensional isotope ratio mass spectrometry (IRMS) data is likely to contain noise/irrelevant features and involve complex statistical interactions that make human inference and interpretation difficult. Further, ML predictions with as far-reaching implications as an extraterrestrial biosignature on an ocean world requires the use of interpretable models (i.e., not “black box” models) with physically and mathematically meaningful feature spaces along with false positive diagnostics. Methods: We use volatile CO2 IRMS data of analogue ocean world seawaters to validate an ML approach to provide biogeochemical context for biosignature detection. We employ a feature selection method called nearest-neighbor projected distance regression (NPDR) that detects statistical interactions and helps elucidate the mechanisms of the Random Forest classification models. Results: We train and validate predictive ML models on volatile CO2 IRMS data of analogue ocean world seawaters to predict major salt components (e.g., MgSO4, NaHCO3), pH, ionic strength, and the presence of biosignatures. Features derived from IRMS measurements are augmented with extracted time-series features. Our results show high test accuracy and interpretability, which is increased by interaction network visualization, sample-wise variable importance scores, and single-sample class probability estimates. We demonstrate an ML mission software solution that triggers autonomous data transmission and biogeochemical sample prediction.

geochemistry↗
NASA NTRSDate not supplied

Development of Solar Flare and Energetic Particle Prediction Portal (SEP 3 )

Solar activity is a primary factor determining the state of the Earth’s space environment, geomagnetic and ionospheric disturbances, and radiation hazards. In the current state of knowledge, machine learning (ML) methods provide essential tools for processing data, investigating relationships among various physical properties and characteristics, uncovering hidden connections, and predicting hazardous solar events. The primary difficulty in developing and applying modern machine-learning tools in heliophysics is that the essential data are scattered among over a hundred data repositories developed by instrument teams of space missions and ground-based observatories. In addition, statistical and ML methods require long time series of homogeneous measurements. To facilitate ML-ready data preparation and access, we have developed an interactive database of solar flares integrating the most essential datasets (https://solarflare.njit.edu/). The database performs an initial data processing and is automatically updated. In addition, we are developing the Solar Energetic Particle Prediction Portal (SEP3, https://sun.njit.edu/SEP3), which hosts web applications that allow users to retrieve the database records. The Portal has a search page for browsing the events from the most widely used catalogs and a dedicated space to share the most recent achievements of the team. The interactive widget can display soft X-ray and proton flux time series from GOES satellites and the flare records. The data portal has been used to evaluate the forecasts of solar proton events and investigate machine-learning approaches to SEP prediction.

SMD↗
NASA NTRS2021

Assessing Relay Communications for Mars Sample Return Surface Mission Concepts

The Mars Sample Return (MSR) Campaign would be a 3-mission campaign concept supported by NASA and ESA to return samples from the Mars surface. MSR would, for the first time ever, present a need to communicate with multiple surface assets that are co-located on Mars in a coordinated effort to accomplish the unified objective of fetching, transporting, and returning samples from Mars. Currently, Mars surface assets relay data to and from Earth using a number of orbiters in what’s known as the Mars Relay Network (MRN). This network is characterized by a small number of surface assets distributed across the Martian globe and a larger number of orbiters to provide relay services. As of June 2020, there are two surface assets for which five orbiters are providing relay. During the MSR Campaign, there would be two rovers and a lander that all would require relay communication from a small number of Mars orbiters to meet the aggressive MSR timeline. The inversion of the current MRN paradigm, a system of many surface assets requiring relay and few orbiters to provide relay, necessitates the unique challenge of optimally allocating relay passes to maximize the operational capability of all assets. The allocation must consider a large number of trade variables including Mars asset operational requirements and Earth ground system constraints, including staffing schedules, operations planning across time zones, and more. To address these telecommunication challenges, the Mars Asset Relay Mission Link Allocation Design Environment (MARMLADE) tool was developed. It is a MATLAB-based tool to assign orbiter passes or Direct-From-Earth (DFE) links to each of the three surface assets and quantify the operational efficiency of each surface asset.MARMLADE uses a data set of simulated Mars relay orbiter geometry and telecommunication capabilities provided by JPL’s Telecom Orbit Analysis and Simulation Tool (TOAST) software to compute which asset should get each pass based on a series of heuristics and predictions of all assets’ states. Within MARMLADE, the user can provide inputs including the option for time-based pass splitting, fixed FWD data rate capabilities, DFE communication capabilities, and link parameters allowing for the assessment of complex operations and hardware trades using surface mission operational efficiency as a primary figure of merit. As the MSR mission concepts continue to mature, MARMLADE is being used to assess ability of all MSR elements to meet the surface mission timeline requirements and to provide relay link allocations to each of the MSR surface assets.This paper will describe the motivation and design of the MARMLADE tool and how it is being used to perform campaign and mission level trades, generate requirements, and support development of the MSR surface mission scenarios.

Lee, Charles↗
NASA NTRS2015

The Tracking Meteogram, an AWIPS II Tool for Time-Series Analysis

A new tool has been developed for the National Weather Service (NWS) Advanced Weather Interactive Processing System (AWIPS) II through collaboration between NASA's Short‐term Prediction Research and Transition (SPoRT) and the NWS Meteorological Development Laboratory (MDL). Referred to as the "Tracking Meteogram", the tool aids NWS forecasters in assessing meteorological parameters associated with moving phenomena. The tool aids forecasters in severe weather situations by providing valuable satellite and radar derived trends such as cloud top cooling rates, radial velocity couplets, reflectivity, and information from ground‐based lightning networks. The Tracking Meteogram tool also aids in synoptic and mesoscale analysis by tracking parameters such as the deepening of surface low pressure systems, changes in surface or upper air temperature, and other properties. The tool provides a valuable new functionality and demonstrates the flexibility and extensibility of the NWS AWIPS II architecture. In 2014, the operational impact of the tool was formally evaluated through participation in the NOAA/NWS Operations Proving Ground (OPG), a risk reduction activity to assess performance and operational impact of new forecasting concepts, tools, and applications. Performance of the Tracking Meteogram Tool during the OPG assessment confirmed that it will be a valuable asset to the operational forecasters. This presentation reviews development of the Tracking Meteogram tool, performance and feedback acquired during the OPG activity, and future goals for continued support and extension to other application areas.

Burks, Jason Eric↗
NASA NTRS1990

The modulation of a radar signal from the ocean surface due to slope and hydrodynamic effects

The modulation of the signal level of a tower-based radar scatterometer illuminating a small area of the ocean surface is predicted from direct measurements of the hydrodynamic conditions of the ocean surface and compared with X band radar measurements taken simultaneously. The radar backscatter is assumed to be due to Bragg resonance between the signal and the ripple waves. The effects of the slope of the ocean surface due to passing long waves are modeled by converting a measured wave height series into a slope time series. Laser-slope-gauge measurements of the spectrum of the ripple waves that ride upon the long ocean waves are used to predict the hydrodynamic effects. Comparisons with the measured radar signal show that the slope modulation alone gives a poor prediction of the modulation. Inclusion of the hydrodynamic effects in the simulation appears more promising, although it also is not entirely successful with this data set.

West, James C.↗
NASA NTRS1978

Analysis of stellar occultation data - Effects of photon noise and initial conditions

An occultation light curve can be analyzed to provide information about a planetary atmosphere. Temperature, pressure, and number density profiles for the atmosphere of Mars are derived from a series of boundary layer equations, which invert equal increments of altitude (as opposed to time) in order to predict the noise quality of the occultation. Numerical results are given for a noisy isothermal light curve, with special attention to error analysis.

French, R. G.↗
NASA NTRS1995

Periodogram analysis of sunspot numbers and the relation with solar activities

The time series of average monthly sunspot numbers during 1900-1992 is studied by using power spectral analysis. This prediction method is used to study the sunspot periodicities relations between its, and with the other periodicities by solar activities. There are periodicities (between few days and 5 years) overwhelm on the mean solar cycle. ( 11 year cycle). These periodicities have the same relation with variations of solar constant and solar radiation reaching the Earth's atmosphere in the last solar cycle. These periods are related to the solar magnetic activity and to the modulation of solar features due to solar rotation.

Hady, Ahmed A.↗
NASA NTRS2011

Daily and Hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We distributed monthly GFED3 emissions during 2003-2009 on a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) active fire observations. We found that patterns of daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of bunting in savannas. On diurnal timescales, our analysis of the GOES active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top-down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

Mu, M.↗
NASA NTRS2011

Daily and 3-hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003.2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS) ]derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top ]down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

Mu, M.↗
NASA NTRS1993

Basic studies of baroclinic flows

A fully nonlinear 3-dimensional numerical model (GEOSIM), previously developed and validated for several cases of geophysical fluid flow, has been used to investigate the dynamical behavior of laboratory experiments of fluid flows similar to those of the Earth's atmosphere. The phenomena investigated are amplitude vacillation, and the response of the fluid system to uneven heating and cooling. The previous year's work included hysteresis in the transition between axisymmetric and wave flow. Investigation is also continuing of the flows in the Geophysical Fluid Flow Cell (GFFC), a low-gravity Spacelab experiment. Much of the effort in the past year has been spent in validation of the model under a wide range of external parameters including nonlinear flow regimes. With the implementation of a 3-dimensional upwind differencing scheme, higher spectral resolution, and a shorter time step, the model has been found capable of predicting the majority of flow regimes observed in one complete series of baroclinic annulus experiments of Pfeffer and co-workers. Detailed analysis of amplitude vacillation has revealed that the phase splitting described in the laboratory experiments occurs in some but not all cases. Through the use of animation of the models output, a vivid 3-dimensional view of the phase splitting was shown to the audience of the Southeastern Geophysical Fluid Dynamics Conference in March of this year. A study on interannual variability was made using GEOSIM with periodic variations in the thermal forcing. Thus far, the model has not predicted a chaotic behavior as observed in the experiments, although there is a sensitivity in the wavenumber selection to the initial conditions. Work on this subject, and on annulus experiments with non-axisymmetric thermal heating, will continue. The comparison of GEOSIM's predictions will result from the Spacelab 3 GFFC experiments continued over the past year, on a 'back-burner' basis. At this point, the study (in the form of a draft of a journal article) is nearly completed. The results from GEOSIM compared very well with the experiments, and the use of the model allows the demonstration of flow mechanics that were not possible with the experimental data. For example, animation of the model output shows that the forking of the spiral bands is a transient phenomenon, due to the differential east-west propagation of convection bands from different latitudes.

Miller, Timothy L.↗
NASA NTRS2014

Soil Dust Aerosols and Wind as Predictors of Seasonal Meningitis Incidence in Niger

Background: Epidemics of meningococcal meningitis are concentrated in sub-Saharan Africa during the dry season, a period when the region is affected by the Harmattan, a dry and dusty northeasterly trade wind blowing from the Sahara into the Gulf of Guinea.Objectives: We examined the potential of climate-based statistical forecasting models to predict seasonal incidence of meningitis in Niger at both the national and district levels.Data and methods: We used time series of meningitis incidence from 1986 through 2006 for 38 districts in Niger. We tested models based on data that would be readily available in an operational framework, such as climate and dust, population, and the incidence of early cases before the onset of the meningitis season in January-May. Incidence was used as a proxy for immunological state.

Soil dust↗
NASA NTRS1992

Intelligent sensor-model automated control of PMR-15 autoclave processing

An intelligent sensor model system has been built and used for automated control of the PMR-15 cure process in the autoclave. The system uses frequency-dependent FM sensing (FDEMS), the Loos processing model, and the Air Force QPAL intelligent software shell. The Loos model is used to predict and optimize the cure process including the time-temperature dependence of the extent of reaction, flow, and part consolidation. The FDEMS sensing system in turn monitors, in situ, the removal of solvent, changes in the viscosity, reaction advancement and cure completion in the mold continuously throughout the processing cycle. The sensor information is compared with the optimum processing conditions from the model. The QPAL composite cure control system allows comparison of the sensor monitoring with the model predictions to be broken down into a series of discrete steps and provides a language for making decisions on what to do next regarding time-temperature and pressure.

Hart, S.↗
NASA NTRS2008

Mapping Snow Grain Size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Lyapustin, Alexei↗
NASA NTRS2000

Follow on Validation of Force-Limited Vibration Testing

A second sounding rocket experiment was performed in the summer of 1998 in a continuing effort to validate the force limits techniques used in random vibration tests. The accuracy of the force limiting prediction techniques has not clearly been sufficiently confirmed with in-flight data as of this time. The flight was on board one of the BlackBrant series of sounding rockets. This vehicle is the one most commonly used for suborbital scientific payloads by NASA. An aluminum double deck structure simulating a dynamic source and load was flown. The hardware was instrumented with accelerometers and force sensors that measured input acceleration, forces and acceleration responses on the load. Force limiting analysis methods are compared with the flight measurements in order to evaluate analysis predictions methods and test procedures. This sounding rocket flight is the second in a series of flights that will be performed.

Kaufman, Daniel S.↗