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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Temperature Corrected Bootstrap Algorithm

A temperature corrected Bootstrap Algorithm has been developed using Nimbus-7 Scanning Multichannel Microwave Radiometer data in preparation to the upcoming AMSR instrument aboard ADEOS and EOS-PM. The procedure first calculates the effective surface emissivity using emissivities of ice and water at 6 GHz and a mixing formulation that utilizes ice concentrations derived using the current Bootstrap algorithm but using brightness temperatures from 6 GHz and 37 GHz channels. These effective emissivities are then used to calculate surface ice which in turn are used to convert the 18 GHz and 37 GHz brightness temperatures to emissivities. Ice concentrations are then derived using the same technique as with the Bootstrap algorithm but using emissivities instead of brightness temperatures. The results show significant improvement in the area where ice temperature is expected to vary considerably such as near the continental areas in the Antarctic, where the ice temperature is colder than average, and in marginal ice zones.

Comiso, Joey C.↗
NASA NTRSDate not supplied

Establishing the Framework for e-VTOL Flight Mission Scenario Development for Urban Air Mobility Research using Cognitive Task Analysis

This paper focuses on applying specific protocol aspects of Cognitive Task Analysis (CTA) methods to formulate non-routine flight mission use-case scenarios in support of a potential research study at the National Aeronautics and Space Administration (NASA) Langley Research Center under the Air Traffic Management – eXploration (ATM-X) Urban Air Mobility (UAM) sub-project. The objective of use-case scenarios is to provide a hypothetical situation to elicit expert knowledge and feedback in a specific subject matter area. Use-case scenarios in the field of aviation, including UAM, can help to support the safe integration of these vehicles into the National Airspace System (NAS) through the examination of airspace procedures during non-routine events. Inherent in this type of research is the need to study in-flight non-routine scenarios that the UAM vehicle could encounter. Situations categorized as non-routine events are any events that occur outside of the original flight plan such as a mechanical malfunction or other in-flight emergency requiring special procedures, which can include diversions to a specified, or non-specified, emergency landing area. In specific instances, some non-routine events could be considered as “routine” during in-flight operations, such as the need to perform a go-around due to a balked landing. As the rate of technological expansion surrounding UAM grows rapidly in modern aviation, so does the need for research that focuses on the safe integration of these vehicles including research with a focus on the methods and development of airspace operational procedures. Research rooted in CTA methods provides a framework for developing use-case scenarios appropriate for environments where mental demands are substantial. Substantial mental demands exist while operating in the NAS for pilots, airline dispatchers, and air traffic controllers among many others. With the focus on UAM in mind, it is critical to frame a baseline use-case scenario using information as it pertains to current-day airspace operations. According to Dr. Robert R. Hoffman’s “Protocols for Cognitive Task Analysis,” one of the first steps to applying CTA methods to research is the concept of “bootstrapping” in which the researcher(s) familiarize themselves with the domain that is being studied. To formulate the non-routine flight mission use-case scenarios for the purpose of this potential NASA ATM-X UAM research study, and to inform the baseline use-case scenario, the practice of bootstrapping was utilized to acquire knowledge of current-day airspace procedures.

Heidi S Glaudel↗
NASA NTRS1982

Evaluation of the CEAS trend and monthly weather data models for soybean yields in Iowa, Illinois, and Indiana

The CEAS models evaluated use historic trend and meteorological and agroclimatic variables to forecast soybean yields in Iowa, Illinois, and Indiana. Indicators of yield reliability and current measures of modeled yield reliability were obtained from bootstrap tests on the end of season models. Indicators of yield reliability show that the state models are consistently better than the crop reporting district (CRD) models. One CRD model is especially poor. At the state level, the bias of each model is less than one half quintal/hectare. The standard deviation is between one and two quintals/hectare. The models are adequate in terms of coverage and are to a certain extent consistent with scientific knowledge. Timely yield estimates can be made during the growing season using truncated models. The models are easy to understand and use and are not costly to operate. Other than the specification of values used to determine evapotranspiration, the models are objective. Because the method of variable selection used in the model development is adequately documented, no evaluation can be made of the objectivity and cost of redevelopment of the model.

French, V.↗
NASA NTRS1970

Bootstrap Unloader-Patent

Bootstrap unloading circuits for sampling transducer voltage sources without drawing current

Pfiffner, H. J.↗
NASA NTRS1982

Evaluation of the Williams-type spring wheat model in North Dakota and Minnesota

The Williams type model, developed similarly to previous models of C.V.D. Williams, uses monthly temperature and precipitation data as well as soil and topological variables to predict the yield of the spring wheat crop. The models are statistically developed using the regression technique. Eight model characteristics are examined in the evaluation of the model. Evaluation is at the crop reporting district level, the state level and for the entire region. A ten year bootstrap test was the basis of the statistical evaluation. The accuracy and current indication of modeled yield reliability could show improvement. There is great variability in the bias measured over the districts, but there is a slight overall positive bias. The model estimates for the east central crop reporting district in Minnesota are not accurate. The estimate of yield for 1974 were inaccurate for all of the models.

Leduc, S.↗
NASA NTRS2023

Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid-Latitude Stations

The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid-latitude magnetometers to predict the probability that dB/dt will exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts of dB/dt in the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over-rely on dB/dt values in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in the dB/dt threshold crossings.

Michael Coughlan↗
NASA NTRS2014

ISS Local Environment Spectrometers (ISLES)

In order to study the complex interactions between the space environment surrounding the ISS and the ISS surface materials, we propose to use lowcost, high-TRL plasma sensors on the ISS robotic arm to probe the ISS space environment. During many years of ISS operation, we have been able to condut effective (but not perfect) extravehicular activities (both human and robotic) within the perturbed local ISS space environment. Because of the complexity of the interaction between the ISS and the LEO space environment, there remain important questions, such as differential charging at solar panel junctions (the so-called "triple point" between conductor, dielectric, and space plasma), increased chemical contamination due to ISS surface charging and/or thruster activation, water dumps, etc, and "bootstrap" charging of insulating surfaces. Some compelling questions could synergistically draw upon a common sensor suite, which also leverages previous and current MSFC investments. Specific questions address ISS surface charging, plasma contactor plume expansion in a magnetized drifting plasma, and possible localized contamination effects across the ISS.

Krause, Linda Habash↗
NASA NTRS2018

Costing at the Speed of Light: How Your Concurrent Engineering Design Team can Bootstrap Your Organizations Programmatic Capabilities

What do you do when it is necessary to generate reasonable cost estimates at the earliest Concept Maturity Levels and you have never flown any similar missions before? This paper describes the current and future Team X cost processes and methods, how they are being used to expand our data frontiers and cost modelling capabilities, and how this enables the ability to estimate early and estimate often.

Ramirez, Matthew↗
NASA NTRS2020

Estimate Early and Estimate Often: How Your Concurrent Engineering Design Team Can Bootstrap Your Organizations Programmatic Capabilities

What do you do when it is necessary to generate reasonable cost estimates at the earliest Concept Maturity Levels and you have never flown any similar missions before? This paper describes the current and future Team X and A-Team cost processes and methods, how they are being used to expand our data frontiers, cost modelling capabilities and how this enables the ability to estimate early and estimate often.

Hihn, Jairus↗
NASA NTRS1969

Bootstrap unloader

Circuit can sample a number of transducers in sequence without drawing from them. This bootstrap unloader uses a differential amplifier with one input connected to a circuit which is the equivalent of the circuit to be unloaded, and the other input delivering the proper unloading currents.

Pfiffner, H. J.↗
NASA NTRS1982

Resistors Improve Ramp Linearity

Simple modification to bootstrap ramp generator gives more linear output over longer sweep times. New circuit adds just two resistors, one of which is adjustable. Modification cancels nonlinearities due to variations in load on charging capacitor and due to changes in charging current as the voltage across capacitor increases.

Kleinberg, L. L.↗
NASA NTRS1982

Comparison of CEAS and Williams-type models for spring wheat yields in North Dakota and Minnesota

The CEAS and Williams-type yield models are both based on multiple regression analysis of historical time series data at CRD level. The CEAS model develops a separate relation for each CRD; the Williams-type model pools CRD data to regional level (groups of similar CRDs). Basic variables considered in the analyses are USDA yield, monthly mean temperature, monthly precipitation, and variables derived from these. The Williams-type model also used soil texture and topographic information. Technological trend is represented in both by piecewise linear functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test of each model (1970-1979) demonstrate that the models are very similar in performance in all respects. Both models are about equally objective, adequate, timely, simple, and inexpensive. Both consider scientific knowledge on a broad scale but not in detail. Neither provides a good current measure of modeled yield reliability. The CEAS model is considered very slightly preferable for AgRISTARS applications.

Barnett, T. L.↗
NASA NTRS2009

Performance of an SOI Boot-Strapped Full-Bridge MOSFET Driver, Type CHT-FBDR, under Extreme Temperatures

Electronic systems designed for use in deep space and planetary exploration missions are expected to encounter extreme temperatures and wide thermal swings. Silicon-based devices are limited in their wide-temperature capability and usually require extra measures, such as cooling or heating mechanisms, to provide adequate ambient temperature for proper operation. Silicon-On-Insulator (SOI) technology, on the other hand, lately has been gaining wide spread use in applications where high temperatures are encountered. Due to their inherent design, SOI-based integrated circuit chips are able to operate at temperatures higher than those of the silicon devices by virtue of reducing leakage currents, eliminating parasitic junctions, and limiting internal heating. In addition, SOI devices provide faster switching, consume less power, and offer improved radiation-tolerance. Very little data, however, exist on the performance of such devices and circuits under cryogenic temperatures. In this work, the performance of an SOI bootstrapped, full-bridge driver integrated circuit was evaluated under extreme temperatures and thermal cycling. The investigations were carried out to establish a baseline on the functionality and to determine suitability of this device for use in space exploration missions under extreme temperature conditions.

Patterson, Richard↗
NASA NTRS1981

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Barnett, T. L.↗
NASA NTRS1981

Evaluation of the CEAS model for barley yields in North Dakota and Minnesota

The CEAS yield model is based upon multiple regression analysis at the CRD and state levels. For the historical time series, yield is regressed on a set of variables derived from monthly mean temperature and monthly precipitation. Technological trend is represented by piecewise linear and/or quadriatic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-79) demonstrated that biases are small and performance as indicated by the root mean square errors are acceptable for intended application, however, model response for individual years particularly unusual years, is not very reliable and shows some large errors. The model is objective, adequate, timely, simple and not costly. It considers scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Barnett, T. L.↗
NASA NTRS2014

A Spurious Jump in the Satellite Record: Has Antarctic Sea Ice Expansion Been Overestimated?

Recent estimates indicate that the Antarctic sea ice cover is expanding at a statistically significant rate with a magnitude one-third as large as the rapid rate of sea ice retreat in the Arctic. However, during the mid-2000s, with several fewer years in the observational record, the trend in Antarctic sea ice extent was reported to be considerably smaller and statistically indistinguishable from zero. Here, we show that much of the increase in the reported trend occurred due to the previously undocumented effect of a change in the way the satellite sea ice observations are processed for the widely used Bootstrap algorithm data set, rather than a physical increase in the rate of ice advance. Specifically, we find that a change in the intercalibration across a 1991 sensor transition when the data set was reprocessed in 2007 caused a substantial change in the long-term trend. Although our analysis does not definitively identify whether this change introduced an error or removed one, the resulting difference in the trends suggests that a substantial error exists in either the current data set or the version that was used prior to the mid- 2000s, and numerous studies that have relied on these observations should be reexamined to determine the sensitivity of their results to this change in the data set. Furthermore, a number of recent studies have investigated physical mechanisms for the observed expansion of the Antarctic sea ice cover. The results of this analysis raise the possibility that much of this expansion may be a spurious artifact of an error in the processing of the satellite observations.

Cryosphere↗
NASA NTRS2001

Studies of Antarctic Sea Ice Concentrations from Satellite Data and Their Applications

Large changes in the sea ice cover have been observed recently. Because of the relevance of such changes to climate change studies it is important that key ice concentration data sets used for evaluating such changes are interpreted properly. High and medium resolution visible and infrared satellite data are used in conjunction with passive microwave data to study the true characteristics of the Antarctic sea ice cover, assess errors in currently available ice concentration products, and evaluate the applications and limitations of the latter in polar process studies. Cloud-free high resolution data provide valuable information about the natural distribution, stage of formation, and composition of the ice cover that enables interpretation of the large spatial and temporal variability of the microwave emissivity of Antarctic sea ice. Comparative analyses of co-registered visible, infrared and microwave data were used to evaluate ice concentrations derived from standard ice algorithms (i.e., Bootstrap and Team) and investigate the 10 to 35% difference in derived values from large areas within the ice pack, especially in the Weddell Sea, Amundsen Sea, and Ross Sea regions. Landsat and OLS data show a predominance of thick consolidated ice in these areas and show good agreement with the Bootstrap Algorithm. While direct measurements were not possible, the lower values from the Team Algorithm results are likely due to layering within the ice and snow and/or surface flooding, which are known to affect the polarization ratio. In predominantly new ice regions, the derived ice concentration from passive microwave data is usually lower than the true percentage because the emissivity of new ice changes with age and thickness and is lower than that of thick ice. However, the product provides a more realistic characterization of the sea ice cover, and are more useful in polar process studies since it allows for the identification of areas of significant divergence and polynya activities. Also, heat and salinity fluxes are proportionately increased in these areas compared to those from the thicker ice areas. A slight positive trend in ice extent and area from 1978 through 2000 is observed consistent with slight continental cooling during the period. However, the confidence in this result is only moderate because the overlap period for key instruments is just one month and the sensitivity to changes in sensor characteristics, calibration and threshold for the ice edge is quite high.

Comiso, Josefino C.↗