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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

(abstract) Measurement of Aerodynamic Roughness Using Radar Backscatter over Vegetated Surfaces

Gathering of local information from remote platforms is often a more cost-effective way of obtaining the required data type than are local measurements. A good example is the collection of wind regime data in order to infer the amount and type of aeolian transport at a given site. This paper describes a potential mechanism for remotely inferring the wind regime from synthetic aperture radar data and describes a project to estimate the practicality of using such a mechanism. Both radar backscatter and wind flow are functions of the roughness of the site under study. It is therefore reasonable to suspect that a fairly well-behaved relationship might exist between normalized radar backscatter coefficient and aerodynamic roughness length. Aeolian transport of small particles depends in turn on wind flow and is an important quantity to measure for several economic-related reasons, but the direct measurement of wind flow regime generally involves construction of wind towers and many days of data collection, making such data extremely expensive and prohibitive in areas that cannot be easily accessed. For both desert and vegetated sites where extensive wind profile data already exist, a NASA airborne multifrequency, multipolarization radar was used to acquire radar data at several incidence angles. At the vegetated sites, vegetation height varied from tens of centimeters to several meters, and surface roughness was a few centimeters.

radar↗
NASA NTRSDate not supplied

Artificial Intelligence (AI) Methods for Augmenting the IMPACT Tool Evidence Library

Development of the Evidence Library for use with the IMPACT probability risk assessment tool took several years and involved a staggering amount of effort from a multi-disciplinary team. A very significant amount of the labor effort to collect, assess and finalize the Clinical Finding Form (CliFF) for each of the 119 medical conditions was provided by physician subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team. Many AI tools such as ChatGPT are excellent at summarizing large amounts of information and the current project was initiated to determine how such tools might streamline laborious processes, e.g., review and summarization of many scientific research publications, to execute key steps more efficiently in the process of developing CliFFs. The process for collecting the evidence which is found in the CliFFs is well documented in the Evidence Library Methods document (ELM; HRP-48036*). Using ELM and the CliFF development instructions as a guideline, a team of developers is leveraging Microsoft Azure AI tools and services along with open-source frameworks, to construct an AI-assisted automated pipeline. This pipeline is designed to search, retrieve, and process the necessary data sources, and ultimately help generate the final version of a CliFF. Currently, the large language model evaluates the relevance of each source material to spaceflights, either as direct evidence or as an analog. Additionally, the model assists in extracting keywords and generating brief summaries to enhance augmented retrieval and search processes in later stages of CliFF development. Once the data is ready, the model can perform semantic search and retrieval, generating and extracting valuable information for the CliFF. For instance, it can handle epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. The steps that required reading and summarizing articles were viewed as providing the greatest return on investment since large language models are very efficient and accurate in summarizing large amounts of text. Since labor effort to complete the original CliFF was not recorded with sufficient granularity, comparisons with an AI tool-generated CliFF will provide merely an approximation of time saved. Upon completion of the process, the CliFF for the medical condition “appendicitis” generated with the support of AI-based methods will serve as a proof-of-concept and will be compared to the original appendicitis CliFF to determine if use of the tools resulted in content and conclusory similarity. Based upon the results from face validation of the two CliFFs, modifications to the process will be made if necessary and additional condition CliFFs will be evaluated. Ultimately, CliFFs for the entire set of medical conditions will be created with the assistance of AI tools. Depending on the cost savings realized, CliFFs for additional medical conditions can be created to expand the Evidence Library. Future direction includes specifying the characteristics of the reviewer (prompting the AI tools to generate output assuming the reviewer is a sub-specialist physician, or nurse or EMT/medic) to determine if the effects on AI-generated output are different based on knowledge, skills and abilities. *Exploration Medical Capability Evidence Library Methods, HRP-48036 Rev A, July 2022.

Ali Al↗
NASA NTRSDate not supplied

Wildfire Emergency Response Hazard Extraction and Analysis of Trends (HEAT) through Natural Language Processing and Time Series

A methodology for Hazard Extraction and Analysis of Trends (HEAT) is proposed and conducted on a data set of wildfire incident response forms, known as ICS-209-PLUS.The HEAT processes: (1) extract a set of hazards from a data set, (2) calculate hazard-relevant metrics in a primary analysis, (3) analyze trends over time in metrics using timeseries, and (4) examine potential explanations for metric trends using a secondary analysis. Hazards are extracted from narrative data in the ICS-209-PLUS based on a framework previously developed by the authors, using natural language processing. Metrics examined for each hazard include operational time to occurrence, rate of occurrence, frequency, and severity. Primary results include a taxonomy of hazards present in the data set with relevant quantitative metrics. The most frequent hazards identified are environmental and include hazardous terrain. Most hazards occur on average between 35-55% containment. Incidents with hazards tend to have a higher average severity score when compared to the average score for all incidents. Time series of the metrics and relevant predictors, including fire characteristics, fire intensity, and operations, are created to facilitate further analysis. Secondary results used to determine which factors best predict hazard frequency include a correlation matrix and regression analysis. These findings are relevant to safety for current, as well as emerging wildfire operations, and are an exploratory first step in developing historical data-driven risk assessment models.

Sequoia R. Andrade↗
NASA NTRS1978

Investigation of the unsteady pressure distribution on the blades of an axial flow fan

The unsteady response of a stator blade caused by the interaction of the stator with the wakes of an upstream rotor was investigated. Unsteady pressure distributions were measured using a blade instrumented with a series miniature pressure transducers. The influence of several geometrical and flow parameters - rotor/stator spacing, stator solidity and stator incidence angle - were studied to determine the unsteady response of the stator to these parameters. A major influence on the stator unsteady response is due to the stator solidity. At high solidities the blade-to-blade interference has a larger contribution. While the range of rotor/stator spacings investigated had a minor influence, the effect of stator incidence angle is significant. The data indicate the existence of an optimum positive incidence which minimizes the unsteady response.

Henderson, R. E.↗
NASA NTRS2009

Surface Wind Vector and Rain Rate Observation Capability of Future Hurricane Imaging Radiometer (HIRAD)

The Hurricane Imaging Radiometer (HIRAD) is the next-generation Stepped Frequency Microwave Radiometer (SFMR), and it will offer the capability of simultaneous wide-swath observations of both extreme ocean surface wind vector and strong precipitation from either aircraft (including UAS) or satellite platforms. HIRAD will be a compact, lightweight, low-power instrument with no moving parts that will produce valid wind observations under hurricane conditions when existing microwave sensors (radiometers or scatterometers) are hindered by precipitation. The SFMR i s a proven aircraft remote sensing system for simultaneously observing extreme ocean surface wind speeds and rain rates, including those of major hurricane intensity. The proposed HIRAD instrument advances beyond the current nadir viewing SFMR to an equivalent wide-swath SFMR imager using passive microwave synthetic thinned aperture radiometer technology. The first version of the instrument will be a single polarization system for wind speed and rain rate, with a dual-polarization system to follow for wind vector capability. This sensor will operate over 4-7 GHz (C-band frequencies) where the required tropical cyclone remote sensing physics has been validated by both SFMR and WindSat radiometers. HIRAD incorporates a unique, technologically advanced array antenna and several other technologies successfully demonstrated by NASA s Instrument Incubator Program. A brassboard (laboratory) version of the instrument has been completed and successfully tested in a test chamber. Development of the aircraft instrument is underway, with flight testing planned for the fall of 2009. Preliminary Observing System Simulation Experiments (OSSEs) show that HIRAD will have a significant positive impact on surface wind analyses as either a new aircraft or satellite sensor. New off-nadir data collected in 2008 by SFMR that affirms the ability of this measurement technique to obtain wind speed data at non-zero incidence angle will be presented, as well as data from the brassboard instrument chamber tests.

Miller, Timothy↗
NASA NTRSDate not supplied

Alternate Inversion Paradigm for Spectroheliogram Data

Over the past five years, new methods to reconstruct spectrally pure maps of the Sun from spectroheliogram data have emerged, essentially unlocking this long-abandoned method of obtaining both spatial and spectral information over a large field of view simultaneously. The original inversion method determined the plasma’s emission measure distribution as a function of temperature at every spatial location in the field of view. To complete this inversion, a response matrix had to be created mapping the emission measure in different (temperature, space) bins to detector, requiring assumptions on the thermal and ionization equilibrium and abundance state of the plasma. We have since derived a new method of the inversion that does not require these atomic assumptions to be made. Instead, we use only the different locations of spectral lines from the same ion species and the possible ratios of those single-species spectral lines, removing the need for a priori knowledge on the state of the emitting plasma. In this presentation, we demonstrate this method using observed data from the Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) and simulated data from the EUV CME and Coronal Connectivity Observatory (ECCCO) investigation.

Amy Winebarger↗
DOE OSTI2021

Explorative Visualization for Traffic Safety using Adaptive Study Areas

The pressing need to improve traffic safety has become a societal concern in many cities around the world. Many traffic accidents are not occurring as stand-alone events but as consequences of other road incidents and hazards. To capture the traffic safety indications from a holistic aspect, this paper presents a suite of visualization techniques to explore large traffic safety datasets collected from different sources using adaptive study areas which include the whole region (Hamilton County, Ohio, U.S.) as well as smaller sub-areas. In the present study, these data source include (1) Hamilton County’s 911 emergency response data, which includes traffic incidents as well as other types of incidents throughout the county, and (2) Tennessee crash data, which contains only vehicle crashes with more detail on the circumstances of each crash. We find that both abstract and spatial visualization techniques are used to derive a better understanding of traffic safety patterns for different traffic participants in various urban environments. In addition to the entire region of Hamilton County, safety is examined on the highways, in the downtown area, and in a shopping district east of the city center. It is possible to characterize incidents in the different areas, gain a better understanding of common incident patterns, and identify outliers in the data. Finally, a textured tile calendar is presented to compare spatiotemporal patterns.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2015

Municipality Level Simulations of Dengue Fever Incidence in Puerto Rico Using Ground Based and Remotely Sensed Climate Data

Dengue fever (DF) is caused by a virus transmitted between humans and Aedes genus mosquitoes through blood feeding. In recent decades incidence of the disease has drastically increased in the tropical Americas, culminating with the Pan American outbreak in 2010 which resulted in 1.7 million reported cases. In Puerto Rico dengue is endemic, however, there is significant inter‐annual, intraannual, and spatial variability in case loads. Variability in climate and the environment, herd immunity and virus genetics, and demographic characteristics may all contribute to differing patterns of transmission both spatially and temporally. Knowledge of climate influences on dengue incidence could facilitate development of early warning systems allowing public health workers to implement appropriate transmission intervention strategies. In this study, we simulate dengue incidence in several municipalities in Puerto Rico using population and meteorological data derived from ground based stations and remote sensing instruments. This data was used to drive a process based model of vector population development and virus transmission. Model parameter values for container composition, vector characteristics, and incubation period were chosen by employing a Monte Carlo approach. Multiple simulations were performed for each municipality and the results were compared with reported dengue cases. The best performing simulations were retained and their parameter values and meteorological input were compared between years and municipalities. Parameter values varied by municipality and year illustrating the complexity and sensitivity of the disease system. Local characteristics including the natural and built environment impact transmission dynamics and produce varying responses to meteorological conditions.

Remote Sensing↗
NASA NTRS2013

Aerodynamic Measurements of a Variable-Speed Power-Turbine Blade Section in a Transonic Turbine Cascade at Low Inlet Turbulence

Aerodynamic measurements obtained in a transonic linear cascade were used to assess the impact of large incidence angle and Reynolds number variations on the 3-D flow field and midspan loss and turning of a 2-D section of a variable-speed power-turbine (VSPT) rotor blade. Steady-state data were obtained for ten incidence angles ranging from +15.8 deg to −51.0 deg. At each angle, data were acquired at five flow conditions with the exit Reynolds number (based on axial chord) varying over an order-of-magnitude from 2.12×10(exp 5) to 2.12×10(exp 6). Data were obtained at the design exit Mach number of 0.72 and at a reduced exit Mach number of 0.35 as required to achieve the lowest Reynolds number. Midspan total-pressure and exit flow angle data were acquired using a five-hole pitch/yaw probe surveyed on a plane located 7.0 percent axial chord downstream of the blade trailing edge plane. The survey spanned three blade passages. Additionally, three-dimensional half-span flow fields were examined with additional probe survey data acquired at 26 span locations for two key incidence angles of +5.8 deg and −36.7 deg. Survey data near the endwall were acquired with a three-hole boundary-layer probe. The data were integrated to determine average exit total-pressure and flow angle as functions of incidence and flow conditions. The data set also includes blade static pressures measured on four spanwise planes and endwall static pressures. Tests were conducted in the NASA Glenn Transonic Turbine Blade Cascade Facility. The measurements reflect strong secondary flows associated with the high aerodynamic loading levels at large positive incidence angles and an increase in loss levels with decreasing Reynolds number. The secondary flows decrease with negative incidence as the blade becomes unloaded. Transitional flow is admitted in this low inlet turbulence dataset, making it a challenging CFD test case. The dataset will be used to advance understanding of the aerodynamic challenges associated with maintaining efficient power turbine operation over a wide shaft-speed range. deg

Flegel-McVetta, Ashlie B.↗
NASA NTRS2023

Machine Learning Framework for Hazard Extraction and Analysis of Trends (HEAT) in Wildfire Response

This research proposes a natural language processing enabled risk analysis framework, named Hazard Extraction andAnalysis of Trends (HEAT), and applies the framework to the ICS-209-PLUS data set of wildfire incident responseforms. The HEAT framework produces safety- and risk- relevant analyses, consisting of: (1) a set of hazards extractedfrom text data, (2) a primary analysis using hazard-relevant metrics, such as rate and severity, to form an FMEA-styletable and risk matrix, (3) a time series analysis of metric trends, and (4) a secondary analysis examining potentialpredictors for hazards. Results from HEAT provide quantitative risk-relevant information for high-level hazards doc-umented in existing-state operations. Because of the generalizability of the steps and limited data requirements, HEATcan be applied to any dataset containing narrative text, thus providing a framework for data-driven machine learning-enabled quantitative risk analysis across a variety of domains. To demonstrate HEAT in a case study, we apply theframework to the ICS-209-PLUS dataset of wildland fire incident response forms. Hazards identified in wildfire re-sponse arise from environmental conditions, the mission, and the wildland urban interface. The resulting risk matrixidentifies evacuations as high-risk hazards, while all other identified hazards are medium or serious risk.

natural language processing↗
NASA NTRS1979

Lightning hazards overview: Aviation requirements and interests

A ten-year history of USAF lightning incidents is presented along with a discussion of the problems posed by lightning to current aircraft, and the hazards it constitutes to the electrical and electronic subsystems of new technology aircraft. Lightning technical protection technical needs, both engineering and operational, include: (1) in-flight data on lightning electrical parameters; (2) tech base and guidelines for protection of advanced systems and structures; (3) improved laboratory test techniques; (4) analysis techniques for predicting induced effects; (5) lightning strike incident data from general aviation; (6) lightning detection systems; (7) pilot reports on lightning strikes; and (8) better training in lightning awareness.

Corn, P. B.↗
NASA NTRS1997

Oscillating Cascade Aerodynamics at Large Mean Incidence Angles

In a cooperative program with Pratt & Whitney, researchers obtained fundamental separated flow unsteady aerodynamic data in the NASA Lewis Research Center's Oscillating Cascade. These data fill a void that has hindered the understanding and prediction of subsonic and transonic stall flutter. For small-amplitude torsional oscillations, unsteady pressure distributions were measured on airfoils with cross sections representative of an advanced, low-aspect-ratio fan blade. Data were obtained for two mean incidence angles with a subsonic inflow. At high mean incidence angles (alpha = 10 deg), the mean flow separated at the leading edge and reattached at about 40 percent of the chord. For comparison purposes, data were also obtained for a low incidence angle (a = 0 deg) attached flow.

Buffum, Daniel H.↗
NASA NTRS2014

Aerodynamic Measurements of a Variable-Speed Power-Turbine Blade Section in a Transonic Turbine Cascade

The purpose of this thesis is to document the impact of incidence angle and Reynolds number variations on the three-dimensional flow field and midspan loss and turning of a two-dimensional section of a variable-speed power-turbine (VSPT) rotor blade. Aerodynamic measurements were obtained in a transonic linear cascade at NASA Glenn Research Center in Cleveland, Ohio. Steady-state data were obtained for 10 incidence angles ranging from +15.8deg to -51.0deg. At each angle, data were acquired at five flow conditions with the exit Reynolds number (based on axial chord) varying over an order-of-magnitude from 2.12×105 to 2.12×106. Data were obtained at the design exit Mach number of 0.72 and at a reduced exit Mach number of 0.35 as required to achieve the lowest Reynolds number. Midspan tota lpressure and exit flow angle data were acquired using a five-hole pitch/yaw probe surveyed on a plane located 7.0 percent axial-chord downstream of the blade trailing edge plane. The survey spanned three blade passages. Additionally, three-dimensional half-span flow fields were examined with additional probe survey data acquired at 26 span locations for two key incidence angles of +5.8deg and -36.7deg. Survey data near the endwall were acquired with a three-hole boundary-layer probe. The data were integrated to determine average exit total-pressure and flow angle as functions of incidence and flow conditions. The data set also includes blade static pressures measured on four spanwise planes and endwall static pressures.

Flegel, Ashlie B.↗
DOE OSTI2020

A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study

Abstract At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case–control and one nested case–cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.

Nutrition & Dietetics↗
DOE OSTI2025

Reviewing Incident Neutron Energy assignments in 238 U(n,f) Fission Yield Data

Fission yield evaluations rely not only on precise measurements, but also on clear documentation of experimental conditions especially the energy of incident neutrons inducing fission. In the EXFOR database, following the convention adopted in past evaluations, the incident neutron energy has traditionally been divided in only three energy groups: thermal, fast and 14 MeV. The information is stored in the EN-DUMMY field, which often serves as a placeholder in the presence of broad-spectrum or reactor-based sources. This work reviews 238 U(n,f) entries in EXFOR that use EN-DUMMY, identifying cases where better energy estimates can be made based on the original publications, facility characteristics, or reconstructed neutron spectra. We assign new cross-section-weighted average energies where possible. The revised independent and cumulative fission yield data are compiled for future use in model development and evaluation efforts.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1986

Multiple incidence angle SIR-B experiment over Argentina

The Shuttle Imaging Radar (SIR-B), the second synthetic aperture radar (SAR) to fly aboard a shuttle, was launched on October 5, 1984. One of the primary goals of the SIR-B experiment was to use multiple incidence angle radar images to distinguish different terrain types through the use of their characteristic backscatter curves. This goal was accomplished in several locations including the Chubut Province of southern Argentina. Four descending image acquisitions were collected providing a multiple incidence angle image set. The data were first used to assess stereo-radargrammetric techniques. A digital elevation model was produced using the optimum pair of multiple incidence angle images. This model was then used to determine the local incidence angle of each picture element to generate curves of relative brightness vs. incidence angle. Secondary image products were also generated using the multi-angle data. The results of this work indicate that: (1) various forest species and various structures of a single species may be discriminated using multiple incidence angle radar imagery, and (2) it is essential to consider the variation in backscatter due to a variable incidence angle when analyzing and comparing data collected at varying frequencies and polarizations.

Cimino, Jobea↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗