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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
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 NTRS2015

Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum

The rapid change in Arctic sea ice in recent decades has led to a rising demand for seasonal sea ice prediction. A recent modeling study that employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May. Here we show that satellite observations show no evidence of predictive skill in May. However, we find that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July. Our results highlight that late spring to mid summer melt pond information is required to improve the prediction skill of the seasonal sea ice minimum. Furthermore, satellite observations indicate a much higher percentage of melt pond formation in May than does the aforementioned model simulation, which points to the need to reconcile model simulations and observations, in order to better understand key mechanisms of melt pond formation and evolution and their influence on sea ice state.

sea ice↗
NASA NTRS2015

Transformational Phenomena as Predictors of Aircraft Accidents: What Goes Around Comes Around

Flight crew confusion, excessive stressworkload, and ineffective Crew Resource Management among other issues arising from dependence on automated systems on the flight deck have been identified as major causal factors in multiple fatal accidents, significant incidents and near misses. Many Loss of Control events have been traced to display of erroneous flight data and auto-flight system mode status to the crew. Safety was compromised by not having sufficiently obvious and unambiguous information available in order to permit quick diagnosis of aircraft status and then appropriate action to regain control of the aircraft energy state or trajectory. Continually evolving training and operational requirements related to aircraft automation have also presented critical challenges to commercial aviation. During times of high demand and low supply of experienced pilots (which is today the case in South-East Asia, for instance), basic training in manual flight may be minimal and as low as a few hundred flight hours on light aircraft before beginning training on advanced, highly automated aircraft. Predominant use of automation may cause aircrew trained in this way to have trouble performing traditionally simple operations such as manually switching to other runways or overriding the autopilot in tight situations. Inadequate crew training andor experience coupled with attempted manual flight in highly automated airplanes may more easily lead to loss of aircraft control in unusual situations such as high-altitude stallsupsets, traffic avoidance or maneuvering. Loss of basic piloting skills through increasing dependence on automation may exacerbate this problem. Finally, design changes by nature take a long time and are very costly. Incorporating novel automated functionality into new aircraft designs is technically feasible and desirable. However, it may take many years for these changes to have a significant impact on tomorrows fleet, given the time it takes to develop a new aircraft and for these aircraft to become a significant fraction of the fleet. This paper will review and summarize the findings and recommendations from a 2004 study of the topic, Increasing reliance on flight deck automation conducted by the Future Aviation Safety Team at the behest of the Joint Safety Strategy Initiative (JSSI) within the Joint Aviation Authorities (JAA) organization that existed at the time. Although this study was conducted more than ten years ago, its findings and recommendations are as relevant now as then and into the future.

safety↗
NASA NTRS2019

State Predictor of Classification Cognitive Engine Applied to Channel Fading

This study presents the application of machine learning (ML) to a space-to-ground communication link, showing how ML can be used to detect the presence of detrimental channel fading. Using this channel state information, the communication link can be used more efficiently by reducing the amount of lost data during fading. The motivation for this work is based on channel fading observed during on-orbit operations with NASA's Space Communication and Navigation (SCaN) testbed on the International Space Station (ISS). This paper presents the process to extract a target concept (fading and not-fading) from the raw data. The pre-processing and data exploration effort is explained in detail, with a list of assumptions made for parsing and labelling the dataset. The model selection process is explained, specifically emphasizing the benefits of using an ensemble of algorithms with majority voting for binary classification of the channel state. Experimental results are shown, highlighting how an end-to-end communication system can utilize knowledge of the channel fading status to identity fading and take appropriate action. With a laboratory testbed to emulate channel fading, the overall performance is compared to standard adaptive methods without fading knowledge, such as adaptive coding and modulation.

Fading↗
NASA NTRS2020

Sensorimotor Predictors of Post-Landing Functional Task Performance

Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study is to identify a set of behavioral, neuroimaging and genetic measures that can potentially be used to predict early performance following G-transitions such as return to Earth on a set of sensorimotor tasks. Astronauts are being recruited who previously participated in sensorimotor field tests and/or dynamic posturography (MedB) within R+1 days following long-duration spaceflight.

Wood, S. J.↗
NASA NTRS2020

Overview of a Generalized Numerical Predictor-Corrector Targeting Guidance with Application to Human-Scale Mars Entry, Descent, and Landing

Recent advances in planetary entry guidance algorithms are motived by precision landing criteria for human-scale Mars missions and improved in-space computing capabilities. An NPC targeting guidance algorithm, originally developed for the Mars Surveyor Program 2001 Missions, has been modified and extended to permit a fully generalized, flexible, and robust approach to spacecraft aerocapture and EDL guidance design. This paper describes this generalized targeting NPC guidance (NPCG) and how its modifications enable its use in precision targeting for human missions. System modeling, trajectory propagation, and guidance segment definition and design are described. Finally, the NPCG capability and performance is demonstrated using a human-scale Mars entry, descent, and landing mission simulation.

Rafael A Lugo↗
NASA NTRS2021

Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

This global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically-triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP) and Gravity Recovery and Climate Experiment (GRACE) remote sensing data, Catchment Land Surface Model (CLSM) simulations and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300-km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011-2016, unless the CLSM model-only soil water content is already high (≥ 50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015-2019) more generally updates the soil water conditions towards higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

Anne Felsberg↗
NASA NTRS2018

Modeling Key Predictors of Airport Runway Configurations Using Learning Algorithms

Advanced traffic flow management automation will need accurate predictions of airport runway configurations. Terminal area weather and traffic demand are generally considered to be the most significant factors in predicting runway configuration. Weather information is forecasted across multiple features, including wind direction, wind speed, gusts, cloud ceilings, visibility, temperature, and precipitation, among many others. We use machine learning techniques on historical weather and runway data to determine weather features that correlate well with runway configurations. We analyze the predictive capability of weather features using different learning models trained on data from four major U.S. airports: Atlanta (ATL), Washington – Dulles (IAD), New York – Kennedy (JFK), and San Francisco (SFO). Wind direction alone is strongly correlated with runway configurations above all other examined factors, as expected. This correlation is the most significant component of the ~80% prediction accuracy in selecting between the two most frequently used runway configurations. However, individual airports show variations on how well the runway configuration decisions correlate with wind direction. While wind direction was identified as the most significant indicator of configuration decisions in ATL, IAD, and JFK, it did not emerge as such at SFO. Traffic demand was not found to be a strong factor in predicting runway configurations at any of the airports analyzed. In rare instances, when high demand cannot be accommodated within the current configuration, temporary changes are likely to be attributable to demand. However, these occurrences are so limited in number that their overall effect is not sufficient to consider traffic demand as a major indicator of runway configuration at the airports analyzed.

Bilimoria, Karl D.↗
NASA NTRSDate not supplied

Sensorimotor Predictors: Examining the Relationship Between Measures of Post-Landing Sensorimotor Functional Task Performance

Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to Earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study is to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on sensorimotor functional tasks. To date, we have recruited fifteen astronauts who returned from the International Space Station on Soyuz and participated in sensorimotor field tests and/or posturography within one day following long-duration spaceflight. We are specifically utilizing a combination of three quantitative post-flight functional task outcomes(relative to pre-flight baselines): tandem walk, recovery from fall and dynamic posturography, along with a subjective self-rating of post-flight decrements and recovery. The recovery from fall is performed with eyes open on a stable support, allowing the use of vestibular, visual and proprioceptive feedback for task performance. In contrast, the dynamic posturography measures are performed with eyes closed on asway-referenced unstable support, requiring reliance on vestibular feedback for task performance. Tandem walk is performed on a stable surface with eyes open and eyes closed. Fourteen of the 15 subjects performed the field tests. These were nominally performed during three timepoints on the first postflight day, while posturography was performed only once during the third time point after direct return to JSC. More than 20% were unable to complete the initial field testing in the medical tent, while all participants completed testing during the third time point at JSC. As expected, there was considerable variability among all performance outcome measures, with more variability post-flight relative to preflight. Given the variability in all post-flight outcomes, we have been examining the relationships in performance across tasks. While there is a strong association within tests obtained at different landing daytime points, our preliminary findings suggest that by R+24 hrs performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. This underscores the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. We expect that further examining specific behavioral, neuroimaging and genetic sensorimotor biomarkers with post-flight functional task performance will improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures.

S J Wood↗
NASA NTRSDate not supplied

Sensorimotor Predictors: Examining the Relationship Between Measures of Post-Landing Sensorimotor Functional Task Performance

Spaceflight drives adaptive changes in healthy individuals appropriate for sensorimotor function in a microgravity environment. These changes are maladaptive for return to Earth's gravity. The inter-individual variability of sensorimotor decrements is striking, although poorly understood. The goal of this study is to identify a set of behavioral, neuroimaging and genetic measures that can be used to predict early post-flight performance on sensorimotor functional tasks. To date, we have recruited fifteen astronauts who returned from the International Space Station on Soyuz and participated in sensorimotor field tests and/or posturography within one day following long-duration spaceflight. We are specifically utilizing a combination of three quantitative post-flight functional task outcomes(relative to pre-flight baselines): tandem walk, recovery from fall and dynamic posturography, along with a subjective self-rating of post-flight decrements and recovery. The recovery from fall is performed with eyes open on a stable support, allowing the use of vestibular, visual and proprioceptive feedback for task performance. In contrast, the dynamic posturography measures are performed with eyes closed on asway-referenced unstable support, requiring reliance on vestibular feedback for task performance. Tandem walk is performed on a stable surface with eyes open and eyes closed. Fourteen of the 15 subjects performed the field tests. These were nominally performed during three timepoints on the first post flight day, while posturography was performed only once during the third time point after direct return to JSC. More than 20% were unable to complete the initial field testing in the medical tent, while all participants completed testing during the third time point at JSC. As expected, there was considerable variability among all performance outcome measures, with more variability post-flight relative to preflight. Given the variability in all post-flight outcomes, we have been examining the relationships in performance across tasks. While there is a strong association with in tests obtained at different landing daytime points, our preliminary findings suggest that by R+24 hrs performance on one post-flight test does not necessarily correlate with performance on other post-flight tests. This underscores the importance of a comprehensive post-flight test battery including different types of tasks with varying sensory feedback. We expect that further examining specific behavioral, neuroimaging and genetic sensorimotor biomarkers with post-flight functional task performance will improve both our understanding of the individual variability and our strategy to optimize sensorimotor countermeasures.

Yiri De Dios↗
NASA NTRSDate not supplied

Predicting Maximum Thermal Response in a Li-ion Cell as a Thermal Runaway Predictor for a UAV Fight

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances to spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Moreover, a thermal runaway can propagate from a single "failed" cell to a whole battery pack, resulting in a more serious event. The commercial and automotive sectors need to mitigate thermal runaway events. An electric aircraft (or air taxi) has no alternative in the event of a failure, so preventing such events is paramount. Currently, battery prognostics algorithms only predict the state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle), but do not predict the maximum temperature during a flight or the likelihood of thermal runaway. Our current work focuses on adding the thermal model (based on Birkl's OCV model, in Ref. [1]) to the existing hybrid electrochemical model (single-particle model with lumped parameters as in Ref. [2]) to determine end-of-discharge, end-of-life, and maximum temperature during discharge.

Thermal runaway↗
NASA NTRS2024

DNA Break Clustering as a Predictor of Cell Death across Various Radiation Qualities: Influence of Cell Size, Cell Asymmetry, and Beam Orientation

Cosmic radiation, composed of high charge and energy (HZE) particles, causes cellular DNA damage that can result in cell death or mutation that can evolve into cancer. In this work, a cell death model is applied to several cell lines exposed to HZE ions spanning a broad range of linear energy transfer (LET) values. We hypothesize that chromatin movement leads to the clustering of multiple double strand breaks (DSB) within one radiation-induced foci (RIF). The survival probability of a cell population is determined by averaging the survival probabilities of individual cells, which is function of the number of pairwise DSB interactions within RIF. The simulation code RITCARD was used to compute DSB. Two clustering approaches were applied to determine the number of RIF per cell. RITCARD outputs were combined with experimental data from four normal human cell lines to derive the model parameters and expand its predictions in response to ions with LET ranging from ∼0.2keV/μmto∼3000keV/μm. Spherical and ellipsoidal nuclear shapes and two ion beam orientations were modeled to assess the impact of geometrical properties on cell death. The calculated average number of RIF per cell reproduces the saturation trend for high doses and high-LET values that is usually experimentally observed. The cell survival model generates the recognizable bell shape of LET dependence for the relative biological effectiveness (RBE). At low LET, smaller nuclei have lower survival due to increased DNA density and DSB clustering. At high LET, nuclei with a smaller irradiation area either because of a smaller size or a change in beam orientation have a higher survival rate due to a change in the distribution of DSB/RIF per cell. If confirmed experimentally, the geometric characteristics of cells would become a significant factor in predicting radiation-induced biological effects.

cell survival↗