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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Depth Dose Distribution Study within a Phantom Torso after Irradiation with a Simulated Solar Particle Event at NSRL

The adequate knowledge of the radiation environment and the doses incurred during a space mission is essential for estimating an astronaut's health risk. The space radiation environment is complex and variable, and exposures inside the spacecraft and the astronaut's body are compounded by the interactions of the primary particles with the atoms of the structural materials and with the body itself Astronauts' radiation exposures are measured by means of personal dosimetry, but there remains substantial uncertainty associated with the computational extrapolation of skin dose to organ dose, which can lead to over- or underestimation of the health risk. Comparisons of models to data showed that the astronaut's Effective dose (E) can be predicted to within about a +10% accuracy using space radiation transport models for galactic cosmic rays (GCR) and trapped radiation behind shielding. However for solar particle event (SPE) with steep energy spectra and for extra-vehicular activities on the surface of the moon where only tissue shielding is present, transport models predict that there are large differences in model assumptions in projecting organ doses. Therefore experimental verification of SPE induced organ doses may be crucial for the design of lunar missions. In the research experiment "Depth dose distribution study within a phantom torso" at the NASA Space Radiation Laboratory (NSRL) at BNL, Brookhaven, USA the large 1972 SPE spectrum was simulated using seven different proton energies from 50 up to 450 MeV. A phantom torso constructed of natural bones and realistic distributions of human tissue equivalent materials, which is comparable to the torso of the MATROSHKA phantom currently on the ISS, was equipped with a comprehensive set of thermoluminescence detectors and human cells. The detectors are applied to assess the depth dose distribution and radiation transport codes (e.g. GEANT4) are used to assess the radiation field and interactions of the radiation field with the phantom torso. Lymphocyte cells are strategically embedded at selected locations at the skin and internal organs and are processed after irradiation to assess the effects of shielding on the yield of chromosome damage. The initial focus of the present experiment is to correlate biological results with physical dosimetry measurements in the phantom torso. Further on, the results of the passive dosimetry within the anthropomorphic phantoms represent the best tool to generate reliable data to benchmark computational radiation transport models in a radiation field of interest. The presentation will give first results of the physical dose distribution, the comparison with GEANT4 computer simulations based on a Voxel model of the phantom, and a comparison with the data from the chromosome aberration study.

Berger, Thomas↗
NASA NTRS2012

Impact of Aerosols on Convective Clouds and Precipitation

Aerosols are a critical.factor in the atmospheric hydrological cycle and radiation budget. As a major agent for clouds to form and a significant attenuator of solar radiation, aerosols affect climate in several ways. Current research suggests that aerosols have a major impact on the dynamics, microphysics, and electrification properties of continental mixed-phase convective clouds. In addition, high aerosol concentrations in urban environments could affect precipitation variability by providing a significant source of cloud condensation nuclei (CCN). Such pollution . effects on precipitation potentially have enormous climatic consequences both in terms of feedbacks involving the land surface via rainfall as well as the surface energy budget and changes in latent heat input to the atmosphere. Basically, aerosol concentrations can influence cloud droplet size distributions, the warm-rain process, the cold-rain process, cloud-top heights, the depth of the mixed-phase region, and the occurrence of lightning. Recently, many cloud resolution models (CRMs) have been used to examine the role of aerosols on mixed-phase convective clouds. These modeling studies have many differences in terms of model configuration (two- or three-dimensional), domain size, grid spacing (150-3000 m), microphysics (two-moment bulk, simple or sophisticated spectral-bin), turbulence (1st or 1.5 order turbulent kinetic energy (TKE)), radiation, lateral boundary conditions (i.e., closed, radiative open or cyclic), cases (isolated convection, tropical or midlatitude squall lines) and model integration time (e.g., 2.5 to 48 hours). Among these modeling studies, the most striking difference is that cumulative precipitation can either increase or decrease in response to higher concentrations of CCN. In this presentation, we review past efforts and summarize our current understanding of the effect of aerosols on convective precipitation processes. Specifically, this paper addresses the following topics: observational evidence of the effect of aerosols on precipitation processes, and results from (CRM) simulations. Note that this presentation is mainly based on a recent paper published in Geophy. Rev. (Tao et al. 2012).

Tao, Wei-Kuo↗
NASA NTRS2014

Forecasting Sensorimotor Adaptability from Baseline Inter-Trial Correlations

One of the greatest challenges surrounding adaptation to the spaceflight environment is the large variability in symptoms, and corresponding functional impairments, from one crewmember to the next. This renders preflight training and countermeasure development difficult, as a "one-size-fits-all" approach is inappropriate. Therefore, it would be highly advantageous to know ahead of time which crewmembers might have more difficulty adjusting to the novel g-levels inherent to spaceflight. Such knowledge could guide individually customized countermeasures, which would enable more efficient use of crew time, both preflight and inflight, and provide better outcomes. The primary goal of this project is to look for a baseline performance metric that can forecast sensorimotor adaptability without exposure to an adaptive stimulus. We propose a novel hypothesis that considers baseline inter-trial correlations, the trial-to-trial fluctuations in motor performance, as a predictor of individual sensorimotor adaptive capabilities. To-date, a strong relationship has been found between baseline inter-trial correlations and adaptability in two oculomotor systems. For this project, we will explore an analogous predictive mechanism in the locomotion system. METHODS: Baseline Inter-trial Correlations: Inter-trial correlations specify the relationships among repeated trials of a given task that transpire as a consequence of correcting for previous performance errors over multiple timescales. We can quantify the strength of inter-trial correlations by measuring the decay of the autocorrelation function (ACF), which describes how rapidly information from past trials is "forgotten." Processes whose ACFs decay more slowly exhibit longer-term inter-trial correlations (longer memory processes), while processes whose ACFs decay more rapidly exhibit shorterterm inter-trial correlations (shorter memory processes). Longer-term correlations reflect low-frequency activity, which is more easily measured in the frequency domain. Therefore, we use the power spectrum (PS), which is the Fourier transform of the ACF, to describe our inter-trial correlations. The decay of the PS yields a straight line on a log-log frequency plot, which we quantify by Beta = - (slope of PS on log-log axes). Hence, Beta is a measure of the strength of inter- trial correlations in the baseline data. Larger Beta values are indicative of longer inter-trial correlations. Experimental Approach: We will begin by performing a retrospective analysis of treadmill-gait adaptation data previously collected by Dr. Bloomberg and colleagues. Specifically, we will quantify the strength of inter-trial correlations in the baseline step cadence and heart rate data and compare it to the locomotor adaptability performance results already described by these investigators. Incorporating these datasets will also allow us to explore the applicability of (and potential limitations surrounding) the use of Beta in forecasting physiological performance. We will also perform a new experiment, in which Beta will be derived from baseline data collected during over-ground (non-treadmill) walking, which will enable us to consider locomotor performance, through the parameter Beta, under the most functionallyrelevant, natural gait condition. This experiment will incorporate two baseline and five post-training over-ground locomotion tests to explore the consistency and potential adaptability of the Beta values themselves. HYPOTHESES: We hypothesize that the strength of baseline inter-trial correlations of step cadence and heart rate will relate to locomotor adaptability. Specifically, we anticipate that individuals who show weaker longer-term inter-trial correlations in baseline step cadence data will be the better adaptors, as step cadence can be modified in real-time (i.e., online corrections are an inherent property of the locomotor system; analogous to results observed in the VOR). Conversely, because heart rate is not altered mid-beat, we expect that individuals who demonstrate stronger longer-term correlations in heart rate will be the better adaptors (analogous to results observed in the saccadic system). CONCLUSIONS: At the conclusion of this project we hope to uncover a baseline predictor of locomotor adaptability. If our hypotheses hold true, our results will demonstrate that the temporal structure of baseline behavioral data contains important information that may aid in forecasting adaptive capacities. The ability to predict such adaptability in the sensorimotor system has significant implications for spaceflight, where astronauts must adjust their motor programs following a change in g-level to retain movement accuracy.

Beaton, K. H.↗
NASA NTRS2012

Towards Reliable Evaluation of Anomaly-Based Intrusion Detection Performance

This report describes the results of research into the effects of environment-induced noise on the evaluation process for anomaly detectors in the cyber security domain. This research was conducted during a 10-week summer internship program from the 19th of August, 2012 to the 23rd of August, 2012 at the Jet Propulsion Laboratory in Pasadena, California. The research performed lies within the larger context of the Los Angeles Department of Water and Power (LADWP) Smart Grid cyber security project, a Department of Energy (DoE) funded effort involving the Jet Propulsion Laboratory, California Institute of Technology and the University of Southern California/ Information Sciences Institute. The results of the present effort constitute an important contribution towards building more rigorous evaluation paradigms for anomaly-based intrusion detectors in complex cyber physical systems such as the Smart Grid. Anomaly detection is a key strategy for cyber intrusion detection and operates by identifying deviations from profiles of nominal behavior and are thus conceptually appealing for detecting "novel" attacks. Evaluating the performance of such a detector requires assessing: (a) how well it captures the model of nominal behavior, and (b) how well it detects attacks (deviations from normality). Current evaluation methods produce results that give insufficient insight into the operation of a detector, inevitably resulting in a significantly poor characterization of a detectors performance. In this work, we first describe a preliminary taxonomy of key evaluation constructs that are necessary for establishing rigor in the evaluation regime of an anomaly detector. We then focus on clarifying the impact of the operational environment on the manifestation of attacks in monitored data. We show how dynamic and evolving environments can introduce high variability into the data stream perturbing detector performance. Prior research has focused on understanding the impact of this variability in training data for anomaly detectors, but has ignored variability in the attack signal that will necessarily affect the evaluation results for such detectors. We posit that current evaluation strategies implicitly assume that attacks always manifest in a stable manner; we show that this assumption is wrong. We describe a simple experiment to demonstrate the effects of environmental noise on the manifestation of attacks in data and introduce the notion of attack manifestation stability. Finally, we argue that conclusions about detector performance will be unreliable and incomplete if the stability of attack manifestation is not accounted for in the evaluation strategy.

cyber defense↗
NASA NTRS2016

Forecasting Sensorimotor Adaptability from Baseline Inter-Trial Correlations

One of the greatest challenges for sensorimotor adaptation to the spaceflight environment is the large variability in symptoms, and corresponding functional impairments, from one crewmember to the next. This renders preflight training and countermeasure development difficult, as a "one-size-fits-all" approach is inappropriate. Therefore, it would be highly advantageous to know ahead of time which crewmembers might have more difficulty adjusting to the novel g-levels inherent to spaceflight. This information could guide individually customized countermeasures, which would enable more efficient use of crew time and provide better outcomes. The principal aim of this work is to look for baseline performance metrics that relate to locomotor adaptability. We propose a novel hypothesis that considers baseline inter-trial correlations, the trial-to-trial fluctuations ("noise") in motor performance, as a predictor of individual adaptive capabilities.

Beaton, K. H.↗
NASA NTRS2016

An Optimized Multicolor Point-Implicit Solver for Unstructured Grid Applications on Graphics Processing Units

In the field of computational fluid dynamics, the Navier-Stokes equations are often solved using an unstructuredgrid approach to accommodate geometric complexity. Implicit solution methodologies for such spatial discretizations generally require frequent solution of large tightly-coupled systems of block-sparse linear equations. The multicolor point-implicit solver used in the current work typically requires a significant fraction of the overall application run time. In this work, an efficient implementation of the solver for graphics processing units is proposed. Several factors present unique challenges to achieving an efficient implementation in this environment. These include the variable amount of parallelism available in different kernel calls, indirect memory access patterns, low arithmetic intensity, and the requirement to support variable block sizes. In this work, the solver is reformulated to use standard sparse and dense Basic Linear Algebra Subprograms (BLAS) functions. However, numerical experiments show that the performance of the BLAS functions available in existing CUDA libraries is suboptimal for matrices representative of those encountered in actual simulations. Instead, optimized versions of these functions are developed. Depending on block size, the new implementations show performance gains of up to 7x over the existing CUDA library functions.

Zubair, Mohammad↗
NASA NTRS2015

Random Access Frame (RAF) System Neutral Buoyancy Evaluations

The Random Access Frame (RAF) concept is a system for organizing internal layouts of space habitats, vehicles, and outposts. The RAF system is designed as a more efficient improvement over the current International Standard Payload Rack (ISPR) used on the International Space Station (ISS), which was originally designed to allow for swapping and resupply by the Space Shuttle. The RAF system is intended to be applied in variable gravity or microgravity environments. This paper discusses evaluations and results of testing the RAF system in a neutral buoyancy facility simulating low levels of gravity that might be encountered in a deep space environment.

human spaceflight↗
NASA NTRS2018

Atmospheric Trace Gas (NO2 and Ozone) Dynamics over Coastal Waters near Polluted Urban Regions

At the interface between the land, oceans, and atmosphere, coastal regions are highly dynamic environments, characterized by strong variability in both water and air quality. Variability in atmospheric composition is associated with highly variable anthropogenic emissions, as well as complex meteorological processes that influence the circulation and accumulation of atmospheric pollutants at the land-ocean interface. Assessing the spatial and temporal dynamics of atmospheric pollutants, aerosols, and absorbing trace gases in coastal areas is critical for improving modeling of coastal tropospheric air quality, developing accurate satellite retrievals of coastal ocean color and biological processes, determining impacts of atmospheric pollution on human health, and assessing the ecological implications of atmospheric pollutant deposition for coastal terrestrial and aquatic ecosystems.Here, we present new measurements of atmospheric trace gas (NO2, and ozone) dynamics across a range of estuarine and coastal waters near urban regions. Measurements were conducted from research vessels using NASA's shipboard Pandora spectrometers, as part of recent multidisciplinary, multiplatform field campaigns, including the 2016 KORUS OC/AQ field campaign in the Yellow Sea and East Sea/Sea of Japan, the 2017/2018 OLWETS field campaign in the Chesapeake Bay estuary, and the 2018 LISTOS field campaign in the Long Island Sound. Shipboard measurements over these coastal waters were integrated with measurements from a ground-based Pandora network to examine differences in air quality over the land and over the ocean. Measurements were combined with air-parcel back-trajectory simulations to determine the origin of air masses over the coastal ocean. Comparisons with satellite retrievals of atmospheric composition reveal the benefits and limitations of polar-orbit satellite observations in capturing variability in atmospheric pollution gradients over land-water boundaries.

Tzortziou, Maria↗
NASA NTRS2022

Are Soybean Models Ready for Climate Change Food Impact Assessments?

An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO 2 concentration [CO 2 ] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO 2 ]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO 2 ] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO 2 ] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO 2 ] and temperature is needed to reduce the uncertainty from the individual models.

Agricultural Model Inter-comparison and Improvemen↗
NASA NTRSDate not supplied

What Can We Learn From Resilient Pilot Behaviors? The Case of Energy Management While Flying a Star

Recently, there has been increased interest in documenting flightcrew behaviors that contribute to safe operations. Instead of only capturing errors, new efforts are attempting to understand how pilots manage complexity and variability in the operational environment to ensure a safe mission. This approach highlights pilot responses to events and conditions that fall outside typical TEM threats; e.g., revised ATC clearances. This approach presents a two-sided coin: characterize flightcrew resilience /or/ generate insights regarding complexity in the operational environment that is not adequately managed by current flight deck interface designs, procedures, and training. To capture operational complexity, we have been analyzing flight path management tied to flying an RNAV STAR. Because ATC often requests revisions—e.g., descend late—and because RNAV STARs may not align with airplane performance limits, flightcrews need to monitor, anticipate threats to RNAV STAR compliance, and devise ways to accommodate unexpected challenges. In this paper, we identify general strategies that can support response adaptation and explore methods to facilitate training these strategies.

flight operations↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
NASA NTRS2003

Constraints on Variability of Brightness and Surface Magnetism on Time Scales of Decades to Centuries in the Sun and Sun-Like Stars: A Source of Potential Terrestrial Climate Variability

The following summarizes the most important, results of our research: (1) Conciliation of solar and stellar photometric variability; (2) Demonstration of an inverse correlation between the global temperature of the terrestrial lower troposphere, inferred from the NASA Microwave Sounding Unit (MSU)) radiometers, and the total area of the Sun covered by coronal holes from January 1979 to present (up to May 2000); (3) Identification of a possible climate mechanism amplifying the impact of solar ultraviolet irradiance variations; (4) Exploration of natural variability in an ocean-atmosphere climate model; (5) Presentation of a review of the sun's coronal influence on the terrestrial space environment; (6) Quantification of stellar variability as an influence on the analysis of periodic radial velocities that imply the presence of a planetary companion.

Baliunas, Sallie L.↗
NASA NTRS2001

Physical and Chemical Aspects of Fire Suppression in Extraterrestrial Environments

A fire, whether in a spacecraft or in occupied spaces on extraterrestrial bases, can lead to mission termination or loss of life. While the fire-safety record of US space missions has been excellent, the advent of longer duration missions to Mars, the moon, or aboard the International Space Station (ISS) increases the likelihood of fire events, with more limited mission termination options. The fire safety program of NASA's manned space flight program is based largely upon the principles of controlling the flammability of on-board materials and greatly eliminating sources of ignition. As a result, very little research has been conducted on fire suppression in the microgravity or reduced-gravity environment. The objectives of this study are: to obtain fundamental knowledge of physical and chemical processes of fire suppression, using gravity and oxygen concentration as independent variables to simulate various extraterrestrial environments, including spacecraft and surface bases in Mars and moon missions; to provide rigorous testing of analytical models, which include comprehensive descriptions of combustion and suppression chemistry; and to provide basic research results useful for technological advances in fire safety, including the development of new fire-extinguishing agents and approaches, in the microgravity environment associated with ISS and in the partial-gravity Martian and lunar environments.

Takahashi, F.↗
NASA NTRS2014

Commonality between Reduced Gravity and Microgravity Habitats for Long Duration Missions

Many conceptual studies for long duration missions beyond Earth orbit have assumed unique habitat designs for each destination and for transit habitation. This may not be the most effective approach. A variable gravity habitat, one designed for use in microgravity, lunar, Martian, and terrestrial environments may provide savings that offset the loss of environment-specific optimization. However, a brief analysis of selected flown spacecraft and Constellation-era conceptual habitat designs suggests that one cannot simply lift a habitat from one environment and place it in another that it was not designed for without incurring significant human performance compromises. By comparison, a conceptual habitat based on the Skylab II framework but designed specifically to accommodate variable gravity environments can be shown to yield significant advantages while incurring only minimal human performance compromises.

Howard, Robert↗
NASA NTRS2019

A Comparison of ARTEMIS Data with the Lunar Plasma Design Environment for NASA Crewed Missions

NASA’s Gateway will provide the capability for sustaining a human presence in cis-lunar space. Operations of the Gateway will include spacecraft dockings, extra vehicular activities (EVA), and high-power solar arrays. NASA’s experience with the International Space Station highlighted the importance of evaluating spacecraft charging effects for such operations. For crewed spacecraft, which tend to employ the use of dielectric surfaces in this dynamic plasma environment, reliance on spacecraft charging simulation packages, such as the NASA/Air Force Spacecraft Charging Analyzer Program (Nascap-2k) [Mandell et al., 2006] and Spacecraft Plasma Interaction System (SPIS) [Roussel et al., 2008], is required to understand the risks to hardware and humans. The variability in the lunar plasma environment as the Moon revolves around the Earth, lunar wake effects, and a strong dependency on photoemission and secondary electron emission creates challenges for spacecraft charging analysis. The Design Specification for Natural Environments (DSNE) [NASA, MSFC] is the primary resource for space environments affecting NASA’s crewed missions, and the DSNE provides plasma environments in a standard form for input into simulation packages. NASA developed the existing lunar plasma environment using data from Geotail [Nishida, 1994] along with published lunar plasma wake models [Halekas et al., 2005] based on Lunar Prospector. Since 2011, NASA’s twin Acceleration Reconnection Turbulence & Electrodynamics of Moon’s Interaction with the Sun (ARTEMIS) satellites [Angelopoulos, 2010] have been collecting high resolution plasma and fields observations within the lunar plasma environment providing a much larger dataset of the plasma properties in cislunar space. This research compares the existing lunar plasma environment definition with ARTEMIS data and makes recommendations on the refinement of the environment definition for future lunar missions.

ARTEMIS mission↗
NASA NTRS1984

The solar flare heavy ion environment for single-event upsets - A summary of observations over the last solar cycle, 1973-1983

A summary of observations of the flux of 25 to 400 MeV/nucleon heavy ions from solar flares is presented covering the period from late 1973 to early 1984. Distributions of flare occurrence frequency versus fluence, energy spectra, and composition are presented for the 30 events observed during this period, to quantify the variability of the heavy ion environment. A comparison of these data to a model environment suggests some refinements to the model. LET spectra based on the worst case flare observed are presented to illustrate the significance of the flare ion distribution and the importance of accurate shielding estimates.

Chenette, D. L.↗
NASA NTRS1993

Statistical analysis of environmental variability within the CELSS breadboard project's biomass production chamber

Variability in the aerial and root environments of NASA's Breadboard Project's Biomass Production Chamber (BPC) was determined. Data from two lettuce and two potato growouts were utilized. One growout of each crop was conducted prior to separating the upper and lower chambers; the other was subsequent to separation. There were little or no differences in pH, EC, or solution temperature between the upper and lower chamber or within a chamber. Variation in the aerial environment within a chamber was two to three times greater than variation between chambers for air temperature, relative humidity, and PPF. High variability in air velocity, relative to tray position, was observed. Separating the BPC had no effect on PPF, air velocity, solution temperature, pH, or EC. Separation reduced the gradient in air temperature and relative humidity between the upper and lower chambers, but increased the variability within a chamber. Variation between upper and lower chambers was within 5 percent of environmental set-points and of little or no physiological significance. In contrast, the variability within a chamber limits the capability of the BPC to generate statistically reliable data from individual tray treatments at this time.

Stutte, G. W.↗
NASA NTRS2003

Assessment and Control of Spacecraft Charging Risks on the International Space Station

Electrical interactions between the F2 region ionospheric plasma and the 160V photovoltaic (PV) electrical power system on the International Space Station (ISS) can produce floating potentials (FP) on the ISS conducting structure of greater magnitude than are usually observed on spacecraft in low-Earth orbit. Flight through the geomagnetic field also causes magnetic induction charging of ISS conducting structure. Charging processes resulting from interaction of ISS with auroral electrons may also contribute to charging albeit rarely. The magnitude and frequency of occurrence of possibly hazardous charging events depends on the ISS assembly stage (six more 160V PV arrays will be added to ISS), ISS flight configuration, ISS position (latitude and longitude), and the natural variability in the ionospheric flight environment. At present, ISS is equipped with two plasma contactors designed to control ISS FP to within 40 volts of the ambient F2 plasma. The negative-polarity grounding scheme utilized in the ISS 160V power system leads, naturally, to negative values of ISS FP. A negative ISS structural FP leads to application of electrostatic fields across the dielectrics that separate conducting structure from the ambient F2 plasma, thereby enabling dielectric breakdown and arcing. Degradation of some thermal control coatings and noise in electrical systems can result. Continued review and evaluation of the putative charging hazards, as required by the ISS Program Office, revealed that ISS charging could produce a risk of electric shock to the ISS crew during extra vehicular activity. ISS charging risks are being evaluated in ongoing ISS charging measurements and analysis campaigns. The results of ISS charging measurements are combined with a recently developed detailed model of the ISS charging process and an extensive analysis of historical ionospheric variability data, to assess ISS charging risks using Probabilistic Risk Assessment (PRA) methods. The PRA analysis (estimated frequency of occurrence and severity of the charging hazards) are then used to select the hazard control strategy that provides the best overall safety and mission success environment for ISS and the ISS crew. This paper presents: 1) a summary of ISS spacecraft charging analysis, measurements, observations made to date, 2) plans for future ISS spacecraft charging measurement campaigns, and 3) a detailed discussion of the PRA strategy used to assess ISS spacecraft charging risks and select charging hazard control strategies

Koontz, Steve↗