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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

GONG Catalog of Solar Filament Oscillations Near Solar Maximum

We have cataloged 196 filament oscillations from the Global Oscillation Network Group Hα network data during several months near the maximum of solar cycle 24 (2014 January–June). Selected examples from the catalog are described in detail, along with our statistical analyses of all events. Oscillations were classified according to their velocity amplitude: 106 small-amplitude oscillations (SAOs), with velocities <10 km s-1, and 90 large-amplitude oscillations (LAOs), with velocities >10 km s-1. Both SAOs and LAOs are common, with one event of each class every two days on the visible side of the Sun. For nearly half of the events, we identified their apparent trigger. The period distribution has a mean value of 58 ± 15 minutes for both types of oscillations. The distribution of the damping time per period peaks at τ/P = 1.75 and 1.25 for SAOs and LAOs, respectively. We confirmed that LAO damping rates depend nonlinearly on the oscillation velocity. The angle between the direction of motion and the filament spine has a distribution centered at 27° for all filament types. This angle agrees with the observed direction of filament-channel magnetic fields, indicating that most of the cataloged events are longitudinal (i.e., undergo field-aligned motions). We applied seismology to determine the average radius of curvature in the magnetic dips, R ≈ 89 Mm, and the average minimum magnetic field strength, B ≈ 16 G. The catalog is available to the community online and is intended to be expanded to cover at least 1 solar cycle.

Filaments↗
NASA NTRS2019

On the Maximum Expected Electric Field in Electrically Small, Undermoded Enclosures

This paper reports the experimental validation of an improved statistical model for the prediction of maximum expected electric field in electrically small and under-moded enclosures. The aerospace community is interested in application of Hill's statistical models to design of avionics boxes for shielding effectiveness and for tailoring EMC test requirements for critical applications. However, it is observed that the probability distribution for mean-squared electric field in an electrically small enclosure differs from the exponential distribution which is widely used in reverberation chamber testing. It is postulated here that the difference is attributable to the under-moded character of the small enclosure.

Bremner, Paul↗
NASA NTRS2019

On the Maximum Expected Electric Field in Electrically Small, Undermoded Enclosures

This paper reports the experimental validation of an improved statistical model for the prediction of maximum expected electric field in electrically small and under-moded enclosures. The aerospace community is interested in application of Hill’s statistical models to design of avionics boxes for shielding effectiveness and for tailoring EMC test requirements for critical applications. However, it is observed that the probability distribution for mean-squared electric field (|E(sub x)|(exp 2)) in an electrically small enclosure differs from the exponential distribution which is widely used in reverberation chamber testing. It is postulated here that the difference is attributable to the under-moded character of the small enclosure. We will define “under-moded” as the condition where a single excitation frequency does not excite enough closely spaced resonant modes to achieve Hill’s assumption of an isotropic (or fully diffuse) plane wave field in the enclosure.

Bremner, P. G.↗
NASA NTRS2016

Maximum Likelihood Synchronization for Pulse Position Modulation with Inter-Symbol Guard Times

Deep space optical communications promises orders-of-magnitude growth in communication capacity, supporting high data rate applications such as video streaming and high-bandwidth science instruments. Pulse position modulation is the modulation format of choice for deep space applications, and by inserting inter-symbol guard times between the symbols, the signal carries the timing information needed by the demodulator. Accurately extracting this timing information is crucial to demodulating and decoding this signal. In this paper we propose a low complexity maximum likelihood timing estimator for pulse position modulation with inter-symbol guard times which significantly outperforms the prior art in this domain. We show that this estimator can achieve the same performance as prior estimators with an order-of-magnitude less signal flux, or multiple orders-of-magnitude less flux-accumulation time. Further we show that this estimator achieves the Cramer-Rao bound, making it asymptotically efficient. This method does not require an explicit synchronization sequence, freeing up channel resources for data transmission.

Rogalin, Ryan↗
NASA NTRS2020

The Multi-Mission Maximum Likelihood Framework threeML: Multi-wavelength Astronomy in Practice

The Multi-Mission Maximum Likelihood framework (threeML)is a flexi-ble python-based framework for multiwavelength data analysis in astronomy. ThreeMLallows joint likelihood fits of data recorded by many different instruments, from radio to gamma rays. This is achieved by encapsulating data access into instrument-specific plugins, leaving the rest of the analysis agnostic of the data format. In this paper, I out-line threeML’s design and major components, with a focus on the modeling language(astromodels) and the data-access plugins

Henrike Fleischhack↗
NASA NTRS2021

Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multi-model composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8,834 sites globally)in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multi-model composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2=0.81 at test point, 0.63 at 0.1°,0.62 at 0.5°) than the multi-model mean (R2=0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

environmental modeling↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗
NASA NTRSDate not supplied

Spaceflight Maximum Allowable Concentrations for Ethyl Acetate

Ethyl acetate is a simple organic compound that occurs naturally and is used industrially as a solvent. It has been detected in the ISS atmosphere and is known to off-gas from building materials. As NASA astronauts have been and will be exposed to ethyl acetate during space missions, Spaceflight Maximum Allowable Concentrations (SMACs) were developed following an extensive review of the available literature.

E Spencer Williams↗
NASA NTRS2020

A GaN-based Four-Switch Buck-Boost Converter using Ripple Correlation Control for Maximum Power Point Tracking in Dynamic Deep Space Environments

As the demand for high-performance power conversion in spacecraft continues to grow and spacecraft mass and volume budgets become increasingly tight, it is essential to design DC-DC converters with higher efficiency and power density. Although photovoltaic (PV) efficiency has increased over time, solar irradiance and temperatures can fluctuate dramatically in deep space. This causes significant variations in the maximum power point (MPP) of the PV array, which can decrease the overall system efficiency unless accounted for. Thus, it is imperative to track the MPP of the PV panels to maintain optimal efficiency. This paper presents the experimental development of a four-switch, GaN-based buck-boost converter with an implementation of the Ripple Correlation Control (RCC) MPPT algorithm for dynamic deep space environments. Due to the use of GaN HEMTs, the experimental system achieves better efficiency and power density compared to the previous state of the art implementations. A simulation of the prototype buck-boost converter was implemented in SaberRD (Synopsis), and a digital design of the RCC-based MPPT controller utilizing the StateAMS tool is presented. The simulation results show that this controller swiftly and precisely converged to the MPP of the source PV panels in a dynamic solar irradiance condition.

Mahmud, Sadab↗
NASA NTRS2022

Merging of the Case 2 Regional Coast Colour and Maximum‑Peak Height Chlorophyll‑a Algorithms: Validation and Demonstration of Satellite‑derived Retrievals Across US Lakes

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L−1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15−M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15−M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L−1; if the MPH(P) value was ≤ 10 µg L−1, the C2RCC value was selected, as long as that value was < 15 µg L−1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.

Satellite↗
NASA NTRSDate not supplied

Evaluation of the Maximum Soluble Water Recovery from Pretreated Urine Brines in Low Earth Orbit

The Urine Processor Assembly (UPA) on the International Space Station recovers water from pretreated urine by thin-film, vacuum compression distillation (VCD). A number of iterative improvements to the distiller assembly (DA) have enabled increases in on-orbit water recovery and improved distillate water quality at on-orbit recoveries up to 87% by volume relative to the volume of pretreated urine. The phosphoric acid-based pretreatment formulation has enabled these higher recoveries on-orbit since May of 2016. Application of simple empirical chemistry models have allowed water recovery of sulfuric-acid pretreated urine from the Russian Orbiting segment to be distilled with phosphoric acid pretreated urine from the U.S. Orbiting Segment. Returned liquid brine from brine filters in the UPA brine loop have enabled empirical measurement of gypsum solubility as a function of recovery, conductivity, or mass density in the real brines of ISS. Calculations of maximum soluble recovery, MSR, are based on preventing the precipitation of gypsum, which is the first mineral to precipitate for both types of pretreated urine. A statistical urine model of urine composition provides an estimate of worst-case urine from a crew of three during one year of operations on ISS. The MSR is defined based on the worst-case concentrations of urinary calcium and urinary sulfate that are pretreated, pooled, and distilled. The potential to increase water recoveries above 87% without gypsum precipitation are evaluated. The potential benefits and costs of the addition of a conductivity sensor or a Coriolis mass flow meter to the brine loop are evaluated.

Dean L Muirhead↗
NASA NTRSDate not supplied

Predicting Maximum Temperatures of a Li-ion Battery on a Simulated Flight Profile using a Model-based Prognostics

One of the challenges in using Li-ion packs in aeronautics is their safety, and thermal runaway (TR) is a major concern. The current engineering solutions to prevent a Li-ion pack from a catastrophic TR require additional mass and volume to isolate cells. The excess mass could be reduced by improving detection and, thus, preventing a TR event. One of the possible early warning indicators of a TR is crossing a threshold temperature. We have developed an approach, based on the Unscented Kalman Filter (UKF), to predict the likelihood of reaching the threshold temperature for simulated flight profiles. The current battery prognostics algorithms for aerospace predict state-of-charge (SOC) and end-of-life (EOL) [1]. We extended this two-level algorithm to predict the maximum temperature during discharge. The amount of heat generated in a cell depends on factors such as cell chemistry, cell packaging, total cycles, operating temperature, and abuse history [2]. Our semi-empirical thermal model depends on three phenomenological parameters which account for those factors. In addition, a two-parameter reduced-order model is developed to predict the temperature rise for short bursts of “random-walk” (RW) discharge current sequence, which simulates a flight's current-loading profile. The performance of these models on different datasets and types of current loading will be presented. To predict the maximal temperatures for future cycles we must estimate the evolution of thermal parameters as the batteries age. It is found that the parameters of the 3-parametric thermal model cannot be estimated only from the RW data. To address the issue, we will present two alternative approaches: i) expanding the datasets to include discharge profiles beyond RWs; ii) model reduction to a two-parametric model. The two approaches will be illustrated by an application to the cycling data from a commercial LG 18650 cell. References: 1. M. Daigle, C.S. Kulkarni, End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models, in: AIAA Infotech @ Aerospace, American Institute of Aeronautics and Astronautics, San Diego, California, USA, 2016. 2. M. Börner, et. al, Correlation of aging and thermal stability of commercial 18650-type lithium ion batteries, Journal of Power Sources. 342 (2017) 382–392.

Thermal runaway↗
NASA NTRSDate not supplied

Frequency Domain Quasi-Maximum Likelihood Identification of Low Order Aeroservoelastic Models from Flight-Test Data

Background and Motivation - Low Order Equivalent System (LOES) - From handling qualities analysis - Traditionally simplifying complex control law and plant - More easily understood form - Extend LOES to a complex model due to aeroelasticity - Maximum likelihood (Filter Error) System Identification - Z = H_loes (U + W) + V - There are a lot of parameters - System identification usually simplifies by assuming a value - Output Error and Equation Error - Results in biased estimates of the parameters - We are proposing a new method to solving this problem

Jeffrey Ouellette↗
NASA NTRS2022

Spacecraft Maximum Allowable Concentrations for Airborne Contaminants: Revision B

The enclosed table lists official Spacecraft Maximum Allowable Concentrations (SMACs) for selected airborne contaminants. They are based upon experiments conducted at standard pressure and oxygen environments and may or may not be applicable to altered atmospheres. These are guideline values set by the National Aeronautics and Space Administration (NASA)/Johnson Space Center (JSC) Toxicology Group in cooperation with the National Research Council Committee on Toxicology (NRCCOT) or through publication in the peer-reviewed scientific literature. Based on documented guidance (NRC, 1992; NRC, 2016), NASA has established SMACs for 60 chemical compounds that are particularly relevant to atmospheric contamination of the International Space Station (ISS) and targets of Exploration. Some long‐term limits (1000‐days) have also been established to support manned deep‐space exploration. Summaries of these SMACs are presented in tabular form as part of this publication. Short-term (1- and 24-hour) SMACs apply to off nominal situations, such as accidental releases aboard a spacecraft. These limits permit risk of minor, reversible effects, such as mild mucosal irritation. In contrast, the long term SMACs are set to fully protect healthy crewmembers from adverse effects resulting from continuous exposure to specific air pollutants for up to 1000 days. Because allergic reactions or chemical idiosyncrasy to certain airborne pollutants are very difficult to predict, crewmembers with allergies or unusual sensitivity to trace pollutants may not be afforded complete protection, even when long-term SMACs are not exceeded. Conversely, exceedance of a SMAC does not mean that health impairment is certain (there are many other factors that influence ultimate health outcomes), although it does indicate that the crew may be subject to increased risks that must be closely evaluated. Environmental pollutant control to mitigate exposure will likely be triggered.

SMACs↗
NASA NTRS2023

Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy Data Fusion to Map Global Surface Ozone Concentration and Associated Uncertainty

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R 2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m −3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

Ozone↗
NASA NTRS2024

Spacecraft Maximum Allowable Concentrations for Airborne Contaminants: Revision C

The enclosed table lists official Spacecraft Maximum Allowable Concentrations (SMACs) for selected airborne contaminants. They are based upon experiments conducted at standard pressure and oxygen environments and may or may not be applicable to altered atmospheres. The values listed in this summary document are applicable only to ambient conditions at standard temperature and oxygen (14.7 psi and 21% O2 at 25°C). These are guideline values set by the National Aeronautics and Space Administration (NASA)/Johnson Space Center (JSC) Toxicology Group in cooperation with the National Research Council Committee on Toxicology (NRCCOT), through publication in the peer- reviewed scientific literature, or NASA Technical Memoranda. Based on documented guidance (NRC, 1992; NRC, 2016), NASA has established SMACs for 58 chemical compounds that are particularly relevant to atmospheric contamination of the International Space Station (ISS) and targets of Exploration. Some long-term limits (1000-days) have also been established to support manned deep-space exploration. Summaries of these SMACs are presented in tabular form as part of this publication. This document provides a tabular summary of values that have been previously established based on guidelines established by NASA and the National Research Council’s Committee on Toxicology and vetted and published through the NRCCOT and/or non-NASA scientific literature and NASA Technical Memoranda. Short‐term (1‐ and 24‐hour) SMACs apply to off‐nominal situations, such as accidental releases aboard a spacecraft. These limits permit risk of minor, reversible effects, such as mild mucosal irritation. In contrast, the long‐term SMACs are set to fully protect healthy crewmembers from adverse effects resulting from continuous exposure to specific air pollutants for up to 1000 days. Because allergic reactions or chemical idiosyncrasy to certain airborne pollutants are very difficult to predict, crewmembers with allergies or unusual sensitivity to trace pollutants may not be afforded complete protection, even when long‐term SMACs are not exceeded.

SMACs↗
DOE OSTI2021

Distributed measurement of minimum and maximum in-situ stress in substrates

A system for performing distributed measurements of in-situ stress includes an expandable element with at least one fiber optic sensor. The expandable element can be positioned at various depths in a hole in a substrate. A pressurizing device expands (and contracts) the expandable element when the expandable element is inserted in the hole in the substrate to exert pressure on the hole wall. A pressure sensor provides a sensor output indicative of a pressure applied to the hole wall by the expandable element. The fiber optic sensor and an optical interrogator measure strain along a length of the sensor in a continuous, high spatial resolution manner Based on the measured strain and pressure sensor output, the system determines various properties of the substrate such as, minimum principal stress, maximum principal stress, and/or principal stress direction associated with one or more fractures in the substrate, as well as substrate modulus.

Ohanian, III, Osgar John↗