Results for “Step” — Page 74

NASA NTRS2022

Spatial Characterization of PACE OCI ETU Using Time-Delay Mode

The OCI (Ocean Color Instrument) is the main sensor on the upcoming PACE (Plankton Aerosol Cloud ocean Ecosystem) mission. OCI has two hyperspectral CCD sensors covering 340nm to 885nm and 9 SWIR (Short Wave IR) bands from 940nm to 2260nm. SWIR bands have nominal 1km ground pixel size and CCD bands have native 1/8 km ground pixel size in diagnostic mode that will be aggregated into 1km pixels to improve SNR and meet the data rate constraints. OCI has a rotating telescope that is synchronized to the readout of the CCD and SWIR detectors. Full pre-launch system level testing for the OCI ETU (Engineering Test Unit) was completed in June 2021.With time-delayed scan mode, a sub-pixel level time-delay step is applied to the detector readout. This sub-pixel level time-delay step causes a sub-pixel level shift in the start of the data collection. After collecting time-delay step scans with different step sizes, a scan profile with sub-pixel resolution can be constructed. 1/8 and 1/4 of CCD pixel resolutions were achieved using this mode. In this paper, the OCI time-delayed scan mode will be described as well as how it was used to calculate OCI’s high spatial resolution PSF (Point Spread Function), IFOV (instantaneous Field of View), MTF (Modulation Transfer Function), and BBR (Band to Band Registration).

PACE↗

NASA NTRSDate not supplied

Toward Consistent Long-term Records of Cloud Fraction from MODIS and VIIRS for CERES

In order to produce a long-term and stable climate record of Earth’s energy budget for NASA’s Clouds and Earth’s Radiant Energy System (CERES) project, a consistent cloud fraction record across different measurement platforms is a crucial first step. As Aqua satellite is approaching the end of its operational lifetime, NOAA-20 VIIRS observations on the JPSS-2 spacecraft will be used for cloud detection to continue the long-term Earth energy budget record. Two steps are designed to achieve this goal. The first step is a quick approach that revises the CERES NOAA-20 VIIRS Edition 1 A (CV Ed1A) cloud mask to incorporate Cross-track Infrared Sounder (CrIS) water vapor and CO_2 channels into the VIIRS cloud mask (CV Ed1B) to produce a consistent cloud fraction with CERES MODIS Edition 4 (CM Ed4) to avoid potential discontinuity when Aqua orbit drifts beyond tolerance before the next Edition is completed. The second step is to develop, as part of CERES next edition (Ed5), a unified MODIS and VIIRS cloud mask using common channels to produce consistent cloud products with improved radiative transfer models, refined clear sky predictions, new GMAO reanalysis products, and the latest radiance collections of MODIS and VIIRS data. This poster will present cloud fraction comparison between CV Ed1B and CM Ed4, as well as the preliminary results of ongoing Ed5 including pixel level cloud mask results, monthly global cloud fraction differences between MODIS and VIIRS (consistency), and initial validation using CALIPSO data (accuracy).

CERES↗

NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Across engineering there is an evolving need to increase reliance on physics-based simulation to develop, design and optimize engineering systems. This increased reliance on modeling and simulation has highlighted a growing need to transform the confidence that modeling and simulation analysts have in their results into credibility for systems engineers to design and field systems more quickly and with less physical testing. For isolated components of a complex system, where a single discipline may drive product design, this is less of a concern as the relationship is often straightforward and easy to explain. However, when these isolated components are integrated, and are expected to operate in a multi-disciplinary context in which safety critical systems are involved, new concepts and model assurance standards are required. In this paper we address this challenge by showing how a model validation hierarchy can be exploited to identify those model validation experiments that will contribute most to increasing confidence and credibility of modeling and simulation predictions. The approach that is adopted contains four main steps. The first step is the construction of a model validation hierarchy that links subsystems, assemblies, and components to a hierarchy of physical experiments that can be used support model validation. This hierarchy connects the concerns of systems engineers to those of the modeling and simulation analyst in a clear and logical way. The structure and content of this hierarchy is then used in a second step to establish which physical phenomena have the greatest impact on overall system performance metrics. A gap analysis technique, based upon modeling and simulation concerns, is then used to prioritize the important physical phenomenon. Unfortunately, a common outcome of such gap analyses is the identification of many important gaps and so, in the final step of our process, we advocate the use of a global sensitivity analysis as a means to complete the prioritization.

Verification and Validation↗

NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Across engineering there is an evolving need to increase reliance on physics-based simulation to develop, design and optimize engineering systems. This increased reliance on modeling and simulation has highlighted a growing need to transform the confidence that modeling and simulation analysts have in their results into credibility for systems engineers to design and field systems more quickly and with less physical testing. For isolated components of a complex system, where a single discipline may drive product design, this is less of a concern as the relationship is often straightforward and easy to explain. However, when these isolated components are integrated, and are expected to operate in a multi-disciplinary context in which safety critical systems are involved, new concepts and model assurance standards are required. In this paper we address this challenge by showing how a model validation hierarchy can be exploited to identify those model validation experiments that will contribute most to increasing confidence and credibility of modeling and simulation predictions. The approach that is adopted contains four main steps. The first step is the construction of a model validation hierarchy that links subsystems, assemblies, and components to a hierarchy of physical experiments that can be used support model validation. This hierarchy connects the concerns of systems engineers to those of the modeling and simulation analyst in a clear and logical way. The structure and content of this hierarchy is then used in a second step to establish which physical phenomena have the greatest impact on overall system performance metrics. A gap analysis technique, based upon modeling and simulation concerns, is then used to prioritize the important physical phenomenon. Unfortunately, a common outcome of such gap analyses is the identification of many important gaps and so, in the final step of our process, we advocate the use of a global sensitivity analysis as a means to complete the prioritization.

Verification and Validation↗

NASA NTRSDate not supplied

Technology Assessment for Producing Propellant From Lunar Water

Introduction: The presence of water ice in permanently shadowed regions (PSRs) on the lunar surface may enable a sustained human presence on the Moon with minimal need for consumables. One of the most valuable uses of lunar water is to produce rocket propellant. A sustained human presence on the Moon will re-quire frequent launches from the lunar surface, and each launch will require tons of propellant. However, many other volatiles are likely to be present in the areas where lunar water is stable1. Producing propellant from lunar water in-situ will require the separation and purification of water before it can be electrolyzed into oxygen and hydrogen gas. The Resource Exploration and Science of OUR Cosmic Environment (RESOURCE) project, funded by the Solar System Exploration Research Virtual Institute (SSERVI) program, includes a task to demonstrate the end-to-end process of water extraction, capture, purification and electrolysis to produce pure/dry oxygen gas sourced from an icy regolith mixture. Water Purification: The requirements for water purity are driven by the type of water electrolyzer used. For example, the water electrolyzer currently used on the International Space Station is based on proton ex-change membrane (PEM) technology. PEM-based electrolyzers require water to be ultrapure and deionized (>10 MegaOhm-cm) so that electrical conductivity is limited to the membranes themselves, where electrical current is used to convert H2O into H2 and O2.. A practical first step toward purifying water that is sourced from a lunar PSR is to use freeze distillation to separate water from other volatiles2. A sub-micronic filter can also be used to ensure that a minimal amount of dust is present in any lunar water that is captured. The combination of freeze distillation and sub-micronic filtration will result in relatively pure water but would require a deionization step if being fed into a PEM electrolyzer. Water Electrolysis: An alternative to PEM electrolysis is to use a Solid Oxide Electrolyzer (SOE). This technology operates at a much higher operating temperature (~800oC) and has a limited pressure range but does not require water to be ultrapure and deionized. Another advantage of SOE technology is that it produces pure dry oxygen gas without the need of an additional drying step. PEM electrolyzers produce humid oxygen gas and the humidity must be removed prior to liquefaction. Assessment: In order to compare PEM and SOE technologies side-by-side, they must be compared at a system level that includes water purification and gas drying steps. Engineers at the Johnson Space Center up-dated and utilized a detailed In-Situ Resource Utilization (ISRU) system model to compare and select the best option for the RESOURCE project which will conclude with a laboratory demonstration of the process of producing pure/dry oxygen from an icy regolith mixture in 2025.

A.J. Paz↗

NASA NTRSDate not supplied

Modern Scientific Data Governance Framework

Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.

Rahul Ramachandran↗

NASA NTRSDate not supplied

Technology Assessment for Producing Propellant From Lunar Water

Introduction: The presence of water ice in permanently shadowed regions (PSRs) on the lunar surface may enable a sustained human presence on the Moon with minimal need for consumables. One of the most valuable uses of lunar water is to produce rocket propellant. A sustained human presence on the Moon will re-quire frequent launches from the lunar surface, and each launch will require tons of propellant. However, many other volatiles are likely to be present in the areas where lunar water is stable1. Producing propellant from lunar water in-situ will require the separation and purification of water before it can be electrolyzed into oxygen and hydrogen gas. The Resource Exploration and Science of OUR Cosmic Environment (RESOURCE) project, funded by the Solar System Exploration Research Virtual Institute (SSERVI) program, includes a task to demonstrate the end-to-end process of water extraction, capture, purification and electrolysis to produce pure/dry oxygen gas sourced from an icy regolith mixture. Water Purification: The requirements for water purity are driven by the type of water electrolyzer used. For example, the water electrolyzer currently used on the International Space Station is based on proton ex-change membrane (PEM) technology. PEM-based electrolyzers require water to be ultrapure and deionized (>10 MegaOhm-cm) so that electrical conductivity is limited to the membranes themselves, where electrical current is used to convert H2O into H2 and O2.. A practical first step toward purifying water that is sourced from a lunar PSR is to use freeze distillation to separate water from other volatiles2. A sub-micronic filter can also be used to ensure that a minimal amount of dust is present in any lunar water that is captured. The combination of freeze distillation and sub-micronic filtration will result in relatively pure water but would require a deionization step if being fed into a PEM electrolyzer. Water Electrolysis: An alternative to PEM electrolysis is to use a Solid Oxide Electrolyzer (SOE). This technology operates at a much higher operating temperature (~800oC) and has a limited pressure range but does not require water to be ultrapure and deionized. Another advantage of SOE technology is that it produces pure dry oxygen gas without the need of an additional drying step. PEM electrolyzers produce humid oxygen gas and the humidity must be removed prior to liquefaction. Assessment: In order to compare PEM and SOE technologies side-by-side, they must be compared at a system level that includes water purification and gas drying steps. Engineers at the Johnson Space Center up-dated and utilized a detailed In-Situ Resource Utilization (ISRU) system model to compare and select the best option for the RESOURCE project which will conclude with a laboratory demonstration of the process of producing pure/dry oxygen from an icy regolith mixture in 2025.

ISRU↗

NASA NTRSDate not supplied

Functional Task Tests in Partial Gravity During Parabolic Flight

BACKGROUND Understanding how critical mission tasks are performed in partial gravity such as on the moon or Mars is necessary to define effective and comprehensive countermeasure strategies for preserving crew performance during exploration missions. We studied the performance of tasks such as standing, walking, and jumping during the partial gravity phases of parabolic flight. We hypothesized that the acute effects of partial gravity on vestibular, proprioceptive, and sensorimotor functions would negatively impact performance. METHODS Twelve subjects were tested over three flights of 30 parabolas each, including 10 parabolas at 0.25g, 10 parabolas at 0.5g, and 10 parabolas at 0.75g. Subjects also performed tests in 1g between parabolas. During the sit-to-stand with obstacle walk task, subjects rose from a seated position and walked as quickly as possible straight ahead towards a cone (4 m distance), walked around the cone making a 180° left turn, returned, and sat in the chair. On the way to and from the cone, subjects stepped over a 30 cm high obstacle. For the recovery from fall task, subjects lay prone for the pull-up phase and initial 10 sec of the parabola. Then they were asked to rise as quickly as possible and maintain a quiet stance for 10 sec. The tandem rail balance task involved standing with both feet on a 4.5 cm wide rail. The time ended when subjects either stepped off the rail or grabbed on to support straps. The jump down task started with the subjects standing on a 30 cm high platform, then the subjects were instructed to step off the platform, land with both feet simultaneously, and settle in a quiet stance. The cone of stability task involved subjects leaning about the ankles as far as they could in the anterior, posterior, and lateral directions without unfolding their arms or taking a step. The center of pressure distance between endpoints was calculated. Data were collected using inertial measurement units (Opal V2, APDM, Portland, OR) worn on the head and trunk, heart rate monitors (RS800CX, Polar, Kempele, Finland), and a force plate (Bertec, Columbus, OH). RESULTS Gravity level had a significant effect on performance, with the greatest changes from 1g tending to be at the 0.25g level (Table 1). Lower gravity levels were associated with increased times to complete the sit-to-stand obstacle walk task and the recovery from fall task, decreased change in heart rate during the recovery from fall task, decreased rail balance times, and increased cone of stability distance in the anterior-posterior direction. Table 1. Functional task performance at different gravity levels during parabolic flight. Measure0.25g0.5g0.75g1gp-valueSit-to-stand with obstacle walk time (sec)9.8 ±1.4*7.2 ±0.76.9 ±0.8*7.4 ±0.9<0.001Recovery from fall time to settle (sec)5.3 ± 0.9*4.6 ± 0.84.2 ± 0.64.3 ± 0.70.002Recovery from fall change in heart rate (bpm)6.0 ± 7.3*11.2 ± 6.5*14.9 ± 4.915.9 ± 6.5<0.001Rail balance with eyes open time (sec)2.7 ±0.8*4.8 ±2.16.6 ±4.78.4 ±6.70.031Rail balance with eyes closed time (sec)1.6 ±0.4*2.1 ±0.42.4 ±0.62.6 ±0.8<0.001Jump down time to settle (sec)2.1 ± 0.41.9 ± 0.42.0 ± 0.31.8 ± 0.30.328Cone of stability –anterior-posterior (cm)20.8 ±2.7*18.8 ±2.218.6 ±1.717.9 ±2.00.039Cone of stability –lateral (cm)26.8 ±6.624.4 ±2.123.0 ±2.123.8 ±2.30.215p-value: one-way repeated measures analysis of variance; *Significant pairwise difference from 1g (p<0.05). DISCUSSION These data suggest that there is a dose-response relationship between gravity level and functional task performance. The largest changes in performance were expected at the lowest gravity level (0.25g) because subjects would no longer be able to use the gravitational reference for the perception of upright. Understanding the extent of performance deficits informs the risks and design of countermeasures for exploration spaceflight missions. ACKNOWLEDGEMENT This work is supported by NASA’s Human Research Program Human Health Countermeasures Element.

T R Macaulay↗

NASA NTRSDate not supplied

A Wall-Distance Method for Turbulence Modeling

The distance from a grid point to the closest wall surface, wall distance, is a funda- mental quantity in turbulence modeling. Efficiency of wall-distance calculations has become more critical as the size of computational grids has significantly increased in recent years. This paper reports on an initial implementation of a new search-based wall-distance method that is suitable for general unstructured computational fluid dynamics (CFD) grids and tailored for requirements specific for turbulence modeling. The method represents a two-step approach to calculate the wall distance. In the first step, the wall distance is approximated for each grid point as the minimum distance from this point to a vertex of a triangular face at the wall. The point-to-vertex distance calculation is relatively inexpensive but may lead to a significant error in the wall-distance ap- proximation, especially for grid points near the wall. In the second step, for grid points located within a predefined distance ( threshold ) from the wall, the wall distance is computed as the minimum distance to wall faces. As a result, the wall distance is exact for all grid points within the threshold. This two-step approach reduces the computational cost yet achieves high and controllable accuracy in the evaluation of the wall distance. Algorithmic enhancements are presented to improve efficiency of wall-distance computations. Comprehensive assessment of the new method is reported for large-scale unstructured CFD grids generated for the Fifth AIAA CFD High-Lift Prediction Workshop. The performance of the new wall-distance method compares favorably with performance of two established methods implemented in high-performance CFD codes.

Wall Distance↗

NASA NTRS1967

The Effects of Controlled Environment on the Growth of Hydrogenomonas Bacteria in Continuous Cultures

The continuous culture of Hydrogenomonas is the intermediate step of a three-step closed life support cycle directed to the conversion of the human metabolic wastes, urea and carbon dioxide, into breathable oxygen and a food supplement. The potential advantages of this scheme have been discussed in an earlier paper. The other two steps of the complete cycle are: (1) The collection and recovery of the C02 removed from the cabin atmosphere by a regenerable C02 absorber, so that relatively pure C02 gas can be fed into the gas phase of the continuous culture; and (2) the electrolysis of water to produce breathable oxygen for the cabin supply, and by-product hydrogen, which is also fed to the gas phase of the closed culture vessel. There are three specific advantages in the use of this culture: (1) the bacteria use waste urea as a sole nitrogen source during growth, together with the C02 waste as a carbon source; the harvested excess of cells from a steady-state culture is a potential food for man; (2) the organism is not photosynthetic, so the equipment designer need not confine the culture in thin layers to have light available to all parts as is necessary in photosynthesis; and (3) the culture uses the by-product hydrogen from electrolysis as an energy source. Some oxygen must also be supplied to the culture environment, because the energy of the growth process is derived from the metabolic hydrogen-oxygen reaction, with the production of water that can be recycled to the electrolysis cell.

ENVIRONMENTAL CONTROL↗

NASA NTRS1969

Variable-mesh method of solving differential equations

Multistep predictor-corrector method for numerical solution of ordinary differential equations retains high local accuracy and convergence properties. In addition, the method was developed in a form conducive to the generation of effective criteria for the selection of subsequent step sizes in step-by-step solution of differential equations.

Van Wyk, R.↗

NASA NTRS1971

An Empirical Method for Determining the Lunar Gravity Field

A method has been devised to determine the spherical harmonic coefficients of the lunar gravity field. This method consists of a two-step data reduction and estimation process. In the first step, a weighted least-squares empirical orbit determination scheme is applied to Doppler tracking data from lunar orbits to estimate long-period Kepler elements and rates. Each of the Kepler elements is represented by an independent function of time. The long-period perturbing effects of the earth, sun, and solar radiation are explicitly modeled in this scheme. Kepler element variations estimated by this empirical processor are ascribed to the non-central lunar gravitation features. Doppler data are reduced in this manner for as many orbits as are available. In the second step, the Kepler element rates are used as input to a second least-squares processor that estimates lunar gravity coefficients using the long-period Lagrange perturbation equations.

Ferrari, A. J.↗

NASA NTRS1972

Application of an approach and landing system model to the space shuttle orbiter vehicle

An analytic model and procedure are described which can be used to estimate probability distributions of touchdown conditions, without utilizing a Monte Carlo simulation. Example applications are presented, including one-step and two-step flare strategies for the space shuttle orbiter. The computed probability distributions for the two-step flare are compared with the corresponding histograms from an independent Monte Carlo simulation of the same situation. The computed distributions are very good fits to the Monte-Carlo-generated histograms for pertinent longitudinal and lateral variables at three points (including touchdown) along the vehicle's final approach trajectory. The model, thus verified, has more inherent power for assessing the effects of system element changes than conventional Monte Carlo techniques.

Johnson, W. A.↗

NASA NTRS1972

Analytical and experimental investigation of aircraft metal structures reinforced with filamentary composites. Phase 2: Structural fatigue, thermal cycling, creep, and residual strength

Specimens representative of metal aircraft structural components reinforced with boron filamentary composites were manufactured and tested under cyclic loading, cyclic temperature, or continuously applied loading to evaluate some of the factors that affect structural integrity under cyclic conditions. Bonded, stepped joints were used throughout to provide composite-to-metal transition regions at load introduction points. Honeycomb panels with titanium or aluminum faces reinforced with unidirectional boron composite were fatigue tested at constant amplitude under completely reversed loading. Results indicated that the matrix material was the most fatigue-sensitive part of the design, with debonding initiating in the stepped joints. However, comparisons with equal weight all-metal specimens show a 10 to 50 times improved fatigue life. Fatigue crack propagation and residual strength were studied for several different stiffened panel concepts, and were found to vary considerably depending on the configuration. Composite-reinforced metal specimens were also subjected to creep and thermal cycling tests. Thermal cycling of stepped joint tensile specimens resulted in a ten percent decrease in residual strength after 4000 cycles.

Blichfeldt, B.↗

NASA NTRS1972

Low energy electron experiment for AE-C and AE-D

The low energy electron experiment (LEE) will provide differential measurements of the energy influx and angular distributions of electrons and protons on the Atmosphere Explorer C and D missions. The detectors consist of cylindrical electrostatic analyzers for species and energy selection and Spiraltron electron multipliers as particle sensors. The C version will contain three detectors measuring the two species from 0.2 to 25 keV in 16 logarithmically spaced steps, and 5 keV electrons continuously. Angular distributions will be acquired utilizing the spin of the spacecraft. The D version will contain 19 detectors, one proton stepped energy analyzer and two electron stepped energy analyzers at two different angles, again over the energy range 0.2 to 2.5 keV. In addition it will contain 16 fixed energy detectors which will obtain high-time-resolution angular distributions in the spacecraft 1 RPO mode at 5 energies between 0.2 and 5 keV. Increase of these energies by a factor of 3.5 will be possible by ground command.

Hoffman, R. A.↗

NASA NTRS1972

Hill-climbing controller for plants with any dynamics and rapid drifts.

Description of extremal control systems which utilize extrapolation procedures to optimize plant operations or processes involving any kind of dynamics before and after the static extremal characteristic. The optimization sequence starts with an 'action of drift detection' - i.e., a measurement of the drift rate of the output. Then a 'search action' is initiated which consists of stepped shifts of the input variable; the output response due to these changes in the input variable is evaluated in the light of the predicted shift and is used to provide information on the shape of the extremal characteristic at the actual operating point. The position of the extremum is estimated by a parabolic extrapolation of the shape of the characteristic from the operating point to the extremum. During the 'control action' the operating point is adjusted to the position corresponding to the predicted optimum of the process by first applying a forcing step to the input variable to obtain a quicker response at the beginning of the transient state, and then applying a definitive step to bring the input variable back to the position which corresponds to the predicted extremum. A 'rest time' may be introduced before starting the next optimization sequence.

Jacob, H. G.↗

NASA NTRS1973

Enhanced diffusion welding

Surfaces of unrecrystallized alloys are sanded and polished. This is followed by a two-step welding process by which the strength of the parent metal is retained at the weld joint. The first step forces the surfaces into intimate contact at a temperature where the metal still has good ductility. The second step causes diffusion, recrystallization, and grain growth across the original weld interface.

Holko, K. H.↗

NASA NTRS1973

Lunar gravity derived from long-period satellite motion - A proposed method.

A new method has been devised to determine the spherical harmonic coefficients of the lunar gravity field. This method consists of a two-step data reduction and estimation process. In the first step, a weighted least-squares empirical orbit determination scheme is applied to Doppler tracking data from lunar orbits to estimate long-period Kepler elements and rates. Each of the Kepler elements is represented by an independent function of time. The long-period perturbing effects of the earth, sun, and solar radiation are explicitly modeled in this scheme. Kepler element variations estimated by this empirical processor are then ascribed to the non-central lunar gravitation features. Doppler data are reduced in this manner for as many orbits as are available. In the second step, the Kepler element rates are used as input to a second least-squares processor that estimates lunar gravity coefficients using the long-period Lagrange perturbation equations.

Ferrari, A. J.↗