Results for “data consistent” — Page 15

NASA NTRS1975

Parametric test of a zirconium (4) oxide-polyacrylic acid dual layer hyperfiltration membrane with spacecraft washwater

Performance data consisting of solute rejections and product flux were measured, as dependent on the operation parameters. These parameters and ranges were pressure (500,000 n/m2 to 700,000 n/m2), temperature (74 C to 95 C), velocity (1.6 M/sec to 10 M/sec), and concentration (up to 14x). Tests were carried out on analog washwater. Data presented include rejections of organic materials, ammonia, urea, and an assortment of ions. The membrane used was deposited in situ on a porcelain ceramic substrate.

Brandon, C. A.↗

DOE OSTI2024

An assessment of ocean alkalinity enhancement using aqueous hydroxides: kinetics, efficiency, and precipitation thresholds

Abstract. Ocean alkalinity enhancement (OAE) is a promising approach to marine carbon dioxide removal (mCDR) that leverages the large surface area and carbon storage capacity of the oceans to sequester atmospheric CO2 as dissolved bicarbonate (HCO3-). One OAE method involves the conversion of salt in seawater into aqueous alkalinity (NaOH), which is returned to the ocean. The resulting increase in seawater pH and alkalinity causes a shift in dissolved inorganic carbon (DIC) speciation toward carbonate and a decrease in the surface ocean pCO2. The shift in the pCO2 results in enhanced uptake of atmospheric CO2 by the seawater due to gas exchange. In this study, we systematically test the efficiency of CO2 uptake in seawater treated with NaOH at aquarium (15 L) and tank (6000 L) scales to establish operational boundaries for safety and efficiency in advance of scaling up to field experiments. CO2 equilibration occurred on the order of weeks to months, depending on circulation, air forcing, and air bubbling conditions within the test tanks. An increase of ∼0.7–0.9 mol DIC per mol added alkalinity (in the form of NaOH) was observed through analysis of seawater bottle samples and pH sensor data, consistent with the value expected given the values of the carbonate system equilibrium calculations for the range of salinities and temperatures tested. Mineral precipitation occurred when the bulk seawater pH exceeded 10.0 and Ωaragonite exceeded 30.0. This precipitation was dominated by Mg(OH)2 over hours to 1 d before shifting to CaCO3,aragonite precipitation. These data, combined with models of the dilution and advection of alkaline plumes, will allow the estimation of the amount of carbon dioxide removal expected from OAE pilot studies. Future experiments should better approximate field conditions including sediment interactions, biological activity, ocean circulation, air–sea gas exchange rates, and mixing zone dynamics.

Ringham, Mallory C. (ORCID:0000000348020185)↗

DOE OSTI2022

Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Introduction Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance. Objective The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies. Methods The data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method. Results The results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals. Conclusion Compared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

Liu, Meng↗

NASA NTRS1998

Development of the X-33 Aerodynamic Uncertainty Model

An aerodynamic uncertainty model for the X-33 single-stage-to-orbit demonstrator aircraft has been developed at NASA Dryden Flight Research Center. The model is based on comparisons of historical flight test estimates to preflight wind-tunnel and analysis code predictions of vehicle aerodynamics documented during six lifting-body aircraft and the Space Shuttle Orbiter flight programs. The lifting-body and Orbiter data were used to define an appropriate uncertainty magnitude in the subsonic and supersonic flight regions, and the Orbiter data were used to extend the database to hypersonic Mach numbers. The uncertainty data consist of increments or percentage variations in the important aerodynamic coefficients and derivatives as a function of Mach number along a nominal trajectory. The uncertainty models will be used to perform linear analysis of the X-33 flight control system and Monte Carlo mission simulation studies. Because the X-33 aerodynamic uncertainty model was developed exclusively using historical data rather than X-33 specific characteristics, the model may be useful for other lifting-body studies.

Cobleigh, Brent R.↗

NASA NTRSDate not supplied

Playing with DIRT: Building the Framework for a Comprehensive In-Situ Soil Materials Testing Database

Long-term, sustainable planetary exploration will require the ability to "live off the land," relying on In-Situ Resource Utilization (ISRU) and In-Situ Construction as core capabilities. Reduction of both risk and launch mass for lunar construction will require evaluating and comparing regolith materials for use as feedstocks suitable for in-situ beneficiation and fabrication of building components. These material assessment capabilities will inform lunar infrastructure design decisions, with co-benefits for terrestrial construction using in-situ materials. Current planetary construction technology development relies on lunar mapping and orbital data, Apollo-era sample analyses, current simulant inventories, and tests conducted using analog site soils. Requirements for lunar infrastructure design decisions and construction systems will be determined based on specific environmental conditions, mission architectures, and the materials available within traverse range of lunar feedstock processing depots. Critical for success is the capability to identify, evaluate and make effective use of a wide range of materials as they are found in-situ on the lunar surface. This paper discusses the development of a digital repository for data on soil and regolith properties, beginning with their structural performance in both cementitious and non-cementitious building material formulations. The Database for In-situ Resource Testing (DIRT) compiles a catalog of raw materials, additives, and formulations, with notations pertaining to material sources and preparation techniques entered via a web-based user interface. Design of consistent data schemas for site-sourced materials evaluation will facilitate linkage with relevant terrestrial and planetary materials databases while enabling guided data input via templates for participation by broader groups of collaborators. Results of these analyses are compiled in a centralized repository to generate insights applicable for regolith resources and landing sites yet to be precisely defined. Collection of thorough records of material characteristics, applications and performance will support innovative construction solutions not only for space infrastructure but for sustainable, resilient design in the terrestrial built environment.

Sarah Joey Seitz↗

NASA NTRS2001

MODIS Snow-Cover Products

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. These products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to the currently available operation products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set is generated for long-term climates studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the product. The snow product suite starts with a 500-m resolution swath snow-cover map which is gridded to the Integerized Sinusoidal Grid to produce daily and eight-day composite tile products. The sequence then proceeds to a climate-modeling grid product at 5-km spatial resolution, with both daily and eight-day composite products. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented. Near-term enhancements include daily snow albedo and fractional snow cover.

Hall, Dorothy K.↗

NASA NTRS2002

Hierarchies of Models: Toward Understanding Planetary Nebulae

Stars like our sun (initial masses between 0.8 to 8 solar masses) end their lives as swollen red giants surrounded by cool extended atmospheres. The nuclear reactions in their cores create carbon, nitrogen and oxygen, which are transported by convection to the outer envelope of the stellar atmosphere. As the star finally collapses to become a white dwarf, this envelope is expelled from the star to form a planetary nebula (PN) rich in organic molecules. The physics, dynamics, and chemistry of these nebulae are poorly understood and have implications not only for our understanding of the stellar life cycle but also for organic astrochemistry and the creation of prebiotic molecules in interstellar space. We are working toward generating three-dimensional models of planetary nebulae (PNe), which include the size, orientation, shape, expansion rate and mass distribution of the nebula. Such a reconstruction of a PN is a challenging problem for several reasons. First, the data consist of images obtained over time from the Hubble Space Telescope (HST) and spectra obtained from Kitt Peak National Observatory (KPNO) and Cerro Tololo Inter-American Observatory (CTIO). These images are of course taken from a single viewpoint in space, which amounts to a very challenging tomographic reconstruction. Second, the fact that we have two disparate and orthogonal data types requires that we utilize a method that allows these data to be used together to obtain a solution. To address these first two challenges we employ Bayesian model estimation using a parameterized physical model that incorporates much prior information about the known physics of the PN. In our previous works we have found that the forward problem of the comprehensive model is extremely time consuming. To address this challenge, we explore the use of a set of hierarchical models, which allow us to estimate increasingly more detailed sets of model parameters. These hierarchical models of increasing complexity are akin to scientific theories of increasing sophistication, with each new model/theory being a refinement of a previous one by either incorporating additional prior information or by introducing a new set of parameters to model an entirely new phenomenon. We apply these models to both a simulated and a real ellipsoidal PN to initially estimate the position, angular size, and orientation of the nebula as a two-dimensional object and use these estimates to later examine its three-dimensional properties. The efficiency/accuracy tradeoffs of the techniques are studied to determine the advantages and disadvantages of employing a set of hierarchical models over a single comprehensive model.

Knuth, Kevin H.↗

NASA NTRS2003

Hierarchies of Models: Toward Understanding Planetary Nebulae

Stars like our sun (initial masses between 0.8 to 8 solar masses) end their lives as swollen red giants surrounded by cool extended atmospheres. The nuclear reactions in their cores create carbon, nitrogen and oxygen, which are transported by convection to the outer envelope of the stellar atmosphere. As the star finally collapses to become a white dwarf, this envelope is expelled from the star to form a planetary nebula (PN) rich in organic molecules. The physics, dynamics, and chemistry of these nebulae are poorly understood and have implications not only for our understanding of the stellar life cycle but also for organic astrochemistry and the creation of prebiotic molecules in interstellar space. We are working toward generating three-dimensional models of planetary nebulae (PNe), which include the size, orientation, shape, expansion rate and mass distribution of the nebula. Such a reconstruction of a PN is a challenging problem for several reasons. First, the data consist of images obtained over time from the Hubble Space Telescope (HST) and spectra obtained from Kitt Peak National Observatory (KPNO) and Cerro Tololo Inter-American Observatory (CTIO). These images are of course taken from a single viewpoint in space, which amounts to a very challenging tomographic reconstruction. Second, the fact that we have two disparate and orthogonal data types requires that we utilize a method that allows these data to be used together to obtain a solution. To address these first two challenges we employ Bayesian model estimation using a parameterized physical model that incorporates much prior information about the known physics of the PN. In our previous works we have found that the forward problem of the comprehensive model is extremely time consuming. To address this challenge, we explore the use of a set of hierarchical models, which allow us to estimate increasingly more detailed sets of model parameters. These hierarchical models of increasing complexity are akin to scientific theories of increasing sophistication, with each new model/theory being a refinement of a previous one by either incorporating additional prior information or by introducing a new set of parameters to model an entirely new phenomenon. We apply these models to both a simulated and a real ellipsoidal PN to initially estimate the position, angular size, and orientation of the nebula as a two-dimensional object and use these estimates to later examine its three-dimensional properties. The efficiency/accuracy tradeoffs of the techniques are studied to determine the advantages and disadvantages of employing a set of hierarchical models over a single comprehensive model.

Knuth, Kevin H.↗

NASA NTRS2009

Microfossils in Carbonaceous Meteorites

Microfossils of large filamentous trichomic prokaryotes have been detected during in-situ investigations of carbonaceous meteorites. This research has been carried out using the Field Emission Scanning Electron Microscope (FESEM) to examine freshly fractured interior surfaces of the meteorites. The images obtained reveal that many of these remains are embedded in the meteorite rock matrix. Energy Dispersive X-Ray Spectroscopy (EDS) studies establish that the filamentous microstructures have elemental compositions consistent with the meteorite matrix, but are often encased within carbon-rich electron transparent sheath-like structures infilled with magnesium sulfate. This is consistent with the taphonomic modes of fossilization of cyanobacteria and sulphur bacteria, since the life habits and processes of these microorganisms frequently result in distinctive chemical biosignatures associated with the properties of their cell-walls, trichomes, and the extracellular polymeric substances (EPS) of the sheath. In this paper the evidence for biogenicity presented includes detailed morphological and morphometric data consistent with known characteristics of uniseriate and multiseriate cyanobacteria. Evidence for indigeneity includes the embedded nature of the fossils and elemental compositions inconsistent with modern biocontaminants.

Hoover, Richard B.↗

DOE OSTI2025

Welch Method and Bootstrapping Applied to Subcritical Gamma Noise

We measured the prompt neutron decay constant 𝛼 of the CROCUS zero-power reactor at the Swiss Federal Institute of Technology Lausanne using cross-power spectral density (CPSD) analysis of gamma-gamma correlations from two trans-stilbene organic scintillators positioned near the reactor core. We measured critical and subcritical states, with water levels ranging from 960 mm (critical) to 800 mm (𝜌=−1.4 $ subcritical). Our analysis used the Welch method, dividing signal segments for fast Fourier transform (FFT) frequency analysis and applying bootstrapping uncertainty quantification that uses Welch-defined segments. Results demonstrated a clear increase in the measured 𝛼 as reactor reactivity decreased, distinguishing critical from subcritical conditions. At the 960-mm critical level, 𝛼 was estimated at 155.9 ± 0.7 s −1 , and for the 800-mm subcritical level, 𝛼 increased significantly to 367.3 ± 6.9 s –1 . A linear regression of subcritical states yielded a critical estimate of 154.0 ± 3.1 s –1 , aligning with the static 𝛼 estimate at critical. The bootstrapping method produced normally distributed 𝛼 estimates, confirming data consistency. The gamma CPSD 𝛼 estimates clearly distinguish reactor states and improve monitoring of zero-power reactors. The future deployment of modular and microreactors as potential candidates for noise analysis is demonstrated in CROCUS, particularly zero-power mock-ups of new designs. The improvement of noise analysis in the subcritical domain from this work will support experimental data for reactor deployment and procedure.

CROCUS↗

NASA NTRS1972

Measurements of the structure and circulation of the stratosphere and mesosphere, 1970

Complete data from a total of 26 meteorological rocket soundings of the stratosphere and mesosphere conducted from Barrow, Alaska; Churchill, Canada; and Wallops Island, Va., are presented. These data consist of temperature, pressure, density, and wind profiles from 16 acoustic grenade soundings that cover the 30- to 90-km altitude range, and temperature, pressure, and density profiles from 10 pitot probe soundings that cover the 25- to 120-km altitude range. Errors for each of the 16 grenade soundings are also included. No analysis of the meteorological significance of the data is attempted.

Smith, W. S.↗

NASA NTRS1989

Time and frequency-domain identification and verification of BO-105 dynamic models

Mathematical models for the dynamics of the DLR BO 105 helicopter are extracted from flight test data using two different approaches: frequency-domain and time-domain identification. Both approaches are reviewed. Results from an extensive data consistency analysis are given. Identifications for 6 degrees of freedom (DOF) rigid body models are presented and compared in detail. The extracted models compare favorably and their prediction capability is demonstrated in verification results. Approaches to extend the 6 DOF models are addressed and first results are presented. System identification is broadly defined as the deduction of system characteristics from measured data. It provides the only possibility to extract both non-parametric (e.g., frequency responses) and parametric (e.g., state space matrices) aircraft models from flight test data and therefore gives a reliable characterization of the dynamics of the actually existing aircraft. Main applications of system identification are seen in areas where higher accuracies of the mathematical models are required: Simulation validation, control system design (in particular model-following control system design for in-flight simulation), and handling qualities.

Kaletka, Juergen↗

NASA NTRS1989

Time and frequency-domain identification and verification of BO-105 dynamic models

Mathematical models for the dynamics of the DLR BO 105 helicopter are extracted from flight test data using two different approaches: frequency-domain and time-domain identification. Both approaches are reviewed. Results from an extensive data consistency analysis are given. Identifications for 6 degrees of freedom (DOF) rigid body models are presented and compared in detail. The extracted models compare favorably and their prediction capability is demonstrated in verification results. Approaches to extend the 6 DOF models are addressed and first results are presented. System identification is broadly defined as the deduction of system characteristics from measured data. It provides the only possibility to extract both non-parametric (e.g., frequency responses) and parametric (e.g., state space matrices) aircraft models from flight test data and therefore gives a reliable characterization of the dynamics of the actually existing aircraft. Main applications of system identification are seen in areas where higher accuracies of the mathematical models are required: Simulation validation, control system design (in particular model-following control system design for in-flight simulation), and handling qualities.

Kaletka, Juergen↗

DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Soil Greenhouse Gas Fluxes 04/2020-06/2024

Both raw and processed data measured using a LI-COR 7810 Greenhouse Gas Analyzer in the TEMPEST experiment, part of COMPASS-FME (https://compass.pnnl.gov/FME/COMPASSFME), at Smithsonian Environmental Research Center. This ecosystem-scale experiment probes the effects of saltwater versus freshwater flooding in a coastal deciduous forest.The data consist of both concentrations and fluxes of carbon dioxide and methane measured using static chambers on the soil surface, approximately every two weeks from early 2020 to mid 2024. Some of the measurement points are controls, and some subject to root-exclusion techniques; all measurements are embedded in the TEMPEST control, freshwater, and saltwater plots (see Hopple et al. 2023). These data were generated to understand changing soil greenhouse gas (CO2 and CH4) production and consumption. File types are comma-separated value (.csv) for data, and markdown (.md) for supplementary information.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

MODIS Snow-Cover Products

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently-available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at about 5.6-km spatial resolution, with both daily and 8-day composite products. Each pixel of the CMG contains fraction of snow cover from 40 - 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02 - 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Hall, Dorothy K.↗

NASA NTRS1975

Parametric evaluation of a dual-layer dynamic hyperfiltration membrane for recovery of spacecraft wash water at elevated temperature

Performance data consisting of solute rejections and product flux have been measured, as dependent on the operational parameters. These parameters were pressure, 5,000,000 N/sq m (750 psia) to 7,000,000 N/sq m (1040 psia); temperature, 347 K (165 F) to 368 K (200 F); velocity, 1.6 m/s to 10 m/s; and concentration (up to 14x). Tests were carried out on analog wash water. Data taken include rejections of organic materials (TOC), ammonia, urea, and an assortment of ions. The membrane used was a dual-layer, polyacrylic acid over zirconium oxide, deposited in situ on a porcelain ceramic substrate.

Brandon, C. A.↗

DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

An operational data compression system using minicomputers.

Functional description of the real-time programmable data compression system that has been developed for operation between the data acquisition and satellite service ground stations and the ATS 1 and 3 satellites. The source data consist of TV pictures from these satellites that are received, compressed, transmitted, and reconstructed in real-time. General purpose minicomputers and specially designed I/O channels are used for compression and reconstruction. The installed compression system has passed all system input-output tests. Because of the great flexibility of the minicomputers, it is possible to vary the compression and to perform auxiliary tasks, such as data formatting.

Davisson, L. D.↗