Search NASA⌕ Search

SEARCH · Search NASA

Results for “random variables”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Restricted Collision List method for faster Direct Simulation Monte-Carlo (DSMC) collisions

The ‘Restricted Collision List’ (RCL) method for speeding up the calculation of DSMC Variable Soft Sphere collisions, with Borgnakke–Larsen (BL) energy exchange, is presented. The method cuts down considerably on the number of random collision parameters which must be calculated (deflection and azimuthal angles, and the BL energy exchange factors). A relatively short list of these parameters is generated and the parameters required in any cell are selected from this list. The list is regenerated at intervals approximately equal to the smallest mean collision time in the flow, and the chance of any particle re-using the same collision parameters in two successive collisions is negligible. The results using this method are indistinguishable from those obtained with standard DSMC. The CPU time saving depends on how much of a DSMC calculation is devoted to collisions and how much is devoted to other tasks, such as moving particles and calculating particle interactions with flow boundaries. For 1-dimensional calculations of flow in a tube, the new method saves 20% of the CPU time per collision for VSS scattering with no energy exchange. With RCL applied to rotational energy exchange, the CPU saving can be greater; for small values of the rotational collision number, for which most collisions involve some rotational energy exchange, the CPU may be reduced by 50% or more.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Strong Correspondence in Evapotranspiration and Carbon Dioxide Fluxes Between Different Eddy Covariance Systems Enables Quantification of Landscape Heterogeneity in Dryland Fluxes

Abstract The eddy covariance method is widely used to investigate fluxes of energy, water, and carbon dioxide at landscape scales, providing important information on how ecological systems function. Flux measurements quantify ecosystem responses to environmental perturbations and management strategies, including nature‐based climate‐change mitigation measures. However, due to the high cost of conventional instrumentation, most eddy covariance studies employ a single system, limiting spatial representation to the flux footprint. Insufficient replication may be limiting our understanding of ecosystem behavior. To address this limitation, we deployed eight lower‐cost eddy covariance systems in two clusters around two conventional eddy covariance systems in the Chihuahuan Desert of North America for a period of 2 years. These dryland settings characterized by large temperature variations and relatively low carbon dioxide fluxes represented a challenging setting for eddy covariance. We found very good closure of energy and water balance across all systems (within ±9% of unity). We found very good correspondence between the lower‐cost and conventional systems' fluxes of sensible heat (with concordance correlation coefficient (CCC) of ≥0.87), latent energy (evapotranspiration; CCC ≥ 0.89), and useful correspondence in the net ecosystem exchange ((NEE); with CCC ≥ 0.4) at the daily temporal resolution. Relative to the conventional systems, the low‐frequency systems were characterized by a higher level of random error, particularly in the NEE fluxes. Lower‐cost systems can enable wider deployment affording better replication and sampling of spatiotemporal variability at the expense of greater measurement noise that might be limiting for certain applications. Replicated eddy covariance observations may be useful when addressing gaps in the existing monitoring of critical and underrepresented ecosystems and for measuring areas larger than a single flux footprint.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Highly Textured BaTiO 3 via Templated Grain Growth and Resulting Polarization Reversal Dynamics

Samples of bulk textured polycrystalline BaTiO 3 ceramics were fabricated using a templated grain growth ( TGG ) approach in order to investigate effects of polycrystallinity and texture related to ferroelectric domain reversal under high‐power drive conditions. Barium titanate platelets were formed via two‐step topochemical conversion of bismuth titanate platelets grown via molten salt synthesis, then aligned via tape casting within a matrix of fine BaTiO 3 powder. The coarse‐grained parts showed a high degree of crystallographic texture after sintering. Combined with ceramics of similar density and polycrystallinity, but random orientation and commercial single‐crystal specimens, this sample set enabled direct isolation of crystallographic texture and polycrystallinity as the primary variables for high‐power polarization reversal studies. These studies have also demonstrated a link between grain size and polarization reversal time that strongly suggests that grain boundaries serve effectively as nucleation sites during the ferroelectric switching process.

Meier, William↗
DOE OSTI2025

Flood Susceptibility Mapping Using Machine Learning and Geospatial-Sentinel-1 SAR Integration for Enhanced Early Warning Systems

This study presents a comprehensive framework for flood susceptibility mapping by integrating geospatial factors with both statistical and machine learning models. Thirteen Flood-related factors, including DEM, slope, TWI, NDVI, etc., are extracted as features of models, and historical flood data derived from Sentinel-1 SAR from 2018 to 2023 are used as the target variables of the models. These datasets are analyzed using a frequency-based statistical model and three machine learning models, including Random Forest, XGBoost, and CNN, to generate flood susceptibility maps. The performance of each model is evaluated through AUC; and SHAP scores are separately generated for Machine learning (ML) models to explain each feature contribution in the ML model. The generated susceptibility maps are validated by high-flood-risk locations monitored by flood sensors, BLE inundation models, and flood-prone areas suggested by the Local Community Task Force. The results indicate that the XGBoost model outperforms all other models, with an AUC of 0.92 and demonstrates the highest alignment with recommended high-flood-risk locations, while the frequency-based statistical model showed the weakest performance with an AUC of 0.65. SHAP value graphs highlight the elevation, slope, and TWI as the most influential features across all models. The susceptibility maps generated by the machine learning model show strong agreement with the BLE map and high-flood-risk areas identified by the local Community Task Force.

Google Engine↗
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
NASA NTRS1980

Packet communications in satellites with multiple-beam antennas and signal processing

A communication satellite with a multiple-beam antenna and onboard signal processing is considered for use in a 'message-switched' data relay system. The signal processor may incorporate demodulation, routing, storage, and remodulation of the data. A system user model is established and key functional elements for the signal processing are identified. With the throughput and delay requirements as the controlled variables, the hardware complexity, operational discipline, occupied bandwidth, and overall user end-to-end cost are estimated for (1) random-access packet switching; and (2) reservation-access packet switching. Other aspects of this network (eg, the adaptability to channel switched traffic requirements) are examined. For the given requirements and constraints, the reservation system appears to be the most attractive protocol.

Davies, R.↗
NASA NTRS1984

Revised radiometric calibration technique for LANDSAT-4 Thematic Mapper data

Depending on detector number, there are random fluctuations in the background level for spectral band 1 of magnitudes ranging from 2 to 3.5 digital numbers (DN). Similar variability is observed in all the other reflective bands, but with smaller magnitude in the range 0.5 to 2.5 DN. Observations of background reference levels show that line dependent variations in raw TM image data and in the associated calibration data can be measured and corrected within an operational environment by applying simple offset corrections on a line-by-line basis. The radiometric calibration procedure defined by the Canadian Center for Remote Sensing was revised accordingly in order to prevent striping in the output product.

Murphy, J.↗
NASA NTRS1990

Interannual variation of seasonal means and subseasonal variability of cloud streets off the east coast of North America 1984-1987

Convection waves are a strong function of the original forcing by convection in the boundary layer. This convection can take the form of random or 2-D spatial structure. When clouds are formed as a result of 2-D boundary layer forcing, they are called cloud streets. Convective waves may be important in the global momentum balance. Global circulation models presently overestimate wind speeds in the middle and upper troposphere, and do not account for convective waves. If the hypothesis that convective waves significantly contribute to momentum transport in the troposphere is correct, then climatology of cloud streets will help understand convective waves' role in the global momentum balance. If the hypothesis is incorrect, then cloud street climatology is still useful because cloud streets show areas where large amounts of sensible and latent heat are transferred from the ocean to the atmosphere. Interannual variation of seasonal means and subseasonal variability of cloud street occurrence off the east coast of North America is examined and compared. The months of Dec., Jan., and Feb. during 1984-85, 1985-86, and 1986-87 are examined along with each season.

Rademacher, Kurt R.↗
NASA NTRS1992

Accretion rates of protoplanets. II - Gaussian distributions of planetesimal velocities

In the present growth-rate calculations for a protoplanet that is embedded in a disk of planetesimals with triaxial Gaussian velocity dispersion and uniform surface density, the protoplanet is on a circular orbit. The accretion rate in the two-body approximation is found to be enhanced by a factor of about 3 relative to the case where all planetesimals' eccentricities and inclinations are equal to the rms values of those disk variables having locally Gaussian velocity dispersion. This accretion-rate enhancement should be incorporated by all models that assume a single random velocity for all planetesimals in lieu of a Gaussian distribution.

Greenzweig, Yuval↗
NASA NTRS1992

Local design optimization for composite transport fuselage crown panels

Composite transport fuselage crown panel design and manufacturing plans were optimized to have projected cost and weight savings of 18 and 45 percent, respectively. These savings are close to those quoted as overall NASA Advanced Composite Technology (ACT) program goals. Three local optimization tasks were found to influence the cost and weight of fuselage crown panels. The effects are summarized of each task and the task associated with a design cost model is described in detail. Studies were performed to evaluate the relationship between manufacturing cost and design details. A design tool was developed to aid in these studies. The development of the design tool included combining cost and performance constraints with a random search optimization algorithm. The resulting software was used in a series of optimization studies that evaluated the sensitivity of design variables, guidelines, criteria, and material selection on cost. The effect of blending adjacent design points in a full scale panel subjected to changing load distributions and local variations was shown to be important. Technical issues and directions for future work were identified.

Swanson, G. D.↗
NASA NTRS1992

Local design optimization for composite transport fuselage crown panels

Composite transport fuselage crown panel design and manufacturing plans were optimized to have projected cost and weight savings of 18 percent and 45 percent, respectively. These savings are close to those quoted as overall NASA ACT program goals. Three local optimization tasks were found to influence the cost and weight of fuselage crown panels. This paper summarizes the effect of each task and describes in detail the task associated with a design cost model. Studies were performed to evaluate the relationship between manufacturing cost and design details. A design tool was developed to aid in these investigations. The development of the design tool included combining cost and performance constraints with a random search optimization algorithm. The resulting software was used in a series of optimization studies that evaluated the sensitivity of design variables, guidelines, criteria, and material selection on cost. The effect of blending adjacent design points in a full scale panel subjected to changing load distributions and local variations was shown to be important. Technical issues and directions for future work were identified.

Swanson, G. D.↗
NASA NTRS1995

Estimating errors in least-squares fitting

While least-squares fitting procedures are commonly used in data analysis and are extensively discussed in the literature devoted to this subject, the proper assessment of errors resulting from such fits has received relatively little attention. The present work considers statistical errors in the fitted parameters, as well as in the values of the fitted function itself, resulting from random errors in the data. Expressions are derived for the standard error of the fit, as a function of the independent variable, for the general nonlinear and linear fitting problems. Additionally, closed-form expressions are derived for some examples commonly encountered in the scientific and engineering fields, namely ordinary polynomial and Gaussian fitting functions. These results have direct application to the assessment of the antenna gain and system temperature characteristics, in addition to a broad range of problems in data analysis. The effects of the nature of the data and the choice of fitting function on the ability to accurately model the system under study are discussed, and some general rules are deduced to assist workers intent on maximizing the amount of information obtained form a given set of measurements.

Richter, P. H.↗
NASA NTRS2011

Acquisition of Skill Proficiency Over Multiple Sessions of a Novel Rover Simulation

Following long-duration exploration transits, adaptive changes in sensorimotor function may impair the crew's ability to safely perform manual control tasks such as operating pressurized rovers. Postflight performance will also be influenced by the level of preflight skill proficiency they have attained. The purpose of this study was to characterize the acquisition of skills in a motion-based rover simulation over multiple sessions, and to investigate the effects of varying the simulation scenarios. METHODS: Twenty healthy subjects were tested in 5 sessions, with 1-3 days between sessions. Each session consisted of a serial presentation of 8 discrete tasks to be completed as quickly and accurately as possible. Each task consisted of 1) perspective-taking, using a map that defined a docking target, 2) navigation toward the target around a Martian outpost, and 3) docking a side hatch of the rover to a visually guided target. The simulator utilized a Stewart-type motion base (CKAS, Australia), single-seat cabin with triple scene projection covering 150 deg horizontal by 50 deg vertical, and joystick controller. Subjects were randomly assigned to a control group (tasks identical in the first 4 sessions) or a varied-practice group. The dependent variables for each task included accuracy toward the target and time to completion. RESULTS: The greatest improvements in time to completion occurred during the docking phase. The varied-practice group showed more improvement in perspective-taking accuracy. Perspective-taking accuracy was also affected by the relative orientation of the rover to the docking target. Skill acquisition was correlated with self-ratings of previous gaming experience. DISCUSSION: Varying task selection and difficulty will optimize the preflight acquisition of skills when performing novel operational tasks. Simulation of operational manual control will provide functionally relevant evidence regarding the impact of sensorimotor adaptation on early surface operations and what countermeasures are needed. Learning Objective: The use of a motion-based simulation to investigate decrements in the proficiency to operate pressurized rovers during early surface operations of space exploration missions, along with the acquisition of skill proficiency needed during the preflight phase of the mission.

Dean, S. L.↗
NASA NTRS2016

Neural Network (NN) Retrievals of Stratocumulus Cloud Properties Using Multiangle Polarimetric Observations During ORACLES

The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign is taking place in the SouthEast Atlantic during the Austral Spring for three consecutive years from 20162018. The study area encompasses one of the Earths three semipermanent subtropical Stratocumulus (Sc) cloud decks,and experiences very large aerosol optical depths, mainly biomass burning, originating from Africa. Over time, cloud optical depth (COD), lifetime and cloud microphysics (number concentration, effective radii Reff and precipitation) are expected to be influenced by indirect aerosol effects. These changes play a key role in the energetic balance of the region, and are part of the core investigation objectives of the ORACLES campaign, which acquires measurements of clean and polluted scenes of above cloud aerosols (ACA). Simultaneous retrievals of aerosol and cloud optical properties are being developed (e.g. MODIS, OMI), butstill challenging, especially for passive, single viewing angle instruments. By comparison, multiangle polarimetric instruments like RSP (Research Scanning Polarimeter) show promise for detection and quantification of ACA, however, there are no operational retrieval algorithms available yet. Here we describe anew algorithm to retrieve cloud and aerosol optical properties from observations by RSP flown on the ER2and P3 during the 2016 ORACLES campaign. The algorithm is based on training a NN, and is intended to retrieve aerosol and cloud properties simultaneously. However, the first step was to establish the retrievalscheme for low level Sc cloud optical properties. The NN training was based on simulated RSP total and polarized radiances for a range of COD, Reff, and effective variances, spanning 7 wavelength bands and 152 viewing zenith angles. Random and correlated noise were added to the simulations to achieve a morerealistic representation of the signals. Before introducing the input variables to the network, the signals are projected on a principle component plane that retains the maximal signal information but minimizes the noise contribution. We will discuss parameter choices for the network and present preliminary results of cloudretrievals from ORACLES, compared with standard RSP low-levelcloud retrieval method that has been validated against in situ observations.

Cloud-aerosol↗
NASA NTRSDate not supplied

Uncertainty Improvement in the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel

Within the past decade, a measurement uncertainty analysis (MUA) team was assembled at the NASA Glenn Research Center to assist the wind tunnel characterization team with quantification of uncertainty estimates for variables of interest in wind tunnels and test cells across the center. The initial analysis performed by the team was conducted on the 8- by 6- Foot Supersonic Wind Tunnel using results from the 1996/97 full calibration test entry. These MUA results were published in 2016 which included recommendations for methods to improve the uncertainty estimates for various variables of interest, such as changes to regression models, tunnel operation philosophy, and instrumentation choices. The wind tunnel characterization team utilized the proposed methods in 2019 during a full characterization test entry. Following publication of the 2019 test section characterization results, the MUA team pursued an update to the uncertainty estimates for the facility, which validated previous recommendations and revealed significantly reduced systematic uncertainties across most variables of interest. Inclusion of within-test repeat points in the 2019 test entry allowed for higher fidelity estimates of random uncertainty to be generated, as well. This collaboration between the facility, wind tunnel characterization, and MUA teams serves as an example of the data quality benefits that can be achieved through rigorous analysis of the sources of uncertainty in a ground-test facility.

Uncertainty↗
NASA NTRSDate not supplied

Uncertainty Improvement in the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel

Within the past decade, a measurement uncertainty analysis (MUA) team was assembled at NASA Glenn Research Center to assist the wind tunnel characterization team with quantification of uncertainty estimates for variables of interest in wind tunnels and test cells across the center. The initial analysis performed by the team was conducted on the 8- by 6-Foot Supersonic Wind Tunnel using results from the 1996/97 full calibration test entry. These MUA results were published in 2016 which included recommendations for methods to improve the uncertainty estimates for various variables of interest, such as changes to regression models, tunnel operation philosophy, and instrumentation choices. The wind tunnel characterization team utilized the proposed methods in 2019 during a full characterization test entry. Following publication of the 2019 test section characterization results, the MUA team pursued an update to the uncertainty estimates for the facility, which validated previous recommendations and revealed significantly reduced systematic uncertainties across most variables of interest. Inclusion of within-test repeat points in the 2019 test entry allowed for higher fidelity estimates of random uncertainty to be generated, as well. This collaboration between the facility, wind tunnel characterization, and MUA teams serves as an example of the data quality benefits that can be achieved through rigorous analysis of the sources of uncertainty in a ground-test facility.

Uncertainty↗
DOE OSTI2022

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

DNA-Directed Non-Langmuir Deposition of Programmable Atom Equivalents

Particle assembly at interfaces via programmed DNA interactions allows for independent modification of both nanoparticle–surface interaction strength and the magnitude of interparticle repulsion. Together, these factors allow for modification of the deposited thin film morphology via alterations in DNA binding sequence. Importantly, both Langmuir and random sequential adsorption models yield insights into the thermodynamics of deposition but cannot fully explain particle coverage as a function of all relevant variables, indicating that the particle deposition mechanism for DNA-grafted colloids is more complex than prior adsorption phenomena. Here, it is shown that these deviations from standard behavior arise from the fact that each nanoparticle is attached to the surface via multiple weak DNA duplex interactions, enabling diffusion of adsorbed colloids across the substrate. Thus, surface migration of individual particles causes reorganization of the deposited monolayer, leading to the unusual behavior of coverage increasing at elevated temperatures that are just below the particle desorption temperature. The programmability of DNA-directed particle deposition therefore allows for precise control over the morphology of monolayer films, as well as the ability to generate crystalline materials with controllable surface roughness and grain size through layer-by-layer growth. As a result, the increased control over thin film morphology potentially enables tailoring of mechanical and optical properties and holds promise for use in a variety of applications.

59 BASIC BIOLOGICAL SCIENCES↗