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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗
NASA NTRS2000

Predictive Multiple Model Switching Control with the Self-Organizing Map

A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framework. Multiple SOMs collectively model the global response of a nonautonomous system to a finite set of representative prototype controls. Each SOM provides a codebook representation of the dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the global minimization of a similarity metric. The SOM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme that selects the best available model for the applied control. SOM based linear models are used to predict the response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal.

Motter, Mark A.↗
NASA NTRS1981

Applications systems verification and transfer project. Volume 8: Satellite snow mapping and runoff prediction handbook

The purpose of the handbook is to update the various snowcover interpretation techniques, document the snow mapping techniques used in the various ASVT study areas, and describe the ways snowcover data have been applied to runoff prediction. Through documentation in handbook form, the methodology developed in the Snow Mapping ASVT can be applied to other areas.

Bowley, C. J.↗
NASA NTRS2015

Rift Valley Fever Prediction and Risk Mapping: 2014-2015 Season

Extremes in either direction (+-) of precipitation temperature have significant implications for disease vectors and pathogen emergence and spread Magnitude of ENSO influence on precipitation temperature cannot be currently predicted rely on average history and patterns. Timing of event and emergence disease can be exploited (GAP) in to undertake vector control and preparedness measures. Currently - no risk for ecologically-coupled RVFV activity however we need to be vigilant during the coming fall season due the ongoing buildup of energy in the central Pacific Ocean. Potential for the dual-use of the RVF Monitor system for other VBDs Need to invest in early ground surveillance and the use of rapid field diagnostic capabilities for vector identification and virus isolation.

RVF↗
NASA NTRS1994

Electron acceleration at nearly perpendicular collisionless shocks. 3: Downstream distributions

Spacecraft observations at the Earth's bow shock and at interplanetary shocks have established that the largest fluxes of accelerated suprathermal electrons occur in so-called shock spike events immediately downstream of the shock ramp. Previous theoretical efforts have mainly focused on explaining upstream energetic electron beams. Here we investigate the general motion and acceleration of energetic electrons in a curved, nearly perpendicular shock by numerically integrating the orbits of solar wind halo electrons in shock fields generated by a hybrid simulation (core electron fluid and kinetic ions). Close to the angle Theta(sub Bn) = 90 degs between the upstream magnetic field and shock normal, the calculations result in a (perpendicular) temperature increase proportional to the magnetic field ratio and give the highest phase space densities in the overshoot. For a steep distribution, the temperature change can correspond to an enhancement of the distribution by several orders of magnitude. These results are in agreement with predictions from adiabatic mapping. With smaller angles Theta(sub Bn), the overshoot and downstream densities fall off quickly, because the adiabatic energy gain is less and fewer electrons transmit. The shock curvature also leads to an accumulation of electrons close to 90 degs. Without pitch angle scattering, energization is only significant within a few (approximately 5 to 10 degs) degrees of the point of tangency. However, shock spike events appear to be observed more easily and farther away from 90 degs. Given that over a region of several degrees around 90 degs the theory gives enhancements of up to approximately 4 orders of magnitude, such electrons could in principle account for the typically observed enhancements of 1 to 2 orders of magnitude, if they were distributed over Theta(sub Bn). To test the idea that scattering could efficiently redistribute the energetic electrons, we have conducted test particle simulations in which artificial pitch angle scattering is included. We find that such a process can indeed be very efficient and can explain observations of shock spike events far away from Theta(sub Bn) is approximately 90 degs. The scattering naturally leads to much higher phase space densities at smaller Theta(sub Bn) than what a local one-dimensional mapping would predict and thus can account for an observed discrepancy with adiabatic theory stated in the literature.

Krauss-Varban, D.↗
DOE OSTI2023

MACHINE LEARNING-ENABLED PREDICTION OF TRANSIENT INJECTION MAP IN AUTOMOTIVE INJECTORS WITH UNCERTAINTY QUANTIFICATION

Accurate prediction of injection profiles is a critical aspect of linking injector operation with engine performance and emissions. However, highly resolved injector simulations can take one to two weeks of wall-clock time, which is incompatible with engine design cycles with desired turnaround times of less than a day. Hence, it is important to reduce the time-to-solution of the internal flow simulations by several orders of magnitude to make it compatible with engine simulations. This work demonstrates a data-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field. Then, autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process models are finally used to predict the spatiotemporal void fraction field at the injector exit for unknown operating conditions. The Gaussian process models produce principled uncertainty estimates associated with the emulated flowfields, which provide the engine designer with valuable information of where the data-driven predictions can be trusted in the design space. The Bayesian flowfield predictions are compared with the corresponding predictions from a deep neural network, which has been transfer-learned from static needle simulations from a previous work by the authors. The emulation framework can predict the void fraction field at the exit of the orifice within a few seconds, thus achieving a speed-up factor of up to 38 x 10(6) over the traditional simulation-based approach of generating transient injection maps.

machine learning↗
DOE OSTI2024

CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.

47 OTHER INSTRUMENTATION↗
NASA NTRS1998

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Feature Map

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a Self Organizing Feature Map (SOFM). Local linear modeling of nonlinear autonomous systems with the SOFM is extended to a control framework where the modeled system is nonautonomous, driven by an exogenous input. This extension to a control framework is based on the consideration of a finite number of subregions in the control space. Multiple self organizing feature maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experimental knowledge gained from decades of operation. Each SOFM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOFM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal. Each SOFM provides a codebook representation of the tunnel dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the minimization of a similarity metric which is the essence of the self organizing feature of the map. Thus, the SOFM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme than selects the best available model for the applied control. Experimental results of controlling the wind tunnel, with the proposed method, during operational runs where strict research requirements on the control of the Mach number were met, are presented. Comparison to similar runs under the same conditions with the tunnel controlled by either the existing controller or an expert operator indicate the superiority of the method.

Motter, Mark A.↗
NASA NTRS1999

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Map

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a self-organizing map (SOM). Multiple self-organizing maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experiential knowledge gained from decades of operation. Each SOM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal.

Mark A. Motter↗
DOE OSTI2026

High-resolution modeling of indoor radon exposure with uncertainty quantification in Utah

Indoor radon accounts for 37% of population-level exposure to ionizing radiation in the United States. However, radon metrics are typically reported at coarse spatial scales, potentially obscuring meaningful local variation. We developed a high-resolution modeling framework to estimate indoor radon concentrations across Utah while explicitly quantifying predictive uncertainty. A total of 19,497 residential radon measurements collected between 2006 and 2017 were combined with environmental and housing characteristics and analyzed using a geospatial neural network that accommodates spatial dependence and nonlinear associations. Predictions were generated on a uniform hexagonal grid at 0.73 km2 resolution (H3 level 8). Out-of-sample predictions aggregated to the H3 level 8 grid showed good agreement with observed concentrations (Pearson r=0.64), while household-level predictions exhibited more moderate agreement (r=0.45). The model produced well-calibrated uncertainty estimates, with 24.1% of held-out observations exceeding the predicted 75th-percentile threshold. Maps of predicted radon concentrations and the probability of exceeding the U.S. EPA action level of 148 Bq/m3 (4 pCi/L) revealed substantial fine-scale spatial heterogeneity that was not apparent in conventional coarse-resolution summaries, with greater local variability observed in densely monitored urban counties than in sparsely sampled regions. High-resolution radon models that explicitly quantify uncertainty provide a useful framework for characterizing the spatial distribution of indoor radon and identifying areas of elevated exceedance risk. These findings highlight the value of fine-scale monitoring data and uncertainty-aware modeling approaches for radon exposure assessment, environmental risk characterization, and radon-related health research.

Wu, Yunhan [ORNL] (ORCID:0000000178842994)↗
DOE OSTI2022

Inferring Water Content from Neutron Die-away Curves for Planetary Science Applications

Signatures of liquid water on planetary bodies may provide evidence for past, present, or future life elsewhere in the solar system. Multiple current and upcoming planetary science missions are prioritizing the search for water, using innovative technologies and creative algorithms. For this project, we explore the capabilities of an instrument similar to the Dragonfly spacecraft to map water content on the surface of Titan, the largest moon of Saturn. We simulate Dragonfly’s neutron observations for a variety of soil compositions and develop multiple techniques to extract water content from the resultant neutron data. Additionally, we supplement neutron observations with gamma spectroscopy in an attempt to further refine water content estimates. This work is part of a larger study to employ Gaussian process regression to estimate a map of water content along the surface of Titan. Using the estimated map, an algorithm based on prediction difference mapping is employed to optimally move multiple Dragonfly detectors around the surface of Titan, fully autonomously.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Global Network of Slow Solar Wind

The streamer belt region surrounding the heliospheric current sheet (HCS) is generally treated as the primary or sole source of the slow solar wind. Synoptic maps of solar wind speed predicted by the Wang-Sheeley-Arge model during selected periods of solar cycle 23, however, show many areas of slow wind displaced from the streamer belt. These areas commonly have the form of an arc that is connected to the streamer belt at both ends. The arcs mark the boundaries between fields emanating from different coronal holes of the same polarity and thus trace the paths of belts of pseudostreamers, i.e., unipolar streamers that form over double arcades and lack current sheets. The arc pattern is consistent with the predicted topological mapping of the narrow open corridor or singular separator line that must connect the holes and, thus, consistent with the separatrix-web model of the slow solar wind. Near solar maximum, pseudostreamer belts stray far from the HCS-associated streamer belt and, together with it, form a global-wide web of slow wind. Recognition of pseudostreamer belts as prominent sources of slow wind provides a new template for understanding solar wind stream structure, especially near solar maximum.

solar wind↗
NASA NTRS1988

Predictions for arc polarization

Maps for the prediction of the polarization of light echo arcs are presented. It is noted that the results can be compared with observations in order to test the light-echo model of Katz (1987) and Milgrom (1987) and to determine its parameters. As long as the model assumptions of single scattering and planar geometry remain valid, the polarization is shown to be independent of the geometrical thickness and the optical depth of the dust layer. Cases such as the light source being in the plane of scatterers and arcs consisting of nearly forward scattered light are considered.

Katz, J. I.↗
NASA NTRS2016

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

Babcock, Chad↗
NASA NTRS2017

Simulation-Based Height of Burst Map for Asteroid Airburst Damage Prediction

Entry and breakup models predict that airburst in the Earth's atmosphere is likely for asteroids up to approximately 200 meters in diameter. Objects of this size can deposit over 250 megatons of energy into the atmosphere. Fast-running ground damage prediction codes for such events rely heavily upon methods developed from nuclear weapons research to estimate the damage potential for an airburst at altitude. (Collins, 2005; Mathias, 2017; Hills and Goda, 1993). In particular, these tools rely upon the powerful yield scaling laws developed for point-source blasts that are used in conjunction with a Height of Burst (HOB) map to predict ground damage for an airburst of a specific energy at a given altitude. While this approach works extremely well for yields as large as tens of megatons, it becomes less accurate as yields increase to the hundreds of megatons potentially released by larger airburst events. This study revisits the assumptions underlying this approach and shows how atmospheric buoyancy becomes important as yield increases beyond a few megatons. We then use large-scale three-dimensional simulations to construct numerically generated height of burst maps that are appropriate at the higher energy levels associated with the entry of asteroids with diameters of hundreds of meters. These numerically generated HOB maps can then be incorporated into engineering methods for damage prediction, significantly improving their accuracy for asteroids with diameters greater than 80-100 m.

Burst Map↗
NASA NTRS1974

A summary of ERTS data applications in Alaska

ERTS has proven to be an exceedingly useful tool for the preparation of urgently needed resource surveys in Alaska. For this reason the wide utilization of ERTS data by federal, state and industrial agencies in Alaska is increasingly directed toward the solution of operational problems in resource inventories, environmental surveys, and land use planning. Examples of some applications are discussed in connection with surveys of potential agricultural lands; mapping of predicted archaeological sites; permafrost terrain and aufeis mapping; snow melt enhancement from Prudhoe Bay roads; geologic interpretations correlated ith possible new petroleum fields, with earthquake activity, and with plate tectonic motion along the Denali fault system; hydrology in monitoring surging glaciers and the break-up characteristics of the Chena River watershed; sea-ice morphology correlated with marine mammal distribution; and coastal sediment plume circulation patterns.

Miller, J. M.↗
DOE OSTI2024

Using GANs to predict milling stability from limited data

Milling is a key manufacturing process that requires the selection of operating parameters that provide efficient performance. However, the presence of chatter, a self-excited vibration causing poor surface finish and potential damage to the machine and cutting tool, makes it challenging to select the appropriate parameters. To predict chatter, stability maps are commonly used, but their generation requires expensive data, making it difficult to employ these maps in industry. Therefore, there is a pressing need for an approach that can accurately predict stability maps using limited experimental data. This study introduces the new Encoder GAN (EGAN) approach based on Generative Adversarial Networks (GANs) that predicts stability maps using limited experimental data. The approach consists of the encoder, generator, and discriminator subnetworks and uses the trained encoder and generator to predict the target stability map. This versatile method can be applied to various tool setups and can accurately predict stability maps with limited experimental data (five to 10 cutting tests) even when there is little information available for unknown parameters. In conclusion, the study evaluates the proposed approach using both numerical data and experiments and demonstrates its superior performance compared to state-of-the-art benchmarks.

42 ENGINEERING↗