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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (~16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008).

Case, Jonathan L.↗
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
NASA NTRS1993

Analysis of hyper-spectral AVIRIS image data over a mixed-conifer forest in Maine

An introduction to some of the potential uses of hyperspectral data for ecosystem analysis is presented. The examples given are derived from a digital dataset acquired over a sub-boreal forest in central Maine in 1990 by the NASA-JPL Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument gathers data from 400 to 2500 nm in 224 channels at bandwidths of approximately 10 nm. As a preview to the uses of the hyperspectral data, several products from this dataset were extracted. They range from the traditional false color composite made from simulated Thematic Mapper bands and the well known normalized difference vegetation index to much more exotic products such as fractions of vegetation, soil and shade based on linear spectral mixing models and estimates of the leaf water content at the landscape level derived using spectrum-matching techniques. Our research and that of many others indicates that the hyperspectral datasets carry much important information which is only beginning to be understood. This analysis gives an initial indication of the utility of hyperspectral data. Much work still remains to be done in algorithm development and in understanding the physics behind the complex information signal carried in the hyperspectral datasets. This work must be carried out to provide the fullest science support for high spectral resolution data to be acquired by many of the instruments to be launched as part of the Earth Observing System program in the mid-1990's.

Lawrence, William T.↗
DOE OSTI2025

Transfer learning of neural surrogates on multifidelity groundwater simulations

Multifidelity data used in the paper published in Advances in Water Resources 206 (2025) 105140, https://doi.org/10.1016/j.advwatres.2025.105140 The code used to process the data is openly available on GitHub at https://github.com/Model-Reduction-and-UQ-Group/Transfer_Learning_K_reconstruction Computationally inexpensive surrogates of process-based models, such as deep neural networks, enable ensemble-based computations used in risk assessment, data assimilation, etc. However, generation of large datasets required to train a neural network can be as expensive as the ensemble simulations themselves. We ameliorate this challenge by using data from multifidelity (MF) groundwater simulations and transfer learning (TL) to reduce data generation costs while maintaining model accuracy. As a computational example, we train a deep convolutional neural network (CNN) to reconstruct permeability fields from saturation maps derived from a multiphase flow model. Starting with very low- and low-fidelity data generated on increasingly coarse meshes, we pretrain the CNN, followed by output-layer training and fine-tuning using only a limited number of high-fidelity samples. We demonstrate the surrogate’s robustness when interpreting low-quality inputs—such as interpolated maps or data affected by noise—which has strong implications for the applicability in practical hydrogeological scenarios. This multilevel MF-TL strategy achieves a favorable trade-off between computational efficiency and predictive accuracy, significantly outperforming high-fidelity-only approaches under the same computational budget.

Chiofalo, Alessia [University of Bologna] (ORCID:0↗
NASA NTRS2003

Iced Aircraft Flight Data for Flight Simulator Validation

NASA is developing and validating technology to incorporate aircraft icing effects into a flight training device concept demonstrator. Flight simulation models of a DHC-6 Twin Otter were developed from wind tunnel data using a subscale, complete aircraft model with and without simulated ice, and from previously acquired flight data. The validation of the simulation models required additional aircraft response time histories of the airplane configured with simulated ice similar to the subscale model testing. Therefore, a flight test was conducted using the NASA Twin Otter Icing Research Aircraft. Over 500 maneuvers of various types were conducted in this flight test. The validation data consisted of aircraft state parameters, pilot inputs, propulsion, weight, center of gravity, and moments of inertia with the airplane configured with different amounts of simulated ice. Emphasis was made to acquire data at wing stall and tailplane stall since these events are of primary interest to model accurately in the flight training device. Analyses of several datasets are described regarding wing and tailplane stall. Key findings from these analyses are that the simulated wing ice shapes significantly reduced the C , max, while the simulated tail ice caused elevator control force anomalies and tailplane stall when flaps were deflected 30 deg or greater. This effectively reduced the safe operating margins between iced wing and iced tail stall as flap deflection and thrust were increased. This flight test demonstrated that the critical aspects to be modeled in the icing effects flight training device include: iced wing and tail stall speeds, flap and thrust effects, control forces, and control effectiveness.

Ratvasky, Thomas P.↗
DOE OSTI2021

Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System

Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Grid, head direction, and conjunctive cell populations each span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures for different dataset dimensions, variations in spatial tuning, and forms of noise. We quantify its ability to decode simulated animal trajectories contained within these topological structures. We also identify regimes under which mixtures of populations form product topologies that can be detected. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

SU-E-T-452: Identifying Inefficiencies in Radiation Oncology Workflow and Prioritizing Solutions for Process Improvement and Patient Safety

Purpose: Successful radiation therapy requires multi-step processes susceptible to unnecessary delays that can negatively impact clinic workflow, patient satisfaction, and safety. This project applied process improvement tools to assess workflow bottlenecks and identify solutions to barriers for effective implementation. Methods: We utilized the DMAIC (define, measure, analyze, improve, control) methodology, limiting our scope to the treatment planning process. From May through December of 2014, times and dates of each step from simulation to treatment were recorded for 507 cases. A value-stream map created from this dataset directed our selection of outcome measures (Y metrics). Critical goals (X metrics) that would accomplish the Y metrics were identified. Barriers to actions were binned into control-impact matrices, in order to stratify them into four groups: in/out of control and high/low impact. Solutions to each barrier were then categorized into benefit-effort matries to identify those of high benefit and low effort. Results: For 507 cases, the mean time from simulation to treatment was 235 total hours. The mean process and wait time were 60 and 132 hours, respectively. The Y metric was to increase the ratio of all non-emergent plans completed the business day prior to treatment from 47% to 75%. Project X metrics included increasing the number of IMRT QAs completed at least 24 hours prior to treatment from 19% to 80% and the number of non-IMRT plans approved at least 24 hours prior to treatment from 33% to 80%. Intervals from simulation to target contour and from initial plan completion to plan approval were identified as periods that could benefit from intervention. Barriers to actions were binned into control-impact matrices and solutions by benefit-effort matrices. Conclusion: The DMAIC method can be successfully applied in radiation therapy clinics to identify inefficiencies and prioritize solutions for the highest impact.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

The HalfDome multi-survey cosmological simulations: N-body simulations

Upcoming cosmological surveys have the potential to reach groundbreaking discoveries on multiple fronts, including the neutrino mass, dark energy, and inflation. Most of the key science goals require the joint analysis of datasets from multiple surveys to break parameter degeneracies and calibrate systematics. To realize such analyses, a large set of mock simulations that realistically model correlated observables is required. In this paper we present the N-body component of the HalfDome cosmological simulations, designed for the joint analysis of Stage-IV cosmological surveys, such as Rubin LSST, Euclid, SPHEREx, Roman, DESI, PFS, Simons Observatory, CMB-S4, and LiteBIRD. Our 300TB initial data release includes full-sky lightcones and halo catalogs between z = 0–4 for 11 fixed cosmology realizations, as well as an additional run with local primordial non-Gaussianity (f NL = 20). The simulations evolve 61443 particles in a 3.75 h -1 Gpc box, reaching a minimum halo mass of ∼6 × 1012 h -1 M ⊙ and maximum scale of k ∼ h Mpc-1. Our data is publicly available: instructions to access the data and plans for future data releases can be found at https://halfdomesims.github.io.

Bayer, Adrian E↗
DOE OSTI2024

Identifying Meteorological Drivers for Errors in Modeled Winds along the Northern California Coast

Abstract An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore of California, using the Weather Research and Forecasting Model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along Northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-yr simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme (the chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-yr simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic-scale northerly flows driven by the North Pacific high and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset.

17 WIND ENERGY↗
NASA NTRS1991

On the estimation of climatological Z-R relationships

A statistical framework for climatological Z-R parameter estimation is developed and simulation experiments are conducted to examine sampling properties of the estimators. Both parametric and nonparametric models are considered. For parametric models, it is shown that Z-R parameters can be estimated by maximum likelihood, a procedure with optimal large sample properties. A general nonparametric framework for climatological Z-R estimation is also developed. Nonparametric procedures are attractive because of their flexibility in dealing with certain types of measurement errors common to radar data. Simulation experiments show that even under favorable assumptions on error characteristics of radar and raingages, large datasets are required to obtain accurate Z-R parameter estimates. Another important conclusion is that estimation results are generally quite sensitive to radar and raingage measurement thresholds. For fixed sample size, the simulation results can be used to provide quantitative assessments of the accuracy of Z-R model parameter estimates. These results are particularly useful for error analysis of precipitation products that are derived using climatological Z-R relations. One example is the large-area rainfall estimates derived using the height-area rainfall threshold (HART) technique.

Krajewski, Witold F.↗
DOE OSTI2015

WE-EF-207-02: The Rotate-Plus-Shift C-Arm Trajectory: Theory and First Clinical Results

Purpose: The proposed method enables the acquisition of a complete dataset for 3D reconstruction of C-Arm data using less than 180° rotation. Methods: Typically a C–arm cone–beam CT scan is performed using a circle–like trajectory around a region of interest. Therefore an angular range of at least 180° plus fan–angle must be covered to ensure a completely sampled data set. This fact defines some constraints on the geometry and technical specifications of a C–arm system, for example a larger C radius or a smaller C opening respectively. This is even more important for mobile C-arm devices which are typically used in surgical applications.To overcome these limitations we propose a new trajectory which requires only 180° minusfan–angle of rotation for a complete data set. The trajectory consists of three parts: A rotation of the C around a defined iso–center and two translational movements parallel to the detector plane at the begin and at the end of the rotation (rotate plus shift trajectory). This enables the acquisition of a completely sampled dataset using only 180° minus fan–angle of rotation. Results: For the evaluation of the method we show simulated and measured data. The results show, that the rotate plus shift scan yields equivalent image quality compared to the short scan which is assumed to be the gold standard for C-arm CT today. Compared to the pure rotational scan over only 165°, the rotate plus shift scan shows strong improvements in image quality. Conclusion: The proposed method makes 3D imaging using C–arms with less than 180° rotation range possible. This enables integrating full 3D functionality into a C- arm device without any loss of handling and usability for 2D imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A Subgrid Approach for Modeling Microtopography Effects on Overland Flow: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication. A new subgrid model was implemented in the Advanced Terrestrial simulator (ATS) to capture micro-topography effects on surface flow. Model evaluation was performed on disparate landscapes including: 1) seven individual ice-wedge polygons and a cluster of ice-wedge polygons from Barrow Alaska, 2) hummocky microtopography from Spruce field site in Minnesota, 3) synthetic rills, and 4) synthetic multGaussian realizations. Results of the subgrid model are compared to benchmark (fine-scale) and null hypothesis (model ignoring microtopography). Our finding confirms that the effects of small-scale spatial heterogeneities can be captured in the coarsened models. The dataset contains variety of files including meshes, input files, and subgrid parameters required to run the simulations. Python scripts (jupyter notebooks) for post-processing and files for geometric analyses are also included. More details are provided in file "simulations_info", which has been provided in both .docx and .pdf formats. This dataset is a zip file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Constraining solar electron number density via neutrino flavor data at Borexino

Understanding the physics of the deep solar interior, and the more exotic environs of core-collapse supernovae (CCSN) and binary neutron-star (NS) mergers, is of keen interest in many avenues of research. To date, this physics is based largely on simulations via forward integration. While these simulations provide valuable constraints, it could be insightful to adopt the "inverse approach" as a point of comparison. Within this paradigm, parameters of the solar interior are not output based on an assumed model, but rather are inferred based on real data. Here, we take the specific case of solar electron number density, which historically is taken as output from the standard solar model. We show how one may arrive at an independent constraint on that density profile based on available neutrino flavor data from the Earth-based Borexino experiment. The inference technique's ability to offer a unique lens on physics can be extended to other datasets, and to analogous questions for CCSN and NS mergers, albeit with simulated data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds

Here, we demonstrate, on a scramjet combustion problem, a constrained probabilistic learning approach that augments physics-based datasets with realizations that adhere to underlying constraints and scatter. The constraints are captured and delineated through diffusion maps, while the scatter is captured and sampled through a projected stochastic differential equation. The objective function and constraints of the optimization problem are then efficiently framed as non-parametric conditional expectations. Different spatial resolutions of a large-eddy simulation filter are used to explore the robustness of the model to the training dataset and to gain insight into the significance of spatial resolution on optimal design.

42 ENGINEERING↗
DOE OSTI2020

BEPAM Model Code and CABBI Simulation Results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land"

This dataset contains BEPAM model code and input data to the replicate the results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land." The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output_0213-2020_Complete_daycent-agversion-[rental payment level]%_[biomass price].gms. (BEPAM-CRP model-Sep2020.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

biomass price↗
NASA NTRSDate not supplied

Particle Interaction Physics Model Formulation for Plume-Surface Interaction Erosion and Cratering

As part of the Game Changing Development (GCD) Program, funded by NASA’s Space Technology Mission Directorate (STMD), the development of simulation capability for the prediction of extra-terrestrial Plume Surface Interaction (PSI) environments has been undertaken by the Fluid Dynamics Branch at NASA/MSFC. The Predictive Simulation Capability (PSC) Element is focused on creating simulation capability for the reliable and accurate prediction of PSI in Martian (~650 Pa) and Lunar (vacuum) ambient environments. In addition to the predictive simulation capability, the GCD Program also contains a companion Ground Testing Element for development of focused datasets for validation of predictive capability as well as a Flight-focused Instrumentation Element. This paper will present the status of implementing and maturing particle-particle interaction constituent physics models essential in simulating the landing surface granular material flow under PSI effects. This gas-particle multi-phase interaction modeling of plume impingement flow on the extra-terrestrial soil material is performed with the Gas-Granular Flow Solver (GGFS) addressed in a companion paper. The response of regolith particle flow induced by lander PSI requires accurate representation of the regolith granular material fluidic behavior and gas-granular interactions. The lunar regolith, as the extreme example, is poorly sorted with broad particle size distributions and large fines content. It has significant cohesion, due to interlocking particle shapes for the very jagged particles. The combination of particle shape and size distribution has been identified as major drivers in the complex particle flow response and resulting crater shape characteristics of extraterrestrial granular material. Constituent models for spherical particles can be formulated directly from particle kinetics theory. Complex particle shapes can be modeled by gluing together elemental spherical shapes into composite particles, requiring a Discrete Element Model (DEM) particle kinetics modeling approach to extract data and formulate constituent models. Mixture constituent models for poly-disperse mixtures (i.e, containing distribution of particle sizes) have recently been developed. The required non-spherical particle mixture granular material response closure models are then obtained through small-scale unit physics DEM simulations for the range of particle shapes, mixtures and packing densities. The granular material response closure models are then implemented in the Eulerian granular flow formulation. This DEM-based constituent model extraction process and formulation of poly-disperse particle mixtures has been successfully developed by small business and academic partners in the development of the Gas-Granular Flow Solver (GGFS) simulation program simulation framework. The currently implemented capabilities have reached the capability level of modeling bi-disperse, non-spherical particle mixtures is being continuously extended towards computational modeling of full range irregular particle mixtures. Under the GCD project, this technology is being further developed, transferred to NASA analysts, and matured towards application readiness. The predictive simulation capability team under the GCD project has acquired the modeling tools and processes of the DEM based constituent model formulation from the GGFS development team and is developing the capability to replicate the existing process. This is the first important step towards the ability of the NASA team to independently perform such model development in a production setting. Further efforts are underway to migrate the DEM based model simulation process performed with the academic based tools to more capable Open Source, highly parallelized simulation tools for efficient operation on NASA HPC assets. Evaluation of the currently implemented (such as mono-disperse and bi-disperse spherical and irregular shape particle constituent model applications) and continuously evolving full-range particle physics models in the GGFS tool is performed by the NASA team to advance application readiness of the simulations. Application testing for complex PSI erosions and cratering scenarios such as the Apollo LM is performed for axi-symmetric and full 3D simulations to aid the tool developers in achieving practical application readiness for NASA projects. Important validation and application testing will further be performed against experimental data generated under the GCD PSI project experimental component.

Peter A Liever↗
DOE OSTI2011

Production of BaBar Skimmed Analysis Datasets Using the Grid

The BABAR Collaboration, based at Stanford Linear Accelerator Center (SLAC), Stanford, US, has been performing physics reconstruction, simulation studies and data analysis for 8 years using a number of compute farms around the world. Recent developments in Grid technologies could provide a way to manage the distributed resources in a single coherent structure. We describe enhancements to the BABAR experiment's distributed skimmed dataset production system to make use of European Grid resources and present the results with regard to BABAR's latest cycle of skimmed dataset production. We compare the benefits of a local and Grid-based systems, the ease with which the system is managed and the challenges of integrating the Grid with legacy software. We compare job success rates and manageability issues between Grid and non-Grid production.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Idaho National Laboratory Quality Of Service Dataset

The code is designed to run tests to generate and collect data from a Wi-Fi network using OPENWRT or a simulated a 5G network using Open5gs and UERANSIM. The tests simulate the network performing downloads or uploads of various files with a varying number of concurrent users. The tests use tcpdump to collect the network traffic but only stores the summarized data. The summarized datasets will be included.

Krome, Cameron [Idaho National Laboratory (INL), I↗