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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Output Data: gcam_usa_v5p3_im3

Output databases and tables supporting the publication: Khan, Z., Zhao, M., Ahsan, H., Wolfram, P., Vernon, C., Rice, J., Iyer, G., Binsted, M., Snyder, A., Graham, N., Kyle, P., 2023. Coevolution of future water, energy and land systems across the United States in response to national and global socioeconomic, climate, and energy policy drivers. (In progress) Earths Future, DOI: XXX with further details available at the meta-repository: https://immm-sfa.github.io/khan-etal_2023_im3gcamusa Folder: gcam_usa_im3_outputs (GCAM USA v 5.3 used for IM3 experiment Output Databases): database_rcp45cooler_ssp3.tar.gz database_rcp45cooler_ssp5.tar.gz database_rcp45hotter_ssp5.tar.gz database_rcp45hotter_ssp5.tar.gz database_rcp85cooler_ssp3.tar.gz database_rcp85cooler_ssp5.tar.gz database_rcp85hotter_ssp5.tar.gz database_rcp85hotter_ssp5.tar.gz Folder: gcam_usa_im3_gcamextractor_outputs (gcamextractor post-processing of the GCAM databases for specific downstream models ): dataGCAM_cerf.tar.gz (For CERF Model) dataGCAM_demeter.tar.gz (For Demeter model) dataGCAM_go.tar.gz (For GO model) gcamDataTable.tar.gz (All IM3 scenarios)

Climate Change↗
NASA NTRS1999

Inlet Noise Reduction by Shielding for the Blended-Wing-Body Airplane

Noise shielding benefits associated with an advanced unconventional subsonic transport concept, the Blended-Wing-Body, were studied using a 4- percent scale, 3-engine nacelle model. The study was conducted in the Anechoic Noise Research Facility at NASA Langley Research Center. A high- frequency, wideband point source was placed inside the nacelles of the center engine and one of the side engines in order to simulate broadband engine noise. The sound field of the model was measured with a rotating microphone array that was moved to various stations along the model axis and with a fixed array of microphones that was erected behind the model. Ten rotating microphones were traversed a total of 22 degrees in 2-degree increments. Seven fixed microphones covered an arc that extended from a point in the exhaust exit plane of the center engine (and directly below its centerline) to a point 30 degrees above the jet centerline. While no attempt was made to simulate the noise emission characteristics of an aircraft engine, the model source was intended to radiate sound in a frequency range encompassing 1, 2, and 3 times the blade passage of a typical full-scale engine. In this study, the Blended-Wing-Body model was found to provide significant shielding of inlet noise. In particular, noise radiated downward into the forward sector was reduced by 20 to 25 dB overall in the full-scale frequencies from 2000 to 4000 Hz, decreasing to 10 dB or less at the lower frequencies. Also, it was observed that noise associated with the exhaust radiates into the sector directly below the model downstream to reduce shielding efficiency.

Clark, Lorenzo R.↗
DOE OSTI2022

Hybrid large-eddy simulation with adaptive downstream anisotropic eddy viscosity model

Turbulence pervades most flows of engineering interest, and its prediction remains a challenge on both accuracy and cost. One promising predictive approach that reduces cost combines large-eddy simulation (LES) with simulation based on Reynolds averaged Navier–Stokes equations (RANS). This study presents a method to overcome stability and accuracy issues associated with these hybrid LES-RANS methods. The method developed involves extracting the Reynolds stresses from the upstream LES solution and then using that information to convert the downstream RANS model from a scalar eddy-viscosity model to an anisotropic nonlinear eddy-viscosity model. The method developed differs from the downstream tensorial eddy-viscosity model by being independent of the coordinate system. The method developed was evaluated by computing film cooling of a flat plate with the coolant injected through one row of circular holes. Results obtained show instabilities at the LES-to-RANS interface to be eliminated. Also, the method developed yielded solutions that compare reasonably well with those from LES, even though a significant portion of the flow is computed by the adapted anisotropic RANS model instead of LES, which significantly reduced the number of grid points and computational time needed. Since modification of the downstream RANS model is based on information extracted from the upstream LES solution, the method developed is adaptive to the problem being studied.

42 ENGINEERING↗
DOE OSTI2023

Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.

13 HYDRO ENERGY↗
NASA NTRS1979

Low-speed wind-tunnel investigation of wing fins as trailing-vortex-alleviation devices on a transport airplane model

The trailing-vortex-alleviation effectiveness of both a one- and a two-fin configuration (semicircular with a radius of 0.043 semispan) on a jumbo-jet transport airplane model in its landing configuration was investigated in the Langley V/STOL tunnel, by the trailing-wing sensor technique. The fins were located on the upper surface of the transport model wing along the 30-percent-chord line. The fin configurations were effective in reducing the vortex-induced rolling moment, by amounts varying from 28 to 60 percent, on the trailing wing model located at a distance of 7.8 transport model wing spans downstream of the transport model. The flow over the fins and over the transport airplane model wing downstream of the fins was observed to be separated and turbulent. All fin configurations caused a reduction in maximum lift coefficient, a positive increment in drag coefficient, and an increment in nose-up pitching-moment coefficient on the transport airplane model.

Croom, D. R.↗
DOE OSTI2025

Point cloud-based diffusion models for the Electron-Ion Collider

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

Araz, Jack Y. [Stony Brook Univ., NY (United State↗
DOE OSTI2025

DEMographic MicrOSimulation (DEMOS) v2.0

DEMOS is an agent-based simulation framework used to evolve population demographic characteristics or lifecycle events such as education, marital status etc. DEMOS modules are designed to capture the interdependencies of short-term and long-term lifecycle events often influential in downstream transportation and land use modeling. An important facet of DEMOS is that it is dynamic, meaning it captures the impact of an agent's demographic characteristics in year 't' on their demographic status in year 't+1'. This has important consequences on medium- and long-term downstream modeling in different domains, such as transportation where decisions such as household vehicle transactions (i.e., buying, selling, or replacing a vehicle) depend current or past household vehicle holdings as well as household transitions. Other downstream modeling that can be enabled by DEMOS include building technology adoption or retrofit decisions, and more. DEMOS modules include: individual age progression year by year, individual mortality events, household restructuring events including marriage match-making; household birth events; individual education participation; individual labor force participation; child leaving household; household in-migration and out-migration for the region; household residence location choice and household mandatory destination choice (work and school). This version of DEMOS is tailored to be integrated with the land use simulation software UrbanSim.

Caicedo, Juan [UrbanSim, Inc.]↗
NASA NTRSDate not supplied

Towards an Optimal Estimation Retrieval of Cirrus Cloud Optical and Microphysical Properties Using Hyperspectral Shortwave Instruments and A Fast Radiative Transfer Algorithm

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral instruments produce high-resolution and information-dense radiance spectra, thus offering the opportunity to reduce uncertainties in retrieval products. An optimal estimation-based cirrus cloud optical and microphysical product retrieval is under development for the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Earth Surface Mineral Dust Source Investigation (EMIT) instruments, and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder). The spectral coverage of all three instruments includes the ultraviolet, visible, and near-infrared. In this study a very fast radiative transfer model, the Principal Component-based Radiative Transfer Model in the solar spectral region (PCRTM-Solar), is used for forward modeling computations. This will reduce the computational burden in the forward radiance and Jacobian calculations as the cost function is minimized, enabling the entire AVIRIS, EMIT, and CLARREO-Pathfinder spectral range to be used in the optimal estimation framework. Using the entire spectrum will maximize the information content, resulting in a more robust and more accurate retrieval. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water. Preliminary results will be shown.

Jeffrey Mast↗
NASA NTRSDate not supplied

Towards an Optimal Estimation Retrieval of Cirrus Cloud Optical and Microphysical Properties Using Hyperspectral Shortwave Instruments and A Fast Radiative Transfer Algorithm

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral shortwave instruments produce high-resolution and information-dense spectra, thus offering the opportunity to reduce uncertainties in retrieval products. We are in the process of developing a retrieval that uses the very fast Principal Component Radiative Transfer Model in the solar spectral region (PCRTM-Solar) in the forward model calculations. This retrieval will use measured reflectances from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder) instruments. In this manuscript we present progress towards a reference retrieval employing a widely used, verified, accurate, yet computationally slower radiative transfer modeling technique. The reference retrieval, while too slow for using the complete hyperspectral measurement, will allow us to study the behavior of retrieval products and help us verify results from our in-development fast retrieval. Both retrievals will use the optimal estimation retrieval framework. In this manuscript we present results from an uncertainty analysis considering three uncertainty sources for a cirrus cloud retrieval in the form of error covariance matrices: reflectance uncertainty due to water vapor, the reflectance uncertainty due to ice crystal scattering assumptions, and the instrument measurement uncertainty. Results show that the uncertainty due to habit selection is the largest, while that due to water vapor is at most 0.6% relative to channel reflectance. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water.

Cirrus cloud↗
NASA NTRS1996

Measurements in Vortex Wakes Shed by Conventional and Modified Subsonic Aircraft

A theoretical and experimental program is underway at NASA Ames Research Center to first obtain a better understanding of the hazard posed by the vortex wakes of subsonic transports, and then to develop methods on how to modify the wake-generating aircraft in order to make the vortices less hazardous. This paper summarizes results obtained in the 80- by 120-Foot Wind Tunnel at NASA Ames Research Center on the characteristics of the vortex wakes that trail from 0.03 scale models of a B-747 and of a DC-10. Measurements are first described that were taken in the wakes with a hot-film anemometer probe, and with wings that range in size from 0.2 to 1.0 times the span of the wake generating models at downstream distances of 81 ft and 162 ft. behind the wake-generating model; i.e., at scale distances of 0.5 and 1.0 mile. The data are then used to evaluate the accuracy of a vortex-lattice method for prediction of the loads induced on following wings by vortex wakes.

Rossow, Vernon J.↗
NASA NTRS1987

Analytical prediction of the unsteady lift on a rotor caused by downstream struts

A two-dimensional, inviscid, incompressible procedure is presented for predicting the unsteady lift on turbomachinery blades caused by the upstream potential disturbance of downstream flow obstructions. Using the Douglas-Neumann singularity superposition potential flow computer program to model the downstream flow obstructions, classical equations of thin airfoil theory are then employed, to compute the unsteady lift on the upstream rotor blades. The method is applied to a particular geometry which consists of a rotor, a downstream stator, and downstream struts which support the engine casing. Very good agreement between the Douglas-Neumann program and experimental measurements was obtained for the downstream stator-strut flow field. The calculations for the unsteady lift due to the struts were in good agreement with the experiments in showing that the unsteady lift due to the struts decays exponentially with increased axial separation of the rotor and the struts. An application of the method showed that for a given axial spacing between the rotor and the strut, strut-induced unsteady lift is a very weak function of the axial or circumferential position of the stator.

Taylor, A. C., III↗
NASA NTRS2014

The Influence of Slope Breaks on Lava Flow Surface Disruption

Changes in the underlying slope of a lava flow impart a significant fraction of rotational energy beyond the slope break. The eddies, circulation and vortices caused by this rotational energy can disrupt the flow surface, having a significant impact on heat loss and thus the distance the flow can travel. A basic mechanics model is used to compute the rotational energy caused by a slope change. The gain in rotational energy is deposited into an eddy of radius R whose energy is dissipated as it travels downstream. A model of eddy friction with the ambient lava is used to compute the time-rate of energy dissipation. The key parameter of the dissipation rate is shown to be rho R(sup 2/)mu, where ρ is the lava density and mu is the viscosity, which can vary by orders of magnitude for different flows. The potential spatial disruption of the lava flow surface is investigated by introducing steady-state models for the main flow beyond the steepening slope break. One model applies to slow-moving flows with both gravity and pressure as the driving forces. The other model applies to fast-moving, low-viscosity, turbulent flows. These models provide the flow velocity that establishes the downstream transport distance of disrupting eddies before they dissipate. The potential influence of slope breaks is discussed in connection with field studies of lava flows from the 1801 Hualalai and 1823 Keaiwa Kilauea, Hawaii, and 2004 Etna eruptions.

disruption↗
DOE OSTI2026

Leveraging Large Language Models for Understanding Fundamental Principles of Catalysis

Heterogeneous catalysis presents a distinct challenge for artificial intelligence (AI). Data sets are often small and inconsistently reported, catalyst representations are not standardized, and extracting fundamental knowledge requires integrating performance data, spectroscopic characterizations, and mechanistic models across multiple scales. Language offers a unifying representation across these modalities, making catalysis well suited for leveraging large language models (LLMs). By standardizing how catalytic data is represented, LLMs make dispersed experimental results more accessible to downstream statistical modeling. In this perspective, we focus our discussion around three opportunities where LLMs can significantly contribute to catalysis: (1) text to properties; (2) text to structure; and (3) text to mechanistic models. The discussion is followed by a perspective section on LLM-readiness of data, aligning LLM outputs with scientific correctness, and bridging lab-scale discovery to industrial deployment. Across each area, the most productive applications couple dispersed chemical knowledge with physics-grounded validation to produce verifiable hypotheses and actionable representations.

Catalysts↗
DOE OSTI2025

Machine Learning Techniques for Data Reduction of Climate Applications

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.

Li, Xiao [University of Florida]↗
NASA NTRS1975

The Bulk Lunar Electrical Conductivity

The electrical conductivity structure was studied of a spherically layered moon consistent with the very low frequency magnetic data collected on the lunar surface and by Explorer 35. In order to obtain good agreement with the lunar surface magnetometer observations, the inclusion of a void cavity behind the moon requires a conductivity at shallow depths higher than that of models having the solar wind impinging on all sides. By varying only the source parameters, a conductivity model can be found that yields a good fit to both the tangential response upstream and the radial response downstream. This model also satisfies the dark side tangential response in the frequency range above 0.006 Hz, but the few data points presently available below this range do not seem to agree with the theory.

Leavy, Donald Lucien↗
DOE OSTI2019

Railcar Dynamics Model of the ENSA/DOE Multimodal Transportation Campaign Rail Conveyance System

Railcar dynamics models can be applied to calculate loads transmitted to used nuclear fuel (UNF) during normal conditions of transportation (NCT). This work details the development of a railcar dynamics model for the Equipos Nucleares of Spain-U.S. Department of Energy (ENSA/DOE) multimodal transportation campaign. The model was developed using NUCARS and represents the Kasgro 12-axle railcar and the ENSA ENUN-32P cask and cradle used during the campaign. The dynamics of the cask and cradle are captured by modeling them as a single degree-of-freedom (SDOF) system with optimized stiffness and damping parameters. The model was tuned using a statistical design method and manual parameter adjustment, and validated by comparing the predicted response to data collected from captive track tests. The SDOF cask and cradle model simplifies downstream analyses used to predict loads on UNF. Previous methods fed the railcar deck motion into computationally expensive finite element models of the cask and cradle; capturing the dynamics of the cask and cradle in the railcar model bypasses this step. The model provides information useful for understanding the shock and vibration environment of UNF during rail transportation, and the agreement achieved between model and test provides confidence that the methods used in this work could be used for other vehicle configurations such as the ATLAS railcar.

Kadooka, Kevin↗
DOE OSTI2024

Augmented Human Analysis (AHA)

Radio frequency (RF) signal monitoring generally emphasizes intentionally generated signals, such as WiFi, Bluetooth, or cellular transmissions. However, electronic devices also produce unintended radiated emissions (UREs), which could also be useful in RF spectrum analysis. In either case, deriving intelligence from RF signals is typically a human-intensive process requiring significant domain knowledge. In the Augmented Human Analysis (AHA) project, we investigate the utility of dimensionally aligned signal projection (DASP) and machine learning (ML) algorithms for accelerating RF analysis workflows. We find that while DASP algorithms can indeed highlight signal characteristics relevant for classification tasks, the choice of algorithmic hyperparameters greatly affects performance. To address this challenge, we evaluate the quality of DASP outputs using the silhouette score, which measures how well data points cluster; high silhouette scores indicate good clustering, and thus good hyperparameter values. This approach is critical for machine learning pipelines as the DASP parameters cannot be directly optimized during model training. By identifying good DASP parameters, and thus good DASP outputs, as a preprocessing step, we can decrease the amount of effort required for downstream ML model training. We demonstrate our workflow using a dataset of UREs from common household devices, showing that even without the aid of ML, proper selection of DASP parameters enables clustering by device type.

42 ENGINEERING↗