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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers

Deep neural networks (DNNs) have heavily relied on traditional computational units, such as CPUs and GPUs. However, this conventional approach brings significant computational burden, latency issues, and high power consumption, limiting their effectiveness. This has sparked the need for lightweight networks such as ExtremeC3Net. Meanwhile, there have been notable advancements in optical computational units, particularly with metamaterials, offering the exciting prospect of energy-efficient neural networks operating at the speed of light. Yet, the digital design of metamaterial neural networks (MNNs) faces precision, noise, and bandwidth challenges, limiting their application to intuitive tasks and low-resolution images. In this study, we proposed a large kernel lightweight segmentation model, ExtremeMETA. Based on ExtremeC3Net, our proposed model, ExtremeMETA maximized the ability of the first convolution layer by exploring a larger convolution kernel and multiple processing paths. With the large kernel convolution model, we extended the optic neural network application boundary to the segmentation task. To further lighten the computation burden of the digital processing part, a set of model compression methods was applied to improve model efficiency in the inference stage. The experimental results on three publicly available datasets demonstrated that the optimized efficient design improved segmentation performance from 92.45 to 95.97 on mIoU while reducing computational FLOPs from 461.07 MMacs to 166.03 MMacs. The large kernel lightweight model ExtremeMETA showcased the hybrid design’s ability on complex tasks.

large convolution kernel↗
NASA NTRS2014

Numerical Modeling of Ocular Dysfunction in Space

Upon introduction to microgravity, the near-loss of hydrostatic pressure causes a marked cephalic (headward) shift of fluid in an astronaut's body. The fluid shift, along with other factors of spaceflight, induces a cascade of interdependent physiological responses which occur at varying time scales. Long-duration missions carry an increased risk for the development of the Visual Impairment and Intracranial Pressure (VIIP) syndrome, a spectrum of ophthalmic changes including posterior globe flattening, choroidal folds, distension of the optic nerve sheath, kinking of the optic nerve and potentially permanent degradation of visual function. In the cases of VIIP found to date, the initial onset of symptoms occurred after several weeks to several months of spaceflight, by which time the gross bodily fluid distribution is well established. We are developing a suite of numerical models to simulate the effects of fluid shift on the cardiovascular, central nervous and ocular systems. These models calculate the modified mean volumes, flow rates and pressures that are characteristic of the altered quasi-homeostatic state in microgravity, including intracranial and intraocular pressures. The results of the lumped models provide initial and boundary data to a 3D finite element biomechanics simulation of the globe, optic nerve head and retrobulbar subarachnoid space. The integrated set of models will be used to investigate the evolution of the biomechanical stress state in the ocular tissues due to long-term exposure to microgravity.

intraocular pressure↗
DOE OSTI2024

Modeling Dust Production, Growth, and Destruction in Reionization-era Galaxies with the CROC Simulations. II. Predicting the Dust Content of High-redshift Galaxies

Abstract We model the interstellar dust content of the reionization era with a suite of cosmological, fluid-dynamical simulations of galaxies with stellar masses ranging from ∼10 5 to 10 9 M ⊙ in the first 1.2 Gyr of the Universe. We use a post-processing method that accounts for dust creation and destruction processes, allowing us to systematically vary the parameters of these processes to test whether dust-dependent observable quantities of galaxies at these epochs could be useful for placing constraints on dust physics. We then forward model observable properties of these galaxies to compare to existing data. We find that we are unable to simultaneously match existing observational constraints with any one set of model parameters. Specifically, the models that predict the largest dust masses D / Z ≳ 0.1 at z = 5—because of high assumed production yields and/or efficient growth via accretion in the interstellar medium—are preferred by constraints on total dust mass and infrared (IR) luminosities, but these models produce far too much attenuation in the ultraviolet (UV), preventing them from matching observations of β UV . To investigate this discrepancy, we analyze the relative spatial distribution of stars and dust as probed by IR and UV emission, which appear to exhibit overly symmetric morphologies compared to existing data, likely due to the limitations of the stellar feedback model used in the simulations. Our results indicate that the observable properties of the dust distribution in high redshift galaxies are a particularly strong test of stellar feedback.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2018

Evaluation of MERRA-2-Based Ozone Profile Simulations with the Global Ozonesonde Network

Chemical transport model (CTM) hindcasts of ozone (O3) are useful for filling in observational gaps and providing context for observed O3 variability and trends. We use global networks of ozonesonde stations to evaluate the O3 profiles in two simulations running versions of the NASA Global Modeling Initiative (GMI) chemical mechanism. Both simulations are tied to the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meteorological reanalysis: 1) The GMI CTM, and 2) The MERRA-2 GMI Replay (M2 GMI). Both simulations start in 1980, and are compared against >50,000 ozonesonde profiles from 37 global stations from the tropics to the poles. The comparisons allow us to evaluate how the Replay technique affects modeled O3 distribution, how an updated chemical mechanism in the GMI CTM affects simulated tropospheric O3 amounts, and how observed O3 distributions compare to the full set of model output. In general, M2 GMI O3 is ~10% higher than in the GMI CTM, and shows global near-surface and tropical upper troposphere/lower stratosphere (UT/LS) high biases. The updated chemical mechanism in the GMI CTM reduces these high biases. Both simulations show similar negative biases in tropical free-tropospheric O3, especially during typical biomass burning seasons. The simulations are highly-correlated with ozonesonde measurements, particularly in the UT/LS (r > 0.8), showing the ability of MERRA-2 to capture tropopause height variations. Both simulations show improved correlations with ozonesonde data and smaller O3 biases in recent years. We expect to use the sonde/model comparisons to diagnose causes of disagreement and to gauge the feasibility of calculating multidecadal O3 trends from the model output.

Stauffer, Ryan↗
NASA NTRS2018

Evaluation of MERRA-2-Based Ozone Profile Simulations with the Global Ozonesonde Network

Chemical transport model (CTM) hindcasts of ozone (O3) are useful for filling in observational gaps and providing context for observed O3 variability and trends. We use global networks of ozonesonde stations to evaluate the O3 profiles in two simulations running versions of the NASA Global Modeling Initiative (GMI) chemical mechanism. Both simulations are tied to the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meteorological reanalysis: 1) The GMI CTM, and 2) The MERRA-2 GMI Replay (M2 GMI). Both simulations start in 1980, and are compared against >50,000 ozonesonde profiles from 37 global stations from the tropics to the poles. The comparisons allow us to evaluate how the Replay technique affects modeled O3 distribution, how an updated chemical mechanism in the GMI CTM affects simulated tropospheric O3 amounts, and how observed O3 distributions compare to the full set of model output. In general, M2 GMI O3 is ~10% higher than in the GMI CTM, and shows global near-surface and tropical upper troposphere/lower stratosphere (UT/LS) high biases. The updated chemical mechanism in the GMI CTM reduces these high biases. Both simulations show similar negative biases in tropical free-tropospheric O3, especially during typical biomass burning seasons. The simulations are highly-correlated with ozonesonde measurements, particularly in the UT/LS (r > 0.8), showing the ability of MERRA-2 to capture tropopause height variations. Both simulations show improved correlations with ozonesonde data and smaller O3 biases in recent years. We expect to use the sonde/model comparisons to diagnose causes of disagreement and to gauge the feasibility of calculating multidecadal O3 trends from the model output.

Stauffer, Ryan M.↗
DOE OSTI2024

Modeling scale-up of particle coating by atomic layer deposition

Atomic layer deposition (ALD) is a promising technique to functionalize particle surfaces for energy applications including energy storage, catalysis, and decarbonization. In this work, we present a set of models of ALD particle coating to explore the transition from lab scale to manufacturing. Our models encompass the main particle coating manufacturing approaches including rotary bed, fluidized bed, and continuously vibrating reactors. These models provide key metrics, such as throughput and precursor utilization, required to evaluate the scalability of ALD manufacturing approaches and their feasibility in the context of energy applications. Our results show that designs that force the precursor to flow through fluidized particles transition faster to a transport-limited regime where throughput is maximized. They also exhibit higher precursor utilization. In the context of continuous processes, our models indicate that it is possible to achieve self-extinguishing processes with almost 100% precursor utilization. A comparison with past experimental results of ALD in fluidized bed reactors shows excellent qualitative and quantitative agreement.

25 ENERGY STORAGE↗
NASA NTRS1971

A method for nonlinear exponential regression analysis

A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

Junkin, B. G.↗
NASA NTRSDate not supplied

Spatial and Temporal Deconfliction of Trajectories in the Presence of Uncertainties

Demonstration of conflict-free movement for multi-agent teams in challenging scenarios is crucial in developing trust and trustworthiness in an autonomous transport system. Tolerance verification queries are explored as a mechanism to enforce spatial and temporal deconfliction for a cooperating team of Unmanned Aerial Systems (UAS) with prescribed heterogeneous path-following performance. Obstacles in the environment are modelled as set of polyhedra, whereas each vehicle’s trajectory is represented as a sequence of polynomial curves with C2 continuity, expressed in a Bernstein basis. Each vehicle is modelled as a point mass and a safety distance, informed by the geometry of the UAS and the worst-case path-following error. This defines a tube around the trajectories where each UAS is most likely to fly through. In addition, obstacles in the environment have an associated safety buffer around them to account for the uncertainty in their location and geometric description. The tolerance verification queries explored in this paper combine the safety distance information from each UAS and environmental hazard to compute trajectories that are contained within the safe configuration space. Tolerance verification is also compared with other proximity queries to determine the suitability of each method along the different steps of the trajectory generation algorithm. This paper analyzes the fitness and performance of three proximity queries – collision, tolerance verification, and distance computations – between polyhedral and polynomial curves to ensure deconfliction between obstacles and vehicles, but also between polynomial curves to guarantee safe separation among cooperating UAS.

trajectory generation↗
NASA NTRS2007

Characterizing Aerosols over Southeast Asia using the AERONET Data Synergy Tool

Biomass burning, urban pollution and dust aerosols have significant impacts on the radiative forcing of the atmosphere over Asia. In order to better quanti@ these aerosol characteristics, the Aerosol Robotic Network (AERONET) has established over 200 sites worldwide with an emphasis in recent years on the Asian continent - specifically Southeast Asia. A total of approximately 15 AERONET sun photometer instruments have been deployed to China, India, Pakistan, Thailand, and Vietnam. Sun photometer spectral aerosol optical depth measurements as well as microphysical and optical aerosol retrievals over Southeast Asia will be analyzed and discussed with supporting ground-based instrument, satellite, and model data sets, which are freely available via the AERONET Data Synergy tool at the AERONET web site (http://aeronet.gsfc.nasa.gov). This web-based data tool provides access to groundbased (AERONET and MPLNET), satellite (MODIS, SeaWiFS, TOMS, and OMI) and model (GOCART and back trajectory analyses) databases via one web portal. Future development of the AERONET Data Synergy Tool will include the expansion of current data sets as well as the implementation of other Earth Science data sets pertinent to advancing aerosol research.

Giles, David M.↗
NASA NTRS1986

Ephemerides of the Uranian satellites determined from earth-based astrometric and Voyager imaging observations

Uranian satellite ephemerides were needed by the Voyager project to support both navigation and acquisition of scientific data. This paper outlines the mathematical modeling approach taken to generate those ephemerides and discusses their pre-encounter development, real time updating, and post-encounter refinement. The results presented include the final set of model parameters which generate the ephemerides that best fit the Voyager optical data as well as the earth based observations of the satellites.

Jacobson, R. A.↗
NASA NTRS2011

Power Management and Distribution (PMAD) Model Development: Final Report

Power management and distribution (PMAD) models were developed in the early 1990's to model candidate architectures for various Space Exploration Initiative (SEI) missions. They were used to generate "ballpark" component mass estimates to support conceptual PMAD system design studies. The initial set of models was provided to NASA Lewis Research Center (since renamed Glenn Research Center) in 1992. They were developed to estimate the characteristics of power conditioning components predicted to be available in the 2005 timeframe. Early 90's component and device designs and material technologies were projected forward to the 2005 timeframe, and algorithms reflecting those design and material improvements were incorporated into the models to generate mass, volume, and efficiency estimates for circa 2005 components. The models are about ten years old now and NASA GRC requested a review of them to determine if they should be updated to bring them into agreement with current performance projections or to incorporate unforeseen design or technology advances. This report documents the results of this review and the updated power conditioning models and new transmission line models generated to estimate post 2005 PMAD system masses and sizes. This effort continues the expansion and enhancement of a library of PMAD models developed to allow system designers to assess future power system architectures and distribution techniques quickly and consistently.

Metcalf, Kenneth J.↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRS1995

Envision: An interactive system for the management and visualization of large geophysical data sets

Envision is a software project at the University of Illinois and Texas A&M, funded by NASA's Applied Information Systems Research Project. It provides researchers in the geophysical sciences convenient ways to manage, browse, and visualize large observed or model data sets. Envision integrates data management, analysis, and visualization of geophysical data in an interactive environment. It employs commonly used standards in data formats, operating systems, networking, and graphics. It also attempts, wherever possible, to integrate with existing scientific visualization and analysis software. Envision has an easy-to-use graphical interface, distributed process components, and an extensible design. It is a public domain package, freely available to the scientific community.

Searight, K. R.↗
DOE OSTI2026

Validated Reactive Force Field Quantifies MXene Interfacial Properties, Mechanics, and Thermal Transport

MXenes combine rich surface chemistry, mechanical strength, and high conductivity for a multitude of emerging applications. Predictive modeling supports accelerated materials designs and has been limited by the absence of validated and transferable force fields. Here, we introduce an interpretable, reactive INTERFACE force field (IFF and IFF-R) for Ti 3 C 2 T x MXenes that is trained based on chemical knowledge and achieves quantitative agreement with experiments across lattice parameters (<0.5%), density (<0.2%), liquid contact angles, Raman spectra, and the in-plane elastic modulus (∼320 GPa). The models cover surface terminations from hydroxyl (−OH) to fluorine (−F) groups and are extensible to other chemistries. We introduce pH-resolved surface chemistry and identify dopamine adsorption mechanisms at MXene–aqueous interfaces supported by QCM-D and UV–Vis experiments. The data reveal coplanar and perpendicular binding modes and concentration-dependent multilayer assembly. We predict previously inaccessible properties, including termination-dependent cleavage energies, interlayer shear moduli and dynamic shear failure, nanoindentation and brittle fracture, anisotropic in-plane and out-of-plane thermal conductivities, including the role of defects. Agreement with available experimental data is consistently close and exceeds DFT accuracy across the benchmark properties examined. The IFF/IFF-R model is compatible with CHARMM, AMBER, OPLS, and CVFF force fields for simulations of MXenes with diverse surface terminations, electrolyte interfaces, biointerfaces, and polymer composites without additional parameters. Parameter sets, 3D models, and analysis scripts are provided for community use. The validated, reactive, and transferable IFF framework facilitates predictive design of MXene-based films, membranes, sensing interfaces, and composites.

MXene↗
DOE OSTI2024

Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

Pike, Christopher (ORCID:0000000155888033)↗
NASA NTRSDate not supplied

Harnessing Large Language Models for Scientific Endeavors

The rapid proliferation of Large Language Models (LLMs) such as GPT, Bard, and Llama has revolutionized various sectors, including the scientific community. These models, with their potential to automate and augment tasks, are increasingly being recognized as both a valuable asset and a potential challenge in the realm of scientific research and data management. However, the current LLMs, primarily trained on general corpora, exhibit a limited understanding of scientific concepts and terminologies due to the lack of scientific corpus in their training data. Recognizing this gap, several groups are now advocating for the development of LLMs specifically tailored for scientific applications. A notable initiative in this direction is the Large Language Model effort initiated by NASA's CSDO. This endeavor aims to align LLM efforts across NASA’s Science Mission Directorate, develop a science-specific corpus and validation test set for model training, and create an encoder-only model for various downstream tasks. Moreover, the initiative also plans to develop a decoder-only model to explore the potential benefits and risks associated with a generative LLM for science. Lastly, the project aims to create a science evaluation suite, encompassing various categories of downstream scientific tasks, to serve as a benchmark for assessing the value of any LLM for future use. This presentation will provide an overview and current status of this ongoing initiative, highlighting its potential to reshape the use of LLMs in the scientific domain.

Rahul Ramachandran↗