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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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NASA NTRSDate not supplied

SimUAM: A Comprehensive Microsimulation Toolchain to Evaluate the Impact of Urban Air Mobility in Metropolitan Areas

Over the past several years, Urban Air Mobility (UAM) has galvanized enthusiasm from investors and researchers, marrying expertise in aircraft design, transportation, logistics, artificial intelligence, battery chemistry, and broader policymaking. However, two significant questions remain unexplored: (1) What is the value of UAM in a region’s transportation network? and (2) How can UAM be effectively deployed to realize and maximize this value to all stakeholders, including riders and local economies? To adequately understand the value proposition of UAM for metropolitan areas, the authors develop a holistic multi-modal toolchain, SimUAM, to model and simulate UAM and its impacts on travel behavior. This toolchain has several components: (1) Microsimulation Analysis for Network Traffic Assignment (MANTA): A fast, high-fidelity regional-scale traffic microsimulator, (2) VertiSim: Agranular, discrete-event vertiport and pedestrian simulator, (3) Flexible Engine for Fast-time Evaluation of Flight Environments (Fe3): A high-fidelity, trajectory-based aerial microsimulation. SimUAM, rooted in granular, GPU-based microsimulation, models millions of trips and their movements in the street network and in the air, producing interpretable and actionable performance metrics for UAM designs and deployments. Once the ground-air interface is modeled, the authors find that the market for UAM decreases across all network designs relative to models with static assumptions about transfer times. However, significant improvements can be made to balance the demand and optimize the networks for transfer time, likely increasing the number of benefited trips. The modularity, extensibility, and speed of the platform will allow for rapid scenario planning and sensitivity analysis, effectively acting as a detailed performance assessment tool.

urban air mobility↗
NASA NTRS1998

Depth dependence of absorbed dose, dose equivalent and linear energy transfer spectra of galactic and trapped particles in polyethylene and comparison with calculations of models

A matched set of five tissue-equivalent proportional counters (TEPCs), embedded at the centers of 0 (bare), 3, 5, 8 and 12-inch-diameter polyethylene spheres, were flown on the Shuttle flight STS-81 (inclination 51.65 degrees, altitude approximately 400 km). The data obtained were separated into contributions from trapped protons and galactic cosmic radiation (GCR). From the measured linear energy transfer (LET) spectra, the absorbed dose and dose-equivalent rates were calculated. The results were compared to calculations made with the radiation transport model HZETRN/NUCFRG2, using the GCR free-space spectra, orbit-averaged geomagnetic transmission function and Shuttle shielding distributions. The comparison shows that the model fits the dose rates to a root mean square (rms) error of 5%, and dose-equivalent rates to an rms error of 10%. Fairly good agreement between the LET spectra was found; however, differences are seen at both low and high LET. These differences can be understood as due to the combined effects of chord-length variation and detector response function. These results rule out a number of radiation transport/nuclear fragmentation models. Similar comparisons of trapped-proton dose rates were made between calculations made with the proton transport model BRYNTRN using the AP-8 MIN trapped-proton model and Shuttle shielding distributions. The predictions of absorbed dose and dose-equivalent rates are fairly good. However, the prediction of the LET spectra below approximately 30 keV/microm shows the need to improve the AP-8 model. These results have strong implications for shielding requirements for an interplanetary manned mission.

Flight Experiment↗
DOE OSTI2025

Influence of particle size on NIR spectroscopic characterization of sorghum biomass for the biofuel industry

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum (Sorghum bicolor), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

09 BIOMASS FUELS↗
DOE OSTI2025

Data for Influence of Particle Size on NIR Spectroscopic Characterization of Sorghum Biomass for the Biofuel Industry

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum ( Sorghum bicolor ), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

Biomass Analytics↗
NASA NTRS1993

Ice Accretion Prediction for a Typical Commercial Transport Aircraft

Ice accretion calculations were made for a modern commercial transport using the NASA Lewis LEWICE3D ice accretion code. The ice accretion calculations were made for the wing and horizontal tail using both isolated flow models and flow models incorporating the entire airplane. The isolated flow model calculations were made to assess the validity of using these simplified models in lieu of the entire model in the ice accretion analysis of full aircraft. Ice shapes typifying a rime and a mixed ice shape were generated for a 30 minute hold condition. In general, the calculated ice shapes looked reasonable and appeared representative of a rime and a mixed ice conditions. The isolated flow model simplification was good for the main wing except at the root where it overpredicted the amount of accreted ice relative to the full aircraft flow model. For the horizontal tail the size and amount of predicted ice compared well for the two flow models, but the position of the accretions were more towards the upper surface for the aircraft flow model relative to the isolated flow model. This was attributed to downwash from the main wing which resulted in a lower effective angle of attack for the aircraft tail.

Bidwell, C. S.↗
NASA NTRS1993

Ice accretion prediction for a typical commercial transport aircraft

Ice accretion calculations were made for a modern commercial transport using the NASA Lewis LEWICE3D ice accretion code. The ice accretion calculations were made for the wing and horizonal tail using both isolated flow models and flow models incorporating the entire airplane. The isolated flow model calculations were made to assess the validity of using these simplified models in lieu of the entire model in the ice accretion analysis for full aircraft. Ice shapes typifying a rime and a mixed ice shape were generated for a 30 minute hold condition. In general, the calculated ice shapes looked reasonable and appeared representative of a rime and a mixed ice conditions. The isolated flow model simplification was good for the main wing except at the root where it overpredicted the amount of accreted ice relative to the full aircraft flow model. For the horizontal tail the size and amount of predicted ice compared well for the two flow models, but the position of the accretions were more towards the upper surface for the aircraft flow model relative to the isolated flow model. This was attributed to downwash from the main wing which resulted in a lower effective angle of attack for the aircraft tail.

Bidwell, C. S.↗
DOE OSTI2025

Plant Roots and the Fate of Mineral-Associated Soil Carbon (Final Report)

Atmospheric CO 2 concentrations continue to rise due to human activities, but there are also natural mechanisms operating in ecosystems that can store carbon in soil long-term. The organic carbon derived from organisms in soil becomes associated with minerals, where it can be protected for thousands of years. Plants are particularly important contributors of carbon belowground, via their roots. The long-term protection of carbon contributed to soil has great value to humans as climate change looms. But, to keep growing, living plants and soil microbes also need to be able to access and recycle soil nutrients that are stored along with the carbon. In this DOE-funded work, Cardon (MBL), Keiluweit (UMass Amherst/University of Lausanne), Malmstrom (MSU), and Riley (LBNL) used lab and greenhouse experiments coupled with mathematical modeling to determine mechanisms by which plant roots and their associated microbes can dislodge organic matter from soil minerals, making its components available for recycling, but also making its carbon vulnerable to re-release to the atmosphere as CO 2 .

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1957

Preliminary Wind-Tunnel Investigation of the Performance of Republic F-105 Wing-Root Inlet Configurations at Various Angles of Attack and a Mach Number of 2.01

A 1/13-scale model of the forebody of the Republic F-105 with twin-duct wing-root inlets was tested in the Langley 4- by 4-foot supersonic pressure tunnel through a range of angle of attack from -4 deg to 15 deg at a Mach number of 2.01 and a Reynolds number of approximately 3.4 x 10(exp 6) per foot. The tests were made with four configurations which incorporated varying amounts of sweep and stagger of the inlet leading edges, modifications to the areas of the boundary-layer diverter floor plate, and modifications to the area of the boundary-layer diverter bleed slots. The highest overall pressure recovery at an angle of attack of 0 deg (average total-pressure recovery, 0.84 mass-flow ratio, 0.98) was achieved with configuration having an inlet leading-edge sweep angle of 58 deg with no leading-edge stagger. Stagger was found to improve the angle-of- attack performance, but at a sacrifice in inlet efficiency for an angle of attack of 0 deg. The boundary-layer diverter floor height, of the order of one boundary-layer thickness, was satisfactory for bypassing the fuselage boundary layer. The boundary-layer diverter-plate bleed slots were effective in increasing the total-pressure recovery of the inlet. The total-pressure-recovery contour plots, taken at the compressor-face station, indicate the existence of high-velocity "cores" throughout the inlet operating range.

Kouyoumjian, W. L.↗
DOE OSTI2022

How Generalizable is a Machine-Learning Approach for Modeling Hub-Height Turbulence Intensity?

Hub-height turbulence intensity is essential for a variety of wind energy applications. However, simulating it is a challenging task. Simple analytical models have been proposed in the literature, but they all come with significant limitations. Even state-of-the-art numerical weather prediction models, such as the Weather Research and Forecasting model, currently struggle to predict hub-height turbulence intensity. Here, we propose a machine-learning-based approach to predict hub-height turbulence intensity from other hub-height and ground-level atmospheric measurements, using observations from the Perdigao field campaign and the Southern Great Plains atmospheric observatory. We consider a random forest regression model, which we validate first at the site used for training and then under a more robust round-robin approach, and compare its performance to a multivariate linear regression. The random forest successfully outperforms the linear regression in modeling hub-height turbulence intensity, with a normalized root-mean-square error as low as 0.014 when using 30-minute average data. In order to achieve such low root-mean-square error values, the knowledge of hub-height turbulence kinetic energy (which can instead be modeled in the Weather Research and Forecasting model) is needed. Interestingly, we find that the performance of the random forest generalizes well when considering a round-robin validation (i.e., when the algorithm is trained at one site such as Perdigao or Southern Great Plains) and then applied to model hub-height turbulence intensity at the other location.

17 WIND ENERGY↗
DOE OSTI2023

Mixing in Low Reynolds Number Reacting Impinging Jets in Crossflow

Previous efforts to model uranyl fluoride formation in an impinging jet gas reactor underpredicted spatial mixing and overpredicted chemical conversion into particulates. The previous fluid dynamics model was based on the solution of the Reynolds Averaged Navier Stokes equations. After simulating fluid dynamics, aerosol dynamics were superimposed onto CFD-simulated gas reactant species concentrations. The current work explores the influence of complex unsteady flow features on the overall flow physics and chemistry for a low Reynolds number, opposed flow, impinging jet gas reactor where there is a low Reynolds number cross flow. The objective of this study was to assess the impact of model formulation on scalar mixing and transport. Here, transient flow simulations were performed using Scale Resolving Simulations. Large-Eddy Simulations with the dynamic Smagorinsky turbulence model were performed along with simulations which directly resolved the flow. Average and root-mean-square (RMS) velocities and species concentrations were computed along with modeled and resolved turbulence kinetic energy (TKE), modeled turbulence dissipation, and modeled turbulent viscosity. Lagrangian flow tracers were also used to quantify species concentrations along path lines emanating from the jet tips. Transient simulation data were compared to results from RANS simulations using the k-ω shear stress transport (SST) model and Reynolds Stress Model (RSM). Transient simulations showed spatial mixing patterns which were more consistent with experimental data and helped elucidate the process of particle formation observed in experiments.

42 ENGINEERING↗
DOE OSTI2026

Modelling belowground plant acclimation to low soil nitrogen – a heuristic optimality-based approach

Increased root growth to access greater soil mineral nitrogen resources and increased root exudation to stimulate microbial mineralization of soil organic nitrogen are widely observed plant acclimations to nitrogen limitation. However, their quantitative contribution to plant growth and ecosystem productivity remains largely elusive. Here, we present a novel optimality-based eco-evolutionary model in which plants dynamically regulate carbon partitioning between root growth and exudation to maximize their aboveground growth. Our simulations indicated that the availability of soil mineral and organic nitrogen as well as plant nitrogen demand and nitrogen uptake capacity shape optimal carbon partitioning between root growth and exudation. The simulated carbon allocation patterns aligned with empirical studies on belowground plant responses to varying soil nitrogen resources. Our eco-evolutionary approach represents a paradigmatic change in modelling plant nitrogen foraging, which is essential to generate hypotheses on optimal plant acclimation in future soil environments characterized by more erratic nitrogen availability.

Chakrawal, Arjun (ORCID:0000000345724347)↗
NASA NTRS2015

Effect of Notches on Creep-Fatigue Behavior of a P/M Nickel-Based Superalloy

A study was performed to determine and model the effect of high temperature dwells on notched low cycle fatigue (NLCF) and notch stress rupture behavior of a fine grain LSHR powder metallurgy (PM) nickel-based superalloy. It was shown that a 90 second dwell applied at the minimum stress (min dwell) was considerably more detrimental to the NLCF lives than similar dwell applied at the maximum stress (max dwell). The short min dwell NLCF lives were shown to be caused by growth of small oxide blisters which caused preferential cracking when coupled with high concentrated notch root stresses. The cyclic max dwell notch tests failed mostly by a creep accumulation, not by fatigue, with the crack origin shifting internally to a substantial distance away from the notch root. The classical von Mises plastic flow model was unable to match the experimental results while the hydrostatic stress profile generated using the Drucker-Prager plasticity flow model was consistent with the experimental findings. The max dwell NLCF and notch stress rupture tests exhibited substantial creep notch strengthening. The triaxial Bridgman effective stress parameter was able to account for the notch strengthening by collapsing the notched and uniform gage geometry test data into a singular grouping.

Dwell Notch Fatigue↗
DOE OSTI2024

Plant root associated chitinases: structures and functions

Chitinases degrade chitin, a linear homopolymer of β-1,4-linked N-acetyl-D-glucosamine (GlcNAc) residues found in the cell walls of fungi and the exoskeletons of arthropods. They are secreted by the roots into the rhizosphere, a complex and dynamic environment where intense nutrient exchange occurs between plants and microbes. Here we modeled, expressed, purified, and characterized Zea mays and Oryza sativa root chitinases, and the chitinase of a symbiotic bacterium, Chitinophaga oryzae 1303 for their activities with chitin, di-, tri-, and tetra-saccharides and Aspergillus niger, with the goal of determining their role(s) in the rhizosphere and better understanding the molecular mechanisms underlying plant-microbe interactions. We show that Zea mays basic endochitinase (ZmChi19A) and Oryza sativa chitinase (OsChi19A) are from the GH19 chitinase family. The Chitinophaga oryzae 1303 chitinase (CspCh18A) belongs to the GH18 family. The three enzymes have similar apparent K M values of (20-40 µM) for the substrate 4-MU-GlcNAc 3 . They vary in their pH and temperature optima with OsChi19A activity optimal between pH 5–7 and 30–40°C while ZmChi19A and CspCh18A activities were optimal at pH 7-9 and 50–60°C. Modeling and site-directed mutation of ZmChi19A identified the catalytic cleft and the active residues E147 and E169 strategically positioned at ~8.6Å from each other in the folded protein. Cleavage of 4-MU-GlcNAc 3 was unaffected by the absence of the CBD but diminished in the absence of the flexible C-terminal domain. However, unlike for the soluble substrate, the CBD and the newly identified flexible C-terminal domain were vital for inhibiting Aspergillus niger growth. The results are consistent with the involvement of the plant chitinases in defense against pathogens like fungi that have chitin exoskeletons. In summary, we have characterized the functional features and structural domains necessary for the activity of two plant root chitinases that are believed to be involved in plant defense and a bacterial chitinase that, along with the plant chitinases, may participate in nutrient recycling in the rhizosphere.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Assessing Dynamic Behaviors in Converter- Dominated Power Systems via RMS and EMT Simulations: A Study of Hawaii’s NELHA Microgrid

The transition from conventional power systems to converter-based microgrids has significantly advanced sustainability, clean energy integration, and operational reliability. However, this paradigm shift introduces operational challenges due to the intermittent nature of renewable energy sources and the non-linear characteristics of power electronic loads, inducing voltage fluctuations and harmonic distortions that complicate voltage and frequency regulation. Accurate dynamic modeling is hypothesized to be critical for capturing such effects, enabling reliable simulation and control strategy development. This study introduces an innovative dynamic modeling framework for a real-world converter-based microgrid, utilizing both root mean square (RMS) and electromagnetic transient (EMT) simulation methods. The microgrid was modeled in DIgSILENT PowerFactory, with simulations calibrated against high-resolution field measurements from SEL-735 power quality meters. Results show that RMS simulations effectively characterize steady-state dynamics, while EMT simulations are essential for capturing high-frequency transients and non-linear effects from photovoltaic inverters and variable frequency drives (VFDs). This complementary approach provides a comprehensive understanding of microgrid behavior, providing critical insights for improving simulation accuracy, advancing protection schemes, and improving resilience in future low-inertia power networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1979

A seasonal climate model for earth

A simple seasonal climate model for earth is developed on the basis of a few terms extracted from monthly and zonally averaged climatic data fields represented in latitude by Legendre polynomials and in time by Fourier series. This simple, physically motivated model accounts for the gross features of the present zonally averaged seasonal climate (root mean square error of two deg C). The sensitivities of the seasonal model and a corresponding mean annual model are approximately equal if ice and snow lines (for albedo purposes) are attached to certain mean-annual and instantaneous isotherms. More dynamics (in particular, cryospheric) are needed in the seasonal model to explain the relationship between glacial rhythms and changes in the earth's orbital elements.

North, G. R.↗
DOE OSTI2025

A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms

There has been an increase in extreme weather events, posing a threat to power grid systems, potentially influenced by factors such as population growth, changes in ecosystems, land cover, and land use in the service area, as well as the growth of certain vegetation types. This research seeks to develop a predictive model to mitigate potential damages caused by future winter storms. This research utilizes the Light Gradient Boosting Machine (LightGBM), incorporating the number of power outages experienced at the county level, geographic details, weather information, and lagged outage and lagged weather data. The developed models were broadly divided into two groups, with six models in each group - one group without optimization and another with optimization, totaling 12 trained models. For model optimization, Bayesian optimization was employed using Root Mean Squared Error (RMSE) as the objective function. In results, when comparing Group 2 (the optimized group) with Group 1 (the non-optimized group), it was found that optimization did not always lead to a reduction in RMSE and Mean Absolute Error (MAE). However, in terms of Mean Directional Accuracy (MDA), while all results in Group 1 were below the baseline accuracy of 0.33, all results in Group 2 exceeded 0.33, with some cases showing an increase of more than three times the baseline. The results indicated that, in the optimized model group, Population and Pressure were the most influential factors when using current weather data and geographical information. When using lagged data, lagged recorded outages and lagged Pressure emerged as the most significant factors. Among the 12 developed models, the L-1-2-O model showed the lowest RMSE and MAE, as well as the highest accuracy, with values of 390.62 households and 168.13 households, respectively. To normalize the RMSE and MAE values, each metric was divided by the average number of households among the counties in Texas. For the L-1-2-O model, the scaled RMSE was 0.88% and the scaled MAE was 0.38%. In terms of MDA, which indicates the accuracy of the prediction direction, the L-1-O model achieved the highest score of 0.41. Although this study focused on Texas, which suffered the greatest impact from the winter storms in 2021, with additional validation, the methodology used in this research could be applied to other regions.

Lee, Jangjae [Texas A & M Univ., College Station, ↗
DOE OSTI2020

Validation and Sensitivity Analysis of a 1-D Lake Model Across Global Lakes

Lakes have important influence on weather and climate from local to global scales. However, their prediction using numerical models is notoriously difficult because lakes are highly heterogeneous across the globe, but observations are sparse. In this study, we assessed the performance of a 1-D lake model in simulating the thermal structures of 58 lakes with diverse morphometric and geographic characteristics by following the phase 2a local lake protocol of the Inter-sectoral Impact Model Intercomparison Project (ISIMIP2a). After calibration, the root-mean-square errors (RMSE) were below 2 °C for 70% and 75% of the lakes for epilimnion and full-profile temperature simulations, with an average of 1.71 °C and 1.43 °C, respectively. The model performance mainly depended on lake shape rather than location, supporting the possibility of grouping model parameters by lake shape for global applications. Furthermore, through machine-learning based parameter sensitivity tests, we identified turbulent heat fluxes, wind-driven mixing and water transparency as the major processes controlling lake thermal and mixing regimes. Snow density was also important for modeling the ice phenology of high-latitude lakes. The relative influence of the key processes and the corresponding parameters mainly depended on lake latitude and depth. Turbulent heat fluxes showed a decreasing importance in affecting epilimnion temperature with increasing latitude. Wind-driven mixing was less influential to lake stratification for deeper lakes while the impact of light extinction, on the contrary, showed a positive correlation with depth. Our findings may guide improvements in 1-D lake model parameterizations to achieve higher fidelity in simulating global lake thermal dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Foliar functional traits from imaging spectroscopy across biomes in eastern North America

Summary Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental‐scale functional diversity.

Wang, Zhihui↗