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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Surrogate Model of Flexible Research Platform EnergyPlus Models to Enable Sensitivity Analysis

This letter report describes the surrogate models developed from the EnergyPlus model of Oak Ridge National Laboratory’s Flexible Research Platform. Two data-driven black-box models were developed, and the outputs of the surrogate models were compared with the EnergyPlus model. The two models developed are a multilayer perceptron deep learning model, and a long short-term memory (LSTM) neural network model. The three factors for selecting the black-box models are scalability, computation time, and accuracy. A total of 107 input variables were the dominant variables in determining the outputs of building energy consumptions and thermal comfort. A total of 54 output variables were identified as the prediction targets, including the system- and zone-level outputs. The large set of the simulation cases were generated by integrating sensor errors into an emulator based on EnergyPlus and Python EMS, which includes advanced control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. The surrogate models were developed based on a set of large-scale simulation runs (i.e., 4,000 runs) on a cloud platform. The comparison analysis shows that the two black-box models had good accuracy for predicting new outputs for sensitivity analysis using the root mean square error metric. As a next step, the developed surrogate models will be used to perform sensitivity analysis for different sensor impacts (e.g., sensor types, sensor locations).

42 ENGINEERING↗
NASA NTRS2016

Passive Water Assurance Delivery System

This report provides an analysis and evaluation of proposed design improvements to the internal water delivery system of NASA’s passive vegetable growing system. The VEGGIE system is designed to provide fresh vegetables and psychological benefits for astronauts aboard the International Space Station. Preliminary testing conducted by NASA for the VEGGIE system revealed flaws in the water delivery system. In these tests, the interface between the plants and the water reservoir failed to provide passive water delivery through the system both in microgravity experiments and experiments conducted on Earth. Our team’s goal was to fix this interface such that water can be passively delivered from the water reservoir to the plant rooting pillows to grow vegetative crops from seed to harvest. The system requirements outlined by NASA include: minimal total mass, on-demand passive water delivery, same dimensions as existing fixtures, minimal pressure on system to prevent leaking, zero mold growth, minimal swelling or clogging for non-flammable capillary materials, pressure stabilization between the water reservoir and plant pillow bags, even dispersal of water during initial priming, minimal bubble obstruction of capillary interface, maximum gas availability to plant roots, growth in a mixed artificial media (50:50 Arcillite:Fafard #2), and to avoid overwatering and drought conditions for plants. The major proposed design improvements are to use a single-interface capillary cord design to directly connect the water reservoir to the plant-rooting pillows, and to alter the water reservoir to model a propellant management device (PMD) in order to ensure consistent and long term watering for the VEGGIE system. The plant rooting pillow required minimal changes outside of replacing the capillary mat on the bottom of the pillow with O-ring insertion points the single-interface capillary system. - Research was conducted to determine which materials are able to uptake water through capillary action to grow Outredgeous Lettuce plants from seed to harvest. The primary requirements to be met by the team's design and evaluation of the capillary interface were: continuous passive watering for 90 days using non-flammable capillary materials and a peak water delivery rate of 30 mL/hr./0.15 m 2 . The results of the team's experimentation showed that the capillary material Nomex displayed the highest capillary water delivery potential with a maximum flow rate of 3.6 mL/hr. The Nomex capillary systems were the only capillary material to consistently grow Outredgeous Lettuce plants from seed to harvest, and displayed the highest average flow rate for multiple experiment sets. The Nomex material was previously incorporated into the VEGGIE system using a matted version of the capillary material, though the team recommends using a cord configuration in the single-interface capillary system for greater system stability. Nomex has proven to be a promising material, as it has passed both health and fire standards for use aboard the International Space Station. PMDs are made of materials that utilize surface tension and adhesive forces to overcome adverse accelerations to improve stability and ensure fluid delivery. PMDs are typically used in fuel tanks to ensure fuel delivery. The team recommends using a sponge PMD in order to mitigate bubble obstruction, decrease system weight, and ensure reliable water delivery to the capillary interface. The PMD water reservoir requires a rigid water reservoir and a vent tube for pressure stabilization. Since PMD’s cannot be tested in 1-G (Earth conditions), further theoretical modeling and testing is required for the proposed water reservoir design. The intent of this proposed system is to passively water plants in microgravity, though the technology is not limited to microgravity applications. The testing at The Ohio State University has proven that the design is highly effective on Earth, demonstrating that it could serve as a simple water delivery system in home and office applications. This would make vegetative crops more accessible in all indoor applications, thereby improving indoor air quality and occupant comfort. Additionally, this technology has great potential to be utilized in greenhouse plant production, cutting back on more sophisticated watering system energy and time requirements.

Alexandria Jensen↗
DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗
NASA NTRS1982

Results of buffet tests in a cryogenic wind tunnel

Buffet tests on two semispan wing models with different leading edge sweep show that it is feasibile to use the standard dynamic wing root bending moment technique in a cryogenic wind tunnel. One model was a slender 65 deg swept delta wing with sharp leading edges. The other model was an unswept wing of aspect ratio 1.5 with a British NPL 9510 airfoil section. The results for the 65 deg swept delta wing indicate the importance of matching the reduced frequency parameter in model tests for planforms which are sensitive to reduced frequency parameter if quantitative buffet measurements are required. The unique ability of a pressurized cryogenic wind tunnel to separate the effects of Reynolds number and of static aeroelastic distortion by variations in the tunnel stagnation temperature and pressure were demonstrated.

Boyden, R. P.↗
NASA NTRS2011

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

Newcomer, Michelle↗
NASA NTRS2024

Evaluating Differences Among Crop Models in Simulating Soybean in-Season Growth

Crop models are useful tools for simulating agricultural systems that require continued model development and testing to increase their robustness and improve how they describe our current understanding of processes. Coordinated and “blind” evaluation of multiple models using same protocols and experimental datasets provides unique opportunities to further improve models and enhance their reliability. For soybean [Glycine max (L.) Merr.], there has been limited coordinated multi-model evaluations for the simulation of in-season plant growth dynamics. We evaluated ten dynamic soybean crop models for their simulation of in-season plant growth using data from five experiments conducted in Argentina, Brazil, France, and USA. We evaluated models after a Blind (using only phenology data) and a Full calibration (with in-season and end-of-season variables). Calibration reduced model uncertainty by reducing standard bias for the simulation of in-season variables (biomass, leaf, pod, and stem weights, and leaf area index, LAI). However, we found that most models had difficulty in reproducing leaf growth dynamics, with normalized root mean squared error (nRMSE) of 56% for leaf weight and 43% for LAI (across locations and models after Full calibration). Models with different levels of complexity and experience were capable of simulating final seed yield at maturity with reasonable accuracy (nRMSE of 8–31% after Full calibration). However, the nRMSE for pod weight (of 17–64% after Full calibration) was two-fold larger than that of seed yield. Moreover, the models differed in how they simulated timing from sowing to beginning seed growth (47–93 days) and effective seed filling period (18–54 days), owing to model structural differences in defining the reproductive developmental stages. Overall, we identified the following processes that can benefit from further model improvement: leaf expansion and senescence, reproductive phenology, and partitioning to reproductive growth. Simulation of pod wall tissue and individual seed cohorts is another aspect that many models currently lack. Model improvement can benefit from high-temporal resolution experimental datasets that concurrently account for phenology, plant growth, and partitioning. Further, we recommend collecting reproductive phenology in the field consistent with actual dry matter allocation to organs in the models and collecting multiple observations of seed and pod weight to aid model improvement for simulation of seed growth and yield formation.

Agricultural Model Intercomparison and Improvement↗
DOE OSTI2020

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM-Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time-series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) for relative change in FBC and BBC. The CLM-Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE < 0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R 2 =0.70, P<0.001) and BBC (R 2 =0.26, P<0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows the most critical parameters are turnover rate, carbon-to-nitrogen ratio of fungi and bacteria, and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM-Microbe model would deepen our understanding of microbial contributions to the global C cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Non-invasive imaging reveals convergence in root and stem vulnerability to cavitation across five tree species

Root vulnerability to cavitation is challenging to measure and under-represented in current datasets. This gap limits the precision of models used to predict plant responses to drought because roots comprise the critical interface between plant and soil. In this study, we measured vulnerability to drought-induced cavitation in woody roots and stems of five tree species (Acacia aneura, Cedrus deodara, Eucalyptus crebra, Eucalytus saligna, and Quercus palustris) with a wide range of xylem anatomies. X-ray microtomography was used to visualize the accumulation of xylem embolism in stems and roots of intact plants that were naturally dehydrated to varying levels of water stress. Vulnerability to cavitation, defined as the water potential causing a 50% loss of hydraulic function (P 50 ), varied broadly among the species (–4.51 MPa to –11.93 MPa in stems and –3.13 MPa to –9.64 MPa in roots). The P 50 of roots and stems was significantly related across species, with species that had more vulnerable stems also having more vulnerable roots. While there was strong convergence in root and stem vulnerability to cavitation, the P 50 of roots was significantly higher than the P 50 of stems in three species. However, the difference in root and stem vulnerability for these species was small; between 1% and 31% of stem P 50 . Thus, while some differences existed between organs, roots were not dramatically more vulnerable to embolism than stems, and the differences observed were less than those reported in previous studies. Further study is required to evaluate the vulnerability across root orders and to extend these conclusions to a greater number of species and xylem functional types.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Artificial neural network based predictions of cetane number for furanic biofuel additives

The next generation of alternative fuels is being explored through advanced chemical and biological production techniques for the purpose of finding suitable replacements for diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels offers a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Current work has shown that predictive models, in this case artificial neural networks (ANN's) analyzing quantitative structure property relationships (QSPR's), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel's CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present report improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5'-(furan-2-ylmethylene)bis(2-methylfuran), 5,5'-((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl)methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-square error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furan-based molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model's predictive accuracy to new molecular classes. The approach, bolstered by the improvements presented in this paper, enables chemists to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.

09 BIOMASS FUELS↗
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 OSTI2018

Simulating groundwater uptake and hydraulic redistribution by phreatophytes in a high-resolution, coupled subsurface-land surface model

Several new functions representing groundwater dependent vegetation were incorporated into a coupled subsurface-land surface model, ParFlow.CLM, in order to adequately describe groundwater water uptake, hydraulic redistribution, and plant water stress. The modified model was used to conduct three-dimensional, stand-scale simulations of a Mediterranean oak savanna in California. It performed well and captured daily, hourly and spatial water and energy dynamics, as well as groundwater evapotranspiration rates. The new model was then compared to various approaches, the original ParFlow.CLM and a version using a root water uptake compensation equation. During the dry season, the modified model closely predicted the measured transpiration rate while the original model predicted that it would become zero and the compensation approach overestimated it by nearly double. The modified model also allowed for analysis of several key ecohydrological processes, namely the hydraulic redistribution when plants were both active and dormant, the leaf water potential, and xylem cavitation.

54 ENVIRONMENTAL SCIENCES↗
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.↗