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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

An ectomycorrhizal fungus alters sensitivity to jasmonate, salicylate, gibberellin, and ethylene in host roots

The phytohormones jasmonate, gibberellin, salicylate, and ethylene regulate an interconnected reprogramming network integrating root development with plant responses against microbes. The establishment of mutualistic ectomycorrhizal symbiosis requires the suppression of plant defense responses against fungi as well as the modification of root architecture and cortical cell wall properties. In this work, we investigated the contribution of phytohormones and their crosstalk to the ontogenesis of ectomycorrhizae (ECM) between grey poplar (Populus tremula x alba) roots and the fungus Laccaria bicolor. To obtain the hormonal blueprint of developing ECM, we quantified the concentrations of jasmonates, gibberellins, and salicylate via liquid chromatography-tandem mass spectrometry. Subsequently, we assessed root architecture, mycorrhizal morphology, and gene expression levels (RNA sequencing) in phytohormone-treated poplar lateral roots in the presence or absence of L. bicolor. Salicylic acid accumulated in mid-stage ECM. Exogenous phytohormone treatment affected the fungal colonization rate and/or frequency of Hartig net formation. Colonized lateral roots displayed diminished responsiveness to jasmonate but regulated some genes, implicated in defense and cell wall remodelling, that were specifically differentially expressed after jasmonate treatment. Responses to salicylate, gibberellin, and ethylene were enhanced in ECM. The dynamics of phytohormone accumulation and response suggest that jasmonate, gibberellin, salicylate, and ethylene signalling play multifaceted roles in poplar L. bicolor ectomycorrhizal development.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

On a finite-state inflow application to flap-lag-torsion damping in hover

An aerodynamic model with a coupled set of generalized dynamic wake equations and hybrid equations of motion for an elastic blade are applied here to a two-blade untwisted stiff in-plane hingeless small-scale model rotor with torsionally soft blades. Blade root offset, precone, blade droop, pitch control stiffness, and blade pitch angle are included in the model rotor. Numerical results show that 3D tip relief effects within the nonuniform steady-state inflow are significant to predict steady-state aerodynamic loads and blade deflections. Eigenvalue results confirm the importance of unsteady 3D aerodynamics in predicting lead-lag damping and frequency. Eigenvector analysis correlations reinforced qualitative and quantitative shortcomings associated with quasi-steady 2D aerodynamic theory for aeroelastic applications in hover.

De Andrade, Donizeti↗
NASA NTRS2005

Shock Location Dominated Transonic Flight Loads on the Active Aeroelastic Wing

During several Active Aeroelastic Wing research flights, the shadow of the over-wing shock could be observed because of natural lighting conditions. As the plane accelerated, the shock location moved aft, and as the shadow passed the aileron and trailing-edge flap hinge lines, their associated hinge moments were substantially affected. The observation of the dominant effect of shock location on aft control surface hinge moments led to this investigation. This report investigates the effect of over-wing shock location on wing loads through flight-measured data and analytical predictions. Wing-root and wing-fold bending moment and torque and leading- and trailing-edge hinge moments have been measured in flight using calibrated strain gages. These same loads have been predicted using a computational fluid dynamics code called the Euler Navier-Stokes Three Dimensional Aeroelastic Code. The computational fluid dynamics study was based on the elastically deformed shape estimated by a twist model, which in turn was derived from in-flight-measured wing deflections provided by a flight deflection measurement system. During level transonic flight, the shock location dominated the wing trailing-edge control surface hinge moments. The computational fluid dynamics analysis based on the shape provided by the flight deflection measurement system produced very similar results and substantially correlated with the measured loads data.

Lokos, William A.↗
DOE OSTI2023

Difference in load predictions obtained with effective turbulence vs. a dynamic wake meandering modeling approach

Abstract. According to the international standard for wind turbine design, the effects of wind turbine wakes on structural loads can be considered in two ways: (1) by augmenting the ambient turbulence levels with the effective turbulence model (EFF) and then calculating the resulting loads and (2) by performing dynamic wake meandering (DWM) simulations, which compute wake effects and loads for all turbines on a farm at once. There is no definitive answer in scientific literature as to the consequences of choosing one model over the other, but the two approaches are unarguably very different. The work presented here expounds on these differences and investigates to what extent they affect the simulated structural loads. We consider an idealized 4×4 rectangular array of National Renewable Energy Laboratory 5 MW wind turbines with a spacing of 5 by 8 rotor diameters and three wind speed scenarios at high ambient turbulence. Load simulations are performed in OpenFAST with EFF and in FAST.Farm with the DWM implementation. We compare ambient turbulence, wind farm turbulence, and loads between both approaches. When omnidirectional results are compared, EFF wind farm turbulence intensity is consistently higher by 0.2 % (above-rated wind speed) to 2.7 % (below-rated wind speed). However, for certain wind directions, the EFF turbulence can be lower than FAST.Farm by almost 9 %. Wind speeds within the farm were found to differ by up to 3 m s−1 due to the lack of wake deficits in the EFF approach, leading to longer tails toward low values in the FAST.Farm mean load distributions. Consistent with the turbulence results, the median EFF load standard deviations are also consistently higher, by a maximum of 20 % and 17 % for blade-root out-of-plane and tower-base fore-aft moments, respectively.

17 WIND ENERGY↗
DOE OSTI2026

A fresh perspective on the role of band bending, and related contributors, in light-driven production of electricity and chemicals

It is widely known that semiconductor-based solar energy conversion could power our planet. This is in part because high-quality semiconductor structures are unrivalled in their ability to separate photogenerated electrons and holes. One effective approach to achieving this photoinduced charge separation relies on a phenomenon known as “band bending”. But details to justify why band bending results in photoinduced charge separation are more complex than often appreciated. This underappreciation is an impediment to the rational, hypothesis-driven design of next-generation approaches to solar energy conversion. Herein we show, by means of derivations rooted in physical chemistry, that several phenomena – not just band bending – can facilitate photoinduced charge separation, and that each is influenced by nonequilibrium species concentration and a parameter, such as diffusion coefficient or rate coefficient, that introduces dynamics. To help visualize the impact of each phenomenon, we introduce plots that depict their contributions as free energy, force, flux, force constant, and rate. We reveal that spatial dopant distributions that define band bending are predictors of initial photogenerated species transport rates. But charge separation alone does not guarantee high-efficiency operation. A photogenerated change in energy that is freely available to do useful work is also essential, and is strongly dependent on semiconductor optical properties and reaction kinetics. Notably, this information reveals that specificity of interfacial chemical reactions – even when they are not preceded by charge separation elsewhere – can result in efficient solar energy conversion. We expect that this tutorial will guide researchers in their pursuit to uncover new mechanisms for light to perform useful work.

14 SOLAR ENERGY↗
DOE OSTI2021

How AI Predicts the Untrained and Unseen

Focus Area: Model predictability improvements (Primary); Data optimization (secondary); Data complexity insights (secondary). The Scientific Challenge: If we believe that a future under extreme conditions will look very differently from today, we can likely agree that ML/AI models trained on past and present datasets will not be adequate to make reliable predictions into the future. This is true for water cycling, as well as biogeochemistry and other Earth system components and behaviors. Additionally, ML/AI models are inherently non-physical. Despite the flourishing success of ML/AI in many applications, such as computer vision, natural language process, and gaming, even the most sophisticated AI models don’t understand the very basic physical laws. Therefore, a natural question is: Can we trust ML/AI based predictions of Earth system behaviors that are fundamentally driven by physical laws? So, are physics models with meticulous process representation a better choice? Not exactly. Physical models, when firmly rooted in first principles, work great at predicting behaviors of systems with a well-defined set of boundary conditions and variables. However, as a complex system, the number of parameters and the degree of complexity and dynamics in processes, coupling, and scale dependent emergent behaviors make the Earth system behaviors very challenging to predict with physical models composed of deterministic laws. In addition, due to the lack of fundamental understandings, physical models often implement empirical correlations derived from observations with biases from locality of data generation. Because correlation is not necessarily causation or comply with first principles, scaling of model predictions beyond locality is often invalid. Beyond the limitation of models, physics or AI, our knowledge of the Earth system is limited by the lack of observational technologies and resources. Insufficient data density, dimensionality and diversity only offer a sliced (or projected) view of the Earth system, e.g. Plato’s Cave analogy, limiting our capability to better understand and represent fundamental processes in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Microfabrication of a Chamber for High-Resolution, In Situ Imaging of the Whole Root for Plant–Microbe Interactions

Fabricated ecosystems (EcoFABs) offer an innovative approach to in situ examination of microbial establishment patterns around plant roots using nondestructive, high-resolution microscopy. Previously high-resolution imaging was challenging because the roots were not constrained to a fixed distance from the objective. Here, we describe a new ‘Imaging EcoFAB’ and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period. The device is capable of investigating root–microbe interactions of multimember communities. We examined nine strains of Pseudomonas simiae with different fluorescent constructs to B. distachyon and individual cells on root hairs were visible. Succession in the rhizosphere using two different strains of P. simiae was examined, where the second addition was shown to be able to establish in the root tissue. The device was suitable for imaging with different solid media at high magnification, allowing for the imaging of fungal establishment in the rhizosphere. Overall, the Imaging EcoFAB could improve our ability to investigate the spatiotemporal dynamics of the rhizosphere, including studies of fluorescently-tagged, multimember, synthetic communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Atomic Force Microscopy to Characterize Antimicrobial Peptide-Induced Defects in Model Supported Lipid Bilayers

Antimicrobial peptides (AMPs) interact with bacterial cell membranes through a variety of mechanisms, causing changes extending from nanopore formation to microscale membrane lysis, eventually leading to cell death. Several AMPs also disrupt mammalian cell membranes, despite their significantly different lipid composition and such collateral hemolytic damage hinders the potential therapeutic applicability of the AMP as an anti-microbial. Elucidating the mechanisms underlying the AMP–membrane interactions is challenging due to the variations in the chemical and structural features of the AMPs, the complex compositional variations of cell membranes and the inadequacy of any single experimental technique to comprehensively probe them. (1) Background: Atomic Force Microscopy (AFM) imaging can be used in combination with other techniques to help understand how AMPs alter the orientation and structural organization of the molecules within cell membranes exposed to AMPs. The structure, size, net charge, hydrophobicity and amphipathicity of the AMPs affect how they interact with cell membranes of differing lipid compositions. (2) Methods: Our study examined two different types of AMPs, a 20-amino acid, neutral, α-helical (amphipathic) peptide, alamethicin, and a 13-amino acid, non-α-helical cationic peptide, indolicidin (which intramolecularly folds, creating a hydrophobic core), for their interactions with supported lipid bilayers (SLBs). Robust SLB model membranes on quartz supports, incorporating predominantly anionic lipids representative of bacterial cells, are currently not available and remain to be developed. Therefore, the SLBs of zwitterionic egg phosphatidylcholine (PC), which represents the composition of a mammalian cell membrane, was utilized as the model membrane. This also allows for a comparison with the results obtained from the Quartz Crystal Microbalance with Dissipation (QCM-D) experiments conducted for these peptides interacting with the same zwitterionic SLBs. Further, in the case of alamethicin, because of its neutrality, the lipid charge may be less relevant for understanding its membrane interactions. (3) Results: Using AFM imaging and roughness analysis, we found that alamethicin produced large, unstable defects in the membrane at 5 µM concentrations, and completely removed the bilayer at 10 µM. Indolicidin produced smaller holes in the bilayer at 5 and 10 µM, although they were able to fill in over time. The root-mean-square (RMS) roughness values for the images showed that the surface roughness caused by visible defects peaked after peptide injection and gradually decreased over time. (4) Conclusions: AFM is useful for helping to uncover the dynamic interactions between different AMPs and cell membranes, which can facilitate the selection and design of more efficient AMPs for use in therapeutics and antimicrobial applications.

Swana, Kathleen W.↗
NASA NTRS1984

An investigation into the probabilistic combination of quasi-static and random accelerations

The development of design load factors for aerospace and aircraft components and experiment support structures, which are subject to a simultaneous vehicle dynamic vibration (quasi-static) and acoustically generated random vibration, require the selection of a combination methodology. Typically, the procedure is to define the quasi-static and the random generated response separately, and arithmetically add or root sum square to get combined accelerations. Since the combination of a probabilistic and a deterministic function yield a probabilistic function, a viable alternate approach would be to determine the characteristics of the combined acceleration probability density function and select an appropriate percentile level for the combined acceleration. The following paper develops this mechanism and provides graphical data to select combined accelerations for most popular percentile levels.

Schock, R. W.↗
NASA NTRS1991

Integration of dynamic, aerodynamic, and structural optimization of helicopter rotor blades

Summarized here is the first six years of research into the integration of structural, dynamic, and aerodynamic considerations in the design-optimization process for rotor blades. Specifically discussed here is the application of design optimization techniques for helicopter rotor blades. The reduction of vibratory shears and moments at the blade root, aeroelastic stability of the rotor, optimum airframe design, and an efficient procedure for calculating system sensitivities with respect to the design variables used are discussed.

Peters, David A.↗
NASA NTRS1994

Precision orbit determination for TOPEX/POSEIDON

The TOPEX/POSEIDON mission objective requires that the radial position of the spacecraft be determined with an accuracy better than 13 cm RMS (root mean square). This stringent requirement is an order of magnitude below the accuracy achieved for any altimeter mission prior to the definition of the TOPEX/POSEIDON mission. To satislfy this objective, the TOPEX Precision Orbit determination (POD) Team was established as a joint effort between the NASA Goddard Space Flight Center and the University of Texas at Austin, with collaboration from the University of Colorado and the Jet Propulsion Laboratory. During the prelaunch development and the post launch verification phases, the POD team improved, calibrated, and validated the precision orbit determination computer software systems. The accomplishments include (1) increased accuracy of the gravity and surface force models and (2) improved peformance of both laser ranging and Doppler tracking systems. The result of these efforts led to orbit accuracies for TOPEX/POSEIDON which are significantly better than the original mission requirement. Tests based on data fits, covariance analysis, and orbit comparisons indicate that the radial component of the TOPEX/POSEIDON spacecraft is determined, relative to the Earth's mass center, with an root mean square (RMS) error in the range of 3 to 4 cm RMS. This orbit accuracy, together with the near continuous dual-frequency altimetry from this mission, provides the means to determine the ocean's dynamic topography with an unprecedented accuracy.

Tapley, B. D.↗
NASA NTRS1975

Dynamic characteristics of a 30-centimeter mercury ion thruster

The present work reports on measurements of the fluctuations in the beam current, discharge current, neutralizer keeper current, and discharge voltage of a 30-cm ion thruster made with 60Hz laboratory-type power supplies. The intensities of the fluctuations (ratio of the root-mean-square magnitude to time-average quantity) were found to depend significantly on the beam and magnetic baffle currents. The shape of the frequency spectra of the discharge plasma fluctuations was related to the beam and magnetic baffle currents. The predominant peaks of the beam and discharge current spectra occurred at frequencies less than 30 kilohertz. This discharge chamber resonance could be attributable to ion-acoustic wave phenomena. Cross-correlations of the discharge and beam currents indicated that the dependence on the magnetic baffle current was strong. The measurements revealed that the discharge current fluctuations directly contribute to the beam current fluctuations and that the power supply characteristics can modify these fluctuations.

Serafini, J. S.↗
DOE OSTI2025

Order conditions for nonlinearly partitioned Runge-Kutta methods

Recently, a new class of nonlinearly partitioned Runge–Kutta (NPRK) methods was proposed for nonlinearly partitioned systems of autonomous ordinary differential equations y' = F(y, y). The target class of problems are those in which different scales, stiffnesses, or physics are coupled in a nonlinear way, wherein the desired partition cannot be written in a classical additive or component-wise fashion. Here we use a rooted-tree analysis to derive full-order conditions for NPRKM methods, where M denotes the number of nonlinear partitions. Due to the nonlinear coupling and thereby the mixed product differentials, it turns out that the standard node-colored rooted tree analysis used in analyzing ODE integrators does not naturally apply. Instead we develop a new edge-colored rooted-tree framework to address the nonlinear coupling. The resulting order conditions are enumerated, are provided directly for up to fourth order with M = 2 and third order with M = 3, and are related to existing order conditions of additive and partitioned RK methods. We conclude with an example that shows how the nonlinear order conditions can be used to obtain an embedded estimate of the state-dependent nonlinear coupling strength in a dynamical system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Agile Allocation in the Tundra: A Single Growing Season of Warming Increases Nutrient Availability While Decreasing Fine-Root Length

The majority of plant biomass is located belowground in Arctic ecosystems and plant roots are responsible for the uptake of the nutrients that constrain plant growth in these infertile ecosystems. Despite performing a crucial role connecting primary producers to the soil, roots are relatively understudied in the Arctic and their functional response to a rapidly warming and increasingly variable climate is unknown. Here, we assessed whether one growing season with elevated temperatures would have an impact on nutrient uptake and allocation by applying a warming technique that increased daily air temperatures by 3.2 °C. Destructive sampling was performed at the peak of the growing season to quantify biomass pools of carbon (C) and nitrogen (N), root traits, and uptake of a 15 N tracer ( 15 NH 4 + ) for the dominant plant species, Arctagrostis latifolia. We found that soil nutrient availability increased with short-term warming, but A. latifolia NH 4 + uptake remained unchanged. Fine-root length density and root biomass within the soil profile, however, were both reduced by warming. N allocation patterns across plant tissues were also altered by warming. NH 4 + uptake was best fit with a logistic model that captured the spatial relationship between roots and soil (NH 4 + uptake expressed per length fine root and NH 4 + availability expressed per unit soil volume) rather than a traditional Michaelis–Menten model. Our results indicate that short-term experimental warming can shift plant–soil interactions, suggesting that the tundra’s belowground response to elevated temperatures may be more dynamic than previously recognized.

15N tracer↗
NASA NTRS2021

Mapping Global Forest Age from Forest Inventories, Biomass and Climate Data

Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021).

Simon Besnard↗
NASA NTRS1973

Externally blown flap dynamic loads

Some of the principal results obtained in three series of measurements of fluctuating surface pressures induced on externally blown flaps by jet impingment are presented. Large- and small-scale models and hot- and cold-flow tests are considered. The discussion sets forth scaling parameters and consistent features of the root-mean-square values and spectra of the loading. Implications of these results with regard to sonic fatigue are indicated.

Lansing, D. L.↗
DOE OSTI2021

Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam G CC (70.4 days, p adj < 0.0001) and EC GPP (79.6 days, p adj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam G CC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m –2 yr –1 . Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Response of Ecosystem Productivity to High Vapor Pressure Deficit and Low Soil Moisture: Lessons Learned From the Global Eddy-Covariance Observations

Although there is mounting concern about how high vapor pressure deficit (VPD) and low soil moisture (SM) affect ecosystem productivity, their relative importance is still under debate. Here, we comprehensively quantified the relative impacts of these two factors on ecosystem gross primary production (GPP) using observations from a global network of eddy-covariance towers and two approaches (sensitivity analysis and linear regression model). Both approaches agree that a higher percentage of sites experience GPP reduction from high VPD than from low SM over the growing season. However, the constraint of high VPD and low SM on GPP reduction is tightly linked with climates and plant functional types. Humid and mesic ecosystems including forests and grasslands are dominated by VPD, while the semi-arid and arid ecosystems including shrublands and savannas are dominated by SM. The varying dominant role of these two factors on GPP is closely related to plant stomatal behavior, as predicted by a stomatal conductance model. Additionally, we highlight the non-linear impact of SM on GPP during droughts and the possible underestimation of the SM effects for deep-rooted plants when only using surface-layer SM. Our results shed light on a better understanding of the impacts of VPD and SM on vegetation productivity, with important implications for modeling the response and feedback of ecosystem dynamics to current and future climates.

54 ENVIRONMENTAL SCIENCES↗