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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRS1950

Approximate Turbulent Boundary-Layer Development in Plane Compressible Flow Along Thermally Insulated Surfaces with Application to Supersonic-Tunnel Contour Correction

Numerical solutions of the differential equation obtained from the momentum theorem for the development of a turbulent boundary layer along a thermally insulated surface in two-dimensional and in radial shock-free flow are presented in tabular form for a range of Mach numbers from 0.100 to 10. The solution can be used in a step-wise procedure with any given distribution of favorable pressure gradients and for zero pressure gradients. Solutions are also given for use with moderate adverse pressure gradients. The mean velocity in the boundary layer is approximated by a power-law profile. In view of the stepwise integration methods to be used, the exponent designated the profile shape can be varied along the surface between the integral fraction limits 1/5 and 1/11 through interpolation. Agreement obtained between theoretical and experimental boundary-layer development in a supersonic nozzle at a nominal Mach number of 2 indicates the general validity of the approximations used in the analysis - in particular, the method of extrapolating low-speed skin-friction relations to high Mach number flows. The extrapolation method used assumes that the skin-friction coefficient depend primarily on Reynolds number, provided that the density and the kinematic viscosity are evaluated at surface conditions.

BOUNDARY LAYER - WIND TUNNEL WALLS↗
DOE OSTI2022

Confronting the water potential information gap

Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil–plant–atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. Here, we end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the ‘potential’ to substantially reduce conceptual and modelling uncertainties.

58 GEOSCIENCES↗
DOE OSTI2020

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scattered measurements. Unlike standard GANs relying solely on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even if there is a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and the generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. Here again, we assumed data collected from simultaneous reads at a limited number of sensors for the multiple stochastic processes. Furthermore, three generators were used for the PI-GANs: two of them were feed forward deep neural networks (DNNs), while the other one was the neural network induced by the SDE. For the case where we have one group of data, we employed one feed forward DNN as the discriminator, while for the case of multiple groups of data we employed multiple discriminators in PI-GANs. We solved forward, inverse, and mixed problems without changing the framework of PI-GANs, obtaining both the means and the standard deviations of the stochastic solution and the diffusion coefficient in good agreement with benchmarks. In this work, we have demonstrated the effectiveness of PI-GANs in solving SDEs for about 120 dimensions. In principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Flux Upcycling of Degraded Layered Cathodes to LiNi x Mn y Co z O 2 (NMCs) with Gradient Transition Metal Distribution

The rising demand for lithium-ion batteries (LIBs) has intensified the need for efficient recycling methods to address both supply chain constraints and environmental impacts. Direct upcycling, distinguished by its ability to achieve both the structural and compositional integrity of cathode materials, has gained prominence as a sustainable alternative to conventional pyrometallurgical and hydrometallurgical processes. However, the current direct upcycling methods are typically limited by incorporating Li and/or Ni, significantly constraining the adaptability across diverse LiNi x Mn y Co z O 2 (NMCs). Here, in this study, a versatile molten salt approach is reported that expands the scope of direct upcycling by enabling simultaneous incorporation of Li, Ni, and Mn. This methodology facilitates flexible conversion among diverse NMC compositions, including non-stoichiometric Co/Mn systems such as upcycling degraded LiCoO 2 (D-LCO), LiNi 1/3 Mn 1/3 Co 1/3 O 2 (D-NMC111), LiNi 0.8 Mn 0.1 Co 0.1 O 2 (D-NMC811) to surface Mn enriched NMC111, LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532), and NMC811, respectively. The gradient transition metal distribution in upcycled products, characterized by Mn-enriched outer layers and Co/Ni-enriched cores enhances the interfacial stability of NMC cathodes, addressing critical challenges in long-term performance and structural integrity. These results highlight the potential of flux methods for advancing the upcycling of spent cathodes and producing high-performance materials for next-generation LIBs applications.

lithium -ion batteries↗
NASA NTRS1994

Acoustic instability driven by cosmic-ray streaming

We study the linear stability of compressional waves in a medium through which cosmic rays stream at the Alfven speed due to strong coupling with Alfven waves. Acoustic waves can be driven unstable by the cosmic-ray drift, provided that the streaming speed is sufficiently large compared to the thermal sound speed. Two effects can cause instability: (1) the heating of the thermal gas due to the damping of Alfven waves driven unstable by cosmic-ray streaming; and (2) phase shifts in the cosmic-ray pressure perturbation caused by the combination of cosmic-ray streaming and diffusion. The instability does not depend on the magnitude of the background cosmic-ray pressure gradient, and occurs whether or not cosmic-ray diffusion is important relative to streaming. When the cosmic-ray pressure is small compared to the gas pressure, or cosmic-ray diffusion is strong, the instability manifests itself as a weak overstability of slow magnetosonic waves. Larger cosmic-ray pressure gives rise to new hybrid modes, which can be strongly unstable in the limits of both weak and strong cosmic-ray diffusion and in the presence of thermal conduction. Parts of our analysis parallel earlier work by McKenzie & Webb (which were brought to our attention after this paper was accepted for publication), but our treatment of diffusive effects, thermal conduction, and nonlinearities represent significant extensions. Although the linear growth rate of instability is independent of the background cosmic-ray pressure gradient, the onset of nonlinear eff ects does depend on absolute value of DEL (vector differential operator) P(sub c). At the onset of nonlinearity the fractional amplitude of cosmic-ray pressure perturbations is delta P(sub C)/P(sub C) approximately (kL) (exp -1) much less than 1, where k is the wavenumber and L is the pressure scale height of the unperturbed cosmic rays. We speculate that the instability may lead to a mode of cosmic-ray transport in which plateaus of uniform cosmic-ray pressure are separated by either laminar or turbulent jumps in which the thermal gas is subject to intense heating.

Begelman, Mitchell C.↗
NASA NTRS1990

Limit on overshooting from the convective core in upper main-sequence stars

Overshooting of convective elements far beyond the classical Schwarzschild convective core boundary in theoretical models of massive ZAMS stars is found to lead to significant reductions of stellar luminosity and radius, provided that the temperature gradient in the overshoot region can be approximated by the adiabatic gradient. Comparison of these theoretical models with binary star data for the range of 5-15 solar masses indicates that the ratio of overshoot distance to pressure scale height is probably less than 1.5. This result apparently excludes any overshooting theories that predict very extended adiabatic convective cores.

Stothers, Richard B.↗
DOE OSTI2022

Bioavailable iron titrations reveal oceanic Synechococcus ecotypes optimized for different iron availabilities

Abstract The trace metal iron (Fe) controls the diversity and activity of phytoplankton across the surface oceans, a paradigm established through decades of in situ and mesocosm experimental studies. Despite widespread Fe-limitation within high-nutrient, low chlorophyll (HNLC) waters, significant contributions of the cyanobacterium Synechococcus to the phytoplankton stock can be found. Correlations among differing strains of Synechococcus across different Fe-regimes have suggested the existence of Fe-adapted ecotypes. However, experimental evidence of high- versus low-Fe adapted strains of Synechococcus is lacking, and so we investigated the transcriptional responses of microbial communities inhabiting the HNLC, sub-Antarctic region of the Southern Ocean during the Spring of 2018. Analysis of metatranscriptomes generated from on-deck incubation experiments reflecting a gradient of Fe-availabilities reveal transcriptomic signatures indicative of co-occurring Synechococcus ecotypes adapted to differing Fe-regimes. Functional analyses comparing low-Fe and high-Fe conditions point to various Fe-acquisition mechanisms that may allow persistence of low-Fe adapted Synechococcus under Fe-limitation. Comparison of in situ surface conditions to the Fe-titrations indicate ecological relevance of these mechanisms as well as persistence of both putative ecotypes within this region. This Fe-titration approach, combined with transcriptomics, highlights the short-term responses of the in situ phytoplankton community to Fe-availability that are often overlooked by examining genomic content or bulk physiological responses alone. These findings expand our knowledge about how phytoplankton in HNLC Southern Ocean waters adapt and respond to changing Fe supply.

Gilbert, Naomi E. (ORCID:0000000256915547)↗
DOE OSTI2023

A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed an end-to-end programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. As a genre of physics-informed machine learning (ML), differentiable models couple physics-based formulations to neural networks (NNs) that learn parameterizations (and potentially processes) from observations, here photosynthesis rates. We first demonstrated that the framework was able to correctly recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types (PFTs), we learned parameters that performed substantially better and greatly reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 °C (V c,max25 ) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The distribution and selective decontamination of carbon-14 from nuclear graphite

This work investigates 14 C release behaviour from UK Magnox reactor sourced irradiated graphite under relatively low temperature oxidation conditions: a 1% oxygen in argon atmosphere at temperatures ranging from 600°C to 700°C, and durations from 4 to 120 hours. A method is used for construction of a detailed 14 C release profile with mass loss. The 14 C release profile is predictable between samples, and an empirical release profile is derived to predict releases to higher mass losses than in this work. An accelerated 14 C release rate at small mass losses is observed, indicative of a 14 C-enriched surface region with a depleting concentration gradient into the material, and the source terms and complexities associated with predicting where the 14 C arose from are discussed. Selective decontamination of a fraction of the 14 C is possible, with the limitation of a reducing efficiency with mass loss and time. This work demonstrates a method for determining the distribution of 14 C between near-surface regions and the bulk material in differing sources of irradiated graphite, and provides data in support of making an informed assessment for the adoption of low temperature thermal treatment and isotopic reduction of irradiated graphite wastes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Tailored Synthesis of the Narrowest Zigzag Graphene Nanoribbon Structure by Compressing the Lithium Acetylide under High Temperature

Scientists are searching for the goal-directed methods to synthesize graphene nanoribbons (GNRs) with a particular edge type and width, which determines their electronic transport properties. A series of Li zigzag GNRs (ZGNRs) with different widths were predicted under high pressure with a stoichiometric ratio of Li n +1 C 2 n , which indicates a route to prepare ultranarrow GNRs. Here, with thermodynamics and ab initio Gibbs free-energy calculations by quasi-harmonic approximation, we investigated the phase stabilities of the Li GNR compounds under high pressure and high temperature. We have also identified Li graphenide LiC 2 ( n = ∞) and Li polyacenide Li 3 C 4 (n = 2) experimentally at the predicted pressure and temperature conditions using in situ X-ray diffraction, which can be recognized as the two end members of Li n +1 C 2 n , with the widest and narrowest ZGNR structures. High temperature and the temperature gradient increased the degree of polymerization and facilitated the formation of wider GNR or carbon slices. Furthermore, this suggests that by controlling temperature and pressure, we may get ultranarrow Li ZGNRs composed of a limited number of parallel carbon chains, such as 3- or 4-zigzag GNR, which is ready to be protonated or functionalized to form atomically ordered ZGNRs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Toward Ultraflat Surface Morphologies During Focused Electron Beam Induced Nanosynthesis: Disruption Origins and Compensation

Here we explore emerging applications for nanoscale materials demand precise deposit shape retention from design to deposition. This study investigates the effects that disrupt high-fidelity shapes during focused electron beam induced nanosynthesis. It is shown that process parameters, patterning strategies and deposit topography can impose lateral precursor coverage gradients during growth resulting in unwanted topographic artifacts. The study classifies the evolving surface shapes into four general types and explains the formation and transition from a fundamental point of view. Continuum model calculations and simulations expand the experimental results to provide a comprehensive insight into understand the disruption mechanism. The findings demonstrate that the well-established concept of growth regimes has to be expanded by its lateral gradients as they strongly influence final shape fidelities. Finally, the study is complemented by a compensation strategy that improves the edge fidelity on the lower nanoscale to further push this technique toward the intrinsic limitations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accumulation of Antifreeze Proteins on Ice Is Determined by Adsorption

Antifreeze proteins (AFPs) enable the survival of organisms in freezing environments by binding to ice crystals and suppressing their growth. The rate at which AFPs accumulate on ice is determined by an interplay between their diffusion from the aqueous solution and their adsorption to the ice crystal. To interrogate the relative importance of these two processes, we combined fluorescence experiments with reaction–diffusion modeling. We found that as AFPs accumulate on ice, their concentration in the aqueous solution does not develop a gradient but remains equal to its bulk concentration throughout our experiments. Furthermore, these findings led us to conclude that AFP accumulation on ice crystals, which are smaller than 100 μm in radius, is not limited by the diffusion of AFPs, but by the kinetics of AFP adsorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Depth dependence of climatic controls on soil microbial community activity and composition

Abstract Subsoil microbiomes play important roles in soil carbon and nutrient cycling, yet our understanding of the controls on subsoil microbial communities is limited. Here, we investigated the direct (mean annual temperature and precipitation) and indirect (soil chemistry) effects of climate on microbiome composition and extracellular enzyme activity throughout the soil profile across two elevation-bioclimatic gradients in central California, USA. We found that microbiome composition changes and activity decreases with depth. Across these sites, the direct influence of climate on microbiome composition and activity was relatively lower at depth. Furthermore, we found that certain microbial taxa change in relative abundance over large temperature and precipitation gradients only in specific soil horizons, highlighting the depth dependence of the climatic controls on microbiome composition. Our finding that the direct impacts of climate are muted at depth suggests that deep soil microbiomes may lag in their acclimation to new temperatures with a changing climate.

16S rRNA↗
DOE OSTI2024

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data

In this paper, we propose a personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce an order orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm guaranteed to converge to a stationary point. The unique and common representations learned by perTucker reveal intrinsic statistical patterns in data and provide valuable information for a wide range of downstream analytics, including anomaly detection, source classification, and clustering. We demonstrate perTucker’s effectiveness through a simulation study and two case studies on solar flare detection and tonnage signal classification.

14 SOLAR ENERGY↗
DOE OSTI2026

Reaction Diffusion Modelling of 3D Pillar Electrodes in Single-Catalyst CO 2 Reduction Cascades

Effective electrochemical CO 2 reduction to liquid fuels requires that the local catalytic environment facilitates the desired reactivity, yet a microscopic understanding of this environment is difficult to achieve from experiment alone. In this work, a 3D reaction-diffusion model was developed to explore the effects of electrode surface area and local geometry on the performance of a heterogeneous catalyst that performs a two-step CO 2 reduction cascade reaction to CO and then CH 3 OH under aqueous conditions. Kinetic parameters for the model were inspired by experimental results using a cobalt phthalocyanine (CoPc) catalyst. Three-dimensional architectures composed of arrays of square pillars with varying dimensions and either smooth or periodically modulated surfaces were tested, revealing the extent to which geometry modulates the performance of the cascade reactions. Although structural variations modulate local concentration gradients, we find that electrochemically active surface area predominantly governs the overall cascade reaction. Moreover, the results suggest that supersaturation of CO, with concentrations up to ten-fold higher than the equilibrium solubility limit, might be critical for more efficient conversion to CH 3 OH. For any given geometry, the spatially averaged ratio of [CO] to [CO 2 ] is dictated by the electrochemically active surface area and determines the yield of CH 3 OH. For a fixed surface area, geometries that spatially confine the electrolyte yield moderate local [CO] to [CO 2 ] ratios within small volumes. In contrast, less confining geometries result in a broader distribution of local ratios spread over larger volumes, with both configurations yielding the same spatially averaged [CO] to [CO 2 ] ratio. These insights provide valuable design principles—highlighting the critical importance of surface area and possibly CO supersaturation—for engineering advanced electrode architectures that leverage intermediate trapping and CO supersaturation to enhance overall performance in tandem CO 2 reduction systems.

COMSOL↗
DOE OSTI2024

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

domain adaptation↗
NASA NTRS1973

Entry environmental simulation testing of REI-Mullite TPS

Glassy fibrous mullite coatings exhibit noncatalytic surface characteristics which limit surface temperature rise under certain convective heat flux conditions. Thermal conductivity design curves provide good agreement with test results. The inclusion of shine-in effects results in improved accuracy of transient temperature gradient predictions for the materials.

Florence, D. E.↗