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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Michael Notaro↗
NASA NTRS2016

Advancements in Afterbody Radiative Heating Simulations for Earth Entry

Four advancements to the simulation of backshell radiative heating for Earth entry are presented. The first of these is the development of a flow field model that treats electronic levels of the dominant backshell radiator, N, as individual species. This is shown to allow improvements in the modeling of electron-ion recombination and two-temperature modeling, which are shown to increase backshell radiative heating by 10 to 40%. By computing the electronic state populations of N within the flow field solver, instead of through the quasi-steady state approximation in the radiation code, the coupling of radiative transition rates to the species continuity equations for the levels of N, including the impact of non-local absorption, becomes feasible. Implementation of this additional level of coupling between the flow field and radiation codes represents the second advancement presented in this work, which is shown to increase the backshell radiation by another 10 to 50%. The impact of radiative transition rates due to non-local absorption indicates the importance of accurate radiation transport in the relatively complex flow geometry of the backshell. This motivates the third advancement, which is the development of a ray-tracing radiation transport approach to compute the radiative transition rates and divergence of the radiative flux at every point for coupling to the flow field, therefore allowing the accuracy of the commonly applied tangent-slab approximation to be assessed for radiative source terms. For the sphere considered at lunar-return conditions, the tangent-slab approximation is shown to provide a sufficient level of accuracy for the radiative source terms, even for backshell cases. This is in contrast to the agreement between the two approaches for computing the radiative flux to the surface, which differ by up to 40%. The final advancement presented is the development of a nonequilibrium model for NO radiation, which provides significant backshell radiation at velocities below 10 km/s. The developed model reduces the nonequilibrium NO radiation by 50% relative to the previous model.

Johnston, Christopher O.↗
DOE OSTI2023

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

Abstract Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Paleotribological models using preserved fossil tissue properties reveal functional significance of Eurasian mammoth dental evolution

Eurasian mammoths (Mammuthus) underwent substantial modifications in molar morphology as later-diverging species evolved progressively thinner enamel and increased enamel crest complexity. These features have been hypothesized to reduce whole-tooth wear and extend dental longevity as increasingly graze-dominated diets evolved within the lineage. This hypothesis has yet to be directly tested. Here, in this study, we developed an in-silico wear model using experimentally derived wear rates from fossil and extant proboscidean dental tissues. The models revealed that shifts in tissue topology do not affect whole-tooth wear rate, as inverse trends in lamellar frequency and enamel thickness preserve a consistent surface enamel area fraction; the determining factor of wear. Rather, topological shifts produce a wear-emergent secondary occlusal surface with greater numbers of triturating crests that create a regular, low-relief, file-like shearing pavement. These changes in occlusal architecture likely directly impacted the mastication capacity of Mammuthus dentitions, facilitating their dietary expansion to incorporate fibrous, lower-nutrient graze.

Dental wear↗
DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗
DOE OSTI2024

Machine learning potential assisted exploration of complex defect potential energy surfaces

Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO 2 ) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tumor targeted alpha particle therapy with an actinium-225 labelled antibody for carbonic anhydrase IX

Selective antibody targeted delivery of α particle emitting actinium-225 to tumors has significant therapeutic potential. This work highlights the design and synthesis of a new bifunctional macrocyclic diazacrown ether chelator, H 2 MacropaSqOEt, that can be conjugated to antibodies and forms stable complexes with actinium-225. The macrocyclic diazacrown ether chelator incorporates a linker comprised of a short polyethylene glycol fragment and a squaramide ester that allows selective reaction with lysine residues on antibodies to form stable vinylogous amide linkages. This new H 2 MacropaSqOEt chelator was used to modify a monoclonal antibody, girentuximab (hG250), that binds to carbonic anhydrase IX, an enzyme that is overexpressed on the surface of cancers such as clear cell renal cell carcinoma. This new antibody conjugate (H 2 MacropaSq-hG250) had an average chelator to antibody ratio of 4 : 1 and retained high affinity for carbonic anhydrase IX. H 2 MacropaSq-hG250 was radiolabeled quantitatively with [ 225 Ac]Ac III within one minute at room temperature with micromolar concentrations of antibody and the radioactive complex is stable in human serum for >7 days. Evaluation of [ 225 Ac]Ac(MacropaSq-hG250) in a mouse xenograft model, that overexpresses carbonic anhydrase IX, demonstrated a highly significant therapeutic response. It is likely that H 2 MacropaSqOEt could be used to modify other antibodies providing a readily adaptable platform for other actinium-225 based therapeutics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Navier-Stokes computation of wing/rotor interaction for a tilt rotor in hover

A method has been developed to analyze the wing/rotor interaction of tilt rotor aircraft in hover. The unsteady, thin-layer compressible Navier-Stokes equations are solved using an implicit, finite difference scheme that employs LU-ADI factorization. The rotor is modeled as an actuator disk which imparts a radial and azimuthal distribution of pressure rise and swirl to the flowfield. The 'chimera' approach of grid point blanking is used to update the rotor boundary conditions. Results are presented for both a rotor alone and for wing/rotor interaction where the thrust coefficient is 0.0164 and wing flap deflection is 67 degrees. Many of the complex flow features are captured including the fountain effect, leading and trailing edge separation, and the unsteady wake beneath the wing. Wing surface pressures compare fairly well with experimental data although the time-averaged download is about twenty percent higher than the measured value. This discrepancy is due to a combination of factors that are discussed.

Fejtek, Ian↗
NASA NTRS1993

Microwave properties and characterization of co-evaporated BSCCO thin films

An extensive characterization of Bi-Sr-Ca-Cu-O (BSCCO) thin films deposited by co-evaporation on LaAlO3 and SrTiO3 substrates was performed. The films had a T(sub c) (R = O) of approximately 78 K, and were predominantly c-axis oriented, with critical current densities (J(sub c)) at 4.5 K of 1.6 x 10(exp 6) and 1.1 x 10(exp 6) A cm(sup -2), for the samples on SrTiO3 and LaAlO3, respectively. The microwave properties of the films were examined by three techniques. The complex conductivity sigma(sub *) = sigma(sub 1) - j(sigma(sub 2)) and the magnetic penetration depth (A) were measured by power transmission at 30.6 GHz; the surface resistance (R(sub s)) was measured using a cavity resonator at 58.9 GHz, and the transmission line losses were determined by measuring the quality factor (Q) of a linear microstrip resonator at 10.4 and 20.2 GHz. The complex conductivity for the film on LaAlO3 was determined to be (2.0-j10) x 10(exp 5) S/m at 77 K. It was observed that in the superconducting state sigma(sub 1) deviates from both the Bardeen-Cooper-Schrieffer (BCS) theory and the two-fluid model. Values of lambda were found to be approximately 2.0 and 1.1 microns at 77 K and 20 K respectively, and were obtained for the film on LaAlO3. The value of lambda at 20 K was approximately three times larger than that of BSCCO single crystals. R(sub s) values of 865 and 1391 mOmega were obtained for the films on SrTiO3 and LaAlO3, respectively, at 77 K and 58.9 GHz. Unloaded Q factors at 20 K of approximately 1100 and 800 at 10.4 and 20.2 GHz respectively, were measured for the BSCCO resonator. Unloaded Q values of 290 and 405 at 20 K were obtained at 10.4 GHz and 20.2 GHz respectively, for an all gold (Au) resonator.

Miranda, F. A.↗
NASA NTRS2010

Parametric Geometry, Structured Grid Generation, and Initial Design Study for REST-Class Hypersonic Inlets

Computational design and analysis of three-dimensional hypersonic inlets with shape transition has been a significant challenge due to the complex geometry and grid required for three-dimensional viscous flow calculations. Currently, the design process utilizes an inviscid design tool to produce initial inlet shapes by streamline tracing through an axisymmetric compression field. However, the shape is defined by a large number of points rather than a continuous surface and lacks important features such as blunt leading edges. Therefore, a design system has been developed to parametrically construct true CAD geometry and link the topology of a structured grid to the geometry. The Adaptive Modeling Language (AML) constitutes the underlying framework that is used to build the geometry and grid topology. Parameterization of the CAD geometry allows the inlet shapes produced by the inviscid design tool to be generated, but also allows a great deal of flexibility to modify the shape to account for three-dimensional viscous effects. By linking the grid topology to the parametric geometry, the GridPro grid generation software can be used efficiently to produce a smooth hexahedral multiblock grid. To demonstrate the new capability, a matrix of inlets were designed by varying four geometry parameters in the inviscid design tool. The goals of the initial design study were to explore inviscid design tool geometry variations with a three-dimensional analysis approach, demonstrate a solution rate which would enable the use of high-fidelity viscous three-dimensional CFD in future design efforts, process the results for important performance parameters, and perform a sample optimization.

Ferlemann, Paul G.↗
DOE OSTI2025

Material Extrusion Printing of Poly(Acrylonitrile‐Styrene‐Acrylate) and Poly(Acrylonitrile‐Butadiene‐Styrene) Structures Reinforced with Poly(Phenylene Oxide) Additives for Improved Thermomechanical Properties and their Surface Analysis by Time‐of‐Flight Secondary Ion Mass Spectrometry

Fused filament fabrication offers the ability to 3D print complex geometries made from plastic filament materials; however, these parts are mechanically outperformed by parts created by traditional fabrication methods. To overcome this challenge, a high-performance polymer poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) is incorporated as an additive into two common engineering thermoplastics, poly(acrylonitrile-styrene-acrylate) (ASA) and poly(acrylonitrile-butadiene-styrene) (ABS). Structures printed from these polymer blends are more mechanically robust compared to those prepared from the parent polymers, with low loading levels (1–5 wt%) of PPO improving the elastic strength by up to ≈30% relative to the parent terpolymers. Even at higher loading levels (10 and 20 wt% PPO), there is no evidence of additive aggregation in the model thin films, which is supported by compositional analysis of the copolymers and chemical analysis via time-of-flight secondary ion mass spectrometry. The enhancements in mechanical properties of ASA and ABS blends appear to be a consequence of homogeneous incorporation of the PPO additive. In conclusion, this work explores expanding materials-property space using miscible blends of engineering thermoplastics to improve mechanical performance as a general approach to overcoming challenges with parts created by melt-based material extrusion printing.

additive manufacturing↗
NASA NTRS2023

Explainable deep learning for insights in El Niño and river flows

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections.

SST↗
DOE OSTI2022

Ligand Coverage and Exciton Delocalization Control Chiral Imprinting in Perovskite Nanoplatelets

Chiral perovskites have generated significant recent interest, yet little is known about how their chiro-optical properties arise. In this study, chiral methylammonium lead halide perovskite nanoplatelets (NPLs) with varied halide and ligand compositions are prepared using direct synthetic methods. Circular dichroism (CD) and 1 H NMR studies find a non-linear relationship between the chiroptical properties and the ratio of chiral phenylethylammonium (PEA) to achiral octylamine (OA) ligands on the NPL surface. We use density functional theory (DFT) computations and a chiral imprinted particle-in-a-box model to rationalize the experimentally observed CD spectra and find that the saturation point of the induced chirality depends on the size of the perovskite exciton relative to the size of the ligands. Temperature-dependent CD and 1 H NMR studies, combined with DFT analysis, show that both the CD intensity and sign depend strongly on the structure and orientation of the ligands. Furthermore, this work reveals the complex nature of chiral imprinting in perovskite nanostructures and establishes a simple physical model for ligand-induced chiral imprinting to guide the further development of chiral materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗
NASA NTRS1982

Aerodynamics of an airfoil with a jet issuing from its surface

A simple, two dimensional, incompressible and inviscid model for the problem posed by a two dimensional wing with a jet issuing from its lower surface is considered and a parametric analysis is carried out to observe how the aerodynamic characteristics depend on the different parameters. The mathematical problem constitutes a boundary value problem where the position of part of the boundary is not known a priori. A nonlinear optimization approach was used to solve the problem, and the analysis reveals interesting characteristics that may help to better understand the physics involved in more complex situations in connection with high lift systems.

Tavella, D. A.↗
DOE OSTI2020

Forced Patterns of Sea Level Rise in the Community Earth System Model Large Ensemble From 1920 to 2100

To provide context for observed sea level rise, the forced response (FR) in dynamic sea level (DSL) during the 20th and 21st centuries is examined in the Community Earth System Model Large Ensemble (LE). After accounting for simulation drift, which in the LE is sizable, the DSL FR is found to be complex, both in space and time. Its evolving character is suggested to arise from both the diversity and transient evolution of climate forcing agents and the slow adjustment timescales of the intermediate and deep oceans. Nonetheless, various intervals of spatially coherent change simulated for the recent past and near future are identified, and their characteristics and associated driving mechanisms are identified and discussed. The mid- to late-20th century DSL FR is characterized by an hemispherically asymmetric pattern of change, with depressed rates of rise in the northern oceans. There is also a dipole of change in the Southern Ocean caused by changes in near-surface zonal winds. Through the late 20th and early 21st centuries, a different pattern of rise emerges with elevated rates in the tropics and depressed rates at high latitudes. Zonal and interbasin variations characterize both intervals and involve the pattern and depth of anomalous ocean heat content storage and spatial contrasts in the expansion coefficient tied mainly to base state temperature, with greater rates of rise in warm regions per unit warming. Finally, the relative roles of surface flux and ocean convergence anomalies are examined.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Developments and applications of dynamic models for large eddy simulation of complex flows

The dynamic modeling procedure for large eddy simulation of turbulent flows is reviewed and recent developments in the theoretical aspects and applications are described. Methods for inclusion of backscatter of energy from small to large scale motions are presented. New formulations of the dynamic procedure are proposed which are optimized based on the subgrid scale flux vector or the energy dissipation rate instead of the subgrid scale stress tensor. Recent results from application of the model to forced isotropic turbulence with an inertial subrange, flow over a backward facing step at Reynolds number of 28000, and flow over a concave curved surface are presented.

Moin, P.↗
DOE OSTI2021

Pyroelectric Heat Detection for Calibrated Measurement of Atomic Layer Deposition Reaction Heat

Atomic layer deposition (ALD) precursors and processes have been identified that enable the deposition of materials that span the periodic table. However, the detailed mechanisms, kinetics, thermodynamics, and complexities of even the most well-studied ALD half-reactions remain insufficiently resolved experimentally to test present computational hypotheses. In this work, a pyroelectric calorimeter is designed, implemented, calibrated, and electrically modeled to enable quantitative ALD reaction heat detection. The operation and response of three ALD calorimeter designs were evaluated for use as absolute ALD half-reaction heat detectors. The most effective design was calibrated in situ before being utilized to measure the heat generated by the reaction of trimethylaluminum with a hydroxylated alumina surface to produce 96 μJ/cm 2 over a reaction time of ~30 ms. With thermal and temporal resolutions down to 0.1 μJ/cm 2 and 50 ns, respectively, this sensitive and ultrafast probe is poised to improve our fundamental understanding of numerous ALD reaction mechanisms through quantitative comparison with first-principles computation predictions.

36 MATERIALS SCIENCE↗