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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

NASGRO 3.0: A Software for Analyzing Aging Aircraft

Structural integrity analysis of aging aircraft is a critical necessity in view of the increasing numbers of such aircraft in general aviation, the airlines and the military. Efforts are in progress by NASA, the FAA and the DoD to focus attention on aging aircraft safety. The present paper describes the NASGRO software which is well-suited for effectively analyzing the behavior of defects that may be found in aging aircraft. The newly revised Version 3.0 has many features specifically implemented to suit the needs of the aircraft community. The fatigue crack growth computer program NASA/FLAGRO 2.0 was originally developed to analyze space hardware such as the Space Shuttle, the International Space Station and the associated payloads. Due to popular demand, the software was enhanced to suit the needs of the aircraft industry. Major improvements in Version 3.0 are the incorporation of the ability to read aircraft spectra of unlimited size, generation of common aircraft fatigue load blocks, and the incorporation of crack-growth models which include load-interaction effects such as retardation due to overloads and acceleration due to underloads. Five new crack-growth models, viz., generalized Willenborg, modified generalized Willenborg, constant closure model, Walker-Chang model and the deKoning-Newman strip-yield model, have been implemented. To facilitate easier input of geometry, material properties and load spectra, a Windows-style graphical user interface has been developed. Features to quickly change the input and rerun the problem as well as examine the output are incorporated. NASGRO has been organized into three modules, the crack-growth module being the primary one. The other two modules are the boundary element module and the material properties module. The boundary-element module provides the ability to model and analyze complex two-dimensional problems to obtain stresses and stress-intensity factors. The material properties module allows users to store and curve-fit fatigue-crack growth data. On-line help and documentation are provided for each of the modules. In addition to the popular PC windows version, a unix-based X-windows version of NASGRO is also available. A portable C++ class library called WxWindows was used to facilitate cross-platform availability of the software.

Mettu, S. R.↗
DOE OSTI2013

Shot-noise at a Fermi-edge singularity: Non-Markovian dynamics

For an InAs quantum dot we study the current shot noise at a Fermi-edge singularity in low temperature cross-correlation measurements. In the regime of the interaction effect the strong suppression of noise observed at zero magnetic field and the sequence of enhancement and suppression in magnetic field go beyond a Markovian master equation model. Qualitative and quantitative agreement can however be achieved by a generalized master equation model taking non-Markovian dynamics into account.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

When does global attention help: a unified empirical study on atomistic graph learning

Graph neural networks (GNNs) are widely used as surrogates for costly experiments and first-principles simulations to study the behavior of compounds at atomistic scale, and their architectural complexity is constantly increasing to enable the modeling of complex physics. While most recent GNNs combine more traditional message passing neural networks (MPNNs) layers to model short-range interactions with more advanced graph transformers (GTs) with global attention mechanisms to model long-range interactions, it is still unclear when global attention mechanisms provide real benefits over well-tuned MPNN layers due to inconsistent implementations, features, or hyperparameter tuning. We introduce the first unified, reproducible benchmarking framework–built on HydraGNN–that enables seamless switching among four controlled model classes: MPNN, MPNN with chemistry/topology encoders, GPS-style hybrids of MPNN with global attention, and fully fused localglobal models with encoders. Using seven diverse open-source datasets for benchmarking across regression and classification tasks, we systematically isolate the contributions of message passing, global attention, and encoder-based feature augmentation. Our study shows that encoder-augmented MPNNs form a robust baseline, while fused localglobal models yield the clearest benefits for properties governed by long-range interaction effects. We further quantify the accuracycompute trade-offs of attention, reporting its overhead in memory. Together, these results establish the first controlled evaluation of global attention in atomistic graph learning and provide a reproducible testbed for future model development.

Equivariant graph neural networks↗
DOE OSTI2023

Synergistic effects of climate warming and atmospheric nutrient deposition on the alpine lake ecosystem in the south-eastern Tibetan Plateau during the Anthropocene

Alpine lakes on the Tibetan Plateau are highly sensitive to global change and have been recognized as the sentinel of climate warming. However, anthropogenic impacts in populated area are migrating to these remote areas via transporting particulate nutrients by atmospheric deposition. Whether warming and nutrient deposition would impose additive or synergistic effects on the lake ecosystem remains largely unknown. Here, we present multi-proxy (sediment pigment and geochemistry) records during the past two centuries at the Cuoqia Lake in the southeast Tibetan Plateau. We found that the lake exhibited rapid ecological changes since 1980 AD characterized by an increase in primary productivity due to algal proliferation, with more rapid growth of green algae and diatoms. These findings are in concert with many other lakes (e.g., Moon Lake and Shade Co) in the same area, suggesting a consistent pattern of ecosystem evolution at the region scale. Statistical analyses suggested that nutrient deposition and climate warming were strongly associated with the variations in primary productivity and algae composition, exerting both individual and interactive effects. In addition, scenario analyses with a well-established process-based ecosystem model further revealed that the two factors not only individually, but also synergistically promoted the algal proliferation and community succession. Such synergy is evident in that the effect of lake warming would be more pronounced under higher nutrient deposition scenario, which is potentially due to higher temperature-driven mineralization in warmer conditions, and higher efficiency of nutrient utilization under enhanced light availability attributing to declining ice thickness and duration in cold seasons. Overall, our study proposes the existence and quantifies the synergistic impacts of climate warming and anthropogenic activities in driving the ecological changes in remote alpine lakes on the Tibetan Plateau. The lake ecological consequences driven by individual factor would be worsen by such synergy, so that we cannot predict the lake ecosystem trajectory in the future based on each factor separately, and more efforts than previously expected would be needed for the lake restoration and management.

Zhang, Can↗
NASA NTRS1984

Hole interaction and load introduction effects for compression-loaded laminates with holes

The effects of hole interaction and load introduction for (+ or - 30/90) sub 8 (s) and (+ or - 45) sub 12 (s) laminates with holes was conducted. A hole interaction region was identified in some specimens, and this region was ineffective for carrying load. The membrane stiffness of a specimen with a hole interaction region is less than the membrane stiffness of a similar specimen without a hole interaction region. A load introduction effect that occurs on the sublaminate level was also observed. This effect can cause a nonuniform load diffusion into the interior of the specimen.

Shuart, M. J.↗
NASA NTRS1997

Growth hormone, IGF-I, and exercise effects on non-weight-bearing fast muscles of hypophysectomized rats

The effects of growth hormone (GH) or insulin-like growth factor I (IGF-I) with or without exercise (ladder climbing) in countering the effects of unweighting on fast muscles of hypophysectomized rats during 10 days of hindlimb suspension were determined. Compared with untreated suspended rats, muscle weights were 16-29% larger in GH-treated and 5-15% larger in IGF-I-treated suspended rats. Exercise alone had no effect on muscle weights. Compared with ambulatory control, the medial gastrocnemius weight in suspended, exercised rats was larger after GH treatment and maintained with IGF-I treatment. The combination of GH or IGF-I plus exercise in suspended rats resulted in an increase in size of each predominant fiber type, i.e., types I, I + IIa and IIa + IIx, in the medial gastrocnemius compared with untreated suspended rats. Normal ambulation or exercise during suspension increased the proportion of fibers expressing embryonic myosin heavy chain in hypophysectomized rats. The phenotype of the medial gastrocnemius was minimally affected by GH, IGF-I, and/or exercise. These results show that there is an IGF-I, as well as a GH, and exercise interactive effect in maintaining medial gastrocnemius fiber size in suspended hypophysectomized rats.

NASA Center ARC↗
DOE OSTI2015

Nuclear Effects in Neutrino Interactions at Low Momentum Transfer

This is a study to identify predicted effects of the carbon nucleus environment on neutrino - nucleus interactions with low momentum transfer. A large sample of neutrino interaction data collected by the MINERvA experiment is analyzed to show the distribution of charged hadron energy in a region with low momentum transfer. These distributions reveal a major discrepancy between the data and a popular interaction model with only the simplest Fermi gas nuclear effects. Detailed analysis of systematic uncertainties due to energy scale and resolution can account for only a little of the discrepancy. Two additional nuclear model effects, a suppression/screening effect (RPA), and the addition of a meson exchange current process (MEC), are shown to improve the description of the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2019

Simulated Microgravity Affects Behavior and Cytokine Expression in the Hippocampus of Adult Mice: Influence of Mitochondrial Reactive Oxygen Species

The effects of microgravity, and social isolation on the CNS are poorly understood. We hypothesize that mitochondrial reactive oxygen species (ROS) play an important role in this process. Since mice are social animals, our lab developed a novel social model of hindlimb unloading (HU), enabling us to determine the effects of both social isolation and simulated microgravity. Responses to 30d of HU were compared in wildtype or transgenic MCAT mice who over-express human catalase in mitochondria. Abundance of 4-Hydroxynonenal, Park7 (a redox-sensitive chaperone and sensor of oxidative stress) and corticosterone were measured by ELISA. Cytokines related to inflammation in the hippocampus and in plasma were analyzed by a protein array. Behavioral data was collected over a 24-hour period.Socially housed HU mice were more active and conducted at least two times more exploratory activities, compared to normally loaded mice. Correlation analysis revealed that specific brain and plasma cytokines correspond with specific behaviors. Simulated microgravity and/or social isolation caused changes in cytokine patterns in the hippocampus and in plasma, with significant interaction effects of HU and genotype in expression levels of five cytokines (out of 35). Interestingly, elevation of these generally pro-inflammatory cytokines by HU in WT mice was mitigated in MCAT mice, suggesting a role for mitochondrial ROS signaling in inflammatory CNS responses to microgravity. Interestingly, socially housed mice had also lower level of 4HNE and higher level of Park7 in the hippocampus compared to singly housed animals. The cytokine responses to social isolation were more extensive in brain vs plasma. Further, there was no overlap in the cytokine repertoire regulated in response to microgravity versus, isolation suggesting divergent mechanisms or downstream signaling. These findings implicate a potentially important role for mitochondrial ROS in CNS responses to the challenges posed both by prolonged missions in space and bedrest on Earth

Guttmann, Linda↗
DOE OSTI2015

A comparison of numerical simulations and analytical theory of the dynamics of interacting magnetic vortices

Magnetostatic interactions between vortices in closely spaced planar structures are important for applications including vortex-based magnonic crystals and spin torque oscillator networks. Analytical theories that include magnetostatic interaction effects have been proposed but have not yet been rigorously tested. Here, we compare micromagnetic simulations of the dynamics of magnetic vortices confined in three disks in an equilateral triangle configuration to analytical theories that include coupling. Micromagnetic simulations show that the magnetostatic coupling between the disks leads to splitting of the gyrotropic resonance into three modes and that the frequency splitting increases with decreasing separation. The temporal profiles of the magnetization depend on the vortex polarities and chiralities; however, the frequencies depend only on the polarity combinations and will fall into one of two categories: all polarities equal or one polarity opposite to the others, where the latter leads to a larger frequency splitting. Although the magnitude of the splitting observed in the simulations is larger than what is expected based on purely dipolar interactions, a simple analytical model that assumes dipole-dipole coupling captures the functional form of the frequency splitting and the motion patterns just as well as more complex models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Pyrolysis temperature and soil depth interactions determine PyC turnover and induced soil organic carbon priming

Pyrogenic organic carbon (PyC) is a complex, heterogeneous class of thermally altered organic substrates, but its dynamics and how its behavior changes with soil depth remain poorly understood. We conducted a laboratory incubation study to investigate the interactive effects of pyrolysis temperature and soil depth on the turnover of PyC compared to its precursor wood and native SOC (NSOC). We incubated dual-labeled (13C and 15N) jack pine pyrogenic organic matter produced at 300 °C (PyC300), 450 °C (PyC450), and their precursor pine wood in a fine-loamy, mixed-conifer forest soil for 745 days. A mixture of surface (0–10 cm) and subsurface (50–70 cm) forest soils, with and without labeled biomass were incubated in the dark at 55% soil water field capacity and 25 °C. Total 13C from PyC and wood mineralized as 13C-CO2 (as % of C added to soil) declined with an increase in pyrolysis temperature as follows: 54 ± 7.7% for wood, 3.1 ± 0.2% for PyC300, and 0.94 ± 0.08% for PyC450. Furthermore, after 2 years, soil depth interacted with pyrolysis temperature to affect C turnover, with total wood C losses significantly declining from 70.6% in surface soils to 37.5% in subsurface soil, while total losses of PyC300 and PyC450 were unaffected by differences between surface and subsurface soils. Wood induced negative priming (i.e., decreased mineralization rates) in surface soil at days 3 and 60, while PyC300 induced positive priming (i.e., increased mineralization rates) in subsurface soil at day 60. After 2 years, unlabeled NSOC losses increased from 9.2 ± 0.8% of NSOC in unamended treatments to 16.5 ± = 2.6% of NSOC with PyC450 additions. Our results suggest that PyC pyrolyzed at a given temperature can mineralize at similar rates between soil depths, and high amounts of PyC450 in subsurface soils can stimulate NSOC losses. These findings indicate that soil depth imposes critical controls on PyC dynamics belowground.

13C-labeling↗
DOE OSTI2019

Three-dimensional quantum Hall effect and metal–insulator transition in ZrTe 5

The discovery of the quantum Hall effect (QHE) in two-dimensional electronic systems has given topology a central role in condensed matter physics. Although the possibility of generalizing the QHE to three-dimensional (3D) electronic systems was proposed decades ago, it has not been demonstrated experimentally. Here we report the experimental realization of the 3D QHE in bulk zirconium pentatelluride (ZrTe 5 ) crystals. We perform low-temperature electric-transport measurements on bulk ZrTe 5 crystals under a magnetic field and achieve the extreme quantum limit, where only the lowest Landau level is occupied, at relatively low magnetic fields. In this regime, we observe a dissipationless longitudinal resistivity close to zero, accompanied by a well-developed Hall resistivity plateau proportional to half of the Fermi wavelength along the field direction. This response is the signature of the 3D QHE and strongly suggests a Fermi surface instability driven by enhanced interaction effects in the extreme quantum limit. By further increasing the magnetic field, both the longitudinal and Hall resistivity increase considerably and display a metal–insulator transition, which represents another magnetic-field-driven quantum phase transition. Lastly, our findings provide experimental evidence of the 3D QHE and a promising platform for further exploration of exotic quantum phases and transitions in 3D systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
NASA NTRS1994

Neuromuscular adaptation to actual and simulated weightlessness

The chronic "unloading" of the neuromuscular system during spaceflight has detrimental functional and morphological effects. Changes in the metabolic and mechanical properties of the musculature can be attributed largely to the loss of muscle protein and the alteration in the relative proportion of the proteins in skeletal muscle, particularly in the muscles that have an antigravity function under normal loading conditions. These adaptations could result in decrements in the performance of routine or specialized motor tasks, both of which may be critical for survival in an altered gravitational field, i.e., during spaceflight and during return to 1 G. For example, the loss in extensor muscle mass requires a higher percentage of recruitment of the motor pools for any specific motor task. Thus, a faster rate of fatigue will occur in the activated muscles. These consequences emphasize the importance of developing techniques for minimizing muscle loss during spaceflight, at least in preparation for the return to 1 G after spaceflight. New insights into the complexity and the interactive elements that contribute to the neuromuscular adaptations to space have been gained from studies of the role of exercise and/or growth factors as countermeasures of atrophy. The present chapter illustrates the inevitable interactive effects of neural and muscular systems in adapting to space. It also describes the considerable progress that has been made toward the goal of minimizing the functional impact of the stimuli that induce the neuromuscular adaptations to space.

Non-NASA Center↗
DOE OSTI2022

Post-inflationary dark matter bremsstrahlung

Dark matter may only interact with the visible sector efficiently at energy scales above the inflaton mass, such as the Planck scale or the grand unification scale. In such a scenario, the dark matter is mainly produced out of equilibrium during the period of reheating, often referred to as UV freeze-in. Here, we evaluate the abundance of the dark matter generated from bremsstrahlung off the inflaton decay products assuming no direct coupling between the inflaton and the dark matter. This process generally dominates the production of dark matter for low reheating temperatures where the production through the annihilations of particle in the thermal plasma becomes inefficient. We find that the bremsstrahlung process dominates for reheating temperatures T RH ≲ 10 10 GeV, and produces the requisite density of dark matter for a UV scale ≃ 10 16 GeV. As examples, we calculate numerically the yield of the dark matter bremsstrahlung through gravitation and dimension-6 vector portal effective interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Particle Strain Analysis of Epoxy-Based Composites Following Quasi-Static and Dynamic Compression

We investigate localized strains in polymer-based composites subjected to varying strain-rates and strains. Research on composites is often focused on their bulk mechanical behavior. However, many material phenomena are influenced by their microstructure, such as the mixing and reaction mechanisms of constituents in structural energetic materials. Therefore, the response of composites at the mesoscale is of clear interest; especially as insight into the micro-deformation and damage, and interaction effects between the constituents at the local level will better inform our understanding of the bulk composite constitutive behavior. To more fully understand the composite mechanical behavior at the mesoscale, uniaxial compression experiments were conducted on epoxy reinforced with aluminum (20 or 40 vol%) and nickel (0 or 10 vol%) particles. Additionally, the particle size of aluminum was either 5 or 50 μm in diameter. Experiments were carried out at quasi-static and dynamic strain rates. Resulting microstructures were analyzed to measure the degree of particle strain and compared to the global material strains for different degrees of strain-rate, strain, and particle size and volume fraction. The aluminum particle strain increased with global strain and had a strong dependency on strain rate. Introducing stiffer nickel particles resulted in larger aluminum particle strains. The nickel particle strain was minimal (< 3%) and showed no dependency with strain rate. This research gives insight into the interactions of particle reinforced composite constituents and their deformation at the mesoscale which is important for understanding phenomena such as preignition mechanisms in structural energetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Determinant quantum Monte Carlo study of exhaustion in the periodic Anderson model

The Kondo and periodic Anderson models describe many of the qualitative features of local moments coupled to a conduction band, and thereby the physics of materials such as the heavy fermions. In particular, when the exchange coupling J or hybridization V between the moments and the electrons of the metallic band is large, singlets form, quenching the magnetism. In the opposite, small J or V, limit, the moments survive and the conduction electrons mediate an effective interaction which can trigger long-range, often antiferromagnetic order. In the case of the Kondo model, where the moments are described by local spins, Nozières considered the possibility that the available conduction electrons within the Kondo temperature of the Fermi surface would be insufficient in number to accomplish the screening. Much effort in the literature has been devoted to the study of the temperature scales in the resulting “exhaustion” problem and how the “coherence temperature” where a heavy Fermi liquid forms is related to the Kondo temperature. Here, we study a version of the periodic Anderson model in which some of the conduction electrons are removed in a way which avoids the fermion sign problem and hence allows low-temperature quantum Monte Carlo simulations which can access both singlet formation and magnetic ordering temperature scales. Finally, we are then able to focus on a somewhat different aspect of exhaustion physics than previously considered: the effect of dilution on the critical V for the singlet-antiferromagnetic transition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Introducing Enabling Computational Tools to the Climate Sciences: Multi-Resolution Climate Modeling with Adaptive Cubed-Sphere Grids

The research investigates and advances strategies how to bridge the scale discrepancies between local, regional and global phenomena in climate models without the prohibitive computational costs of global cloud-resolving simulations. In particular, the research explores new frontiers in computational geoscience by introducing high-order Adaptive Mesh Refinement (AMR) techniques into climate research. AMR and statically-adapted variable-resolution approaches represent an emerging trend for atmospheric models and are likely to become the new norm in future-generation weather and climate models. The research advances the understanding of multi-scale interactions in the climate system and showcases a pathway how to model these interactions effectively with advanced computational tools, like the Chombo AMR library developed at the Lawrence Berkeley National Laboratory. The research is interdisciplinary and combines applied mathematics, scientific computing and the atmospheric sciences. In this research project, a hierarchy of high-order atmospheric models on cubed-sphere computational grids have been developed that serve as an algorithmic prototype for the finite-volume solution-adaptive Chombo-AMR approach. The foci of the investigations have lied on the characteristics of both static mesh adaptations and dynamically-adaptive grids that can capture flow fields of interest like tropical cyclones. Six research themes have been chosen. These are (1) the introduction of adaptive mesh refinement techniques into the climate sciences, (2) advanced algorithms for nonhydrostatic atmospheric dynamical cores, (3) an assessment of the interplay between resolved-scale dynamical motions and subgrid-scale physical parameterizations, (4) evaluation techniques for atmospheric model hierarchies, (5) the comparison of AMR refinement strategies and (6) tropical cyclone studies with a focus on multi-scale interactions and variable-resolution modeling. The results of this research project demonstrate significant advances in all six research areas. The major conclusions are that statically-adaptive variable-resolution modeling is currently becoming mature in the climate sciences, and that AMR holds outstanding promise for future-generation weather and climate models on high-performance computing architectures.

58 GEOSCIENCES↗
NASA NTRS2016

Effects of Liner Length and Attenuation on NASA Langley Impedance Eduction

This study explores the effects of liner length and attenuation on the CHE (convected Helmholtz equation) impedance eduction method, in which the surface impedance of an acoustic liner is inferred through an iterative process based on repeated solutions to the convected Helmholtz equation. Wire mesh-over-honeycomb and perforate-over-honeycomb acoustic liners are tested in the NASA Langley Grazing Flow Impedance Tube, and the resultant data are processed using two impedance eduction methods. The first is the CHE method, and the second is a direct method (labeled the KT method) that uses the Kumaresan and Tufts algorithm to compute the impedance directly. The CHE method has been extensively used for acoustic liner evaluation, but experiences anomalous behavior under some test conditions. It is postulated that the anomalies are related to the liner length and/or attenuation. Since the KT method only employs data measured over the length of the liner, it is expected to be unaffected by liner length. A comparison of results achieved with the two impedance eduction methods is used to explore the interactive effects of liner length and attenuation on the CHE impedance eduction method.

Jones, M. G.↗