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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Cellular effects of individual high-linear energy transfer particles and implications for tissue response at low doses

The energy deposition patterns produced by the radiation environment in space can be quite different from those in conventional radiation environments. Furthermore, conventional radiation biological experiments, using randomly distributed particle tracks, cannot access some variables which may be important in determining the health effects of irradiation. Controlled microbeam irradiation provides the means to investigate the effects and unique energy deposition patterns and cell environment for a variety of end points.

Non-NASA Center↗
NASA NTRS2010

Multiscale Modeling of Thermal Conductivity of Polymer/Carbon Nanocomposites

Molecular dynamics simulation was used to estimate the interfacial thermal (Kapitza) resistance between nanoparticles and amorphous and crystalline polymer matrices. Bulk thermal conductivities of the nanocomposites were then estimated using an established effective medium approach. To study functionalization, oligomeric ethylene-vinyl alcohol copolymers were chemically bonded to a single wall carbon nanotube. The results, in a poly(ethylene-vinyl acetate) matrix, are similar to those obtained previously for grafted linear hydrocarbon chains. To study the effect of noncovalent functionalization, two types of polyethylene matrices. -- aligned (extended-chain crystalline) vs. amorphous (random coils) were modeled. Both matrices produced the same interfacial thermal resistance values. Finally, functionalization of edges and faces of plate-like graphite nanoparticles was found to be only modestly effective in reducing the interfacial thermal resistance and improving the composite thermal conductivity

Clancy, Thomas C.↗
DOE OSTI2025

Prediction of Alpha-Particle-Immune Gate-All-Around Field-Effect Transistors (GAA-FET) Based SRAM Design

Alpha particles are known to be a major source of particles creating soft errors in semiconductor devices, such as content flipping in Static Random-Access Memory (SRAM). Recent advancements in transistor nodes have led to the introduction of Gate-All-Around Field Effect Transistors (GAA-FETs), which have better gate control, thus better electrostatics. Moreover, the introduction of bottom dielectric isolation (BDI) eliminates substrate leakage and thus is expected to enhance its radiation hardness. It is thus important to explore if one can design an SRAM that is completely radiation-hard to alpha particles. In this paper, using 3D Technology Computer-Aided-Design (TCAD) simulations, we show that it is possible to design an SRAM using GAA-FET technology so that it is immune to single alpha particle radiation error. In other words, with the design, there will be no single-event upset (SEU) due to alpha particles. We first use ab initio calculations in PHITS to show that there is a maximum linear energy transfer (LET), LET max , for the alpha particle in Si and Si x Ge 1-x . Based on that, by de signing a sub-7nm GAA-FET-based SRAM with BDI, we show that the SRAM does not flip even if the particle strike is in the worst-case scenario for LET > LET max .

42 ENGINEERING↗
DOE OSTI2025

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

In this work we explore the capability of physics-informed neural networks (PINNs) to discover multiple solutions. Many real-world phenomena governed by nonlinear differential equations (DEs), such as fluid flow, exhibit multiple solutions under the same conditions, yet capturing this solution multiplicity remains a significant challenge. A key difficulty lies in providing appropriate initial conditions or guesses, as widely used time-marching schemes and Newton’s method are highly sensitive to these choices when solving complex computational problems. While machine learning models, particularly PINNs, have shown promise in solving DEs, their ability to capture multiple solutions remains underexplored. In this work, we propose a simple and practical approach using PINNs to learn and discover multiple solutions. We first demonstrate that PINNs, when combined with random initialization and deep ensemble method—originally developed for uncertainty quantification—can effectively uncover multiple solutions to nonlinear ordinary and partial DEs. Although training large ensembles of PINNs may appear computationally demanding, this can be done efficiently using vectorization techniques supported by modern deep learning frameworks, allowing many networks to be trained simultaneously. Our approach highlights the critical role of initialization in shaping solution diversity, addressing an often-overlooked aspect of machine learning for scientific computing. Furthermore, we propose utilizing PINN-generated solutions as initial conditions or initial guesses for conventional numerical solvers to enhance accuracy and efficiency in capturing multiple solutions. Extensive numerical experiments, including the Allen–Cahn equation and cavity flow, where our approach successfully identifies both stable and unstable solutions, validate the effectiveness of our method. These findings establish a general and efficient framework for addressing solution multiplicity in nonlinear DEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Bead–Spring Simulation of Ionomer Melts—Studying the Effects of Chain-Length and Associating Group Fraction on Equilibrium Structure and Extensional Flow Behavior

Ionomers are associative polymers with diverse applications ranging from selective membranes and high-performance adhesives to abrasion- and chemical-resistant coatings, insulation layers, vacuum packaging, and foamed sheets. Within equilibrium melt, the ionic or associating groups are known to form thermally reversible, associative clusters whose presence can significantly affect the system’s mechanical, viscoelastic, and transport properties. It is, thus, of great interest to understand how to control such clusters’ size distribution, shape, and stability through the designed choice of polymer architecture and the ionic groups’ fraction, arrangement, and interaction strength. In this work, we represent linear associating polymers using a Kremer–Grest type bead–spring model and perform large-scale MD simulations to explore the effect of polymer chain-length (l) and fraction (f s ) of randomly placed associating groups on the size distribution and stability of formed clusters. We consider different chain-lengths (below and above entanglement), varying fractions of associating groups (represented by ‘sticky’ beads) between 5 and 20%, and a fixed sticky–sticky nonbond interaction strength of four times that between regular non-associating beads. For all melts containing associating groups the equilibrium structure factor S(q) displays a signature ionomer peak at low wave vector q whose intensity increases with increasing f s and l. The average cluster size Nc increases with f s . However, the effect of chain-length on Nc appears to be pronounced only at higher values of f s . Under extensional flows, the computed stress (and viscosity) is higher at higher f s and l regardless of strain rate. Beyond a critical strain rate, we observe fragmentation of the associative clusters, which has interesting effects on the stress/viscous response.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Is there a role for nonsedating antihistamines in motion sickness? Fallout from space research may soon benefit your patients

The rotating chair test, a novel research technique for simulating motion sickness, is used to study the effect of nonsedating oral antihistamines in preventing or forestalling motion sickness. After receiving terfenadine, astemizole, doxepin, or placebo, four groups of male volunteers were rotated at accelerating speed, and they made head movements out of the axis of rotation until they perceived that vomiting would occur if additional head movements were made. Those pretreated with doxepin or terfenadine experienced a statistically significant prophylactic effect, as measured by increased tolerance to Coriolis stimulation. This suggests that selective peripheral H1 antihistamine action may protect against motion sickness.

Clinical Trial↗
DOE OSTI2023

Interplay between disorder, local relaxation, and collective behavior for an ensemble of emitters outside versus inside a cavity

The interplay between collective optical response and molecular static and dynamic disorder is studied using simple effective Hamiltonians for an ensemble of two-level emitters inside and outside a single-mode cavity. We model environmental disorder by randomly modulating the molecular transition frequencies and the coupling between the emitters and the electromagnetic field. We also consider the effects of intermolecular interactions and orientational disorder. We investigate how these effects lead to new features in the steady-state absorption (outside the cavity), transmission spectra (inside the cavity), and the yield of local molecular processes such as a unimolecular reaction. Outside the cavity, the collective behavior is manifested in the linewidth of the steady-state absorption, the emission spectrum, and the local chemical yield. Inside the cavity, however, the collective behavior primarily determines the Rabi splitting. The effects of intermolecular interactions under orientational disorder are also studied. For the most part, for all types of disorder, if we increase disorder, we find a reduction in the collective nature of the molecular response (smaller effective N) and therefore the Rabi splitting contraction occurs with orientational disorder. Furthermore, we find that static disorder is more destructive to collective behavior than dynamic disorder.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1977

Plasma effects on the spontaneous emission of synchrotron radiation from weakly relativistic electrons

A method for computing the spectral emissivity of spontaneous synchrotron radiation is discussed. The Klimontovich (1967) formalism in plasma kinetic theory is adopted in which an ensemble average of the microscopically emitted power is considered. The present method clarifies the meaning of the random phase approximation which is imposed in several existing theories of synchrotron radiation. Both the effects of dielectric polarization and two-particle correlations are included in the present discussion. The theory is applied to the case of a plasma in thermal equilibrium, for which it is shown that the effect of pair correlations on the emissivity vanishes. On the other hand, the effect of dielectric polarization is studied numerically for a wide range of parameters.

Freund, H. P.↗
NASA NTRS1994

Formation Energy of Disordered Alloys form the Energetics of Ordered Compounds

The cluster expansion technique is used in combination with first-principles calculations of the total energy of ordered compounds to study the energetics of the disordered phase for a number of alloy systems. The effect of short-range order, as seen in the energy differences between an alloy with the configuration corresponding to finite temperature and the perfectly random one is studied. The role of relaxation due to large size mismatch is discussed in terms of an effective cluster volume approximation. Very good agreement with measured energies of formation is obtained when short-range order and relaxation effects are taken into account.

Amador, Carlos↗
NASA NTRS1989

Leaf-shape effects in electromagnetic wave scattering from vegetation

A vegetation medium is modeled as a half-space of randomly distributed and oriented leaves of arbitrary shape. In accordance with the first-order radiative transfer theory, the backscattering coefficient for such a half-space is expressed in terms of the scattering amplitudes. For disc- or needle-shaped leaves, the generalized Rayleigh-Gans approximation is used to calculate the scattering amplitudes. This approach is valid for leaf dimensions up to the size of the incident wavelength. To examine the leaf-shape effect, elliptic discs are used to model deciduous leaves, and needles are used to model coniferous leaves. The differences between the scattering characteristics of leaves of different shapes are illustrated numerically for various orientations, frequencies, and incidence angles. It is found that the scattering characteristics of elliptic disc-shaped leaves are sensitive to the three angles of orientation and disc ellipticity. In general, both like and cross polarizations may be needed to differentiate the difference in scattering due to the shapes of the leaves.

Karam, Mostafa A.↗
NASA NTRS2004

Examination of Surface Roughness on Light Scattering by Long Ice Columns by Use of a Two-Dimensional Finite-Difference Time-Domain Algorithm

Natural particles such as ice crystals in cirrus clouds generally are not pristine but have additional micro-roughness on their surfaces. A two-dimensional finite-difference time-domain (FDTD) program with a perfectly matched layer absorbing boundary condition is developed to calculate the effect of surface roughness on light scattering by long ice columns. When we use a spatial cell size of 1/120 incident wavelength for ice circular cylinders with size parameters of 6 and 24 at wavelengths of 0.55 and 10.8 mum, respectively, the errors in the FDTD results in the extinction, scattering, and absorption efficiencies are smaller than similar to 0.5%. The errors in the FDTD results in the asymmetry factor are smaller than similar to 0.05%. The errors in the FDTD results in the phase-matrix elements are smaller than similar to 5%. By adding a pseudorandom change as great as 10% of the radius of a cylinder, we calculate the scattering properties of randomly oriented rough-surfaced ice columns. We conclude that, although the effect of small surface roughness on light scattering is negligible, the scattering phase-matrix elements change significantly for particles with large surface roughness. The roughness on the particle surface can make the conventional phase function smooth. The most significant effect of the surface roughness is the decay of polarization of the scattered light.

Sun, W.↗
DOE OSTI2020

Modeling Nanoconfinement Effects Using Active Learning

Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.

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

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗
NASA NTRS1996

The Challenges Facing Science Data Archiving on Current Mass Storage Systems

This paper discusses the desired characteristics of a tape-based petabyte science data archive and retrieval system required to store and distribute several terabytes (TB) of data per day over an extended period of time, probably more than 115 years, in support of programs such as the Earth Observing System Data and Information System (EOSDIS). These characteristics take into consideration not only cost effective and affordable storage capacity, but also rapid access to selected files, and reading rates that are needed to satisfy thousands of retrieval transactions per day. It seems that where rapid random access to files is not crucial, the tape medium, magnetic or optical, continues to offer cost effective data storage and retrieval solutions, and is likely to do so for many years to come. However, in environments like EOS these tape based archive solutions provide less than full user satisfaction. Therefore, the objective of this paper is to describe the performance and operational enhancements that need to be made to the current tape based archival systems in order to achieve greater acceptance by the EOS and similar user communities.

Peavey, Bernard↗
DOE OSTI2020

Building Durable Multimetallic Electrocatalysts from Intermetallic Seeds

When combined with earth-abundant metals, Pt-based alloy nanoparticles (NPs) can be cost-effective electrocatalysts. However, these NPs can experience leaching of non-noble-metal components under harsh electrocatalytic conditions. In this work, the Skrabalak group has demonstrated a novel NP construct in which Pt-based random alloy surfaces are stabilized against non-noble-metal leaching by their deposition onto intermetallic seeds. These core@shell NPs are highly durable electrocatalysts, with the ability to tune catalytic performance by the core@shell architecture, surface alloy composition, and NP shape. This versatility was demonstrated in a model system in which random alloy (ra-) PtM surfaces were deposited onto ordered intermetallic (i-) PdCu seeds using seed-mediated co-reduction (SMCR). In the initial demonstration, ra-PtCu shells were deposited on i-PdCu seeds, with these core@shell NPs exhibiting higher specific and mass activities for the oxygen reduction reaction (ORR) when compared to similarly sized ra-PtCu NPs. These NPs also showed outstanding durability, maintaining ~85% in specific activity after 5000 cycles. Characterization of the NPs after use revealed minimal loss of Cu. The activity enhancement was attributed to the strained surface that arises from the lattice mismatch between the intermetallic core and random alloy surface. The outstanding durability was attributed to the ordered structure of the intermetallic core. The origin of this durability enhancement was investigated by classical molecular dynamics simulations, where Pt atoms were found to have a lower potential energy when deposited on an intermetallic core than when deposited on a random alloy core. Also, ordering of Cu atoms at the core@shell interface appears to enhance the overall binding between the core and the shell materials. Inspired by this initial demonstration, SMCR has been used to achieve shells of different random alloy compositions, PtM (M = Ni, Co, Cu, or Fe). This advance is significant because ligand effects vary as a function of PtM identity and Pt/M ratio. These features also influence the degree of surface strain imparted from the lattice mismatch between the core and shell materials. Like the initial demonstration, standout features of these core@shell NPs were high durability and resistance to non-noble metal leaching. Moving forward, efforts have been directed toward integrating shape-control to this core@shell NP construct. This integration is motivated by the shape-dependent catalytic performance of NPs derived from the selective expression of specific facets. Considering the initial i-PdCu@ra-PtCu system, NPs with a cubic shape have been achieved by judicious selection of capping ligands during SMCR. Evaluation of these NPs as catalysts for the electrooxidation of formic acid found that the nanocubic shape enhances catalytic performance compared to similar core@shell NPs with a spherical morphology. We envision that SMCR can be applied to other NP systems to achieve highly durable catalysts as the syntheses of monodisperse and shape-controlled intermetallic seeds are advanced. This Account highlights the role of intermetallic cores in providing more durable electrocatalysts. More broadly, the versatility of SMCR is highlighted as a route to integrate architecture, alloy surfaces, and shape within one NP system, and how this achievement is inspiring new high-performance and robust catalysts is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1995

Influence of fiber architecture on the elastic an d inelastic response of metal matrix composites

This three part paper focuses on the effect of fiber architecture (i.e., shape and distribution) on the elastic and inelastic response of metal matrix composites. The first part provides an annotative survey of the literature, presented as a historical perspective, dealing with the effects of fiber shape and distribution on the response of advanced polymeric matrix and metal matrix composites. Previous investigations dealing with both continuously and discontinuously reinforced composites are included. A summary of the state-of-the-art will assist in defining new directions in this quickly reviving area of research. The second part outlines a recently developed analytical micromechanics model that is particularly well suited for studying the influence of these effects on the response of metal matrix composites. This micromechanics model, referred to as the generalized method of cells (GMC), is capable of predicting the overall, inelastic behavior of unidirectional, multi-phased composites given the properties of the constituents. In particular, the model is sufficiently general to predict the response of unidirectional composites reinforced by either continuous or discontinuous fibers with different inclusion shapes and spatial arrangements in the presence of either perfect or imperfect interfaces and/or interfacial layers. Recent developments regarding this promising model, as well as directions for future enhancements of the model's predictive capability, are included. Finally, the third pan provides qualitative results generated using GMC for a representative titanium matix composite system, SCS-6/TlMETAL 21S. Results are presented that correctly demonstrate the relative effects of fiber arrangement and shape on the longitudinal and transverse stress-strain and creep response, with both strong and weak fiber/matrix interfacial bonds. The fiber arrangements include square, square diagonal, hexagonal and rectangular periodic arrays, as well as a random array. The fiber shapes include circular, square and cross-shaped cross sections. The effect of fiber volume fraction on the observed stress-strain response is also discussed, as the thus-far poorly documented strain rate sensitivity effect. In addition to the well documented features of architecture dependent response of continuously reinforced two-phase MMC's, new results involving continuous multi-phase internal architectures are presented. Specifically, stress strain and creep response of composites with different size fibers having different internal arrangements and bond strengths are investigated with the aim of determining the feasibility of using this approach to enhance the transverse toughness and creep resistance of TMC's.

Arnold, Steven M.↗
DOE OSTI2023

Modeling Short-Range Order in Disordered Rocksalt Cathodes by Pair Distribution Function Analysis

Pair distribution function (PDF) analysis is a powerful technique for the characterization of short-range order (SRO) in disordered materials. Accurate interpretation of experimental PDF data is critically reliant on the development of structural models that can account for local variations in site occupancies and bond lengths. To this end, we outline an approach to model SRO using first-principles calculations based on the cluster-expansion formalism. These methods are validated on neutron scattering data from two disordered rocksalt oxyfluorides, Li 1.3 Mn 0.4 Ti 0.3 O 1.7 F 0.3 and Li 1.3 Mn 0.4 Nb 0.2 Ti 0.1 O 1.7 F 0.3 . For each composition, we demonstrate that an average structure without any SRO fails to reproduce several key features in the experimental PDF. To pinpoint the origin of the suspected SRO in these materials, configurational and displacive effects were separately investigated using two disparate models. Special quasi-random structures were relaxed using density functional theory to account for local changes in bond lengths while maintaining a near-random ionic configuration. This leads to slightly improved accuracy but still misrepresents asymmetry in the first few peaks of the PDF. Monte Carlo simulations were performed to model configurational SRO on a fixed lattice, which by itself is shown to have a minimal influence on the PDF. Instead, we find that it is the bond length relaxations within environments created by SRO which controls the details of the PDF, thereby highlighting the subtle but important coupling between configurational and displacive SRO in disordered materials.

36 MATERIALS SCIENCE↗