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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Structural properties of impact ices accreted on aircraft structures

The structural properties of ice accretions formed on aircraft surfaces are studied. The overall objectives are to measure basic structural properties of impact ices and to develop finite element analytical procedures for use in the design of all deicing systems. The Icing Research Tunnel (IRT) was used to produce simulated natural ice accretion over a wide range of icing conditions. Two different test apparatus were used to measure each of the three basic mechanical properties: tensile, shear, and peeling. Data was obtained on both adhesive shear strength of impact ices and peeling forces for various icing conditions. The influences of various icing parameters such as tunnel air temperature and velocity, icing cloud drop size, material substrate, surface temperature at ice/material interface, and ice thickness were studied. A finite element analysis of the shear test apparatus was developed in order to gain more insight in the evaluation of the test data. A comparison with other investigators was made. The result shows that the adhesive shear strength of impact ice typically varies between 40 and 50 psi, with peak strength reaching 120 psi and is not dependent on the kind of substrate used, the thickness of accreted ice, and tunnel temperature below 4 C.

Scavuzzo, R. J.↗
DOE OSTI2025

Curiosity driven exploration to optimize structure–property learning in microscopy

Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2026

First-principles investigation of structure-property relationships in stable and metastable MXenes

Understanding the structure–property relationships in layered transition-metal carbides or nitrides, known as MXenes, is of critical importance for their rational design, synthesis, and application. However, the vast chemical and structural diversity of MXenes, stemming from their wide range of M and X elements, surface terminations, and different atomic coordination environments, makes it challenging to clearly understand these structure–property relationships. Here, in this work, we perform first-principles density functional theory (DFT) calculations and molecular dynamics (MD) simulations to comprehensively investigate the stability and a variety of physical properties of MXenes with different coordination environments. Using Ti- and Mo-based carbide MXenes as model systems, energetic calculations reveal that Ti-based MXenes are most stable in octahedral coordination, whereas Mo-based MXenes preferentially adopt prismatic coordination. This fundamental difference in preferred atomic coordination gives rise to markedly distinct properties between these two systems as a function of the fraction of octahedral and prismatic sites. For instance, the in-plane stiffness of Ti-based MXenes increases as octahedral coordination becomes dominant, but it decreases in the Mo-based MXenes under the same conditions. Additional stability analyses based on mechanical, lattice-dynamical, and temperature-dependent thermodynamic properties demonstrate that many metastable MXenes not only satisfy the strict stability criteria but can also undergo phase transitions among different structures and even become stabilized at elevated temperatures. Although surface terminations, such as F and O atoms, do not alter the energetic ordering or the overall stiffness trends among stable and metastable MXenes, they influence other material properties. For instance, O termination can induce semiconducting behavior in both stable and metastable Ti 2 ⁢CO 2 MXenes. This study significantly advances the fundamental understanding of structure–property relationships in MXenes and provides valuable guidance for developing coordination-based design principles to precisely engineer MXenes with improved properties.

Oyeniran, Noah [University of Alabama, Tuscaloosa,↗
DOE OSTI2024

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning

For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance.

Electrical contact materials↗
DOE OSTI2024

Orientation-dependent structural properties during growth and growth mechanism of CoO films

The orientation-dependent local structural properties of CoO films on sapphire substrates during growth were investigated through linearly-polarized extended X-ray absorption fine structure (EXAFS) measurements. Specifically, CoO(1 1 1) and (100) crystals with a rock-salt structure (Fm3m) were epitaxially grown on alpha-Al2O3(0001) and (1012) substrates, respectively, at 700 degrees C using a radio-frequency sputtering system. The local structural properties of CoO films in the in-plane and out-of-plane orientations were quantitatively determined using linearly-polarized EXAFS at the Co K-edge during growth. The EXAFS analysis revealed that during the initial stages of growth, the local structural properties exhibit significant differences compared to thick films, with short atomic distances and large (small) Debye-Waller factors observed in the out-of-plane (in-plane) orientations. The local structural strain mostly diminished when approximately 20 CoO layers accumulated on the substrate. Density functional theory (DFT) calculations further supported these findings by confirming that cobalt atoms initially form stable bonds with the sapphire surface, leading to the simultaneous growth of oxygen and cobalt layers in a coordinated manner through layer-by-layer growth.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Determination of HART I Blade Structural Properties by Laboratory Testing

The structural properties of higher harmonic Aeroacoustic Rotor Test (HART I) blades were measured using the original set of blades tested in the German-dutch wind tunnel (DNW) in 1994. the measurements include bending and torsion stiffness, geometric offsets, and mass and inertia properties of the blade. the measured properties were compared to the estimated values obtained initially from the blade manufacturer. The previously estimated blade properties showed consistently higher stiffness, up to 30 percent for the flap bending in the blade inboard root section.

Jung, Sung N.↗
NASA NTRSDate not supplied

Integrated Process-Structure-Property Simulations for Additive Manufacturing Using the Open-Source Materialite Package

The microstructure and properties of additively manufactured (AM) metals are strongly dependent on process conditions. Therefore, process-structure-property (PSP) simulations are a useful tool for exploring process parameter space, studying process variations, and quantifying uncertainty in material properties. However, integrating process-structure and structure-property simulations often involves connecting multiple software packages. Each package may use unique data structures and require substantial domain knowledge. This presentation demonstrates PSP simulation capabilities of Materialite, an open-source package developed at NASA Langley Research Center. Materialite simplifies model linkages by using a common data structure and model interface, enabling straightforward simulation across a PSP model chain. Physics-based models, including kinetic Monte Carlo and crystal plasticity, are implemented within the package. The model interface is also intended to simplify implementation of new models and enable integration with external simulation tools. Example use cases include uncertainty quantification with PSP models and GPU-accelerated powder bed fusion AM process models.

additive manufacturing↗
DOE OSTI2026

Attention-based explainability for structure–property relationships

Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.

Slautin, Boris [Independent Researcher]↗
NASA NTRS2015

Finite Element Estimation of Meteorite Structural Properties

The goal of the project titled Asteroid Threat Assessment at NASA Ames Research Center is to develop risk assessment tools. The expertise in atmospheric entry in the Entry Systems and Technology Division is being used to describe the complex physics of meteor breakup in the atmosphere. The breakup of a meteor is dependent on its structural properties, including homogeneity of the material. The present work describes an 11-week effort in which a literature survey was carried for structural properties of meteoritic material. In addition, the effect of scale on homogeneity isotropy was studied using a Monte Carlo approach in Nastran. The properties were then in a static structural response simulation of an irregularly-shape meteor (138-scale version of Asteroid Itokawa). Finally, an early plan was developed for doctoral research work at Georgia Tech. in the structural failure fragmentation of meteors.

Structural Properties↗
NASA NTRS1986

Structural properties of bismuth-bearing semiconductor alloys

The structural properties of bismuth-bearing III-V semiconductor alloys are addressed. Because the Bi compounds are not known to form zincblende structures, only the anion-substituted alloys InPBi, InAsBi, and InSbBi are considered candidates as narrow-gap semiconductors. Miscibility calculations indicate that InSbBi will be the most miscible, and InPBi, with the large lattice mismatch of the constituents, will be the most difficult to mix. Calculations of the hardness of the Bi compounds indicate that, once formed, the InPBi alloy will be harder than the other Bi alloys, and substantially harder than the currently favored narrow-gap semiconductor HgCdTe. Thus, although InSbBi may be an easier material to prepare, InPBi promises to be a harder material. Growth of the Bi compounds will require high effective growth temperatures, probably attainable only through the use of nonequilibrium energy-assisted epitaxial growth techniques.

Berding, M. A.↗
NASA NTRS1988

Structural properties of bismuth-bearing semiconductor alloys

The structural properties of bismuth-bearing III-V semiconductor alloys InPBi, InAsBi, and InSbBi were studied theoretically. Bond energies, bond lengths, and strain coefficients were calculated for pure AlBi, GaBi, and InBi compounds and their alloys, and predictions were made for the mixing enthalpies, miscibility gaps, and critical metastable-to-stable material transition temperatures. Miscibility calculations indicate that InSbBi will be the most miscible, and the InPBi will be the the most difficult to mix. However, calculations of the hardness of the Bi compounds indicate that, once formed, the InPBi alloy will be harder than the other Bi alloys and substantially harder than the currently favored narrow-gap semiconductor HgCdTe.

Berding, M. A.↗
NASA NTRS1992

Energetics and structural properties of twist grain boundaries in Cu

Structural and energetics properties of atoms near a grain boundary are of great importance from theoretical and experimental standpoints. From various experimental work it is concluded that diffusion at low temperatures at polycrystalline materials take place near grain boundary. Experimental and theoretical results also indicate changes of up to 70 percent in physical properties near a grain boundary. The Embedded Atom Method (EAM) calculations on structural properties of Au twist grain boundaries are in quite good agreement with their experimental counterparts. The EAM is believed to predict reliable values for the single vacancy formation energy as well as migration energy. However, it is not clear whether the EAM functions which are fitted to the bulk properties of a perfect crystalline solid can produce reliable results on grain boundaries. One of the objectives of this work is to construct the EAM functions for Cu and use them in conjunction with the molecular static simulation to study structures and energetics of atoms near twist grain boundaries in Cu. This provides tests of the EAM functions near a grain boundary. In particular, we determine structure, single vacancy formation energy, migration energy, single vacancy activation energy, and interlayer spacing as a function of distance from grain boundary. Our results are compared with the available experimental and theoretical results from grain boundaries and bulk.

Karimi, Majid↗
DOE OSTI2025

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗
NASA NTRS1992

Dynamically hot galaxies. I - Structural properties

Results are reported from an analysis of the structural properties of dynamically hot galaxies which combines central velocity dispersion, effective surface brightness, and effective radius into a new 3-space (k), in which the axes are parameters that are physically meaningful. Hot galaxies are found to divide into groups in k-space that closely parallel conventional morphological classifications, namely, luminous ellipticals, compacts, bulges, bright dwarfs, and dwarf spheroidals. A major sequence is defined by luminous ellipticals, bulges, and most compacts, which together constitute a smooth continuum in k-space. Several properties vary smoothly with mass along this continuum, including bulge-to-disk ratio, radio properties, rotation, degree of velocity anisotropy, and 'unrelaxed'. A second major sequence is comprised of dwarf ellipticals and dwarf spheroidals. It is suggested that mass loss is a major factor in hot dwarf galaxies, but the dwarf sequence cannot be simply a mass-loss sequence, as it has the wrong direction in k-space.

Bender, Ralf↗
DOE OSTI2024

Structure–property relations of sodium iron phosphate nuclear waste glasses: Effects of iron redox ratio and glass composition

Iron phosphate glasses, known for their exceptional chemical durability and potential applicability in nuclear waste management, have gained significant attention over the years. The structures of these glasses are complicated by the coexistence of Fe 3+ and Fe 2+ , which plays a crucial role in determining their structures and properties. Here, this work uses molecular dynamics simulations to study the structural changes in Na 2 O–Fe 2 O 3 –P 2 O 5 glasses with varying glass composition and Fe 2+ /Fe 3+ redox ratio. It was found that the redox ratio and modifier contents significantly affected the short-range and medium-range orders in the glasses. Significant changes in the local environments around P 5+ and Fe 3+ were observed, as reflected by the bond distances and coordination numbers. Na + cations are found to preferentially associate with Fe 3+ (rather than Fe 2+ ), whereas Fe 2+ has stronger association with P 5+ than Na + , confirming the structural role of Fe 2+ as a glass modifier. The disruptions in P–O–P linkages upon increasing FeO suggest that FeO causes glass depolymerization. These glasses achieved higher connectivity with increasing Fe 3+ / (Fe 3+ + Fe 2+ ) ratios, conerting phosphorous Q 2 to Q 3 units and iron Q 5 units to Q 4 units. The decrease of nonbridging oxygen fractions with increasing Fe 3+ / (Fe 3+ + Fe 2+ ) ratios, through creating P–O–Fe linkages, is the main reason of enhanced network connectivity. Quantitative structure–property relationship analyses with different structural descriptors were used to correlate with measured properties. The analyses provided valuable insights into structure–property relationships, emphasizing the importance of choosing relevant energy parameters and defining glass network connectivity, particularly in F net descriptors. It was found the Fe–O–P linkage density exhibits strong correlations to measured dissolution rates, supporting the importance of these linkages in improving the chemical durability in iron phosphate glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Correlation effects on coupled electronic and structural properties of doped rare-earth trihydrides

Rare-earth trihydride (𝑅⁢H 3 ) compounds exhibit intriguing coupled electronic and structural properties as a function of doping, hydrogen vacancies, and thermodynamic conditions. Theoretical studies of these materials typically rely on density functional theory (DFT), including the use of small supercells that may underestimate strong correlation effects and structural distortions which in turn may influence their metallicity. Here, we elucidate the roles of lattice distortions and correlation effects on the electronic properties of pristine and doped 𝑅⁢H 3 compounds by adopting DFT +U and quantum Monte Carlo (QMC) methods. Linear-response constrained DFT (LR-cDFT) methods find Hubbard U ≈ 2 eV for 𝑅 𝑑 orbitals and U ≈ 6 eV for H 𝑠 ⁡/N 𝑝 /O 𝑝 orbitals. The small U on Lu 𝑑 orbitals is consistent with QMC calculations on LuH 3 and LuH 2.875 ⁢N 0.125 . In pure face-centered-cubic (FCC) 𝑅⁢H 3 (𝑅 = Lu,Y) compounds, neither DFT nor DFT +U with the self-consistently determined U is enough to create a band gap, however a supercell with hydrogen distortions creates a small gap whose magnitude increases when performing DFT +U with self-consistently determined U values. Furthermore, correlation effects, in turn, have a moderate influence on the coupled structural and electronic properties of doped 𝑅⁢H 3 compounds and may be important when considering the competition between structural distortions and superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1980

Structural properties of superplastically formed/diffusion-bonded orthogonally corrugated core sandwich plates

This paper describes a new superplastically formed/diffusion-bonded (SPF/DB) orthogonally corrugated sandwich structure, and presents formulae and the associated plots for evaluating the effective elastic constants for the core of this new sandwich structure. Comparison of structural properties of this new sandwich structure with the conventional honeycomb core sandwich structure was made under the condition of equal sandwich density. It was found that the SPF/DB orthogonally corrugated sandwich core has higher transverse shear stiffness than the conventional honeycomb sandwich core. However, the former has lower stiffness in the sandwich core thickness direction than the latter.

Ko, W. L.↗