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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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Topics addressed include: composite plates; buckling predictions; missile launch tube modeling; structural/control systems design; optimization of nonlinear R/C frames; error analysis for semi-analytic displacement; crack acoustic emission; and structural dynamics.
Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.
We present evidence of inverse Hall-Petch behavior for a single-phase high entropy alloy (CoCrFeMnNi) in ultra-high vacuum and show that it is associated with low friction coefficients (~0.3). Grain size measurements by STEM validate a recently proposed dynamic amorphization model that accurately predicts grain size-dependent shear strength in the inverse Hall-Petch regime. Wear rates in the initially soft (coarse grained) material were shown to be remarkably low (~10 –6 mm 3 /N-m), the lowest for any HEA tested in an inert environment where oxidation and the formation of mixed metal-oxide films is mitigated. The combined high wear resistance and low friction are linked to the formation of an ultra-nanocrystalline near-surface layer. The dynamic amorphization model was also used to predict an average high angle grain boundary energy (0.87 J/m 2 ). This value was used to explain cavitation-induced nanoporosity found in the highly deformed surface layer, a phenomenon that has been linked to superplasticity.
New experimental findings on semiconductors with the relatively complex 32 atom unit cell skutterudite crystal structure show that these materials possess attractive transport properties and have a good potential for achieving ZT values larger than for state-of- the-art thermoelectric materials. An overview of recent results is provided, and current approaches to experimentally achieving high ZT in skutterudite materials are discussed.
A plethora of two-dimensional (2D) materials have been introduced in physics and engineering in the past two decades. Their robust, membranelike sheets permit (mostly require) deposition, giving rise to solid-solid dry interfaces whose mobility, pinning, and general tribological properties under shear stress are currently being understood and controlled, both experimentally and theoretically. Here, in this Colloquium simulated case studies of twisted graphene systems are used as a prototype workhorse tool to demonstrate and discuss the general picture of 2D material interface sliding. First highlighted is the crucial mechanical difference, often overlooked, between small and large incommensurabilities, which corresponds to, for example, small and large twist angles in graphene interfaces. In both cases, focusing on flat, structurally lubric or “superlubric” geometries, the generally separate scalings with the area of static friction in pinned states and of kinetic friction during sliding are elucidated and reviewed, tangled as they are with the effects of velocity, temperature, load, and defects. The roles of island boundaries and elasticity are also discussed, and compared when possible to results in the literature for systems other than graphene. It is proposed that the resulting picture of pinning and sliding should be applicable to interfaces in generic 2D materials that are of importance for the physics and technology of existing and future bilayer and multilayer systems.
An overview of the Materials and Structures Integrated Technology Plan (ITP) is presented in outline and graphic form. The base R&T funding and the Civil Space Program work breakdown structure is given. Current programs, state-of-the-art, and technology needs are described for each of several research areas including space materials, space structures and structural dynamics, aerothermal materials and structures, computational materials science and chemistry, power and propulsion materials, space environmental effects, thermal protection systems, and space debris.
Evaluation of fibrous materials in cryogenic structural polyurethane adhesive used for bonding Saturn S-IVB tank assembly attachments
Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn 2 As 2 , a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.
Porous structures for ion engine application, discussing ionizer materials preparation, based on powder metallurgy and two-phase tungsten base alloy
Porous structures for ion engine application, discussing ionizer materials preparation, based on powder metallurgy and two-phase tungsten base alloy
This dissertation demonstrates the first of its kind distributed magnetic field sensor based on a fiber optic distributed acoustic sensing (DAS) scheme. Ferromagnetic nickel and Metglas® were dispersed internally within a fiber optic preform and then drawn on an in-house fiber optic draw tower to lengths in the kilometers. Due to the close proximity of the ferromagnetic metals and fiber optic core, the magnetostrictive strain response of the ferromagnetic materials when exposed to a magnetic field would perturbate within the fiber cladding and transfer that strain, internally, to the fiber optic core. Strain resulting from the magnetostrictive effect allows the DAS based sensor to accurately translate strain into readable magnetic field data. Due to the high sensitivity seen in this sensor design, multiparameter sources, acoustic and magnetic fields, were tested and validated and a three dimensional magnetic-field vector sensor was proposed. Numerical analysis of the novel sensor design was first implemented using COMSOL Multiphysics, where inputs such as magnetostrictive element shape, size, distance, and number were first investigated. Upon optimizing system constraints, the sensor design was further modified such that single mode operation was consistent across multiple fiber draws while retaining high strain transfer from the ferromagnetic elements to the fiber optic core. Ferromagnetic material selection was evaluated as a function of the saturation magnetostriction constants and a total of 4 modules were used to fully characterize the complex physics involved in this sensor design. All fabrication and testing were performed in-house using a full scale 3-story fiber draw tower and custom environmental testing stations to imitate naturally occurring events such as magnetic or acoustic point sources. A unique stacking method was used to embed ferromagnetic nickel and Metglas® into a fiber optic preform which when combined with a custom fiber draw process resulted in consistent multi-material fibers drawn to lengths of 1-km. In-house testing facilities included different types of electromagnetic generators, in addition to a soil test bed, and an outdoor test bed which allowed 100 meters of fiber to be tested simultaneously. All tested sensors demonstrated high strain transfer capabilities on the order of 0.01-10 μϵ depending on the materials used, ferromagnetic rod number, and core to metal spacing. Due to the sensitivity of the system the difference between AC and DC was distinct, and directional magnetostriction was studied. Transverse and longitudinal magnetic wave propagation was controlled through a solenoid and rectangular Helmholtz coil, both built in-house. A three-dimensional magnetic field vector sensor was proposed due to the success of the magnetic field sensor, and a design was proposed and initially tested to validate direction as a function of field strength and distance. To summarize, this dissertation explores the first fully distributed magnetic field sensor using DAS based techniques and one of the first multi-material fiber draw processes which can produce consistent single mode fiber up to 1-km. Due to extensive FEA modeling, multiple iterations of the magnetic sensor were fully characterized and an equation describing the relationship between sensor design and strain transfer has been created and validated experimentally. Multi-parameter tests including acoustic and magnetic fields were implemented and an algorithm was developed to separate the mixed signals. Finally, a test was performed to demonstrate the feasibility of sensing magnetic fields directionally. Cumulative results demonstrate a high-quality sensor alternative to current designs which may surpass other magnetic sensors due to innate multi-parameter capabilities, in addition to the inexpensive production cost and extremely long operating lengths.
Small-angle scattering techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov–Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions—two representative soft matter systems—we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Here, our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.
Single wall carbon nanotube (SWCNT)-based materials represent the future aerospace vehicle construction material of choice based primarily on predicted strength-to-weight advantages and inherent multifunctionality. The multifunctionality of SWCNTs arises from the ability of the nanotubes to be either metallic or semi-conducting based on their chirality. Furthermore, simply changing the environment around a SWCNT can change its conducting behavior. This phenomenon is being exploited to create sensors capable of measuring several parameters related to vehicle structural health (i.e. strain, pressure, temperature, etc.) The structural health monitor is constructed using conventional electron-beam lithographic and photolithographic techniques to place specific electrode patterns on a surface. SWCNTs are then deposited between the electrodes using a dielectrophoretic alignment technique. Prototypes have been constructed on both silicon and polyimide substrates, demonstrating that surface-mountable and multifunctional devices based on SWCNTs can be realized.
United States civil space program administered by National Aeronautics and Space Administration (NASA) has a new strategic direction to explore the solar system. This new 'vision for space exploration' encompasses a broad range of human and robotic missions, including the Moon, Mars and destinations beyond. These missions require advanced systems and capabilities that will accelerate the development of many critical technologies, including advanced materials and structural concepts. Specifically, it is planned to develop high-peformance materials for vehicle structures, propulsion systems, and space suits; structural concepts for modular assembly for space infrastructure; lightweight deployable and inflatable structures for large space systems and crew habitats; and highly integrated structural systems and advanced thermal management systems for reducing launch mass and volume. This paper will present several materials challenges in advanced space systems-high performance structural and thermal materials, space durable materials, radiation protection materials, and nano- structural materials. The paper will also address smart materials and structures and examine space environmental effects on materials and methods of mitigating them. Finally, the paper will take a look at the possibility of utilizing materials in situ, i.e., processing and using desired materials on the surface of the Moon and Mars.
Composite materials have been considered for many years as the major advance in the construction of energy efficient aerospace structures. Notable advances have been made in understanding the special design considerations that set composites apart from the usual "isotropic" engineering materials such as the metals. As a result, a number of significant engineering designs have been accomplished. However, one shortcoming of the currently favored composites is their relatively unforgiving behavior with respect to failure (brittleness) under seemingly mild impact conditions and large efforts are underway to rectify that situation, much along the lines of introducing thermoplastic matrix materials. Because of their relatively more pronounced (thermo) viscoelastic behavior these materials respond with "toughness" in fracture situations. From the point of view of applications requiring material strength, this property is highly desirable. This feature impacts several important and distinct engineering problems which have been' considered under this grant and cover the 1) effect of impact damage on structural (buckling) stability of composite panels, the 2) effect of time dependence on the progression of buckling instabilities, and the 3) evolution of damage and fracture at generic thickness discontinuities in structures. The latter topic has serious implications for structural stability problems (buckling failure in reinforced shell structures) as well as failure progression in stringer-reinforced shell structures. This grant has dealt with these issues. Polymer "toughness" is usually associated with uncrosslinked or thermo-plastic polymers. But, by comparison with their thermoset counterparts they tend to exhibit more pronounced time dependent material behavior; also, that time dependence can occur at lower temperatures which places restriction in the high temperature use of these "newer and tougher" materials that are not quite so serious with the thermoset matrix materials. From a structural point of view the implications of this material behavior are potentially severe in that structural failure characteristics are no longer readily observed in short term qualification tests so characteristic for aerospace structures built from typical engineering metals.
The Light Thermal Structures Center at the University of Virginia sponsors educational and research programs focused on the development of reliable, lightweight structures to function in hostile thermal environments. Technology advances in materials and design methodology for light thermal structures will contribute to improved space vehicle design concepts with attendant weight savings. This paper highlights current research activities in three areas relevant to space exploration: low density, high temperature aluminum alloys, composite materials, and structures with thermal gradients. Advances in the development of new aluminum-lithium alloys and mechanically alloyed aluminum alloys are described. Material properties and design features of advanced composites are highlighted. Research studies in thermal structures with temperature gradients include inelastic panel buckling and thermally induced unstable oscillations. Current and future research is focused on the integration of new materials with applications to structural components with thermal gradients.
ComDMFT is a parallel computational package designed to study the electronic structure of correlated quantum materials from first principles. Our approach is based on the combination of first-principles methods and dynamical mean field theories. In version 2.0, we implemented fully-diagrammatic GW+EDMFT from first-principles self-consistently. In this approach, correlated electrons are treated within full GW+EDMFT and the rest are treated within full-GW, seamlessly. Further, this implementation enables the electronic structure calculation of quantum materials with weak, intermediate, and strong electron correlation without prior knowledge of the degree of electron correlation.
Additional information was added to a growing data base from which estimates of finite element model complexities can be made with respect to thermal stress analysis. The manner in which temperatures were smeared to the finite element grid points was examined from the point of view of the impact on thermal stress calculations. The general comparison of calculated and measured thermal stresses is guite good and there is little doubt that the finite element approach provided by NASTRAN results in correct thermal stress calculations. Discrepancies did exist between measured and calculated values in the skin and the skin/frame junctures. The problems with predicting skin thermal stress were attributed to inadequate temperature inputs to the structural model rather than modeling insufficiencies. The discrepancies occurring at the skin/frame juncture were most likely due to insufficient modeling elements rather than temperature problems.