Tensor Methods A Unified and Interpretable Approach for Material Design
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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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Poster for Techconnect Conference
Spacecraft energy dissipation system design optimization, developing semigeometric solution technique
Spacecraft energy dissipation system design optimization, developing semigeometric solution technique
The objective of this educational exercise is to have students build and evaluate simple wing structures, and in doing so, learn about materials choices and lightweight construction methods. A list of equipment and supplies and the procedure for the experiment are presented.
The tunable nature of lasers provides for a wide range of applications. Most applications rely on finding available laser wavelengths to meet the needs of the research. This article presents the concept of compositional tuning, whereby the laser wavelength is designed by exploiting nonstoichiometry. For research where precise wavelengths are required, such as remote sensing, this is highly advantageous. A theoretical basis for the concept is presented and experimental results in spectroscopic measurements support the theoretical basis. Laser operation nicely demonstrates the validity of the concept of designer lasers.
PISCES, the Pacific International Space Center for Exploration Systems. This paper describes the PISCES development plans, particularly in the areas of In-Situ Resource Utilization, Robotics and Education and Outreach.
As the propulsion center of excellence for NASA, Marshall Space Flight Center has undertaken a systematic study of components and their effects on a set of limited critical properties.
No abstract available
This presentation provides a summary of NASA’s composite technology development for launch vehicle applications over the last decade. It provides an overview with assessment of composite analysis.
Invited presentation given at the Telluride meeting
Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.
Requirements exist for an extremely stable, high performance, all-weather tethered aerostat system. This requirement has been satisfied by a 250,000 cubic foot captive buoyant vehicle as demonstrated by over a year of successful field operations. This achievement required significant advancements in several technology areas including composite materials design, aerostatics and aerodynamics, structural design, electro-mechanical design, vehicle fabrication and mooring operations. This paper specifically addresses the materials and structural design aspects of pressurized buoyant vehicles as related to the general class of Lighter Than Air vehicles.
The design, fabrication, and subsequent testing of four power slip rings for synchronous orbit application are described. The synergistic effects of contact materials and slip ring-brush design are studied by means of frequent and simultaneous recording of friction, wear, and electrical noise. Data generated during the test period are presented along with post test analysis data.
Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Additionally, our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.
There is a strong need for developing multifunctional materials to reduce the cost of applied material without compromising the performance of the detectors, devices and sensors. The materials design, processing, growth and fabrication of bulk and nanocrystals and fabrication into devices and sensors involve huge cost and resources including a multidisciplinary team of experts. Because of this reason, prediction of multifunctionality of materials before design and development should be evaluated. Chalcogenides and chalcopyrites are a very exciting class of materials for developing multifunctionality. Materials such as Gallium selenide GaSe and zinc selenide ZnSe have been proven to be excellent examples. GaSe is a layered material and very difficult to grow in large crystal. However, it's ternary and quaternary analogs such as thallium gallium selenide TlGaSe2, thallium gallium selenide sulfide TlGaSe2-xSs, thallium arsenic selenide Tl3AsSe3, silver gallium selenide AgGaGe3Se8, AgGaGe5Se12 and several others have shown great promise for multifunctionality. Several of these materials have shown good efficiency for frequency conversion (nonlinear optical NLO), electro-optic modulation, and acousto-optic tunable filters and imagers suitable for the visible, near-infrared wavelength, mid wave infrared (MWIR), long wave infrared (LWIR) and even up to Tera hertz wavelength (THW) regions. In addition, this class of materials have demonstrated low absorption coefficients and power handling capability in the systems. Also, these crystals do not require post growth annealing, show very large transparency range and fabricability.