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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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CMF studies to date have been largely restricted to low-mass star-forming regions. The present study focuses on a HIGH MASS star forming region, ORION, but observes positions sufficiently far from KL that effects of previously-formed massive stars are not overwhelming
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A hierarchically mesoporous (HM) UiO-66-F 4 shell with ~8 nm mesopores can be iteratively grown on top of UiO-66 nanoparticle (NP) seeds over several cycles templated by Pluronic F-127 micelles. Presumably, the Pluronic micelles that were formed in water can surround UiO-66 NPs to facilitate the overgrowth of an HM-UiO-66-F 4 shell in the presence of Zr IV precursors and BDC-F 4 linkers. The UiO-66 NP seeds play an important role in directing the continuous growth of the HM-UiO-66-F 4 shell, as only a nonporous phase was obtained in their absence. Furthermore, this template-assisted, seed-mediated method can be extended to produce other UiO-66-X (X = (OH) 2 , (COOH) 2 , etc.) shells, demonstrating its generality for the UiO-66 family of metal–organic frameworks. Notably, the incorporation of mesopores into the thrice-overgrown UiO-66@HM|3rd-UiO-66-F 4 materials leads to impressive enhancements in the per-mass uptake capacity of Direct Blue 86, a large anionic dye: 320% better than the parent UiO-66 seeds and 150% better than that for a [UiO-66 + UiO-66-F 4 ] physical mixture at the same mass proportion. Similar enhancements were also observed in the catalyzed oxidation of thioanisole.
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This paper presents extensions to a penalty-free, parallel simulated annealing (SA) algorithm for multi-constrained combinatorial optimization with the aim of embedding multi-fidelity physics models into the annealing procedure. The method uses a low-fidelity, quickly executing model for rapid design space exploration and a high-fidelity model for detailed constraint resolution and on-the-fly bias correction. Machine learning models updated within the annealing procedure were used to bridge the gap between the multi-fidelity models, which led to accurate rapid exploration and efficient detailed constraint resolution. A software implementation of the new multi-fidelity optimization methods, called ML-PSA, was demonstrated on a continuous multi-fidelity optimization problem and a constrained combinatorial PWR lattice design problem. These problems demonstrate some of the features, parallel performance characteristics, and extensible nature of the multi-fidelity SA methods. This paper shows that the developed software and procedure are a general optimization tool that can be applied to a wide variety of scientific and engineering design optimization applications. (authors)
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Colloid-fuel, vortex chamber cavity in nuclear rocket engine