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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Control of Shell Morphology in p-n Heterostructured Water-Processable Semiconductor Colloids: Toward Extremely Efficient Charge Separation

This article describes p-n heterostructured water-borne semiconductor naonoparticles (NPs) with unique surface structures via control of shell morphology. The shell particles, comprising PC60-PC61BM composite, having n-type semiconductor characteristic, notably influence the charge carrier behavior in the core-shell NPs. A one- or two-phase methodology based on a PC60 surfactant-water phase and PC61BM n-type semiconductor-organic phase provided highly specific control over the shell structure of the NPs, which promoted their superior charge separation ability when combined with poly-3-hexyl-thiophene (P3HT). Moreover, the resulting water-borne NPs exhibit shell morphology-dependent carrier quenching and stability, which was characterized via luminescence studies paired with structural analysis. Corresponding to the results, outstanding performances of photovoltaic cells with over 5 % efficiency were achieved. The results suggest that the surrounding shell environments, such as the shell structure, and its electronic charge density, were crucial in determining the overall activity of the core-shell p-n heterostructured NPs. Thus, this work provides a new protocol in the current fields of water-based organic semiconductor colloids.

charge separation↗
DOE OSTI2022

Oxidation Resistance in 1200°C Steam of a FeCrAl Alloy Fabricated by Three Metallurgical Processes

Abstract FeCrAl alloys are a leading candidate material for accident tolerant fuel cladding due to their good performance in both normal light-water reactor operating conditions as well as their resilience to high-temperature accident scenarios. For commercial-scale production, new fabrication techniques need to be investigated. In this study, the effects of fabrication methods on the high-temperature steam oxidation performance of C26M (Fe12Cr6Al2Mo in wt.%) were investigated. Three variants of C26M were manufactured: wrought (cast and forged) (WC26M), powder metallurgy hot isostatic pressing (PMC26M), and laser powder bed fusion additive manufacturing (AMC26M). All three variants were exposed to steam at 1200°C for 2 h. Results showed no significant variation in mass change between the variants after steam exposure. All three variants effectively formed stable protective alumina films with ~0.6–1.3 µ m thickness. This study suggests FeCrAl alloys have excellent resilience to high-temperature steam in nuclear reactor accident scenarios regardless of the fabrication method.

Materials Science↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
DOE OSTI2024

TRACER Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed the first ARM Mobile Facility (AMF1) to Houston, Texas for the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign. The TRACER campaign was conducted from October 1, 2021 to September 30, 2022, with an intensive operational period (IOP) from June 1 to September 30, 2022. To investigate the life cycles of convective cells in the polluted and humid urban environment of Houston, ARM deployed cloud and precipitation radars, including the C-band Scanning ARM Precipitation Radar (CSAPR2), the X/Ka-band Scanning ARM Cloud Radar (SACR), and the Ka-band ARM Zenith cloud Radar (KAZR), as shown in Figure 1. This report presents an analysis of radar data quality, hardware calibrations, and radar data corrections.

54 ENVIRONMENTAL SCIENCES↗