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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 217 records · Page 12

The Spectral Characteristics of Lunar Agglutinates: Visible-Near-Infrared Spectroscopy of Apollo Soil Separates

The lunar surface evolves over time due to space weathering, and the visible–near-infrared spectra of more mature (i.e., heavily weathered) soils are lower in reflectance and steeper in spectral slope (i.e., darker and redder) than their immature counterparts. These spectral changes have traditionally been attributed to the space-weathered rims of soil grains (and particularly nanophase iron therein). However, understudied thus far is the spectral role of agglutinates—the agglomerates of mineral and lithic fragments, nanophase iron, and glass that are formed by micrometeoroid impacts and are ubiquitous in mature lunar soils. We separated agglutinates and non-agglutinates from six lunar soils of varying maturity and composition, primarily from the 125–250 μm size fraction, and measured their visible–near-infrared reflectance spectra. For each soil, the agglutinate spectra are darker, redder, and have weaker absorption bands than the corresponding non-agglutinate and unsorted soil spectra. Moreover, greater soil maturity corresponds to darker agglutinate spectra with weaker absorption bands. These findings suggest that agglutinates (rather than solely the space-weathered rims) play an important role in both the darkening and reddening of mature soils—at least for the size fractions examined here. Comparisons with analog soils suggest that high nanophase iron abundance in agglutinates is likely responsible for their low reflectance and spectrally red slope. Additional studies of agglutinates are needed both to more comprehensively characterize their spectral properties (across size fractions and in mixing with non-agglutinates) and to assess the relative roles of agglutinates and rims in weathering-associated spectral changes. Plain Language Summary - In scientific study of the Moon, one key focus is surface processes: how do physical and chemical properties of the Moon’s surface change over time due to weathering (e.g., bombardment by micrometeoroids and by particles from the Sun)? Such investigations provide valuable insights into the Moon’s history (such as the ages of impact craters) that are often deduced from measurements of reflected light; as a soil is weathered it reflects light differently, which manifests visually as a progressive darkening of the soil. This phenomenon had primarily been attributed to weathering-associated development of rims on individual soil grains, but in this work we explored an alternative cause: soil particles known as agglutinates (misshapen, vesicular agglomerates of mineral fragments, iron, and glass that form due to weathering processes). We isolated agglutinates of six soil samples from the Moon and measured how they reflect light. We find that they reflect light in patterns reminiscent of how the Moon’s surface does when weathered. These findings suggest that agglutinates play a more important role than previously thought in determining the light-reflecting properties of the Moon’s surface, thus warranting greater and more nuanced consideration in future studies of how the Moon’s surface changes over time.

spectroscopy↗

Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.

14 SOLAR ENERGY↗

Solar Forecasting, Net Load Forecasting, and Data-Driven Distributed Solar Visibility Prizes (Final Technical Report)

The American-Made Solar Forecasting Prize, Net Load Forecasting Prize, and Data-Driven Distribution (3D) Solar Visibility Prize is a multimillion-dollar prize competition designed to energize U.S. solar innovation through a series of contests that accelerate the entrepreneurial process from years to months. The activities incentivized by these three prizes will support the governmentwide approach to increase American energy dominance by promoting innovation and early deployment of energy technologies, resulting in wider adoption, which is critical for secure, affordable, and reliable solar energy.

14 SOLAR ENERGY↗