Engineering Papers⌕ Search

DOE OSTI · 2422331

Modulation of apparent optical properties using arrayed mesoscale structures

Abstract

In this study, a method for using arrays of mesoscale structures to modify the apparent optical properties of an opaque composite surface has been theoretically demonstrated to both raise and lower the apparent emissivity as compared to the intrinsic properties of the constitutive materials. For design problems where thermomechanical and optical material properties are both of importance, mesoscale surface structuring can greatly expand the design space. Analysis via the net radiosity method herein illustrates the ability to achieve a wide range of spectral apparent optical properties. Notably, a hexagonal array of spheres on a planar surface can raise the apparent emissivity of a planar surface by 50%. Conversely, a hexagonal enclosure of reradiating surfaces, realized by thin adiabatic walls, can reduce the apparent emissivity of a blackbody by half. As this method of modifying apparent optical properties utilizes structures much larger than the wavelengths of interest, the relationship between intrinsic planar emissivity, geometry, and apparent emissivity can be computed semi-analytically at low computational expense. Passive solar cooling, thermophotovoltaic cells, aerodynamic surfaces exposed to intense heating, and solar absorbers are presented as case studies that could benefit from the use of mesoscale structures on opaque surfaces to modify the apparent optical properties.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Simeroth, David L., Kottke, Peter A., Kucuktas, Onur A., Fedorov, Andrei G.. 2022-06-06. Modulation of apparent optical properties using arrayed mesoscale structures. https://doi.org/10.1016/j.jqsrt.2022.108280

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗