JOURNAL ARTICLE

Effective thermal conductivity of fiberglass insulation.

  • Published In: International Journal of Applied Glass Science, 2024, v. 15, n. 3. P. 307 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Choudhary, Manoj K.; Eastes, Walter 3 of 3

Abstract

Globally, the operational energy usage in buildings accounts for about 30% of the final energy consumption and 26% of the energy‐related emissions. In 2022, the building sector recorded 132 EJ in energy usage and 9.8 Gt of CO2 emissions. Energy‐intensive space heating and air conditioning play a significant role in these statistics, with slightly over half of US home energy usage attributed to them. Thus, energy‐efficient buildings, incorporating effective thermal insulation, are essential for addressing climate change. Fiberglass is the dominant insulation material used in US homes, comprising about 71% of installations. The paper discusses the fundamental aspects of heat transfer in fibrous insulation in general and fiberglass insulation in particular. The thermal performance of a fibrous insulation is characterized by an effective thermal conductivity, which combines conductive and radiative terms. The former represents heat conduction through the gas–fiber network and the latter the absorption and the scattering of thermal radiation by the fibers. The paper describes mathematical formulations for these terms and presents results showing the dependence of the effective conductivity on insulation density, fiber diameter, and temperature. The predicted values of the effective conductivity are found to be in good agreement with the measured ones. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Applied Glass Science. 2024/07, Vol. 15, Issue 3, p307
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:2041-1286
  • DOI:10.1111/ijag.16652
  • Accession Number:177626172
  • Copyright Statement:Copyright of International Journal of Applied Glass Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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