journal article·2025-02

Design Metrics for Highly Reflective Fibrous Insulation

Georgios Koutsakis, Nikhil Deliwala, David R. Clarke

Advanced Materials Technologies· Volume 10· Issue 3
Publisher page

Abstract

Abstract The high melting temperatures, large optical bandgaps, and large refractive indices of oxides favor their use as fibrous insulation at extreme temperatures where radiative heat transfer is significant. In this work, the reflectance of mats of fibers is modeled with the Monte Carlo (MC) method using the fiber optical properties (such as the scattering efficiency and scattering coefficient), the fiber radius, refractive index, volume fraction ( f v ), as well as the thickness of insulation ( L ). Two key metrics are identified for the design of highly reflective insulation. One is a novel metric, the Kuhn scattering length, which is based on an analogy between the anisotropic random walks in MC radiative transfer and polymer physics. It is used to determine approximate the effective thickness of insulation ( f v L ). The second is a size metric, which indicates that fiber mats are most reflective when the fiber radius is . The model is validated on experimental measurements of commercially available insulation consisting of fibers with 3–5 µm radius.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Aerogels and thermal insulation · Advanced Manufacturing and Logistics Optimization · Advanced Cellulose Research Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical Engineering University of New Mexico Albuquerque NM 87131 USA

School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA

Funding

U.S. Air Force

References (18)

Show source-provided references
1. 10.2514/3.81110.2514/3.811 ↗
2. 10.1177/10971963870110010310.1177/109719638701100103 ↗
3. 10.1111/ijac.1404710.1111/ijac.14047 ↗
4. Bohren C. F. · 1983
5. 10.1093/mnras/148.1.9310.1093/mnras/148.1.93 ↗
6. Hulst H. C. · 1981
7. 10.1201/978042932730810.1201/9780429327308 ↗
8. Modest M. F. · 2021
9. Monte carlo methods in practice https://www.scratchapixel.com/lessons/mathematics‐physics‐for‐computer‐graphics/monte‐carlo‐methods‐in‐practice/monte‐carlo‐simulation.html.
10. 10.1073/pnas.201555111810.1073/pnas.2015551118 ↗
11. 10.1016/j.optcom.2017.01.00310.1016/j.optcom.2017.01.003 ↗
12. 10.2514/1.T493710.2514/1.T4937 ↗
13. 10.1364/AO.21.00297810.1364/AO.21.002978 ↗
14. 10.1364/AO.51.00678910.1364/AO.51.006789 ↗
15. 10.1007/BF0239664110.1007/BF02396641 ↗
16. 10.2514/3.59310.2514/3.593 ↗
17. 10.1142/760910.1142/7609 ↗
18. Tokmakoff A. · 2018