An inverse analysis to estimate the thermal properties of nanoporous aerogel composites using the particle swarm optimized deep neural network-Reference-Cited by-同舟云学术

An inverse analysis to estimate the thermal properties of nanoporous aerogel composites using the particle swarm optimized deep neural network

Published:2023-06-16 Issue:6 Volume:84 Page:667-688
ISSN:1040-7790
Container-title:Numerical Heat Transfer, Part B: Fundamentals
language:en
Short-container-title:Numerical Heat Transfer, Part B: Fundamentals

Author:

Dai Jia-Peng¹,Cao Zhan-Wei¹²,Du Shen¹,Li Dong¹,He Ya-Ling¹

Affiliation:

1. Key Laboratory of Thermo-Fluid Science and Engineering of Ministry of Education, School of Energy and Power Engineering, Xi’an Jiaotong University, Shaanxi, P.R. China

2. Science and Technology on Space Physics Laboratory, China Academy of Launch Vehicle Technology, Beijing, P.R. China

Funder

National Key R&D Program of China

Basic Science Center Program for Ordered Energy Conversion of the National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Computer Science Applications,Mechanics of Materials,Condensed Matter Physics,Modeling and Simulation,Numerical Analysis

Link

https://www.tandfonline.com/doi/pdf/10.1080/10407790.2023.2217340

Reference64 articles.

1. An overview on silica aerogels synthesis and different mechanical reinforcing strategies

2. Engineering thermal and mechanical properties of flexible fiber-reinforced aerogel composites

3. Theoretical modeling of carbon content to minimize heat transfer in silica aerogel

4. Synthesis and characterization of ambient-dried microglass fibers/silica aerogel nanocomposites with low thermal conductivity

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