Fathidoost, Mozhdeh ; Yang, Yangyiwei ; Thor, Nathalie ; Bernauer, Jan ; Pundt, Astrid ; Riedel, Ralf ; Xu, Bai-Xiang (2024)
Thermal conductivity analysis of polymer‐derived nanocomposite via image‐based structure reconstruction, computational homogenization, and machine learning.
In: Advanced Engineering Materials, 26 (17)
doi: 10.1002/adem.202302021
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Macroscopic thermal properties of engineered or inherent composites depend substantially on the composite structure and the interface characteristics. While it is acknowledged that unveiling such dependency relation is essential for materials design, the complexity involved in, e.g., microstructure representation and limited data impedes the research progress. Herein, this issue is tackled by machine learning techniques on image-based microstructure and property data predicted from physics simulations, along with experimental validation. The methodology is demonstrated for the model system (Hf₀.₇ Ta₀.₃) C/SiC ultrahigh-temperature ceramic nanocomposite. The structure is reconstructed from scanning electron microscope images, and is resolved by a diffuse-interface representation, which is advantageous in handling complicated structure and interface properties. Subsequently, hierarchical finite element homogenization is carried out to evaluate the effective thermal conductivity. A thorough comparison between the computed results and experimentally measured data, conducted across diverse temperatures and varying interface thermal resistances, reveals a high level of agreement. The observed agreement allows for the inverse estimation of the interface thermal resistance, a parameter typically challenging to ascertain directly through experimental means. Utilizing comprehensive data, a machine learning surrogate model has been meticulously trained to accurately predict the effective thermal conductivity of composite structures with exceptional performance.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Fathidoost, Mozhdeh ; Yang, Yangyiwei ; Thor, Nathalie ; Bernauer, Jan ; Pundt, Astrid ; Riedel, Ralf ; Xu, Bai-Xiang |
| Art des Eintrags: | Bibliographie |
| Titel: | Thermal conductivity analysis of polymer‐derived nanocomposite via image‐based structure reconstruction, computational homogenization, and machine learning |
| Sprache: | Englisch |
| Publikationsjahr: | 1 März 2024 |
| Ort: | Weinheim |
| Verlag: | Wiley-VCH |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Advanced Engineering Materials |
| Jahrgang/Volume einer Zeitschrift: | 26 |
| (Heft-)Nummer: | 17 |
| Kollation: | 11 Seiten |
| DOI: | 10.1002/adem.202302021 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Macroscopic thermal properties of engineered or inherent composites depend substantially on the composite structure and the interface characteristics. While it is acknowledged that unveiling such dependency relation is essential for materials design, the complexity involved in, e.g., microstructure representation and limited data impedes the research progress. Herein, this issue is tackled by machine learning techniques on image-based microstructure and property data predicted from physics simulations, along with experimental validation. The methodology is demonstrated for the model system (Hf₀.₇ Ta₀.₃) C/SiC ultrahigh-temperature ceramic nanocomposite. The structure is reconstructed from scanning electron microscope images, and is resolved by a diffuse-interface representation, which is advantageous in handling complicated structure and interface properties. Subsequently, hierarchical finite element homogenization is carried out to evaluate the effective thermal conductivity. A thorough comparison between the computed results and experimentally measured data, conducted across diverse temperatures and varying interface thermal resistances, reveals a high level of agreement. The observed agreement allows for the inverse estimation of the interface thermal resistance, a parameter typically challenging to ascertain directly through experimental means. Utilizing comprehensive data, a machine learning surrogate model has been meticulously trained to accurately predict the effective thermal conductivity of composite structures with exceptional performance. |
| Freie Schlagworte: | computational thermal homogenization, machine learning, polymer-derived ceramics, two-point statistics |
| ID-Nummer: | Artikel-ID: 2302021 |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Disperse Feststoffe 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Mechanik Funktionaler Materialien Zentrale Einrichtungen Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner |
| Hinterlegungsdatum: | 15 Mär 2024 08:25 |
| Letzte Änderung: | 19 Nov 2024 06:54 |
| PPN: | 516332597 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Thermal Conductivity Analysis of Polymer‐Derived Nanocomposite via Image‐Based Structure Reconstruction, Computational Homogenization, and Machine Learning. (deposited 18 Nov 2024 12:16)
- Thermal conductivity analysis of polymer‐derived nanocomposite via image‐based structure reconstruction, computational homogenization, and machine learning. (deposited 15 Mär 2024 08:25) [Gegenwärtig angezeigt]
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