| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Thor, Nathalie
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2024Exceptional Hardness and Thermal Properties of SiC/(Hf,Ta)C(N)/(B)C Ceramic Composites Derived from Single‐Source Precursor
- 2024Microstructure Characterization and Mechanical Properties of Polymer‐Derived (HfₓTa₁₋ₓ)C/SiC Ceramic Prepared upon Field‐Assisted Sintering Technique/Spark Plasma Sintering
- 2024Thermal Conductivity Analysis of Polymer‐Derived Nanocomposite via Image‐Based Structure Reconstruction, Computational Homogenization, and Machine Learning
- 2024Microstructure Characterization and Mechanical Properties of Polymer‐Derived (Hf<sub><i>x</i></sub>Ta<sub>1−<i>x</i></sub>)C/SiC Ceramic Prepared upon Field‐Assisted Sintering Technique/Spark Plasma Sinteringcitations
- 2024Oxidation Resistance and Microstructural Analysis of Polymer‐Derived (HfₓTa₁₋ₓ)C/SiC Ceramic Nanocomposites
- 2023Microstructural evolution of novel Si(M)(BC)N polymer-derived ceramics upon different heat treatments
- 2022Microstructural evolution of Si(HfₓTa₁₋ₓ)(C)N polymer-derived ceramics upon high-temperature annealcitations
Places of action
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article
Thermal Conductivity Analysis of Polymer‐Derived Nanocomposite via Image‐Based Structure Reconstruction, Computational Homogenization, and Machine Learning
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 (Hf₀.₇Ta₀.₃)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.