A new approach for heat flux estimation in composite materials
An important process for enhancing the productivity of different applications is Heat Flux (HF) distribution estimation. Accurate HF estimation led to the importance of effective thermal management in different applications. However, none of the prevailing research works concentrated on estimating t...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025004505 |
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| Summary: | An important process for enhancing the productivity of different applications is Heat Flux (HF) distribution estimation. Accurate HF estimation led to the importance of effective thermal management in different applications. However, none of the prevailing research works concentrated on estimating the HF for different composite materials, which led to the production of low-quality material during application. Thus, this paper proposes a novel HF distribution using the Finite Element Analysis (FEA) model and proposed classifier approach. For the analysis, the metal, reinforced concrete, and ceramic matrix composite materials are utilized. Initially, the boundary condition and mesh generation are carried out. Next, Computational Fluid Dynamics (CFD) modeling is processed. Then, with the help of Carslaw and Jaeger Contour Plot Construction (CJCPC), the values are visualized. Then, from the visualized plot, the peak values are extracted. Then, the HF distribution is estimated with the help of data generated from FEA. According to the material, the input data are clustered for the estimation. Now, the labeling process is carried out from the extracted features using the Fisher Membership Function-based Fuzzy Inference System (FMF-FIS), and the Entropy Production Rate (EPR) is estimated. Finally, the uniform and non-uniform HF distribution is classified. Thus, the similar composite material is clustered with a clustering time of 28734 ms, and the labeling of HF is done with a fuzzification time of 673 ms. Also, the HF distribution is estimated with a training time of 43922 ms and accuracy of 99.57 %, thus proving better performance than existing works. |
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| ISSN: | 2590-1230 |