Three-dimensional (3D) tensor-based methodology for characterizing 3D anisotropic thermal conductivity tensor
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915199615762432 |
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| author | Wang, Dihui Ban, Heng Jiang, Puqing |
| author_facet | Wang, Dihui Ban, Heng Jiang, Puqing |
| contents | The increasing complexity of advanced materials with anisotropic thermal properties necessitates more generic and efficient methods to determine three-dimensional (3D) anisotropic thermal conductivity tensors with up to six independent components. Current methods rely on a vector-based framework that can handle only up to four independent components, often leading to inefficiencies and inaccuracies. We introduce Three-Dimensional Spatially Resolved Lock-In Micro-Thermography (3D SR-LIT), a novel optical thermal characterization technique combining a 3D tensor-based framework with an efficient area-detection experimental system. For simple tensors (e.g., x-cut quartz, k_xz=k_yz=0), our method reduces uncertainty by over 50% compared to vector-based methods. For complex tensors with six independent components (e.g., AT-cut quartz), 2σ uncertainties remain below 12% for all components. A novel adaptive mapping approach enables high-throughput data acquisition (40 seconds to 3 minutes, depending on tensor complexity), over 35 times faster than current methods, and accommodates samples with 200 nm surface roughness. Extensive numerical validation on 1,000 arbitrary anisotropic tensors ranging from 1 to 1,000 Wm^(-1) K^(-1) further validates the robustness of this methodology. This work highlights significant advancements in thermal characterization of complex anisotropic materials. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_07605 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Three-dimensional (3D) tensor-based methodology for characterizing 3D anisotropic thermal conductivity tensor Wang, Dihui Ban, Heng Jiang, Puqing Materials Science Data Analysis, Statistics and Probability The increasing complexity of advanced materials with anisotropic thermal properties necessitates more generic and efficient methods to determine three-dimensional (3D) anisotropic thermal conductivity tensors with up to six independent components. Current methods rely on a vector-based framework that can handle only up to four independent components, often leading to inefficiencies and inaccuracies. We introduce Three-Dimensional Spatially Resolved Lock-In Micro-Thermography (3D SR-LIT), a novel optical thermal characterization technique combining a 3D tensor-based framework with an efficient area-detection experimental system. For simple tensors (e.g., x-cut quartz, k_xz=k_yz=0), our method reduces uncertainty by over 50% compared to vector-based methods. For complex tensors with six independent components (e.g., AT-cut quartz), 2σ uncertainties remain below 12% for all components. A novel adaptive mapping approach enables high-throughput data acquisition (40 seconds to 3 minutes, depending on tensor complexity), over 35 times faster than current methods, and accommodates samples with 200 nm surface roughness. Extensive numerical validation on 1,000 arbitrary anisotropic tensors ranging from 1 to 1,000 Wm^(-1) K^(-1) further validates the robustness of this methodology. This work highlights significant advancements in thermal characterization of complex anisotropic materials. |
| title | Three-dimensional (3D) tensor-based methodology for characterizing 3D anisotropic thermal conductivity tensor |
| topic | Materials Science Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2501.07605 |