Image based in silico characterisation of the effective thermal properties of a graphite foam

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Abstract

Functional materials’ properties are influenced by microstructures which can be changed during manufacturing. A technique is presented which digitises graphite foam via X-ray tomography and converts it into image-based models to determine properties in silico. By simulating a laser flash analysis its effective thermal conductivity is predicted. Results show ∼1% error in the direction the foam was ‘grown’ during manufacturing but is significantly less accurate in plane due to effective thermal conductivity resulting from both the foam's microstructure and graphite's crystalline structure. An empirical relationship is found linking these by using a law of mixtures. A case study is presented demonstrating the technique's use to simulate a heat exchanger component containing graphite foam with micro-scale accuracy using literature material properties for solid graphite. Compared against conventional finite element modelling there is no requirement to firstly experimentally measure the foam's effective bulk properties. Additionally, improved local accuracy is achieved due to exact location of contact between the foam and other parts of the component. This capability will be of interest in design and manufacture of components using graphite materials. The software used was developed by the authors and is open source for others to undertake similar studies.

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Evans, L. M., Margetts, L., Lee, P. D., Butler, C. A. M., & Surrey, E. (2019). Image based in silico characterisation of the effective thermal properties of a graphite foam. Carbon, 143, 542–558. https://doi.org/10.1016/j.carbon.2018.10.031

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