Thermal image processing for feature extraction from encapsulated phase change materials

Brian Whinery; Yuri Gulak; Vedang Chauhan; Jingzhou Zhao; Jingru Benner; Feng Ye

doi:10.1117/12.2586979

4 January 2021 Thermal image processing for feature extraction from encapsulated phase change materials

Brian Whinery, Yuri Gulak, Vedang Chauhan, Jingzhou Zhao, Jingru Benner, Feng Ye

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116050K (2021) https://doi.org/10.1117/12.2586979
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Encapsulated inorganic particles with high melting points (>300°C) are desired as high-temperature Phase Change Materials (PCMs) for next-generation Latent Heat Thermal Energy Storage (LHTES) systems. One of the many challenges during the development of PCMs is to achieve a high throughput that in turn depends on accurately modeling the relation between process parameters and geometric and thermal properties of the PCMs particle. During the production of the PCMs, a high-speed infrared camera is used to acquire images of the encapsulated material under controlled illumination conditions. This research article focuses on the development of image processing techniques for both geometric and thermal feature extraction during the development of the PCMs. A user-friendly GUI has been designed in MATLAB and preliminary experimental results have demonstrated that the method is fast, accurate and reliable for a high throughput production. The extracted features will be used to develop Machine Learning (ML) models to predict the geometric and thermal properties of the PCM based on the process parameter settings. The ML model will accelerate the search for the optimized process settings to boost the throughput of the production.

Citation Download Citation

Brian Whinery, Yuri Gulak, Vedang Chauhan, Jingzhou Zhao, Jingru Benner, and Feng Ye "Thermal image processing for feature extraction from encapsulated phase change materials", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116050K (4 January 2021); https://doi.org/10.1117/12.2586979

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