Learning thermal radiative properties of porous media from engineered geometric features
نویسندگان
چکیده
Monte Carlo ray tracing (MCRT) simulations are the most reliable non-experimental means for predicting radiative properties of randomly packed structures or porous media, particularly in polydisperse heterogenous environments. These methods can approximate a void region filled with homogeneous solids by averaging across large pool independent random simulations. However, due to stringent precision requirements, high computational cost simulation setup and execution including processing, memory programming requirements parameter-dependent uncertainty MC simulations, required time these overwhelmingly obtaining accurate estimations. The goal this work is build models MCRT calculations using learning methods. This an extension previous work, building that more generalizable realistic: while was limited circular particles assumed knowledge generating parameters proposed based on engineered geometric features constructed from final configuration. Furthermore, ground truth current take approach configurations packing algorithm, rather than radiation distribution function identification method (pack-free). In addition, we study both uniform 2D media circles squares orientation. We also consider opaque transparent materials larger various refractive indices. focus optics dimensions regime which particle sizes compared light wavelength thus render estimation wave propagation properties. design synthetically generated database in-house algorithms computations. use combinations cross-validation data mixture techniques assure not overfit particular class configurations. As result, demonstrate estimate out-sample different generation schemes accuracy.
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ژورنال
عنوان ژورنال: International Journal of Heat and Mass Transfer
سال: 2021
ISSN: ['1879-2189', '0017-9310']
DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2021.121668