Explainable machine learning techniques for hybrid nanofluids transport characteristics: an evaluation of shapley additive and local interpretable model-agnostic explanations

• 主要な著者: Kanti P.K., PrabhakarSharma, Wanatasanappan V.V., Said N.M.
• その他の著者: 57216493630
• フォーマット: 論文
• 出版事項: Springer Science and Business Media B.V. 2025
• 主題: Deep neural networks; Nanofluidics; Thermal conductivity of liquids; Extreme gradient boost; Graphene oxides; Hybrid nanofluid; Nanofluids; Neural-networks; Shapley; Shapley additive explanation; Thermal; Transport characteristics; Transport phenomenon; Titanium dioxide

Comprehending and managing the transport characteristics of nanofluids is critical for improving their efficacy in heat transfer applications, thereby improving thermal management systems. This research focuses on investigating the impact of varying concentrations (0.05?1 vol.%) and temperatures (30...