A hybrid machine LEARNING-CFD method for the innovative analysis of Al2O3 nanoparticle deposition in shell-and-tubes heat exchangers

This study examines the intricate dynamics surrounding the deposition of Al₂O₃ nanoparticles within a heat exchanger, with the aim of optimizing heat transfer efficiency and gaining insights into gas dynamics. A comprehensive investigation of various parameters is conducted, including nanoparticle diameter ranging from 10 to 100 nm, heat flux variations from 500 to 3000 W/m², Reynolds numbers spanning from 308 to 1540, and mass fractions ranging from 0.5 to 8 %. The methodology integrates machine learning algorithms with Eulerian and Lagrange methods, leveraging Python programming to deepen the understanding of complex deposition processes. Through the integration of random forest algorithms and SHAP values, the study achieves a model accuracy of 96.74 %, supported by minimal mean absolute error (6E^-06) and root mean square error (2.5E^-03). Key findings reveal the profound impact of heat flux, particularly at 3000 W/m², on enhancing nanoparticle deposition. Furthermore, a direct correlation is observed between mass fraction and sedimentation, peaking at a mass fraction of 8 %. In laminar flow regimes, the Reynolds number profoundly influences sedimentation, with the sedimentation rate reaching its apex as the Reynolds number decreases. The diameter of nanoparticles also emerges as a crucial factor, with larger diameters correlating with increased sedimentation.