Advanced hybrid modeling of alumina nanoparticle deposition patterns in heat exchangers with triangular tube models

This study meticulously explores the deposition dynamics of aluminum oxide nanoparticles in a triangular tube heat exchanger to enhance heat transfer efficiency and gas dynamics, crucial for mitigating deposition risks. By investigating various parameters such as nanoparticle diameters (10–100 nm), heat flux (1000–3000 W/m²), Reynolds numbers (308–925), mass fractions (0.5–2%), and geometry lengths (50–90 mm), the research provides a comprehensive understanding. Employing Python programming, the methodology integrates machine learning algorithms (RF and DNN) with Eulerian and Lagrange methods, achieving an impressive model accuracy of 84% with low errors. Key findings include the correlation between heightened heat flux and increased nanoparticle deposition, particularly at a 100 nm diameter, and the direct relationship between mass fraction and deposition, peaking at 2% mass fraction and a 100 nm diameter. The Reynolds number significantly influences deposition, peaking with lower Reynolds numbers and larger nanoparticle diameters, shedding light on critical aspects of deposition behavior in heat exchangers. Furthermore, the research identifies tube geometry and nanoparticle size as critical factors affecting deposition patterns.