Numerical Heat Transfer Part B Fundamentals, Год журнала: 2024, Номер unknown, С. 1 - 19

Опубликована: Июль 17, 2024

Nanotechnology has recently led to new possibilities for enhancing heat transfer in exchangers. The remarkable thermal characteristics of graphene oxide (GO)-based nanofluids with nanoscale additions have garnered significant attention particular. When and intricate flow patterns are involved, traditional analytical models frequently fail appropriately forecast the efficiency Analyzed phenomenon laminar a exchanger that swirling fluid dynamics, pressure drop, predicted condensation coefficient (HTC) LHT. functionalized radiated GO was chosen as nanomaterials present Pre-processing Data Methods managing outliers by machine learning (ML) models, like CLAHE algorithm. logarithmic mean temperature difference (LMTD) can be used determine driving power within exchanger. Dynamic Smagorinsky Model (DSLM) Wall-Adapting Local Eddy-viscosity (WALE) turbulence primarily designed capturing turbulent behavior flows. Kern technique Hagen–Poiseuille equation drop pumping needed shell tube through microtube based on Levenberg–Marquardt Momentum Algorithm predict Nusselt number prediction sensitivity HTC an LHT-trained ML model is evaluate performance. methods may effectively maximize performance setting nanofluid accuracy score 99%, demonstrating its exceptional predictive capabilities results great potential improve energy efficiency, save operating costs, advance sustainable practices various industrial applications.

Язык: Английский