Akademik Veri Yönetim Sistemi
COMPUTERS & MATHEMATICS WITH APPLICATIONS, cilt.205, ss.195-211, 2026 (SCI-Expanded, Scopus)
This study introduces an integrated analytical-to-AI framework for modeling and optimizing entropy generation in pulsating non-Newtonian heat and fluid flow specifically within two basic geometries: circular pipe and plane duct geometries. A semi-analytical model, based on the second law of thermodynamics, is first developed using the perturbation method to evaluate entropy generation under fully developed laminar flow and constant heat flux conditions. The model is validated against existing benchmark solutions, confirming its accuracy. Using this model, a comprehensive dataset is created by varying key dimensionless numbers: Brinkman number (Br), power-law index (n), pulsation amplitude (epsilon), and frequency (F). Four machine learning (ML) models are then trained to predict entropy generation, among which Gaussian Process Regression (GPR) shows the highest accuracy and is selected as the surrogate ML model for optimization. It is performed using the Grey Wolf Optimization (GWO) algorithm under different flow scenarios. The results indicate that shear-thinning fluids, especially with high amplitude (epsilon = 0.3) and moderate-to-high frequency pulsation (F = 60.803 for the circular pipe and F = 53.843 for the plane duct), yield the lowest entropy generation (N-savg = 1.850 for the circular pipe and N-savg = 0.911 for the plane duct), while an increase in the power-law index leads to higher N-savg. The frequency range where entropy generation is significantly affected expands with increasing power-law index. These findings demonstrate the combined effect of fluid rheology and pulsation in reducing entropy generation. Furthermore, they emphasize that the proposed framework offers a reliable and efficient approach for analyzing and improving thermal systems using a combination of analytical modeling, machine learning, and optimization.