Optimizing bifurcating channel protrusions for cooling double photovoltaic-thermoelectric generator units under magnetic field - Selimefendigil, Fatih; Oztop, Hakan F. (Pergamon-Elsevier Science Ltd, 2026)
Novel cooling solutions and efficient computational methods are essential for the effective thermal manage ment of multiple PV units. This study proposes a T-shaped cooling channel having various protrusion shapes (rectangular, elliptic, and triangular) positioned near the channel junction under a uniform magnetic field. CFD simulations are performed to analyze the cooling channel for ranges of Reynolds number (50 <= Re <= 300) and Hartmann number (0 <= Ha <= 50). The horizontal location of the protrusions (x0) is varied between 0.023 and 0.027, considering different geometric shapes. The numerical solutions are obtained using the finite element method. Optimization studies are performed to identify the optimal operating parameters (Re, Ha, and x0) for various protrusion geometries. Machine learning models, specifically Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), are utilized to predict the heat transfer coefficients for vertical and horizontal cooling configurations. The most accurate predictive model is subsequently integrated with double PV-TEG units for comprehensive thermal management studies. Higher Re promotes upper-wall recir culation and initiates secondary vortices at protrusion apexes that propagate downstream. Maximum Ha results in significant vortex suppression near the protrusions and channel junctions. In the absence of a magnetic field, vertical channel cooling performance significantly decreases due to large vortex formation at the channel en trance. However, proper protrusion placement combined with higher magnetic field strength improves cooling performance for both channel configurations. Optimization studies are conducted, and compared to the flat base line at minimum flow without a magnetic field, these optimized configurations yield heat transfer enhancement factor values of 3.48 (R), 3.27 (E), and 2.87 (T). The best-performing model, Gaussian Process Regression (GPR), is subsequently integrated with the dual PV-TEG units for comprehensive performance analysis. Compared to the reference baseline (flat-walled, minimum flow, Ha = 0), the optimized T, E, and R-type protrusions achieve temperature reductions of 11.6 degrees C, 14.8 degrees C, and 15.0 degrees C for the vertical unit, and 9.4 degrees C, 11.2 degrees C, and 11.5 degrees C for the horizontal unit. The results demonstrate that the synergistic application of optimally placed protru sions and magnetic field effects significantly enhances cooling performance which, coupled with the machine learning-assisted computational method, offers an effective framework for the advanced thermal management of photovoltaic units.
Keywords: Protruded T-Channel; Machine Learning; Double Pv-Teg; Fem; Magnetic Field