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Numerical investigation of thermal performance in liquid cooling serpentine mini-channel heat sink with various inlet/outlet positions

Chadi Kamel, Belghar Nourredine, Lachi Mohammed, Khoshnaw Fuad

Advances in Materials Science

2025

Vol. 25, nr 3(85)

18--37

This study aims to investigate and optimize the thermal dissipation of a constant heat flux source by conducting a numerical analysis of four serpentine mini-channel heat sink configurations, each characterized by different inlet and outlet arrangements for the cooling fluid. The cooling system under study consists of an upper part made of ABS copolymer resin, incorporating the fluid inlets and outlets (water), and a lower part made of aluminum, which contains the serpentine mini-channel heat sink. The analyzed configurations included four cases: First: a single inlet and a single outlet, Second: two inlets and one outlet, Third: one inlet and two outlets, and Fourth: a variation of the third model with reversed inlet and outlet positions. Numerical simulations, performed using the finite volume method, cover a Reynolds number range from 200 to 600. The analysis focuses on flow behavior, temperature distributions, pressure drop, thermal resistance, the average Nusselt number and the performance evaluation factor (PEF). The results indicate that the configurations with two inlets and one outlet (Case 2) and the reversed inlet/outlet configuration (Case 4) significantly enhance cooling compared to the other configurations. However, the two-inlet, one-outlet case also results in a higher pressure drop. At a Reynolds number of 600, Case 2 achieves the best thermal performance with an average Nusselt number of 20.79 and a minimum thermal resistance of 0.228K/W, while Case 3 exhibits the lowest efficiency. These findings help identify optimal configurations for cooling high heat flux electronic components.

Corrosion rate prediction for underground gas pipelines using a Levenberg-Marquardt artificial neural network (ANN)

Ahmaid Ashref, Khoshnaw Fuad

Advances in Materials Science

2024

Vol. 24, nr 4(82)

5-22

This study addresses the challenge of accurately predicting corrosion rates and estimating the remaining life of underground gas pipelines, which is complicated by the complex interaction of physical factors and environmental conditions. Traditional models are inadequate in capturing these variables, leading to less reliable predictions, which this study aims to address by developing a more accurate and optimized artificial neural network (ANN) model. This study focuses on predicting corrosion rates and estimating the remaining life of underground gas pipelines using ANNs implemented in MATLAB. It incorporates both physical factors, such as maximum corrosion depth and pipe thickness, and environmental variables such as moisture, soil resistivity, and chloride concentration. The analysis identified corrosion depth and wall thickness as significant contributors, influencing material integrity by 20% and 16%, respectively. The optimal ANN model, with a Levenberg-Marquardt structure and one hidden layer of 10 neurons, achieved superior accuracy, with an MSE of 0.038 and R² of 0.9998. The study addresses the challenge of accurately predicting corrosion rates and remaining life in underground gas pipelines by developing an optimised ANN model. Its contribution lies in creating a highly accurate prediction tool that outperforms traditional models and enables more informed decisions for pipeline maintenance and safety.