A novel cascade neural network with heuristic computational analysis for thermal dynamics of rectangular fin model with surface stretching/shrinking
The present study designs a new computational approach to study the thermal performance of a shrinking or stretching longitudinal rectangular fin under both convective and radiative conditions. A dimensionless mathematical model is developed to describe the thermal behavior of the fin, including parameters such as Peclet number, convective and radiative coefficients, temperature ratios, and stretching/shrinking parameters. The hybrid neurocomputing method used to solve the no
Researchers have developed a novel computational method to analyze the thermal performance of rectangular fins that are either shrinking or stretching, considering both heat convection and radiation. This approach utilizes a hybrid neurocomputing technique, combining Cascaded Neural Networks (CNN) with a Genetic Algorithm and Sequential Quadratic Programming (GA-SQP), to solve complex differential equations governing the fin's thermal behavior. The method was tested across various scenarios, demonstrating its accuracy and stability through statistical validation. Results indicate that factors like the Peclet number and temperature ratio positively influence fin tip temperature, while increased radiation coefficient leads to a temperature reduction.
This advanced computational tool offers a reliable and efficient way to understand and predict heat transfer in complex fin designs, which is crucial for optimizing thermal management in various engineering applications.
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