Design of Experiments for Heat Transfer Phenomena and Entropy Generation in Thermal Management of Electronic Components

This work explores the application of Design of Experiments methodologies to systematically investigate and optimize thermal management strategies, with a specific focus on maximizing heat transfer minimizing entropy generation. DOE provides a statistically robust framework for enabling the efficient identification of key parameters influencing heat transfer characteristics. This study employs a carefully designed experimental matrix to systematically vary critical parameters such as Rayleigh number (Ra), component length (L) and component position (P). Analysis of variance (ANOVA) is used to develop empirical models that quantify the relationships between these parameters and both heat transfer performance and entropy generation. The analysis reveals optimal parameter values that effectively reduces entropy generation, leading to a more sustainable and efficient heating solution. Polynomial regressions for the interaction between these parameters are correlated. The findings provide valuable insights and practical guidelines for optimizing thermal management in a wide range of electronic applications.