Integrating Machine Learning Algorithms for Performance Enhancement and Thermal Efficiency Optimization in Finned Heat Transfer Systems

Persuasive thermal control is essential for optimizing device adaptation, prolonging product span, preserving energy, conserving the ambient and evading thermal issues. The information over demonstrates the worth of analyzing methods that promote energy transfer, like pin fins. A common amiable cooling approach that can be used to enrich the effectiveness of energy transfer in a variety of applications is pin fins. These consist of turbine cooling, microchannel cooling, and SAH ducting. Pin-fins have been demonstrated to significantly reduce pressure and transfer heat in these systems, highlighting their efficacy. Improving flow structure, pressure drop reduction, and heat transfer mechanisms all depend on pin-fin designs and combinations being optimized. This paper focuses to severely assess the form of survey on pin fins and examine their thermic interpretation in compensating heat transmission, energy utilization, and apparatus effectiveness with respect to different pin-fin forms and configurations. The current study will examine the impingement of distinct pin-fin shapes on the collective system interpretation in order to assist the research community in creating cooling systems that are more effective and efficient. Moreover, this paper proceeds beyond a simple summary by closely interrogating the advantages and disadvantages of diverse pin-fin designs and their implications on distinct flow types and energy-transfer techniques. Using AI techniques to optimize ducting, random forest showed optimum duct length of 207.69 mm, width of 67.12 mm and height of duct 17.26mm with accuracy of 81.86%,57.12% and 86.37% respectively. This work improves the development of thermic operation tactics for acceptable and sustainable energy sources by critically analyzing the existing literature and identifying areas that need more research.