AI for Mechanical Engineering: Heat Transfer

By Jongmok Lee
http://iailab.kaist.ac.kr/
Industrial AI Lab at KAIST

# 1. Regression Model for Heat FinsÂ¶

## 1.1 Recap: Heat FinsÂ¶

• Fins are widely used to increase the rate of heat transfer from a wall$,$
• The selection of a suitable fin geometry requires a compromise among the cost, weight, and etc.
• The heat transfer effectiveness and efficiency is determined by these fin geometries
• ex) $\eta_f = \frac{\tanh \sqrt{\bar{h}PL^2/KA}}{\sqrt{\bar{h}PL^2/KA}}$ for fin of rectangular cross section (length $L$ and thickness $t$)
• However, for complex, geometry, FDM calculation is required to obtain heat transfer efficiency and effectiveness
• Build regression model that predicts efficiency and effectiveness using fin geometries
• Train regession model to predict heat transfer performance using fin parameters
• Inputs: fin parameters:
• Outer diameter of tube $(D_o)$
• Fin spacing $(\delta)$
• Fin thickness $(t)$
• Thermal conductivity of the material $(k)$
• Convective heat transfer coefficient $(h)$
• Outputs: heat fin performances:
• Efficiency $(\eta)$
• Effectiveness $(\epsilon)$
• In order to dimensionally balance the equation, the input variables are convected into non-dimensional forms subsequently decreasing the number of input variables
• $\delta^* = \frac{\delta}{D_o}$
• $t^* = \frac{t}{D_o}$
• $M = \frac{h}{8k}L_{exp}$
• $\eta = f_1(\delta^*, t^*, M)$
• $\epsilon = f_2(\delta^*, t^*, M)$