**AI for Mechanical Engineering: Heat Transfer**

By Jongmok Lee

http://iailab.kaist.ac.kr/

Industrial AI Lab at KAIST

http://iailab.kaist.ac.kr/

Industrial AI Lab at KAIST

- 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
- $\delta^* = \frac{\delta}{D_o}$
- $t^* = \frac{t}{D_o}$
- $M = \frac{h}{8k}L_{exp}$

- $\eta = f_1(\delta^*, t^*, M)$
- $\epsilon = f_2(\delta^*, t^*, M)$