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ABSTRACT
This study reports the application of Computational Fluid Dynamics (CFD) as a data provider for Artificial Neural Networks (ANNs). Four interrupted plate fins with different geometric parameters were studied experimentally. The CFD modeling was undertaken and the simulation results were verified using the experimental data. After validating the model, more fins with various geometries were modeled. The numerically validated data were used for developing two ANNs. Reynolds number and geometric parameters were determined as ANN inputs, and Nusselt number (Nu) and friction factor (f) were outputs. Moreover, the ANNs were compared to genetic algorithm-based correlations and the ANNs appeared more accurate than the correlations.
Nomenclature
| Aheat | = | heat transfer area (m2) |
| b | = | height of the base plate (m) |
| bJ | = | Bias |
| Cp | = | specific heat capacity (kJ/kg K) |
| Ci | = | Constant |
| Dh | = | hydraulic diameter (m) |
| h | = | heat transfer coefficient (W/m2 K) |
| H | = | fin height (m) |
| f | = | friction factor |
| F | = | transfer function |
| k | = | thermal conductivity (W/m K) |
| L | = | length of the finned surface (m) |
| Nu | = | Nusselt number |
| p | = | fin pitch (m) |
| Q | = | heat transfer rate (W) |
| Re | = | Reynolds number |
| r | = | fin length (m) |
| s | = | fin interruption (m) |
| T | = | temperature (K) |
| t | = | fin thickness (m) |
| ΔP | = | pressure drop (Pa) |
| u | = | velocity (m/s) |
| W | = | width of the finned surface (m) |
| WJI | = | Weight |
| y | = | predicted value |
| Greek symbols | = | |
| µ | = | dynamic viscosity (Pa s) |
| ρ | = | density (kg/m3) |
| Subscripts | = | |
| conv | = | Convection |
| i | = | input layer |
| j | = | hidden layer |
| k | = | output layer |
| lm | = | logarithmic mean |
| Superscripts | = | |
| Num | = | Numerically validated |
| Pred | = | Predicted |
Nomenclature
| Aheat | = | heat transfer area (m2) |
| b | = | height of the base plate (m) |
| bJ | = | Bias |
| Cp | = | specific heat capacity (kJ/kg K) |
| Ci | = | Constant |
| Dh | = | hydraulic diameter (m) |
| h | = | heat transfer coefficient (W/m2 K) |
| H | = | fin height (m) |
| f | = | friction factor |
| F | = | transfer function |
| k | = | thermal conductivity (W/m K) |
| L | = | length of the finned surface (m) |
| Nu | = | Nusselt number |
| p | = | fin pitch (m) |
| Q | = | heat transfer rate (W) |
| Re | = | Reynolds number |
| r | = | fin length (m) |
| s | = | fin interruption (m) |
| T | = | temperature (K) |
| t | = | fin thickness (m) |
| ΔP | = | pressure drop (Pa) |
| u | = | velocity (m/s) |
| W | = | width of the finned surface (m) |
| WJI | = | Weight |
| y | = | predicted value |
| Greek symbols | = | |
| µ | = | dynamic viscosity (Pa s) |
| ρ | = | density (kg/m3) |
| Subscripts | = | |
| conv | = | Convection |
| i | = | input layer |
| j | = | hidden layer |
| k | = | output layer |
| lm | = | logarithmic mean |
| Superscripts | = | |
| Num | = | Numerically validated |
| Pred | = | Predicted |