Genetic Algorithm Optimization of a Compact Heat Exchanger Modeled Using Volume Averaging Theory

This paper proposes and implements a new methodology for optimizing Compact Heat Exchangers (CHXs) using a Volume Averaging Theory (VAT) model and a Genetic Algorithm (GA) optimizer. This method allows for multiple-parameter optimization of CHXs by design of their basic morphological structures, and is applied to a Finned-Tube Heat Exchanger (FTHX). A consistent model is used to describe transport phenomena in a FTHX based on VAT, which allows for the volume averaged conservation of mass, momentum, and energy equations to be solved point by point, with the morphology of the structure directly incorporated into the field equations. The equations differ from known equations and are developed using a rigorous averaging technique, hierarchical modeling methodology, and fully turbulent models with Reynolds stresses and fluxes in the space of every pore. These averaged equations have additional integral and differential terms that must be dealt with in order for the equation set to be closed, and recent work has provided this closure. The resulting governing equation set is relatively simple and is discretized and solved using the finite difference method. Such a computational algorithm is fast running, but still able to present a detailed picture of the temperature fields in both of the fluid flows as well as in the solid structure of the heat exchanger. A GA is integrated with the VAT-based solver to carry out the FTHX optimization, which is a ten parameter problem, and the FTHX’s effectiveness is selected as the fitness function to be optimized. This method of using the VAT-based solver fully integrated with a GA optimizer results in an all-in-one tool for performing multiple-parameter constrained optimization on FTHXs.