Topology Optimization (TO) in the design of structural components is commonly used and well explored. However, its usage in the design of complex thermo-fluid equipment used in aerospace applications is limited and relatively new. This is because the coupling between the fluid dynamics, heat transfer, and the shape is complex and nonlinear. Furthermore, the resulting geometry from a TO analysis is often very complex and difficult to manufacture due to the free forms that can occur. With the advent of Additive Manufacturing (AM), however, it has become possible to directly manufacture complex geometries. This study develops a new Genetic Algorithm (GA) based TO combined with Computational Fluid Dynamics (CFD) to produce optimized fin shapes for heat exchangers used in aerospace applications. To implement this approach, a rectangular shaped baseline fin geometry was created using voxel representation. An initial population is generated by mutating the baseline fin a random number of times. The CFD package OpenFOAM is then used to evaluate the performance of each design, after which the optimization algorithm is applied. The GA sorts the designs using a composite fitness function that is comprised of the overall heat transfer and pressure drop, and generates new generations based on mutation and carryover of top performing designs. The study also explores the sensitivity of the GA to the various GA parameters as well as the effect of varying flow Reynolds number. In general, as Reynolds number increases, the percent improvement in the optimum relative to the baseline increases, with potentially a 60% performance improvement. Overall, the approach enables generation of novel freeform designs that may open new performance space for heat transfer applications.

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