Integrated approach based on surrogate optimization and CFD for the design of helical turbulators

The present work describes an accurate tool based on numerical simulations and genetic algorithms, which is able to automatically choose the turbulator geometrical parameters with helical profile that optimize both the heat exchanged and the pressure losses with a low computation cost. In particular, the proposed methodology is assessed in the design of turbulators for the smoke-pipe of a steam boiler. The flue gases behavior is modeled with the compressible Reynolds-Averaged Navier–Stokes equations, coupled with transport equations for non-reacting chemical species. The optimization employs a surrogate-based optimization, where the single and multi objective genetic algorithm are applied to a response surface. Different objective functions from literature to drive the optimization process are compared, e.g, the heat exchange efficiency, the total entropy generation number, the merit function, and the net profit and total cost per unit transferred heat load. Even if these functions have been yet investigated in different works, showing pros and cons, it is still not clear from a practical point of view, which one should be used for a shape optimization process. The results suggest the use of a different objective function if the main goal is to maximize the heat exchanged and/or to minimize the pressure losses, with similar performance of the parameters based on the laws of thermodynamics or also on economic considerations. Furthermore, the results prove the higher fidelity of numerical simulations in the estimation of the Nusselt number and the friction factor in comparison with empirical correlations. The significance of the present work lies in the proof of the low computational cost and easy application of a shape optimization process for the design-of-things based on numerical simulations.