Steam boiler mixing channel optimization with a surrogate based multi objective genetic algorithm

Nowadays, thanks to ever-increasing computational resources, a viable path to a robust and fast design strategy for both thermal machines and turbomachinery is the coupling of Computational Fluid Dynamics (CFD) and shape optimization algorithms. In general, numerical optimization approaches require less time than the trial and-error procedure traditionally employed, where the designer produces only a tentative initial geometry. This work assesses the capability of a shape optimization algorithm to enhance the design of a steam boiler mixing channel to guarantee negligible NOx production, avoid combustion instabilities especially at lower thermal powers, due to a bad mixing quality of the mixture, and thermal deformation on the burner surface mesh, due to a non uniform distribution of the flame. In particular, the effect of the mixing quality, flow uniformity and the pressure losses at the outlet section of the mixing channel are investigated. The shape optimization approach is here based on a Surrogate Based Optimization (SBO) with the Multi Objective Genetic Algorithm (MOGA), where response surfaces based on the Kriging meta-model are adopted to decrease the computational cost of the proposed approach.