The design optimization of turbomachinery components has witnessed an increased attention in last decade, and is currently used in many companies in the daily design cycle. The adjoint method proves to have the highest potential in this field, however, has still two major shortcomings before its full potential can be used: 1) the shape is mainly parameterized by its grid and the connection to the CAD model is lost, and 2) the optimization process includes only aerodynamic performance and neglects stress and vibration requirements. In the present work, both problems are tackled simultaneously leading to an effective optimization tool applied to a radial turbine for turbocharger applications. The objective is to increase the total to static efficiency of the turbine while limiting the maximum stress level in the blades to a predefined limit. The shape considered is parameterized through a CAD based approach, which serves as the 'master' geometry from which both fluid and solid grid are derived. The efficiency of the turbine is predicted by a Reynolds Averaged Navier Stokes solver, while the maximum stresses in the material are predicted by a Finite Element structural analysis tool. The gradients of the objective are computed using an adjoint approach, which allows for an efficient computation independent of the number of design variables. The shape is optimized using a steepest descent algorithm, demonstrating an increase of over 5% in efficiency while keeping the stress levels near the imposed constraint.
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