Abstract
This paper presents a gradient-based design optimization of a turbocharger radial turbine for automotive applications. The aim is to improve both the total-to-static efficiency and the moment of inertia of the turbine wheel. The search for the optimal designs is accomplished by a high-fidelity adjoint-based optimization framework using a fast sequential quadratic programming algorithm. The proposed method is able to produce improved Pareto-optimal designs, which are trade-offs between the two competing objectives, in only a few iterations. This is realized by redesigning the blade shape and the meridional flow channel for the respective target while satisfying imposed aerodynamic constraints. Furthermore, a comparative study with an evolutionary algorithm suggests that the gradient-based method has found the global Pareto front at a computational cost which is about one order of magnitude lower.
Highlights
The use of the adjoint method [1,2] is nowadays well established for design optimization problems in Computational Fluid Dynamics (CFD)
As can be seen, depending on the chosen weight, the final design is a trade-off solution between the low moment of inertia and high total-to-static efficiency, establishing a Pareto front towards the lower left hand corner
The highest aerodynamic performance is obtained when the moment of inertia is excluded during the optimization (ω J = 1.0)
Summary
The use of the adjoint method [1,2] is nowadays well established for design optimization problems in Computational Fluid Dynamics (CFD). The advantage of the adjoint approach is the efficient computation of sensitivity derivatives of a given objective function at a cost which is essentially independent of the number of design variables This feature makes the adjoint method attractive to tackle large-scale complex design problems by gradient-based optimization methods. Power 2019, 4, 10 design problem where one objective cannot be improved without penalizing the other, which is shown in this paper using a gradient-based optimization method. Buckley et al [19] show evidence of two local optima for practical multi-point airfoil design problems To investigate this point for a realistic turbomachinery design problem, we compare the results of the gradient-based optimization with the outcome of a stochastic evolutionary optimization strategy which has a higher probability to locate the global optimum, but at much larger computational cost.
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