Abstract
Key words:
Computational Fluid Dynamics, Genetic Algorithm, Flow Parameterization, Turbomachinery, Fan Blade, Robust Design, Shape Optimization, Multi-Objective Optimization.
Abstract.
Optimal design techniques are not routinely used in the industry when dealing with complex physical phenomena, due to high computing costs. The parameterization method described in this paper is based on the differentiation and high-order Taylor-series expansion of the discretized Reynolds-Averaged Navier-Stokes equations. A flow database containing the derivatives of the physical variables with respect to the design variables is produced by the Turb’Opty c parameterization tool and thoroughly explored by a multi-objective Genetic Algorithm coupled to the extrapolation tool Turb’Post c . The optimization case of an automotive engine cooling fan blade is fully described. Five geometric parameters have been chosen to characterize the fan blade. Three objective functions have been taken into account: the minimization of the loss coefficient, the maximization of the static pressure rise and the minimization of the torque. Two geometric constraints have been imposed to the extrapolated profiles: the monotonicity of the thickness variation and the convexity of the pressure and suction sides. Quantitative results are finally discussed. A noticeable reduction in CPU time cost has been demonstrated.