Abstract A typical centrifugal impeller characterized by a low flow coefficient and cylindrical blades is redesigned by means of an intelligent automatic search program. The procedure consists of a feasible sequential quadratic programming algorithm (Fletcher, R. Practical Methods of optimization, 2000 (Wiley)) coupled to a lazy learning (LL) interpolator 1 to speed-up the process. The program is able to handle geometric constraints to reduce the computational effort devoted to the analysis of non-physical configurations. The objective function evaluator is an in-house developed structured computational fluid dynamics (CFD) code. The LL approx-imator is called each time the stored database can provide a sufficiently accurate performance estimate for a given geometry, thus reducing the effective CFD computations. The impeller is represented by 25 geometric parameters describing the vane in the meridional and s-0 planes, the blade thickness, and the leading edge shape. The optimization is carried out on the impeller design point maximizing the polytropic efficiency with nearly constant flow coefficient and polytropic head. The optimization is accomplished by maintaining unaltered those geometrical parameters which have to be kept fixed in order to make the impeller fit the original stage. The optimization, carried out on a cluster of 16 PCs, is self-learning and leads to a geometry presenting an increased design point efficiency. The program is completely general and can be applied to any component which can be described by a finite number of geometrical parameters and computed by any numerical instrument to provide performance indices. The work presented in this paper was done under the METHOD EC funded project for the implementation of new technologies for optimization of centrifugal compressors.
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