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contributor authorMontanelli, Hadrien
contributor authorMontagnac, Marc
contributor authorGallard, Franأ§ois
date accessioned2017-05-09T01:24:43Z
date available2017-05-09T01:24:43Z
date issued2015
identifier issn0889-504X
identifier otherturbo_137_09_091006.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/159966
description abstractThis paper presents the application of the gradient span analysis (GSA) method to the multipoint optimization of the twodimensional LS89 turbine distributor. The cost function (total pressure loss) and the constraint (mass flow rate) are computed from the resolution of the Reynoldsaveraged Navier–Stokes equations. The penalty method is used to replace the constrained optimization problem with an unconstrained problem. The optimization process is steered by a gradientbased quasiNewton algorithm. The gradient of the cost function with respect to design variables is obtained with the discrete adjoint method, which ensures an efficient computation time independent of the number of design variables. The GSA method gives a minimal set of operating conditions to insert into the weighted sum model to solve the multipoint optimization problem. The weights associated to these conditions are computed with the utopia point method. The singlepoint optimization at the nominal condition and the multipoint optimization over a wide range of conditions of the LS89 blade are compared. The comparison shows the strong advantages of the multipoint optimization with the GSA method and utopiapoint weighting over the traditional singlepoint optimization.
publisherThe American Society of Mechanical Engineers (ASME)
titleGradient Span Analysis Method: Application to the Multipoint Aerodynamic Shape Optimization of a Turbine Cascade
typeJournal Paper
journal volume137
journal issue9
journal titleJournal of Turbomachinery
identifier doi10.1115/1.4030016
journal fristpage91006
journal lastpage91006
identifier eissn1528-8900
treeJournal of Turbomachinery:;2015:;volume( 137 ):;issue: 009
contenttypeFulltext


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