caa a22 4500 463243935 CHVBK 20180405153320.0 cr unu---uuuuu 170326e20070101xx s 000 0 eng 10.1007/s00158-006-0033-y doi (NATIONALLICENCE)springer-10.1007/s00158-006-0033-y Multidisciplinary and multiple operating points shape optimization of three-dimensional compressor blades [Elektronische Daten] [S. Pierret, R. Filomeno Coelho, H. Kato] The recent progress in simulation technologies in several fields such as computational fluid dynamics, structures, thermal analysis, and unsteady flow combined with the emergence of improved optimization algorithms makes it now possible to develop and use automatic optimization software and methodologies to perform complex multidisciplinary shape optimization process. In the present applications, the MAX optimization software developed at CENAERO is used to perform the optimization. This software allows performing derivative free optimization with very few calls to the computer intensive simulation software. The method employed in this paper combines the use of a genetic algorithm (with real coding of the variables) to an approximate (or meta) model to accelerate significantly the optimization process. The performance of this optimization methodology is illustrated on the optimization of three-dimensional turbomachinery blades for multiple operating points and multidisciplinary objectives and constraints. The NASA rotor 67 geometry is used to demonstrate the capabilities of the method. The aim is to find the optimal shape for three different operating conditions: one at a near peak efficiency point, one at choked mass flow, and one near the stall flow. The three points are analyzed at the same blade rotational speed but with different mass flows. A finite element structural mechanics software is used to compute the static and dynamic mechanical responses of the blade. A Navier-Stokes solver is used to calculate the aerodynamic performance. High performance computers (HPC) are used in this application. Cenaero's HPC infrastructure contains a Linux cluster with 170 3.06GHz Xeon processors. The optimization algorithm is parallelized using MPI. Springer-Verlag, 2006 Optimization nationallicence Turbomachines nationallicence Multiobjectives nationallicence Genetic algorithms nationallicence Approximate models nationallicence Pierret S. CENAERO asbl, Avenue Jean Mermoz 30, Bâtiment Mermoz 1, 6041, Gosselies, Belgium aut Filomeno Coelho R. CENAERO asbl, Avenue Jean Mermoz 30, Bâtiment Mermoz 1, 6041, Gosselies, Belgium aut Kato H. CENAERO asbl, Avenue Jean Mermoz 30, Bâtiment Mermoz 1, 6041, Gosselies, Belgium aut Structural and Multidisciplinary Optimization Springer-Verlag 33/1(2007-01-01), 61-70 1615-147X 33:1<61 2007 33 158 https://doi.org/10.1007/s00158-006-0033-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00158-006-0033-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Pierret S. CENAERO asbl, Avenue Jean Mermoz 30, Bâtiment Mermoz 1, 6041, Gosselies, Belgium aut NATIONALLICENCE 700 1- Filomeno Coelho R. CENAERO asbl, Avenue Jean Mermoz 30, Bâtiment Mermoz 1, 6041, Gosselies, Belgium aut NATIONALLICENCE 700 1- Kato H. CENAERO asbl, Avenue Jean Mermoz 30, Bâtiment Mermoz 1, 6041, Gosselies, Belgium aut NATIONALLICENCE 773 0- Structural and Multidisciplinary Optimization Springer-Verlag 33/1(2007-01-01), 61-70 1615-147X 33:1<61 2007 33 158 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer