An automated multidisciplinary optimization system for multi-objective design of the long blade turbine stage for steam turbines is developed in this paper. The Self-adaptive Multi-objective Differential Evolution (SMODE) algorithm, cubic non-uniform B-Spline curves based on surface modeling technology for three-dimensional turbine blade parameterization method, aerodynamic and mechanical performance of long blade turbine stage evaluation approach are coupled in the presented multidisciplinary optimization system. The aerodynamic performance of long blade turbine stage design candidates is evaluated using three-dimensional Reynolds-Averaged Navier-Stokes (RANS) solutions. The mechanical performance of the designed long rotor blade is analyzed using Finite Element Analysis (FEA) method based on the software ANSYS. Multi-objective design of long blade turbine stage is conducted using the developed multidisciplinary optimization methodology for the maximization of specific power and minimization of maximum Von Mises Stress with constraints on mass flow rate. The design variables are specified by the stator and rotor blade parameterization method. The Pareto solutions of the multidisciplinary optimization design for the long blade turbine stage are obtained. The aerodynamic and strength performance of obtained Pareto solutions improves obviously by comparison of the referenced design. The dynamic frequency with pre-stress of the referenced and optimized long rotor blade is also calculated in order to avoid resonance. The availability of the presented multidisciplinary optimization system for multi-objective design of long blade turbine stage for steam turbines is also demonstrated.
- International Gas Turbine Institute
Multidisciplinary Optimization Design of Long Blade Turbine Stage Based on Parallel Self-Adaptive Multi-Objective Differential Evolution Algorithm
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Li, J, Li, B, Song, L, & Feng, Z. "Multidisciplinary Optimization Design of Long Blade Turbine Stage Based on Parallel Self-Adaptive Multi-Objective Differential Evolution Algorithm." Proceedings of the ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. Volume 8: Microturbines, Turbochargers and Small Turbomachines; Steam Turbines. Seoul, South Korea. June 13–17, 2016. V008T26A009. ASME. https://doi.org/10.1115/GT2016-56180
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