A blackbox optimization of volumetric heating rate for reducing the wetness of the steam flow through turbine blades
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This paper proposes to use a blackbox optimization to obtain the optimal volumetric heating required to reduce the wetness at the last stages of steam turbines. For this purpose, a global multiobjective optimization is utilized through the automatic linking of genetic algorithm and CFD code, where the blackbox function evaluations are performed by CFD runs. The logarithm of number of droplets per volume (LND), the droplet average radius (DAR), and the integral of local entropy (ILE) at the end of the cascade (after the condensation location) are minimized, while the volumetric heating rate is the optimization parameter. The Eulerian–Eulerian approach is implemented to model the two-phase wet steam turbulent flow and the numerical results are validated against well-established experiments. Since higher volumetric heating rates reduce DAR and LND, while increase ILE, according to optimization results, there is an optimum for the volumetric heating rate to reach the best performance of steam turbines. For case studies presented in this work, the optimal volumetric heating rates of 5.21x10^8 and 4.67x10^8 W/m^2 are obtained for two different cases of supersonic and subsonic outlets, respectively. Particularly, these rates improve DAR by 45.7% and 57.5%, and LND by 6.0% and 7.8% for respective cases.