Robust Design Optimization of a Steam Turbine Labyrinth Seal Based on Surrogate Models

Abstract

This work presents a robust multi-objective optimization of a labyrinth seal used in power plants steam turbines. The conflicting objectives of this optimization are to minimize the mass flow and to minimize the total enthalpy increase in order to increase the performance and to reduce the temperature, which results in elevated component utilization. The focus should be the robustness aspect to be involved into the optimization. So that the final design is not only optimized for its deterministic values but also robust under its uncertainties. To achieve a robust and optimized design, surrogate models are trained and used to replace the computational fluid dynamic solver (CFD), so as to speed up the calculations. In contrast to most techniques used in literature, the robustness criteria are directly involved in the multi-objective optimization. This leads to a more robust Pareto front compared to a purely deterministic one. This method needs many design evaluations, which would be not effective, if a CFD solver were used.

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“…A surrogate model can approximate the relationship between uncertainties and design objectives very well, and it can establish its explicit expression [7]. Cremanns et al developed a multiobjective robust optimization method for the steam turbine labyrinth seal based on a surrogate model; the authors also solved the problem of conflict between minimizing mass flow and total enthalpy, thus improving the performance and lowering the temperature [8]. Wang et al used a kriging surrogate model and a 6σ robust design method to optimize the robust design of flexible mechanisms with flexible plates [9].…”