In this study a strategy for a 3D optimisation of the exhaust of a low pressure (LP) steam turbine is presented. The flow domain utilized consists of both the last stage and the exhaust diffuser. The optimisation is done with the help of a hybrid surrogate model for the diffuser flow. In the first part of the paper a numerical model and its validation is presented, which allows for a precise simulation of the diffuser flow including the actual 3D geometry. The second part describes the optimisation procedure and the necessary simplifications applied to this model in order to get a numerical setup that is fast enough to actually perform a design study with roughly 200 design variants within a feasible time. This model is used afterwards to create a surrogate model. Based on this meta model an optimisation is carried out and finally scrutinzed with a flow simulation of the whole exhaust hood.

Renewable energies are increasingly contributing to the overall volume of the electricity grid and demand besides high efficiency, greater flexibility of the conventional fossil power plants. To optimize these objectives, extensive CFD calculations are required in most cases. For example, transient CFD calculations are only rarely combined with an optimizer because of their high demand on computational resources and time. Surrogate models, which are mathematical methods to learn and approximate the relationship between input and output parameters, are a common way to solve these problems. Once they are trained, they can perform the evaluations within seconds and replace the expensive simulation. Of course, real calculations are still needed to generate the training data. Therefore, it is useful to apply efficient and sequentially extensible design plans. This paper presents a new surrogate model method, based on a deep neural network learning the non-stationary hyperparameters of combined Gaussian process covariance matrices. It is used to approximate the complex and time consuming transient CFD simulation of a combined high-intermediate pressure steam turbine double shell outer casing. To minimize the exergy loss, the exhaust geometry is optimized in a single and multi-objective optimization on the surrogate models. The multi-objective optimization also includes the uniform velocity distribution of the steam in different areas of the casing, to predict the thermal loading of the steam turbine inner casing and to avoid an imbalanced thermal loading. A sequential sampling approach combined with a sensitivity analysis is used to find the minimum number of samples needed to train the surrogate models in order to gain sufficient prediction quality. Additionally, the paper describes the initial geometry, its numerical setup and the required control mechanisms to avoid noisy designs, which might complicate the surrogate model training. There is also a comparison of the initial and chosen optimal designs.

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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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