The demand for energy is increasingly covered through renewable energy sources. As a consequence, conventional power plants need to respond to power fluctuations in the grid much more frequently than in the past. Additionally, steam turbine components are expected to deal with high loads due to this new kind of energy management. Changes in steam temperature caused by rapid load changes or fast starts lead to high levels of thermal stress in the turbine components. Therefore, todays energy market requires highly efficient power plants which can be operated under flexible conditions. In order to meet the current and future market requirements, turbine components are optimized with respect to multi-dimensional target functions. The development of steam turbine components is a complex process involving different engineering disciplines and time-consuming calculations. Currently, optimization is used most frequently for subtasks within the individual discipline. For a holistic approach, highly efficient calculation methods, which are able to deal with high dimensional and multidisciplinary systems, are needed. One approach to solve this problem is the usage of surrogate models using mathematical methods e.g. polynomial regression or the more sophisticated Kriging. With proper training, these methods can deliver results which are nearly as accurate as the full model calculations themselves in a fraction of time. Surrogate models have to face different requirements: the underlying outputs can be, for example, highly non-linear, noisy or discontinuous. In addition, the surrogate models need to be constructed out of a large number of variables, where often only a few parameters are important. In order to achieve good prognosis quality only the most important parameters should be used to create the surrogate models. Unimportant parameters do not improve the prognosis quality but generate additional noise to the approximation result. Another challenge is to achieve good results with as little design information as possible. This is important because in practice the necessary information is usually only obtained by very time-consuming simulations. This paper presents an efficient optimization procedure using a self-developed hybrid surrogate model consisting of moving least squares and anisotropic Kriging. With its maximized prognosis quality, it is capable of handling the challenges mentioned above. This enables time-efficient optimization. Additionally, a preceding sensitivity analysis identifies the most important parameters regarding the objectives. This leads to a fast convergence of the optimization and a more accurate surrogate model. An example of this method is shown for the optimization of a labyrinth shaft seal used in steam turbines. Within the optimization the opposed objectives of minimizing leakage mass flow and decreasing total enthalpy increase due to friction are considered.
This work presents guidelines for designing a ring-type inlet duct of an intermediate pressure steam turbine. It looks at aerodynamic characteristics of the flow field in the analyzed ducts as well as the impact of the duct topology on the radial clearances influencing casing deformations in different load cases. To show the reliability of the CFD-program used, calculated results are compared to measured data. The numerical method reflects the main physical effects observed in the reference cases with high accuracy. The inlet duct shape influences the loss of total pressure within the duct and the following stator blades as well as the leakage losses in the whole turbine casing. To minimize these losses, geometric parameters such as area ratios within the duct and the shape of its flow channels, are considered and the influence of these parameters on the losses is quantified. The optimization of the inlet duct leads to a geometry with minimum losses from an aerodynamic and mechanical point of view.
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.
Conventional power plants are obliged to compensate for the fluctuations in power generation, due to the rising amount of renewable energies, to ensure grid stability. Consequently, steam turbines are more frequently facing load variation and startup/shut-down cycles leading to an increase of thermal stress induced by phase change phenomena. The review of existing test facilities providing measurement data of heat transfer coefficients influenced by multiphase phenomena, such as surface wettability and dry-out, revealed the necessity for a new measurement application. This paper presents the design of the Experimental Multi-phase Measurement Application “EMMA” to generate the required conditions in combination with an academic turbine housing geometry. The performed investigations are focused on the local distribution of heat transfer coefficients (HTC) and the surface wettability affected by phase change phenomena. Two main film formation mechanisms can be observed, depending on the thermal gradient between the fluid and the wall. These are a) saturated/superheated steam in contact with a sub-cooled wall leading to film-wise/drop-wise condensation and b) primary condensed wet steam droplets depositing on a superheated wall, leading to evaporation. Both, the liquid film and the local heat transfer are measured simultaneously. An overview of applicable thickness measurement methods for transparent liquid films is given and the applied optical measurement system is further described. Moreover the HTC measurement methods are presented considering the occurring case of phase change.
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