Kaplan Turbine

"In this work a response surface approach was used for the prediction of the hill chart of a small horizontal shaft Kaplan turbine. To this aim artificial neural networks (ANN) were chosen as a fast, reliable, and computationally inexpensive tool. The training of the ANN was based on computational fluid dynamics (CFD).
The optimization of the runner-guide vane stagger correlation is a time consuming and expensive task when obtained either on experimental models or directly on the power plant. The use of CFD coupled with ANN may represent an attractive alternative.In order to obtain a more general prediction tool it was decided to characterize the turbine performance coupled with a generic draft tube.
The draft tube is a key component for the performance of small head power plants. As a matter of fact, the kinetic energy recovery may represent a considerable fraction of the total head, thus strongly affecting the efficiency. It presents a very complex flow environment characterized by unsteady, large scale vortices, and the prediction of its performance is a challenging task for the CFD. Moreover, in the refurbishment and repowering of hydro power plants, the turbine may often be coupled with a pre-existing draft tube.
For these reasons the draft tube was not included in the computational domain, and a simple 1D model was used to account for its influence on the turbine.
The three-dimensional version of the TRAF code developed at the University of Florence, coupled with a two equation turbulence closure has been used to predict the turbine flow features and the operating characteristics. The code exploits the artificial compressibility concept to work with incompressible flows. Steady multirow viscous single-phase analysis has been applied to compute the flow field from the inlet struts to the runner exit.
The CFD results were used for the training of a feed-forward artificial neural network with two hidden layers. As far as the training is concerned, a gradient based back propagation method was employed. In order to improve the generalization ability, a hybrid network made by multiple trained neural networks was used.
The considered input parameters were the stagger angles of both the runner and the guide vane, the mass flow rate and the draft tube recovery coefficient. The output of the neural network were the computed total head and the hydro power plant efficiency. The proposed procedure was applied to an existing power plant in order to optimize the runner-guide vane stagger correlation for all runner positions. Comparisons with the measurements obtained on the hydro power plant are presented and discussed. The predicted coupled positions of the guide vane and runner blades were successfully verified all over the range of the operating mass flows."

In this study, numerical analysis is conducted to investigate the failure modes in Kaplan turbine. All necessary steps for Kaplan turbine failure analysis are presented in this work using the modal analysis computational approach. The modal behaver analysis is carried out on a model of an existing Kaplan turbine blade, which is based on the existing turbine used in Haditha hydropower plant in Iraq. This work investigates the modal behavior of the blade of interest, which aid in predicting structural damage initiation. The Kaplan turbine blade is designed using the commercial software ANSYS and SolidWorks. To simulate the blade in operation, the blade is fixed from one end, and all degrees of freedom are measured. Moreover, the turbine blade is moved and rotated to simulate multiple operational conditions. Both mode shapes and natural frequencies are predicted and analyzed using the two aforementioned commercial software and the numerical formula involving the arrest Lanczos method. It is clear from the results that the natural frequency of the specified mode shape does not match with the natural frequency of the runner blade. Hence, there is no failure due to resonance phenomenon in this specific Kaplan turbine. The future work must investigate other aspect of the failure modes in such turbine, such as unbalance dynamic loading. The Results obtained from this study will help study the different possibilities for detecting the failure of the Kaplan blade by examining the modal behavior of the blade.

Abstract: The paper presents the results of the failure analysis of a Kaplan turbine runner blade from a hydropower station in Romania. In order to determine the causes that led to the crack, the authors carried out metallographic investigations on a sample obtained from the damaged blade. They led to the conclusion, that the cracking of the blade was caused by fatigue, initiated by the numerous non metallic inclusions, which were discovered in the vicinity of the blade surface. The results obtained by metallographic investigations were ...