Unsteady CFD analysis and shape optimization of a hydraulic turbine (original) (raw)
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SHAPE OPTIMIZATION OF TURBINE NGV BY USING CFD COMPUTATIONAL TOOL
The most critical parts of a gas turbine engine are turbine blades and disc. They are designed to operate under severe conditions such as high turbine inlet temperature, high speeds, and high compression ratios. Owing to these operating conditions high rotational speed which is likely to be between 60000 and 100000 rev/min. Design optimization of fluid machinery based on computer simulation has become a reality today because of development of high speed computers. Highly complex flow patterns are being predicted by solving mass, momentum and energy equations and near accurate solutions at an acceptable level can be achieved. The present work aims at optimizing the blade shape and analysis of surrogate model of turbine blade. The sweep and lean variables are modified to enhance the efficiency, based objective. RANS equations are solved to get the flow field and objective function values. Based neural network, model has been constructed and the blade shape has been modified to enhance the performance. The surrogate performances are evaluated for applicability in turbo machinery blade.
Multi-objective shape optimization of runner blade for Kaplan turbine
IOP Conference Series: Earth and Environmental Science, 2014
Automatic runner shape optimization based on extensive CFD analysis proved to be a useful design tool in hydraulic turbomachinery. Previously the authors developed an efficient method for Francis runner optimization. It was successfully applied to the design of several runners with different specific speeds. In present work this method is extended to the task of a Kaplan runner optimization. Despite of relatively simpler blade shape, Kaplan turbines have several features, complicating the optimization problem. First, Kaplan turbines normally operate in a wide range of discharges, thus CFD analysis of each variant of the runner should be carried out for several operation points. Next, due to a high specific speed, draft tube losses have a great impact on the overall turbine efficiency, and thus should be accurately evaluated. Then, the flow in blade tip and hub clearances significantly affects the velocity profile behind the runner and draft tube behavior. All these features are accounted in the present optimization technique. Parameterization of runner blade surface using 24 geometrical parameters is described in details. For each variant of runner geometry steady state three-dimensional turbulent flow computations are carried out in the domain, including wicket gate, runner, draft tube, blade tip and hub clearances. The objectives are maximization of efficiency in best efficiency and high discharge operation points, with simultaneous minimization of cavitation area on the suction side of the blade. Multiobjective genetic algorithm is used for the solution of optimization problem, requiring the analysis of several thousands of runner variants. The method is applied to optimization of runner shape for several Kaplan turbines with different heads.
Aerodynamic Shape Optimization of Axial Turbines in Three Dimensional Flow
2012
Aerodynamic shape optimization of axial gas turbines in three dimensional flow is addressed. An effective and practical shape parameterization strategy for turbine stages is introduced to minimize the adverse effects of three-dimensional flow features on the turbine performance. The optimization method combines a genetic algorithm (GA), with a Response Surface Approximation (RSA) of the Artificial Neural Network (ANN) type. During the optimization process, the individual objectives and constraints are approximated using ANN that is trained and tested using a few three-dimensional CFD flow simulations; the latter are obtained using the commercial CFD package Ansys-Fluent. To minimize three-dimensional effects, the stator and rotor stacking curves are taken as the design variable. They are parametrically represented using a quadratic rational Bézier curve (QRBC) whose parameters are directly and explicitly related to the blade lean, sweep and bow, which are used as the design variables. In addition, a noble representation of the stagger angle in the spanwise direction is introduced. The described strategy was applied to optimize the performance of the E/TU-3 axial turbine stage which is designed and tested in Germany. The optimization objectives introduced the isentropic efficiency and the streamwise vorticity, subject to some constraints. This optimization strategy proved to be successful, flexible and practical, and resulted in remarkable improvements in stage performance.
Structural and Multidisciplinary Optimization, 2018
An approach for multi-discipline automatic optimization of the hydraulic turbine runner shape is presented. The approach accounts hydraulic efficiency, mechanical strength and the weight of the runner. In order to effectively control the strength and weight of the runner, a new parameterization of the blade thickness function is suggested. Turbine efficiency is evaluated through numerical solution of Reynolds-averaged Navier-Stokes equations, while the finite element method is used to evaluate the von Mises stress in the runner. An objective function, being the weighted sum of maximal stress and the blade volume, is suggested to account for both the strength and weight of the runner. Multi-objective genetic algorithm is used to solve the optimization problem. The suggested approach has been applied to automatic design of a Francis turbine runner. Series of threeobjective optimization runs have been carried out. The obtained results clearly indicate that simultaneous account of stress and weight objectives accompanied by thickness variation allows obtaining high efficiency, light and durable turbine runners.
Automatic CFD Analysis Method for Shape Optimization
2007
List of Figures CFD-Based optimization result of a turbine runner. 2 RAE2822 airfoil, degree-16 Bézier curvefit and control polygons associated with the upper and lower surfaces. 3 PARSEC Method Parameters. 4 Sobieczky Method Parameters. 5 Three numerically derived orthogonal basis functions. 6 Steepest descent on a given space (local-global minima) 7 CFD Results (Displaying streamlines). 8 CFD Analysis for a injection valve system(Displaying streamlines). 9 Mount St. Helen's Topography. 10 Mount St. Helen's CFD domain over the Topography. 11 Aeroelasticity analysis by FEM. 12 Wing airfoil aerodynamic forces decomposition. 13 Wing airfoil typical regions.
Applied Mathematical Modelling, 2016
A robust multi-fidelity design optimization methodology has been developed to integrate advantages of high-and low-fidelity analyses, aiming to help designers reach more efficient turbine runners within reasonable computational time and cost. An inexpensive low-fidelity inviscid flow solver handles most of the computational burden by providing data to the optimizer by evaluating objective functions and constraint values in the low-fidelity phase. An open-source derivative-free optimizer, NOMAD, explores the search space, using the multiobjective mesh adaptive direct search optimization algorithm. A versatile filtering algorithm is in charge of connecting low-and high-fidelity phases by selecting among all feasible solutions a few promising solutions which are transferred to the high-fidelity phase. In the highfidelity phase, a viscous flow solver is used outside the optimization loop to accurately evaluate filtered candidates. High-fidelity analyses results are used to recalibrate the low-fidelity optimization problem. The developed methodology has demonstrated its ability to efficiently redesign a Francis turbine blade for new operating conditions.
Multiobjective optimal design of runner blade using efficiency and draft tube pulsation criteria
IOP Conference Series: Earth and Environmental Science, 2012
In the present work new criteria of optimal design method for turbine runner [1] are proposed. Firstly, based on the efficient method which couples direct simulation of 3D turbulent flow and engineering semi empirical formulas, the combined method is built for hydraulic energy losses estimation in the whole turbine water passage and the efficiency criterion is formulated. Secondly, the criterion of dynamic loads minimization is developed for those caused by vortex rope precession downstream of the runner. This criterion is based on the finding that the monotonic increase of meridional velocity component in the direction to runner hub, downstream of its blades, provides for decreasing the intensity of vortex rope and thereafter, minimization of pressure pulsation amplitude. The developed algorithm was applied to optimal design of 640 MW Francis turbine runner. It can ensure high efficiency at best efficiency operating point as well as diminished pressure pulsations at full load regime.
A two-dimensional design method for the hydraulic turbine runner and its preliminary validation
2016
April 10-15, 2016 Abstract The paper presents the approach to solve the inverse problem by means of a two-dimensional axisymmetric flow model in a curvilinear coordinate system on a basis of the runner blade of the model vertical hydraulic turbine of Kaplan type. The Vortex Lattice Method was used to obtain streamline function that is necessary to solve the inverse problem for the designed turbine blades. In order to solve it authors’ own numerical algorithm and code were prepared. The preliminary verification of the prepared algorithm has been based on (1) the results of model Kaplan turbine design obtained by means of the classical method and (2) the results of laboratory tests of the prepared physical turbine model. A comparison of the tested runner blade with the runner blade generated using the developed design method indicates its large utility and applicability.
International Journal of Fluid Machinery and Systems, 2015
A robust multi-fidelity optimization methodology has been developed, focusing on efficiently handling industrial runner design of hydraulic Francis turbines. The computational task is split between low-and high-fidelity phases in order to properly balance the CFD cost and required accuracy in different design stages. In the low-fidelity phase, a physics-based surrogate optimization loop manages a large number of iterative optimization evaluations. Two derivative-free optimization methods use an inviscid flow solver as a physics-based surrogate to obtain the main characteristics of a good design in a relatively fast iterative process. The case study of a runner design for a low-head Francis turbine indicates advantages of integrating two derivative-free optimization algorithms with different local-and global search capabilities.