Investigation On Internal Flow Field And Aerodynamic Optimization Of Steam Turbine Low Pressure Exhausthood

The low pressure exhaust hood is an important through-flow component of a steam turbine. Its main function is to partially recover the turbine leaving kinetic energy into potential energy. This pressure recovery gives the turbine an effective back pressure that is lower than the condenser pressure, thus increasing the turbine work. Studying the flow structure and understanding the flow loss mechanism of the steam turbine low pressure exhaust hood provides a firm foundation for developing high performance low pressure exhaust hoods. The investigation on aerodynamic optimization method of low pressure exhaust hood perfects the design system of steam turbines.Experimental measurement and numerical simulation are the two basic methods to study the internal flow of turbo machinery. The flow structure in a low pressure exhaust hood scaled model is investigated through the experimental measurement and numerical simulation in this paper. On the basis of the experimental and numerical studies, the key geometric parameters are optimized with the aerodynamic optimization system developed based on CFD and surrogate model technique.Main contents and conclusions of the investigation are described in the following:1. According to similarity criterion, an experimental scaled exhaust hood model is designed and manufactured. The test rig is constructed at the experimental condition, and the measurement equipments used for the experiment are installed and adjusted. The satisfactory results are gained from the experimental measurement.2. HWA is used to measure the mean velocity at the diffuser and exhaust volute outlet locations at the uniform inlet condition. Static pressure tabs are located on the flow guide and bearing cone to measure wall static pressure. Results show that the flow in the diffuser is a process of pressure recovery, and the passage vortices in the volute gradually expand reducing the pressure recovery capability of the volute. PIV is used to measure the flow field at the different circumferential planes and the outlet plane, and the vortices structure is captured. Result shows that the strength of vortices formed in the upper part of the hood is weakened along the flow direction.3. The numerical simulation with CFX software is carried out at the same condition of the experimental measurement. The reliability and accuracy of numerical simulation is validated by the comparison of results between CFD and experimental measurement. The results of numerical simulations indicate that pressure recovery is mainly achieved in the diffuser, and most of the total pressure loss occurs in the volute. Complicated vortices formed in the upper part of the hood are mixed along the flow direction, and a pair of large vortices is formed at the volute outlet. According to the flow field at three typical planes and vortex character analysis, passage vortex and vortex behind the flow guide is the main contributor of the energy loss in the exhaust hood.4. The niching micro genetic algorithm instead of the pattern search algorithm is used to get the correlation vector of Kriging model by optimizing the like-hood function. The original and modified Kriging models are used to approximate two non-linear functions to study the approximation accuracy and robustness of the models. Test result indicates that the modified Kriging model is more accurate and robust. In the optimization process, Expected Improvement (EI) function is introduced to identify the next sampled point by considering the prediction and mean squared error of the prediction to avoid the risk of trapping into the local optimum when the optimal strategy is used. Niching micro genetic algorithm coupled penalty function approach which does not require any penalty parameter is introduced to optimize EI function. This method reduces the difficulty of finding appropriate penalty parameters and increased the robustness of the algorithm.5. The modified Kriging model,maximizing EI criterion and niching micro genetic algorithm are coupled to develop an adaptive sequential optimization algorithm(ASKO), and the corresponding code is developed on the Matlab platform. Four numerical and two engineering optimization problems are tested using the ASKO algorithm. Results show that the algorithm proposed in this paper is more accurate and efficient.6. The profile of the flow guide and bearing cone are parameterized using cubic Bezier curve, respectively. A parameterized geometry model is constructed with the UG/NX software. An aerodynamic optimization system for the low pressure exhaust hood has been developed on the Matlab platform. The system integrates four modules. These are the geometry parameterization modelling module, the commercial mesh generator ICEM-CFD, the aerodynamic simulator CFX, and the ASKO optimizer. The aerodynamic optimization is automatic performed without any human intervention. 7. Shape optimization of the low pressure turbine exhaust hood scaled experimental model is performed. The mass averaged pressure recovery coefficient is largely improved at the experimental inlet condition, and the mass averaged total pressure loss coefficient is slightly decreased. The optimized geometry is used in the real exhaust hood with the last three stages turbine. At the THA work condition, the total pressure loss coefficient is reduced by 0.1 and the pressure recovery coefficient is improved is by 0.1.