汽轮机低压排汽系统内部流动及其气动优化设计研究
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摘要
汽轮机低压排汽系统是汽轮机通流部分的一个重要部件,它的主要作用是对汽流扩压把汽流的余速动能部分的转化为压力能。排汽系统中产生的压力恢复使得透平末级的出口压力低于冷凝器的压力,有利于提高机组的出功能力。研究汽轮机低压排汽系统内部的流动结构和理解其内部流动损失机理为研制高性能的排汽系统提供了坚实的基础,而探讨排汽系统气动优化设计方法有利于进一步完善汽轮机的设计体系。
实验测量和数值模拟是研究叶轮机械内部流动的两种基本方法。本文采用实验测量和数值模拟两种手段研究某型号汽轮机低压排汽系统的小尺寸模型内部的流动结构。在实验和数值研究的基础上,结合CFD和代理模型技术开发了一套气动优化系统并选择影响排汽系统气动性能的关键几何参数进行了气动优化设计。
本文主要研究内容和研究成果如下:
1.依据相似准则,设计加工了排汽系统小尺寸实验模型。根据实验测量工况,搭建了排汽系统测试实验台,并对测量仪器进行安装、调试,通过实验测量取得了较好的结果。
2.采用热线风速仪在均匀来流条件下测量排汽系统扩压器出口和排汽蜗壳出口平均速度,并在导流环和轴承锥壁面上布置压力孔测量壁面压力。测试结果表明扩压器中的流动是一个压力恢复过程,而排汽蜗壳中的通道涡随着流动方向不断膨胀,降低了排汽蜗壳的压力恢复能力。采用PIV测速技术对排汽蜗壳不同周向平面和出口平面进行了流场测量,捕捉到了排汽系统内不同特征平面上的流动结构。结果表明,在排汽蜗壳顶部位置形成的涡系沿着流动方向不断减弱。
3.在实验测量工况下,采用CFX软件对实验工况进行了数值模拟。将数值计算得到的结果与实验测量结果比较,验证计算模型的可靠性和准确性。计算结果表明排汽系统的压力恢复主要由扩压器获得,而排汽蜗壳中有较大的总压损失。随着流动向下游的发展,在排汽蜗壳顶部位置形成的复杂涡系不断掺混,在蜗壳出口位置形成了大尺度的反向涡对。通过三个典型截面上流场分析和整个排汽蜗壳内的涡系特征模式分析发现,通道涡和导流环弧背涡是排汽系统内引起能量损失的主要来源。
4.采用小生境微种群遗传算法取代模式搜索算法对Kriging模型的似然函数进行多变量寻优得到相关参数向量,完成了对Kriging模型的改进。采用两个测试函数对改进前后Kriging模型的预测精度和鲁棒性进行了测试,结果表明改进后Kriging模型的预测精度和鲁棒性更优。优化过程中综合考虑Kriging模型的预测值与预测标准差,引入了期望提高函数得到校正点,解决了采用代理模型最优策略得到校正点带来的局部收敛问题。采用小生境微种群遗传算法,结合无惩罚因子的惩罚函数法来最大化期望提高函数,解决了选择惩罚因子难以选择困难,增强了算法的鲁棒性。
5.把改进Kriging模型、最大化期望提高准则和小生境微种群遗传算法结合起来发展了一种自适应序列优化算法(ASKO),并在Matlab平台上开发了相应的计算程序。将ASKO算法应用于四个数值算例和两个工程算例,测试结果表明本文提出的算法收敛速度和精度更高,可以有效地提高优化效率。
6.对排汽系统导流环和轴承锥的型线采用四阶Bezier曲线进行参数化表达,采用三维几何建模软件UG/NX建立低压排汽系统的参数化模型。在Matlab平台上开发了低压排汽系统的气动优化平台。该平台集成了四个模块:UG参数化模型,网格划分软件ICEM-CFD,气动分析软件CFX和ASKO优化算子。该平台能自动完成优化过程无需人工干预。
7.在该平台上对模型低压排汽系统进行了气动优化设计,实验来流条件下排汽系统的压力恢复系数有了大幅度的提高,而总压损失系数有了一定程度降低。将模型排汽系统的优化结果应用于带有末三级叶片的真实排汽系统,结果表明排汽系统优化后在THA工况下,总压损失系数减少0.1,静压恢复系数提高0.1.
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.
实验测量和数值模拟是研究叶轮机械内部流动的两种基本方法。本文采用实验测量和数值模拟两种手段研究某型号汽轮机低压排汽系统的小尺寸模型内部的流动结构。在实验和数值研究的基础上,结合CFD和代理模型技术开发了一套气动优化系统并选择影响排汽系统气动性能的关键几何参数进行了气动优化设计。
本文主要研究内容和研究成果如下:
1.依据相似准则,设计加工了排汽系统小尺寸实验模型。根据实验测量工况,搭建了排汽系统测试实验台,并对测量仪器进行安装、调试,通过实验测量取得了较好的结果。
2.采用热线风速仪在均匀来流条件下测量排汽系统扩压器出口和排汽蜗壳出口平均速度,并在导流环和轴承锥壁面上布置压力孔测量壁面压力。测试结果表明扩压器中的流动是一个压力恢复过程,而排汽蜗壳中的通道涡随着流动方向不断膨胀,降低了排汽蜗壳的压力恢复能力。采用PIV测速技术对排汽蜗壳不同周向平面和出口平面进行了流场测量,捕捉到了排汽系统内不同特征平面上的流动结构。结果表明,在排汽蜗壳顶部位置形成的涡系沿着流动方向不断减弱。
3.在实验测量工况下,采用CFX软件对实验工况进行了数值模拟。将数值计算得到的结果与实验测量结果比较,验证计算模型的可靠性和准确性。计算结果表明排汽系统的压力恢复主要由扩压器获得,而排汽蜗壳中有较大的总压损失。随着流动向下游的发展,在排汽蜗壳顶部位置形成的复杂涡系不断掺混,在蜗壳出口位置形成了大尺度的反向涡对。通过三个典型截面上流场分析和整个排汽蜗壳内的涡系特征模式分析发现,通道涡和导流环弧背涡是排汽系统内引起能量损失的主要来源。
4.采用小生境微种群遗传算法取代模式搜索算法对Kriging模型的似然函数进行多变量寻优得到相关参数向量,完成了对Kriging模型的改进。采用两个测试函数对改进前后Kriging模型的预测精度和鲁棒性进行了测试,结果表明改进后Kriging模型的预测精度和鲁棒性更优。优化过程中综合考虑Kriging模型的预测值与预测标准差,引入了期望提高函数得到校正点,解决了采用代理模型最优策略得到校正点带来的局部收敛问题。采用小生境微种群遗传算法,结合无惩罚因子的惩罚函数法来最大化期望提高函数,解决了选择惩罚因子难以选择困难,增强了算法的鲁棒性。
5.把改进Kriging模型、最大化期望提高准则和小生境微种群遗传算法结合起来发展了一种自适应序列优化算法(ASKO),并在Matlab平台上开发了相应的计算程序。将ASKO算法应用于四个数值算例和两个工程算例,测试结果表明本文提出的算法收敛速度和精度更高,可以有效地提高优化效率。
6.对排汽系统导流环和轴承锥的型线采用四阶Bezier曲线进行参数化表达,采用三维几何建模软件UG/NX建立低压排汽系统的参数化模型。在Matlab平台上开发了低压排汽系统的气动优化平台。该平台集成了四个模块:UG参数化模型,网格划分软件ICEM-CFD,气动分析软件CFX和ASKO优化算子。该平台能自动完成优化过程无需人工干预。
7.在该平台上对模型低压排汽系统进行了气动优化设计,实验来流条件下排汽系统的压力恢复系数有了大幅度的提高,而总压损失系数有了一定程度降低。将模型排汽系统的优化结果应用于带有末三级叶片的真实排汽系统,结果表明排汽系统优化后在THA工况下,总压损失系数减少0.1,静压恢复系数提高0.1.
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.
引文
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