Passive Control Optimization of Condensation Flow of Steam Turbine Blades by Response Surface Method

2021 ◽  
Author(s):  
Shima Yazdani ◽  
Esmail Lakzian ◽  
Bok Jik Lee
Keyword(s):  
Steam Turbine ◽  
Response Surface ◽  
Response Surface Method ◽  
Passive Control ◽  
Turbine Blades ◽  
Control Optimization ◽  
Surface Method ◽  
Steam Turbine Blades ◽  
Condensation Flow

scholarly journals Fuzzy Multi-SVR Learning Model for Reliability-Based Design Optimization of Turbine Blades

Materials ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2341 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Ze Wang ◽  
Cheng-Wei Fei ◽  
Zhe-Shan Yuan ◽  
Jing-Shan Wei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Response Surface ◽  
Turbine Blade ◽  
Response Surface Method ◽  
Fuzzy Theory ◽  
Turbine Blades ◽  
Learning Method ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Surface Method

The effectiveness of a model is the key factor of influencing the reliability-based design optimization (RBDO) of multi-failure turbine blades in the power system. A machine learning-based RBDO approach, called fuzzy multi-SVR learning method, was proposed by absorbing the strengths of fuzzy theory, support vector machine of regression (SVR), and multi-response surface method. The model of fuzzy multi-SVR learning method was established by adopting artificial bee colony algorithm to optimize the parameters of SVR models and considering the fuzziness of constraints based on fuzzy theory, in respect of the basic thought of multi-response surface method. The RBDO model and procedure with fuzzy multi-SVR learning method were then resolved and designed by multi-objective genetic algorithm. Lastly, the fuzzy RBDO of a turbine blade with multi-failure modes was performed regarding the design parameters of rotor speed, temperature, and aerodynamic pressure, and the design objectives of blade stress, strain, and deformation, and the fuzzy constraints of reliability degree and boundary conditions, as well. It is revealed (1) the stress and deformation of turbine blade are reduced by 92.38 MPa and 0.09838 mm, respectively. (2) The comprehensive reliability degree of the blade was improved by 3.45% from 95.4% to 98.85%. (3) It is verified that the fuzzy multi-SVR learning method is workable for the fuzzy RBDO of complex structures just like a multi-failure blade with high modeling precision, as well as high optimization, efficiency, and accuracy. The efforts of this study open a new research way, i.e., machine learning-based RBDO, for the RBDO of multi-failure structures, which expands the application of machine learning methods, and enriches the mechanical reliability design method and theory as well.

An Effective Turbine Blade Parameterization and Aerodynamic Optimization Procedure Using an Improved Response Surface Method

2006 ◽  
Author(s):  
Naixing Chen ◽  
Hongwu Zhang ◽  
Fangfei Ning ◽  
Yanji Xu ◽  
Weiguang Huang
Keyword(s):  
Response Surface ◽  
Turbine Blade ◽  
Response Surface Method ◽  
Simulated Annealing Algorithm ◽  
Turbine Blades ◽  
Objective Response ◽  
Complex Nature ◽  
Aerodynamic Optimization ◽  
Surface Method ◽  
Improved Response Surface Method

This paper describes a procedure for a rapid and accurate 3D aerodynamic optimization of high performance turbine blades. This procedure has been developed to account for the complicated geometrical aspects and the complex nature of the associated fluid flow, while remaining simple, practical and demanding less computing power. The focus has been placed on the blade geometrical representation using a set of simple algebraic equations (blade parameterization) and on the aerodynamic optimization methodology based on the numerical computations by a N.S. solver. The turbine blade, including thickness distribution and camber line for each section of the blade span and radial stacking line, has been defined by polynomials, allowing investigation of the influence of any single-parameter change on blade performance. An improved response surface method, by incorporating a simulated annealing algorithm (RS-SAM), has been found to improve the accuracy and to strengthen the optimum-searching ability. A multi-objective response surface method (MORSM) has also been included for testing. One example is given here to demonstrate the effectiveness of the procedure.

scholarly journals Reliability analysis of turbine blades based on fuzzy response surface method

2015 ◽  
Vol 29 (6) ◽  
pp. 2467-2474 ◽  
Author(s):  
Cheng-Yi Pan ◽  
Wen-Long Wei ◽  
Chun-Yi Zhang ◽  
Lu-Kai Song ◽  
Cheng Lu ◽  
...  
Keyword(s):  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Turbine Blades ◽  
Surface Method

A Proposal of PSO with Presumption Function of the Solution Landscape by Response Surface Method

2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Surface Method

scholarly journals Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Tip Clearance ◽  
Strong Nonlinearity ◽  
Surface Method ◽  
Blade Tip ◽  
Blade Tip Clearance

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

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