Multidisciplinary reliability analysis of turbine blade with shape uncertainty by Kriging model and free-form deformation methods

This work presents an integrated approach for the multidisciplinary reliability analysis of turbine blades with shape uncertainty, including the metamodel, the free-form deformation, and the Monte Carlo simulation. The multidisciplinary analysis of turbine blade includes fluid, structure, and thermal analyses, which is time-consuming during integration with multidisciplinary reliability analysis. The metamodel is constructed by adaptive sampling to reduce computational cost. The shape uncertainty with small size changes in reliability analysis should be considered. The geometry-based multidisciplinary analysis may fail to capture the small size changes during the geometry and mesh regeneration process. The main contribution of this article is to introduce the free-form deformation in multidisciplinary reliability analysis to overcome the aforementioned problems. The mesh-based method supported by free-form deformation is proposed. Failure probability analysis of the multidisciplinary blade system is performed using the Monte Carlo simulation and the surrogate model. Through the numerical simulation, it is found that the failure probability increases as the blade shape uncertainty becomes larger. The methodology in this article provides a valuable and applicative way to calculate the risk of blade in multidisciplinary system.

[1]  Liang Gao,et al.  A general failure-pursuing sampling framework for surrogate-based reliability analysis , 2019, Reliab. Eng. Syst. Saf..

[2]  Bak Kong Low,et al.  Practical second‐order reliability analysis applied to foundation engineering , 2012 .

[3]  Yi Gao,et al.  An active learning kriging model for hybrid reliability analysis with both random and interval variables , 2015 .

[4]  Steve L. Karman,et al.  Geometry Parameterization Method for Multidisciplinary Applications , 2009 .

[5]  Jamshid A. Samareh,et al.  Novel Multidisciplinary Shape Parameterization Approach , 2001 .

[6]  Qiujing Pan,et al.  An efficient reliability method combining adaptive Support Vector Machine and Monte Carlo Simulation , 2017 .

[7]  W SederbergThomas,et al.  Free-form deformation of solid geometric models , 1986 .

[8]  Giacomo Bruno Azzurro Persico,et al.  Uncertainty Quantification of an ORC turbine blade under a low quantile constrain , 2017 .

[9]  Sankaran Mahadevan,et al.  Efficient surrogate models for reliability analysis of systems with multiple failure modes , 2011, Reliab. Eng. Syst. Saf..

[10]  Zhihui Li,et al.  Review of design optimization methods for turbomachinery aerodynamics , 2017 .

[11]  Yongshou Liu,et al.  System reliability analysis through active learning Kriging model with truncated candidate region , 2018, Reliab. Eng. Syst. Saf..

[12]  Sabine Coquillart,et al.  Extended free-form deformation: a sculpturing tool for 3D geometric modeling , 1990, SIGGRAPH.

[13]  Zhenzhou Lu,et al.  An efficient reliability analysis method combining adaptive Kriging and modified importance sampling for small failure probability , 2018 .

[14]  A. Kiureghian,et al.  Multiple design points in first and second-order reliability , 1998 .

[15]  Cheng-Wei Fei,et al.  A stochastic model updating strategy-based improved response surface model and advanced Monte Carlo simulation , 2017 .

[16]  Thomas W. Sederberg,et al.  Free-form deformation of solid geometric models , 1986, SIGGRAPH.

[17]  Guang-Chen Bai,et al.  Reliability-based low-cycle fatigue damage analysis for turbine blade with thermo-structural interaction , 2016 .

[18]  Cheng Lu,et al.  Advanced multiple response surface method of sensitivity analysis for turbine blisk reliability with multi-physics coupling , 2016 .

[19]  Xiaoping Du,et al.  Reliability Analysis With Monte Carlo Simulation and Dependent Kriging Predictions , 2016 .

[20]  Zhigang Sun,et al.  A response surface approach for reliability analysis of 2.5D C/SiC composites turbine blade , 2016 .

[21]  R. Rackwitz,et al.  New light on first- and second-order reliability methods , 1987 .

[22]  Fan Yang,et al.  Non-matching meshes data transfer using Kriging model and greedy algorithm , 2018, Adv. Eng. Softw..

[23]  Yue Zhufeng Parametric modeling and multidisciplinary design optimization for cooling turbine blade , 2007 .

[24]  Yi Gao,et al.  Unified reliability analysis by active learning Kriging model combining with Random‐set based Monte Carlo simulation method , 2016 .

[25]  Xiaoping Du Unified Uncertainty Analysis by the First Order Reliability Method , 2008 .

[26]  João Cardoso,et al.  Review and application of Artificial Neural Networks models in reliability analysis of steel structures , 2015 .

[27]  W. C. Nelson DEPARTMENT OF AERONAUTICAL AND ASTRONAUTICAL ENGINEERING , 2008 .

[28]  Joaquim R. R. A. Martins,et al.  Multidisciplinary design optimization: A survey of architectures , 2013 .

[29]  Kyung K. Choi,et al.  Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty , 2016, Structural and Multidisciplinary Optimization.

[30]  A. Quarteroni,et al.  Shape optimization for viscous flows by reduced basis methods and free‐form deformation , 2012 .

[31]  Yue Zhufeng,et al.  An adaptive reliability method combining relevance vector machine and importance sampling , 2015 .

[32]  Cheng-Wei Fei,et al.  Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression , 2014 .

[33]  Tom Verstraete,et al.  CAD Integrated Multipoint Adjoint-Based Optimization of a Turbocharger Radial Turbine , 2017 .

[34]  Ming Jian Zuo,et al.  A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis , 2018, Reliab. Eng. Syst. Saf..

[35]  Guozhao Wang,et al.  Direct manipulation of free-form deformation using curve-pairs , 2013, Comput. Aided Des..

[36]  Jie Wen,et al.  Multi-objective reliability-based design optimization approach of complex structure with multi-failure modes , 2017 .

[37]  Xiaoping Du,et al.  Simulation-based time-dependent reliability analysis for composite hydrokinetic turbine blades , 2013 .

[38]  G. Xie,et al.  Film cooling performance and flow characteristics of internal cooling channels with continuous/truncated ribs , 2017 .

[39]  Nicolas Gayton,et al.  AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation , 2011 .

[40]  Cheng-Wei Fei,et al.  Novel method and model for dynamic reliability optimal design of turbine blade deformation , 2014 .