A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine Blades

Zhu, Shibai; Yue, Peng; Yu, Zhenning; Wang, Hongtao

doi:10.3390/ma10070698

Cited by 89 publications

(91 citation statements)

References 38 publications

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“…Blades are important components in power and energy equipment. The formability of material and the complexity of structural shape are two key factors during the design and manufacture process of blades . In practical engineering, blades are often manufactured as smooth curved surface shapes to satisfy the performance requirements for harsh environment.…”

Section: Introductionmentioning

confidence: 99%

“…The formability of material and the complexity of structural shape are two key factors during the design and manufacture process of blades. [4][5][6] In practical engineering, blades are often manufactured as smooth curved surface shapes to satisfy the performance requirements for harsh environment. These smooth curved surface shapes can generate wake flow field, secondary flow, and boundary layer separation.…”

Section: Introductionmentioning

confidence: 99%

“…s(4) where, ε is the difference between the system response based on surrogate model and the system output in practical engineering, N is the number of samples which are introduced to construct the surrogate model, M i is the value of the performance of blades based on numerical data, and b M i is the value from response surface model in this study.…”

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confidence: 99%

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Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

Meng

Yang

Zhang³

et al. 2018

Fatigue Fract Eng Mat Struct

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136

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In power and energy systems, both the aerodynamic performance and the structure reliability of turbine equipment are affected by utilized blades. In general, the design process of blade is high dimensional and nonlinear. Different coupled disciplines are also involved during this process. Moreover, unavoidable uncertainties are transported and accumulated between these coupled disciplines, which may cause turbine equipment to be unsafe. In this study, a saddlepoint approximation reliability analysis method is introduced and combined with collaborative optimization method to address the above challenge. During the above reliability analysis and design optimization process, surrogate models are utilized to alleviate the computational burden for uncertainties‐based multidisciplinary design and optimization problems. Smooth response surfaces of the performance of turbine blades are constructed instead of expensively time‐consuming simulations. A turbine blade design problem is solved here to validate the effectiveness and show the utilization of the given approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

Meng

Yang

Zhang³

et al. 2018

Fatigue Fract Eng Mat Struct

Self Cite

136

View full text Add to dashboard Cite

show abstract

“…Under variable amplitude vibration (VAV), Kinyon and Hoeppner [13] concluded that Miner rule is insufficient to predict failure lives of Ti-6Al-4V with multiple load levels. Zhu et al [14] proposed a new damage accumulation model based on Miner rule to consider the coupled damage due to HCF-LCF interaction by introducing four load parameters, and results show that the proposed model provides good predictions. Mlikota et al [15] proposed a micro-model containing the microstructure of carbon steel to simulate the crack growth process, which shows an acceleration effect on short-crack growth rate due to overload.…”

Section: Introductionmentioning

confidence: 99%

Vibration Fatigue Damage Accumulation of Ti–6Al–4V under Constant and Sequenced Variable Loading Conditions

Lin

Yang

et al. 2018

Metals

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Abstract:This study aims to investigate the effect of loading factors on damage accumulation under variable amplitude vibration (VAV). Vibration fatigue experiments are conducted under both constant amplitude vibration (CAV) and VAV loading cases. The effects of loading sequence, loading amplitude, stress difference, and cyclic ratio on damage accumulation are analyzed. It is found the damage accumulation rate is strongly affected by the loading sequence: the fatigue lives can be ranked in descending order as the one-way low-high loading, the constant loading, and the one-way high-low loading. The effect of stress difference on damage accumulation is not significant, while the damage accumulation varies a lot according to the cyclic ratio of the two-level loading blocks and the fatigue life could be extended by increasing the lower loading cycles. Comparing with linear and double linear damage rules, models based on nonlinear damage rules have apparent advantages in predicting accuracy in VAV conditions, in which the nonlinear continuous damage model has the best compromise between availability and precision.

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“…The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.Energies 2019, 12, 1026 2 of 15 strain, stiffness, and deflection, and then provided more accurate stiffness data for a numerical model of load calculations for wind turbines. Thus, many achievements have been obtained in the blade load bearing capacity and parameter measurement methods, and some studies have also focused on structural characteristics and damage analysis of the blades [5][6][7][8][9][10][11][12]. Besides, some surrogate models for wind turbine blade stress/strain prediction due to the significant computational burden of physics-based simulation were constructed [13,14].…”

mentioning

confidence: 99%

GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

Liu

Wang³

et al. 2019

Energies

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This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.

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A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine Blades

Cited by 89 publications

References 38 publications

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

Vibration Fatigue Damage Accumulation of Ti–6Al–4V under Constant and Sequenced Variable Loading Conditions

GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

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