AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

Qin, Qiang; Cao, Xiaolei; Zhang, Shengpeng

doi:10.32604/cmes.2023.022078

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…The MC method is developed based on the Bernoulli law of large numbers to improve the analytical accuracy with the sample size, which is unacceptable in calculation burden for the reliability evaluation of complex structures. Importance sampling [3,4], truncated importance sampling [5,6], linear sampling [7], and directional sampling methods [8,9] are explored to reduce the computation burden of the MC method. To further improve the efficiency of the reliability evaluation for complex structures, the moment methods are explored by numerous scholars.…”

Section: Introductionmentioning

confidence: 99%

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Liang,

Sun,

Sun

et al. 2024

Aerospace

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As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10−3 m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10−4 m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory.

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Section: Introductionmentioning

confidence: 99%

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Liang,

Sun,

Sun

et al. 2024

Aerospace

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“…Pholdee et al used genetic algorithm to optimize Kriging's correlation function parameters to improve the accuracy of their model [21]. Qin et al proposed a new kriging method based on the AK-IS and truncated importance sampling method, whale optimization algorithm (WOA) was used to optimize the proposed model and improve its fitting accuracy [22]. Zhang et al proposed an improved Kriging optimization model based on an improved QPSO algorithm, in which all performance indexes were improved [18].…”

Section: Introductionmentioning

confidence: 99%

Contact Stress Reliability Analysis Model for Cylindrical Gear with Circular Arc Tooth Trace Based on an Improved Metamodel

Zhang,

Chen,

et al. 2024

CMES

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Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace (referred to as CATT gear), a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis. In this study, a mathematical relationship between design parameters and contact stress is formulated using the Kriging Metamodel. To enhance the model's accuracy, we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle Swarm optimization algorithm, leveraging the strengths of each. Additionally, the "parental inheritance + self-learning" optimization model is used to fine-tune the Kriging Metamodel's parameters. Following this, a mathematical model for calculating the contact stress of Variable Hyperbolic Circular-Arc-Tooth-Trace (VH-CATT) gears using the optimized Kriging model was developed. We then examined how different gear parameters affect the VH-CATT gears' contact stress. Our simulation results show: (1) Improvements in R 2 , RMSE, and RMAE. R 2 rose from 0.9852 to 0.9974 (a 1.22% increase), nearing 1, suggesting the optimized Kriging Metamodel's global error is minimized. Meanwhile, RMSE dropped from 3.9210 to 1.6492, a decline of 57.94%. The global error of the GA-IQPSO-Kriging algorithm was also reduced, with RMAE decreasing by 58.69% from 0.1823 to 0.0753, showing the algorithm's enhanced precision. In a comparison of ten experimental groups selected randomly, the GA-IQPSO-Kriging and FEM-based contact analysis methods were used to measure contact stress. Results revealed a maximum error of 12.11667 MPA, which represents 2.85% of the real value. (2) Several factors, including the pressure angle, tooth width, modulus, and tooth line radius, are inversely related to contact stress. The descending order of their impact on the contact stress is: tooth line radius > modulus > pressure angle > tooth width. (3) Complex interactions are noted among various parameters. Specifically, when the tooth line radius interacts with parameters such as pressure angle, tooth width, and modulus, the resulting stress contour is nonlinear, showcasing a multifaceted contour plane. However, when tooth width, modulus, and pressure angle interact, the stress contour is nearly linear, and the contour plane is simpler, indicating a weaker coupling among these factors.

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AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

Cited by 2 publications

References 54 publications

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

Contact Stress Reliability Analysis Model for Cylindrical Gear with Circular Arc Tooth Trace Based on an Improved Metamodel

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