Study on the theory, method and model for mechanical dynamic assembly reliability optimization

Fei, Chengwei; Bai, Guang-Chen

doi:10.1177/0954406214525137

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…where δ i ¼ Y K i À T i represents the deviation between the i-th element of the output vector and the i-th element of the label vector, and the use of 1 2 is intended to make the subsequent calculation of the derivative more convenient.…”

Section: Backpropagation Updatementioning

confidence: 99%

High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

Liang

Tian

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper considers optimizing the performance of high-temperature combustion chamber of an aero-engine based on a concentric and hierarchical model. First, sample data for the design variables are obtained based on Latin hypercube sampling method, and a one-dimensional program is used to obtain the true values of combustion efficiency and total pressure loss corresponding to each group of variables. The obtained data are then pre-processed to establish a dataset. Second, a multi-layer artificial neural network (ANN) architecture is designed and a surrogate model of the combustion-related performance of the combustor is established using a data-driven method. The results of global sensitivity analysis based on variance show that ratio of fuel flow to air flow (fuel–air ratio) and the total inlet pressure are the most important factors influencing the two objective functions. Finally, we optimize the multi-objective combustion-related performance of the surrogate model by applying the particle swarm optimization algorithm to it. The results of experiments show that the ANN-based model could accurately predict the efficiency of combustion and total pressure loss of the chamber, yielding root mean-squared errors of 0.0107 and 0.3032%, respectively. It also had better generalization ability than the cubic polynomial surrogate model. Compared with the cubic polynomial model, it generated an optimal Pareto solution set as prediction that had higher values in both objective functions. The proposed model might require better data that can be obtained using intelligent sampling methods so that deeper neural networks can be designed to reduce error and improve its optimization design.

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Section: Backpropagation Updatementioning

confidence: 99%

High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

Liang

Tian

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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show abstract

“…35,36 Mathematical formulation of a multidisciplinary and multi-objectives problem can, therefore, be considered a concern and is extensively covered by many researchers. 12,[37][38][39] Another important concern in employing optimization in conceptual design is computational time. Surrogate models have been proven as beneficial tools to speed up the optimization process.…”

Section: Multidisciplinary Design Optimizationmentioning

confidence: 99%

A comprehensive computational multidisciplinary design optimization approach for a tidal power plant turbine

Munjulury

Najafabadi

Safavi

et al. 2017

Advances in Mechanical Engineering

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Multidisciplinary design optimization has become a powerful technique to facilitate continuous improvement of complex and multidisciplinary products. Parametric modeling is an essential part with tremendous impact on the flexibility and robustness of multidisciplinary design optimization. This article investigates the effect of relational and non-relational parameterization techniques on the robustness and flexibility of the conceptual design of a multidisciplinary product. Bench marking between relational and non-relational parameterization and their effect on flexibility and robustness indicate that the relational parameterization is an efficient method in the multidisciplinary design optimization process. The inherent properties of the method contribute to an efficient parametric modeling with improved communication between different disciplines. This enhances the performance of the multidisciplinary design optimization process and allows a more flexible and robust design. The considered disciplines are computer-aided design, computational fluid dynamics, finite element analysis, and dynamic simulation. A high-fidelity geometry created in a computer-aided design environment is computer-aided design centric approach and later used in computational fluid dynamics, finite element analysis for a better understanding of the product as it leads to precise outcomes. The proposed approach is implemented for the conceptual design of a novel product, a tidal power plant developed by Minesto AB using a multidisciplinary design optimization process.

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“…The response surface method (RSM) is widely used in reliability optimal design for high efficiency and precision. Zhang et al [6] firstly proposed an extremum response surface method to complete the reliability optimization of a two-link flexible manipulator; Fei et al [7,8,9] studied an importance degree model with the extremum response surface method for the dynamic reliability optimization design of a mechanical assembly relationship such as turbine blade-tip radial clearance. However, the traditional RSM can’t meet the reliability optimization design of complex mechanical structures in modeling accuracy and simulation efficiency.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Fatigue/Creep Optimization of Turbine Bladed Disk with Fuzzy Multi-Extremum Response Surface Method

et al. 2019

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To effectively perform the probabilistic fatigue/creep coupling optimization of a turbine bladed disk, this paper develops the fuzzy multi-extremum response surface method (FMERSM) for the comprehensive probabilistic optimization of multi-failure/multi-component structures, which absorbs the ideas of the extremum response surface method, hierarchical strategy, and fuzzy theory. We studied the approaches of FMERSM modeling and fatigue/creep damage evaluation of turbine bladed disks, and gave the procedure for the fuzzy probabilistic fatigue/creep optimization of a multi-component structure with FMERSM. The probabilistic fatigue/creep coupling optimization of turbine bladed disks was implemented by regarding the rotor speed, temperature, and density as optimization parameters; the creep stress, creep strain, fatigue damage, and creep damage as optimization objectives; and the reliability and GH4133B fatigue/creep damages as constraint functions. The results show that gas temperature T and rotor speed ω are the key parameters that should be controlled in bladed disk optimization, and respectively reduce by 85 K and 113 rad/s after optimization, which is promising to extend bladed disk life and decrease failure damages. The simulation results show that this method has a higher modeling accuracy and computational efficiency than the Monte Carlo method (MCM). The efforts of this study provide a new useful method for overall probabilistic multi-failure optimization and enrich mechanical reliability theory.

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Study on the theory, method and model for mechanical dynamic assembly reliability optimization

Cited by 10 publications

References 31 publications

High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

A comprehensive computational multidisciplinary design optimization approach for a tidal power plant turbine

Probabilistic Fatigue/Creep Optimization of Turbine Bladed Disk with Fuzzy Multi-Extremum Response Surface Method

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