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

Author(s):  
Chengwei Fei ◽  
Wenzhong Tang ◽  
Guangchen Bai

To improve the performance and reliability of gas turbine like an aeroengine, the multi-object multi-discipline (MOMD) reliability optimization design of high press turbine (HPT) blade-tip radial running clearance (BTRRC) was first accomplished based on the mechanical dynamic assembly reliability (MDAR) theory and distributed collaborative response surface method (DCRSM). Four optimization models of MDAR were developed based on the features of assembly machinery and the thought of DCRSM, which are, respectively, called as the direct reliability optimization model (denoted by M1), the multilayer reliability optimization models (denoted by M2), the direct reliability optimization model-based probabilistic analysis (denoted by M3), and the multilayer reliability optimization model-based probabilistic analysis (denoted by M4). Through the MDAR optimization design of BTRRC by the four standard optimization models, some conclusions are drawn as follows: (1) the DCRSM is proved to be effective and feasible for MOMD MDAR optimization design with high computational efficiency and precision; (2) all the reliability optimization results of BTRRC and assembly objects satisfy the requirements of optimization design, and the optimized BTTRC variations are reduced by about 10% and obey the normal distribution, which are quite promising in improving the design and control of HPT BTRRC; (3) in computational efficiency, the computing time of M1 and M3 is far less than those of M2 and M4, meanwhile M3 and M4 are superior to M1 and M2; (4) in computational accuracy, M1 and M2 are better than M3 and M4, as well as M2 and M4 are higher than M1 and M3 theoretically. The presented study does not only fulfill the HPT BTRRC dynamic assembly design from a probabilistic optimization perspective and improve the performance and reliability of gas turbine engine, but also provides a promising approach and four valuable optimization models for MDAR optimization design. Besides, the present efforts are of great significance in enriching the theory and method of mechanical reliability design.

2014 ◽  
Vol 1049-1050 ◽  
pp. 842-845
Author(s):  
Yong Xian Li ◽  
Wen Qiong Zhang ◽  
Song Ping Chen

Large sag due to heavy weight of the propeller shaft is a major cause of rotating vibration, so that to use the hollow shaft is an effective way to reduce the weight. Many scholars and engineers ignored that the outer and inner diameter are discrete and integer variables in optimization design of the propeller shaft. The practicability of an optimal solution is affected by decimal value of the diameter. A practical reliability optimization model with discrete and integer variable of the propeller shaft is proposed, and “Solver” in Microsoft Excel is used to seek the solution of optimization design. The reliability optimization model is composed of minimum weight objective function and constraint conditions such as torsional strength reliability, torsional stiffness reliability, critical speed, torsional stability, manufacturing process, etc. Design results show that the reliability optimization model with integer and variable of propeller shaft and the solving method with “Solver” in Microsoft Excel are more reliable and efficient.


2011 ◽  
Vol 121-126 ◽  
pp. 1019-1022
Author(s):  
Si Zhu Zhou ◽  
Chao Li

The optimization for petroleum high pressure wellhead gate valve stem is significant. Valve stem load calculation method is developed, and petroleum high pressure wellhead gate valve stem optimization model based on MATLAB is established, taking 2 9/16 inch 35MPa wellhead gate valve for an example, the stem weight decreases by 27%, consequently decreasing valve weight.


Author(s):  
Р.И. Кузьмич ◽  
А.А. Ступина ◽  
С.Н. Ежеманская ◽  
А.П. Шугалей

Предлагаются две оптимизационные модели для построения информативных закономерностей. Приводится эмпирическое подтверждение целесообразности использования критерия бустинга в качестве целевой функции оптимизационной модели для получения информативных закономерностей. Информативность, закономерность, критерий бустинга, оптимизационная модель Comparison of two optimization models for constructing patterns in the method of logical analysis of data Two optimization models for constructing informative patterns are proposed. An empirical confirmation of the expediency of using the boosting criterion as an objective function of the optimization model for obtaining informative patterns is given.


2020 ◽  
Vol 34 (12) ◽  
pp. 5041-5051
Author(s):  
Xinda Zhou ◽  
Zhaojun Yang ◽  
Hailong Tian ◽  
Chuanhai Chen ◽  
Liding Wang ◽  
...  

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.


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