quasiparticle swarm optimization for cross-section linear profile error evaluation of variation elliptical piston skirt
Clicks: 95
ID: 259878
2012
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Popular Article
28.2
/100
95 views
15 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #731 of 1,093 articles by views in journal of power sources
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,093 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Variation elliptical piston skirt has better mechanical and thermodynamic properties and it is widely applied in internal combustion engine in recent years. Because of its complex form, its geometrical precision evaluation is a difficult problem. In this paper, quasi-particle swarm optimization (QPSO) is proposed to calculate the minimum zone error and ellipticity of cross-section linear profile, where initial positions and initial velocities of all particles are generated by using quasi-random Halton sequences which sample points have good distribution properties and the particles’ velocities are modified by constriction factor approach. Then, the design formula and mathematical model of the cross-section linear profile of variation elliptical piston skirt are set up and its objective function calculation approach using QPSO to solve the minimum zone cross-section linear profile error is developed which conforms to the ISO/1101 standard. Finally, the experimental results evaluated by QPSO, particle swarm optimization (PSO), improved genetic algorithm (IGA) and the least square method (LSM) confirm the effectiveness of the proposed QPSO and it improves the linear profile error evaluation accuracy and efficiency. This method can be extended to other complex curve form error evaluation such as cam curve profile.
| Reference Key |
wen2012mathematicalquasiparticle
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Xiulan Wen;Yibing Zhao;Youxiong Xu;Danghong Sheng |
| Journal | journal of power sources |
| Year | 2012 |
| DOI |
10.1155/2012/761978
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.