design optimization of the centrifugal pumps via low fidelity models
Clicks: 177
ID: 254742
2018
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
Emerging Content
30.0
/100
177 views
37 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #177 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
Low fidelity model assisted design optimization of turbomachines has reduced the total computational and experimental costs. These models are called surrogate models which mimic the actual experiments or simulations. The surrogate models can generate thousands of approximate results from a few samples, making it easy to locate the optimal solution. Ample articles reported surrogate assisted design optimization of centrifugal pumps. In this article, the authors try to give a brief overview of the surrogate based optimization technique along with its historical applications and trend of the recent use. The various key design parameters which affect the performance of the centrifugal pump have also been discussed. The effectiveness of the surrogate based optimization technique and corresponding performance metrics have been discussed.
| Reference Key |
siddique2018mathematicaldesign
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;M. Hamid Siddique;Arshad Afzal;Abdus Samad |
| Journal | journal of power sources |
| Year | 2018 |
| DOI |
10.1155/2018/3987594
|
| 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.