PSO-BP Neural Network-Based Strain Prediction of Wind Turbine Blades
Current Language EN
English English Türkçe Türkçe Español Español Português Português Pусский Pусский Filipino Filipino Tiếng Việt Tiếng Việt हिन्दी हिन्दी العربية العربية বাংলা বাংলা Deutsch Deutsch 中文 中文

PSO-BP Neural Network-Based Strain Prediction of Wind Turbine Blades

Liu X;Liu Z;Liang Z;Zhu SP;Correia JAFO;De Jesus AMP;;
Materials (Basel, Switzerland) 2019 Vol. 12 pp. -
164
x2019materialspso-bp

Abstract

The full-scale static testing of wind turbine blades is an effective means to verify the accuracy and rationality of the blade design, and it is an indispensable part in the blade certification process. In the full-scale static experiments, the strain of the wind turbine blade is related to the appl …

Keywords

National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine pmid:31212753 pmc6632004 doi:10.3390/ma12121889 xin liu zheng liu abílio m p de jesus

Access

URL:
https://www.pubmed.gov/?cmd=Search&term=Jos%C3%A9%...

Citation

ID: 112237
Ref Key: x2019materialspso-bp
Use this key to autocite in SciMatic or Thesis Manager

References

References

No Bibliography

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
112237
Unique Identifier:
Network:
Scimatic Chain (ID: 481)
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet
Gas fees required in SCI Coins
Buy SCI

Saymatik Web3.0 Wallet

Google Play
App Store