Stochastic numerical solver for nanofluidic problems containing multi-walled carbon nanotubes
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- 作者：Muhammad Asif Zahoor Rajaa ; rasifzahoor@yahoo.com" class="auth_mail" title="E-mail the corresponding author ; Muhammad.asif@ciit-attock.edu.pk" class="auth_mail" title="E-mail the corresponding author ; asif.phdee10@iiu.edu.pk" class="auth_mail" title="E-mail the corresponding author ; Umair Farooqb ; mumairfarooq@hotmail.com" class="auth_mail" title="E-mail the corresponding author ; mumairfarooq1@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Naveed Ishtiaq Chaudharyc ; Naveedch835@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Naveed.ishtiaq@iiu.edu.pk" class="auth_mail" title="E-mail the corresponding author ; Abdul Majid Wazwazd ; wazwaz@sxu.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：Heat transfer ; Multi-walled carbon nanotubes ; Artificial neural networks ; Genetic algorithms ; Interior point method
- 刊名：Applied Soft Computing
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：38
- 期：Complete
- 页码：561-586
- 全文大小：6112 K
文摘
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Novel design of unsupervised ANNs for solving nanofluidic problems in mechanics.
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Hybrid computing GA-IPA is exploited for finding design parameters of networks.
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Design scheme is tested effectively on variant fluid flow and heat transfer scenarios.
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Correctness of scheme is verified by closely matched results from standard solutions.
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Statistical performance indices validate consistent accuracy and convergence.