Volume 38, Issue 1 (8-2019)
2019, 38(1): 1-18 |
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Noghrehabadi A, Mirzaei R, Ghalambaz M. A New Approach for Solving Heat and Mass Transfer Equations of Viscoelastic Nanofluids using Artificial Optimization Method. Computational Methods in Engineering 2019; 38 (1) :1-18
URL: http://jcme.iut.ac.ir/article-1-657-en.html
URL: http://jcme.iut.ac.ir/article-1-657-en.html
1- Department of Mechanical Engineering, Shahid Chamran University, Ahvaz, Iran.
2- Department of Mechanical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran. ,m.ghalambaz@iaud.ac.ir
2- Department of Mechanical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran. ,
Abstract: (3662 Views)
The behavior of many types of fluids can be simulated using differential equations. There are many approaches to solve differential equations, including analytical and numerical methods. However, solving an ill-posed high-order differential equation is still a major challenge. Generally, the governing differential equations of a viscoelastic nanofluid are ill-posed; hence, their solution is a challenging task. In addition, the presence of very tiny nanoparticles (lower than 100 nm) induces new heat and mass transfer mechanisms which can increase the complexity of the behavior of the viscoelastic nanofluids. Therefore, creating or developing new analytical or semi-analytical approaches to solve the governing equations of these types of nanofluids is highly demanded. In the present study, by using a new idea and utilizing an optimization approach, a new solution approach has been presented to solve the governing equations of viscoelastic nanofluids. By using the optimization method, a basic initial guess was changed toward an optimized solution satisfying all boundary conditions and the governing equations. The results indicate the robustness and accuracy of the presented method in dealing with the high-order ill-posed governing differential equations of viscoelastic nanofluids.
Keywords: Viscoelastic nanofluid, Similarity solution, Numerical approach, Optimization method, Neural network
Type of Study: Research |
Subject:
General
Received: 2017/04/15 | Accepted: 2018/10/8 | Published: 2019/08/15
Received: 2017/04/15 | Accepted: 2018/10/8 | Published: 2019/08/15
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