Details
| Original language | English |
|---|---|
| Pages (from-to) | 311-335 |
| Number of pages | 25 |
| Journal | INTERNATIONAL JOURNAL OF HYDROMECHATRONICS |
| Volume | 5 |
| Issue number | 4 |
| Early online date | 16 Nov 2022 |
| Publication status | Published - 2022 |
Abstract
In this work, the hybrid intelligent computing method is developed to solve the Falkner-Skan equations with various wedge angles, which combines efficient Jaya algorithm with classical Runge-Kutta method. Using higher order reduction strategies, the whole problem can be reduced to solving of coupled differential equations with prescribed initial and boundary conditions. The hybrid Jaya Runge-Kutta method is found to yield stable and accurate results and can extract those unknown parameters brought by asymptotic boundary condition in solving the coupled differential equations. In addition, the Jaya algorithm, without the need for tuning the algorithm-specific parameters, is proved to be effective and stable for optimisation problems. By comparing numerical results obtained using the Jaya algorithm with particle swarm optimisation (PSO), genetic algorithm (GA), hyperband, the hybrid Jaya Runge-Kutta method proves to be more stable and accurate, which shows great potential for solving more complicated multifield and multiphase flow problems.
Keywords
- boundary layer flow, Falkner-Skan, hyperband, Jaya algorithm, optimisation, PDEs, Runge-Kutta method
ASJC Scopus subject areas
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Mechanical Engineering
- Materials Science(all)
- Materials Science (miscellaneous)
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In: INTERNATIONAL JOURNAL OF HYDROMECHATRONICS, Vol. 5, No. 4, 2022, p. 311-335.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Integrated intelligent Jaya Runge-Kutta method for solving Falkner-Skan equations with various wedge angles
AU - Guo, Hongwei
AU - Zhuang, Xiaoying
AU - Rabczuk, Timon
N1 - Funding Information: The authors would like to thank Xiaoyu Meng for his help regarding code development.
PY - 2022
Y1 - 2022
N2 - In this work, the hybrid intelligent computing method is developed to solve the Falkner-Skan equations with various wedge angles, which combines efficient Jaya algorithm with classical Runge-Kutta method. Using higher order reduction strategies, the whole problem can be reduced to solving of coupled differential equations with prescribed initial and boundary conditions. The hybrid Jaya Runge-Kutta method is found to yield stable and accurate results and can extract those unknown parameters brought by asymptotic boundary condition in solving the coupled differential equations. In addition, the Jaya algorithm, without the need for tuning the algorithm-specific parameters, is proved to be effective and stable for optimisation problems. By comparing numerical results obtained using the Jaya algorithm with particle swarm optimisation (PSO), genetic algorithm (GA), hyperband, the hybrid Jaya Runge-Kutta method proves to be more stable and accurate, which shows great potential for solving more complicated multifield and multiphase flow problems.
AB - In this work, the hybrid intelligent computing method is developed to solve the Falkner-Skan equations with various wedge angles, which combines efficient Jaya algorithm with classical Runge-Kutta method. Using higher order reduction strategies, the whole problem can be reduced to solving of coupled differential equations with prescribed initial and boundary conditions. The hybrid Jaya Runge-Kutta method is found to yield stable and accurate results and can extract those unknown parameters brought by asymptotic boundary condition in solving the coupled differential equations. In addition, the Jaya algorithm, without the need for tuning the algorithm-specific parameters, is proved to be effective and stable for optimisation problems. By comparing numerical results obtained using the Jaya algorithm with particle swarm optimisation (PSO), genetic algorithm (GA), hyperband, the hybrid Jaya Runge-Kutta method proves to be more stable and accurate, which shows great potential for solving more complicated multifield and multiphase flow problems.
KW - boundary layer flow
KW - Falkner-Skan
KW - hyperband
KW - Jaya algorithm
KW - optimisation
KW - PDEs
KW - Runge-Kutta method
UR - http://www.scopus.com/inward/record.url?scp=85147875383&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2010.05682
DO - 10.48550/arXiv.2010.05682
M3 - Article
AN - SCOPUS:85147875383
VL - 5
SP - 311
EP - 335
JO - INTERNATIONAL JOURNAL OF HYDROMECHATRONICS
JF - INTERNATIONAL JOURNAL OF HYDROMECHATRONICS
SN - 2515-0464
IS - 4
ER -