Modeling on magnetohydrodynamic Stokes flow using machine learning and curve fitting.
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| Title: | Modeling on magnetohydrodynamic Stokes flow using machine learning and curve fitting. |
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| Authors: | Gurbuz-Caldag, Merve1 (AUTHOR) merve.gurbuz@tedu.edu.tr, Pekmen, Bengisen1 (AUTHOR) bengisen.pekmen@tedu.edu.tr |
| Source: | Neural Computing & Applications. Jun2025, Vol. 37 Issue 16, p9603-9619. 17p. |
| Subjects: | Stokes flow, Stream function, Learning curve, Fluid flow, Numerical calculations |
| Abstract: | In this study, neural network (NN) and curve fitting modeling of fluid flow characteristics of the magnetohydrodynamic (MHD) Stokes flow in a lid-driven cavity are utilized. Firstly, the MHD Stokes flow equations are numerically solved by the method of approximate particular solution for the variations of Hartmann number M ∈ [ 1 , 120 ] and the inclination angle a ∈ [ 0 , π ] . The essential data for modeling are extracted from the numerical results. The inputs are M and a, and the outputs are the infinity norm of stream function ψ , v velocity component, vorticity ω and the minimum value of u velocity. In modeling of these outputs, the distinct curve fitting functions are examined. NN is employed for different layer numbers and data partitions. It is obtained that the increase in the number of the hidden layers gives less error and locally weighted quadratic regression fit captures the best behavior in curve fitting. The usage of modeling allows us to be independent from the repeated numerical calculations. The capability of trilayer NN for modeling ψ , u , v , ω in the entire region is also shown. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | In this study, neural network (NN) and curve fitting modeling of fluid flow characteristics of the magnetohydrodynamic (MHD) Stokes flow in a lid-driven cavity are utilized. Firstly, the MHD Stokes flow equations are numerically solved by the method of approximate particular solution for the variations of Hartmann number M ∈ [ 1 , 120 ] and the inclination angle a ∈ [ 0 , π ] . The essential data for modeling are extracted from the numerical results. The inputs are M and a, and the outputs are the infinity norm of stream function ψ , v velocity component, vorticity ω and the minimum value of u velocity. In modeling of these outputs, the distinct curve fitting functions are examined. NN is employed for different layer numbers and data partitions. It is obtained that the increase in the number of the hidden layers gives less error and locally weighted quadratic regression fit captures the best behavior in curve fitting. The usage of modeling allows us to be independent from the repeated numerical calculations. The capability of trilayer NN for modeling ψ , u , v , ω in the entire region is also shown. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09410643 |
| DOI: | 10.1007/s00521-025-11088-7 |
