Thermal performance and SVM-based regression of natural convection in a 3D cavity filled with nanofluids as two phase mixture under combined effects of magnetic field and inner conductive hollow rotating conic object

被引:4
|
作者
Selimefendigil, Fatih [1 ,2 ]
Kocyigit, Yucel [3 ]
Oztop, Hakan F. [4 ,5 ]
机构
[1] King Faisal Univ, Coll Engn, Dept Mech Engn, Al Hasa 31982, Saudi Arabia
[2] Manisa Celal Bayar Univ, Dept Mech Engn, TR-45140 Manisa, Turkiye
[3] Manisa Celal Bayar Univ, Dept Elect & Elect Engn, TR-45140 Manisa, Turkiye
[4] Firat Univ, Technol Fac, Dept Mech Engn, TR-23119 Elazig, Turkiye
[5] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung, Taiwan
关键词
Hollow rotating cone; Two phase flow; Nanofluid; Magnetic field; Finite element method; Support vector machines; CNT-WATER NANOFLUID; HEAT-TRANSFER; HYBRID NANOFLUIDS; FORCED-CONVECTION; MIXED CONVECTION; CUBICAL CAVITY;
D O I
10.1016/j.enganabound.2023.04.015
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this study, a conductive hollow rotating conic object (H-RCO) is developed for convection control and thermal management in a 3D partially heated enclosure under uniform magnetic field with nanofluid considering two phase mixture formulation. Analysis is conducted for different parameters of interest as: Rayleigh number (Ra between 10(4) and 10(6)), angular rotational speed of the H-RCO (Omega between -60 and 60), Hartmann number (Ha between 0 and 50), expansion ratio (r1 between 1.1 and 2.5) and conductivity ratio (KR between 0.01 and 50). The rotational speed and expansion ratio of the object contributes significantly to the overall performance improvements. At the highest speed of the H-RCO, the average Nusselt number (Nu) rises up to 38% when compared to cases of non-rotating object. When object with highest expansion ratio is used at rotational speed of Omega = -40, the average Nu rises by about 36%. The impacts of using magnetic field on the reduction of convective effects are stronger when rotations are active while up to 69% reduction of average Nu is seen at the highest strength. Thermal conductivity of the object at higher speeds contributes slightly to the overall heat transfer. Support vector machine based regression model is used for thermal performance predictions while model with third order polynomial kernel gives the best results as compared to high fidelity 3D computational results.
引用
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页码:311 / 325
页数:15
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