Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks
Nanotechnology is a fundamental component of modern technology. Researchers have concentrated their efforts in recent years on inventing various algorithms to increase heat transmission rates. Using nanoparticles in host fluids to dramatically improve the thermal properties of ordinary fluids is one...
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MDPI AG
2022-08-01
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| Online Access: | https://www.mdpi.com/2075-4442/10/9/209 |
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| author | Anum Shafiq Andaç Batur Çolak Tabassum Naz Sindhu |
| author_facet | Anum Shafiq Andaç Batur Çolak Tabassum Naz Sindhu |
| author_sort | Anum Shafiq |
| collection | DOAJ |
| description | Nanotechnology is a fundamental component of modern technology. Researchers have concentrated their efforts in recent years on inventing various algorithms to increase heat transmission rates. Using nanoparticles in host fluids to dramatically improve the thermal properties of ordinary fluids is one way to address this problem. The article deals with the bio-convective Walter’s B nanofluid with thermophoresis and Brownian diffusion through a cylindrical disk under artificial neural networks (ANNs). In addition, the thermal conductivity, radiation, and motile density of microorganisms are taken into consideration. The Buongiorno model is utilized to investigate the properties of nanofluids in motile microorganisms. By using appropriate similarity variables, a dimensionless system of a differential system is attained. The non-linear simplified system of equations has been numerically calculated via the Runge–Kutta fourth-order shooting process. The consequences of flow parameters on the velocity field, temperature distribution, species volumetric concentration, and microorganism fields are all addressed. Two distinct artificial neural network models were produced using numerical data, and their prediction performance was thoroughly examined. It is noted that according to the error histograms, the ANN model’s training phase has very little error. Furthermore, mean square error values calculated for local Nusselt number, local Sherwood number, and local motile density number, parameters were obtained as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.58</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.24</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.55</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula>, respectively. Both artificial neural network models can predict with high accuracy, according to the findings of the calculated performance parameters. |
| first_indexed | 2024-03-09T23:21:08Z |
| format | Article |
| id | doaj.art-11b3994cc8a94b0a819082429bed704d |
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| language | English |
| last_indexed | 2024-03-09T23:21:08Z |
| publishDate | 2022-08-01 |
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| spelling | doaj.art-11b3994cc8a94b0a819082429bed704d2023-11-23T17:25:22ZengMDPI AGLubricants2075-44422022-08-0110920910.3390/lubricants10090209Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural NetworksAnum Shafiq0Andaç Batur Çolak1Tabassum Naz Sindhu2School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaMechanical Engineering Department, Niğde Ömer Halisdemir University, Niğde 51240, TurkeyDepartment of Statistics, Quaid I Azam University, Islamabad 44000, PakistanNanotechnology is a fundamental component of modern technology. Researchers have concentrated their efforts in recent years on inventing various algorithms to increase heat transmission rates. Using nanoparticles in host fluids to dramatically improve the thermal properties of ordinary fluids is one way to address this problem. The article deals with the bio-convective Walter’s B nanofluid with thermophoresis and Brownian diffusion through a cylindrical disk under artificial neural networks (ANNs). In addition, the thermal conductivity, radiation, and motile density of microorganisms are taken into consideration. The Buongiorno model is utilized to investigate the properties of nanofluids in motile microorganisms. By using appropriate similarity variables, a dimensionless system of a differential system is attained. The non-linear simplified system of equations has been numerically calculated via the Runge–Kutta fourth-order shooting process. The consequences of flow parameters on the velocity field, temperature distribution, species volumetric concentration, and microorganism fields are all addressed. Two distinct artificial neural network models were produced using numerical data, and their prediction performance was thoroughly examined. It is noted that according to the error histograms, the ANN model’s training phase has very little error. Furthermore, mean square error values calculated for local Nusselt number, local Sherwood number, and local motile density number, parameters were obtained as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.58</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.24</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.55</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula>, respectively. Both artificial neural network models can predict with high accuracy, according to the findings of the calculated performance parameters.https://www.mdpi.com/2075-4442/10/9/209motile microorganismsWalter’s B nanofluidvariable thermal conductivityBrownian motionthermophoresisartificial neural network |
| spellingShingle | Anum Shafiq Andaç Batur Çolak Tabassum Naz Sindhu Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks Lubricants motile microorganisms Walter’s B nanofluid variable thermal conductivity Brownian motion thermophoresis artificial neural network |
| title | Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks |
| title_full | Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks |
| title_fullStr | Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks |
| title_full_unstemmed | Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks |
| title_short | Optimization of Bioconvective Magnetized Walter’s B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks |
| title_sort | optimization of bioconvective magnetized walter s b nanofluid flow towards a cylindrical disk with artificial neural networks |
| topic | motile microorganisms Walter’s B nanofluid variable thermal conductivity Brownian motion thermophoresis artificial neural network |
| url | https://www.mdpi.com/2075-4442/10/9/209 |
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