Owing to significant applications in environmental engineering, biomedical systems, and advanced cooling technologies, this study delves into the numerical exploration of thermo-bioconvection process in a two-sided lid-driven magneto-viscoplastic hybrid nanofluid flow within porous enclosure incorporating oxytactic microorganisms. The lower wall moves in x-direction, while the upper wall moves opposite to lower wall. The enclosure is subjected to isothermal heating from the left wall and cooling from the right wall, while the upper and lower walls remain insulated. To describe the rheological behavior of the viscoplastic fluid, the Casson fluid model was utilized. The “finite difference method (FDM) in association with successive over relaxation (SOR), successive under relaxation (SUR), and Gauss–Seidel techniques is utilized to address the challenge of solving the nonlinear coupled governing partial differential equations (PDEs).” A comprehensive analysis is conducted to explore and elucidate the impacts of distinct critical parameters on contours the flow structure, thermal distribution, oxygen, and microorganisms concentrations. Numerical calculations were performed using specified parameter values in the range, i.e., 0.1 ≤ Rab ≤ 10; 0.05 ≤ Pe ≤ 0.5; 105 ≤ Ra ≤ 107, 25 ≤ Re ≤ 75, 0 ≤ Ha ≤ 20, 10−4 ≤ Da ≤ 10−2, Casson fluid parameter (0.1 ≤ <InlineEquation ID="IEq1"> <EquationSource Format="TEX">${\beta }_{1}\le 1)$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi>β</mi> <mn>1</mn> </msub> <mrow> <mo>≤</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </mrow> </math></EquationSource> </InlineEquation>; 0% ≤ <InlineEquation ID="IEq2"> <EquationSource Format="TEX">${\phi }_{\text{hnf}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>ϕ</mi> <mtext>hnf</mtext> </msub> </math></EquationSource> </InlineEquation> ≤ 4%, and Schmidt number (0.1 ≤ Sc ≤ 1). The outcomes of this work indicate that the “average Nusselt number (Nuavg)” enhances with higher "Ra," "Re," and "<InlineEquation ID="IEq3"> <EquationSource Format="TEX">${\phi }_{\text{hnf}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>ϕ</mi> <mtext>hnf</mtext> </msub> </math></EquationSource> </InlineEquation>" values but decreases with "Da" and "Ha," whereas average Sherwood number (Shavg) decreases with increasing "Ra," "Da," and "Pe." Nnavg decreases with higher values of "Ra" and "Ha," but increases with increasing "Re," "Da," "<InlineEquation ID="IEq4"> <EquationSource Format="TEX">${\phi }_{\text{hnf}}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>ϕ</mi> <mtext>hnf</mtext> </msub> </math></EquationSource> </InlineEquation>," and "Pe." Moreover, the implementation and testing of the “artificial neural network (ANN)” technique are carried out to predict the overall thermal behavior. Computational simulations establish a benchmark for comparison, validating the accuracy and efficiency of ANN predictions. Moreover, this study highlights the capability of ANN in modeling intricate fluid dynamics, providing insights into optimizing bioconvection applications across engineering and environmental systems.

Thermo-bioconvection in two-sided lid-driven magneto-hybrid nanofluid flow within porous enclosure containing oxytactic microorganisms using artificial neural network

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发表日期：

2025年6月29日

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Anil Ahlawat,
Shilpa Chaudhary,
K. Loganathan,
Rifaqat Ali,
H. Thameem Basha

摘要

Owing to significant applications in environmental engineering, biomedical systems, and advanced cooling technologies, this study delves into the numerical exploration of thermo-bioconvection process in a two-sided lid-driven magneto-viscoplastic hybrid nanofluid flow within porous enclosure incorporating oxytactic microorganisms. The lower wall moves in x-direction, while the upper wall moves opposite to lower wall. The enclosure is subjected to isothermal heating from the left wall and cooling from the right wall, while the upper and lower walls remain insulated. To describe the rheological behavior of the viscoplastic fluid, the Casson fluid model was utilized. The “finite difference method (FDM) in association with successive over relaxation (SOR), successive under relaxation (SUR), and Gauss–Seidel techniques is utilized to address the challenge of solving the nonlinear coupled governing partial differential equations (PDEs).” A comprehensive analysis is conducted to explore and elucidate the impacts of distinct critical parameters on contours the flow structure, thermal distribution, oxygen, and microorganisms concentrations. Numerical calculations were performed using specified parameter values in the range, i.e., 0.1 ≤ Ra_b ≤ 10; 0.05 ≤ Pe ≤ 0.5; 10⁵ ≤ Ra ≤ 10⁷, 25 ≤ Re ≤ 75, 0 ≤ Ha ≤ 20, 10⁻⁴ ≤ Da ≤ 10⁻², Casson fluid parameter (0.1 ≤ ${\beta }_{1}\le 1)$ $β_{1} \leq 1)$ ; 0% ≤ ${\phi }_{\text{hnf}}$ $ϕ_{hnf}$ ≤ 4%, and Schmidt number (0.1 ≤ Sc ≤ 1). The outcomes of this work indicate that the “average Nusselt number (Nu_avg)” enhances with higher "Ra," "Re," and " ${\phi }_{\text{hnf}}$ $ϕ_{hnf}$ " values but decreases with "Da" and "Ha," whereas average Sherwood number (Sh_avg) decreases with increasing "Ra," "Da," and "Pe." Nn_avg decreases with higher values of "Ra" and "Ha," but increases with increasing "Re," "Da," " ${\phi }_{\text{hnf}}$ $ϕ_{hnf}$ ," and "Pe." Moreover, the implementation and testing of the “artificial neural network (ANN)” technique are carried out to predict the overall thermal behavior. Computational simulations establish a benchmark for comparison, validating the accuracy and efficiency of ANN predictions. Moreover, this study highlights the capability of ANN in modeling intricate fluid dynamics, providing insights into optimizing bioconvection applications across engineering and environmental systems.