Neural artificial networking for nonlinear Darcy–Forchheimer nanofluidic slip flow

Khan, M. Ijaz; Shoaib, Mohammed; Zubair, Ghania; Kumar, R. Naveen; Prasannakumara, B. C.; Mousa, Abd Allah A.; Malik, M.Y.; Raja, Muhammad Asif Zahoor

doi:10.1007/s13204-022-02528-0

Cited by 21 publications

(6 citation statements)

References 45 publications

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“…Machine learning is a specific subset of artificial intelligence. Neural networks are a subset of machine learning that mimics the functioning of the human brain to do various tasks such as pattern recognition, picture segmentation, and text mining (see [36][37][38][39]). The approach for solving differential equations using PINN is beyond the data dependency and embraces the physics knowledge gained by the neural units.…”

Section: Physic Informed Neural Networkmentioning

confidence: 99%

Analyzing magnetic dipole impact in fluid flow with endothermic/exothermic reactions: neural network simulation

R S,

Sharma

et al. 2024

Phys. Scr.

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The consequence of exothermic/endothermic chemical reactions and Arrhenius activation on the heat and mass transport of the liquid flow past a cylinder in the incidence of a magnetic dipole is considered in the current investigation. Magnetic dipoles are used in medical applications such as magnotherapy and spectroscopy, to produce static magnetic fields. Scientists and engineers can improve the effectiveness of chemical reactions or heat transfer operations by analyzing the impact of reactions on flow and building systems with optimized flows. The modelled equations are converted into non-dimensional ordinary differential equations (ODEs) by using similarity variables. The resultant equations are solved by employing the physics-informed neural network (PINN) technique. Additionally, the comparison of PINN with the numerical method Runge Kutta Fehlberg's fourth-fifth order (RKF-45) is studied. The effects of different parameters on the temperature, concentration, and velocity profiles for endothermic/exothermic instances are shown graphically. The thermal, velocity, and concentration profiles get stronger as the curvature parameter values increase for both endothermic and exothermic cases. The influence of activation energy parameters, chemical reaction parameters, and endothermic/exothermic reaction parameters on the thermal and concentration is also depicted.

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Section: Physic Informed Neural Networkmentioning

confidence: 99%

Analyzing magnetic dipole impact in fluid flow with endothermic/exothermic reactions: neural network simulation

R S,

Sharma

et al. 2024

Phys. Scr.

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“…All of this is feasible due to an increase in the rate of heat transmission of nanofluids. Several investigations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] were conducted to demonstrate a considerable increase in the rate of heat exchange in instances of hybrid nanofluids under various situations. All these investigations inspired the researchers to develop more effective nanofluids with improved heat transfer and thermal conductivity.…”

Section: Introductionmentioning

confidence: 99%

Radiative Impact on Jeffery Trihybrid Convective Nanoflow over an Extensible Riga Plate: Multiple Linear Regression Analysis

Bhargavi,

2024

Contemp. Math.

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Solar thermal systems utilize solar energy to generate heat, and the incorporation of nanoparticles such as Al2O3-Cu-Ni with a water base can elevate their efficiency. These nanofluids, composed of aluminum oxide, copper, and nickel nanoparticles dispersed in water, enhance heat absorption and transfer within the system. This improvement contributes to heightened overall performance and effectiveness of solar thermal systems. Cupronickel alloy helps in the process of desalination. Hence, this study examines the heat exchange properties in the context of a boundary layer flow of a trihybrid over a variable-thickness Riga plate stretched and heated by convective heat with non-Newtonian fluid (Jeffery) in the presence of thermal radiation. The governing equations of the boundary layer are transformed into a system of ordinary differential equations through appropriate similarity transformations, and those equations are resolved utilizing a boundary value problem program. The engineering parameters are analyzed through the application of multiple linear regression. The key finding of the investigation is that the Prandtl number, and thickness index number all have a positive impact on the Nusselt number. The presence of radiation and a uniform heat source improves the Nusselt number, physically this energy transfer improvement assists in higher solar collector efficacy; and converts that energy to usable heat. The rationale behind selecting trihybrid nanoparticles Al2O3, Cu, and Ni lies in the balance and inertness of Al2O3, with metals Cu, and Ni, both possessing more thermal conductivity.

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“…In recent years, the application of machine learning techniques in nanomaterials and pharmaceutical technologies has gained significant attention from researchers. Several studies have used backpropagated neural networks to model various systems [39,40], including Darcy-Forchheimer slip flow models for nanomaterials and ferrofluids [41], non-Fourier heat flux models for transient heat exchange [42], and ternary nanofluid flow between parallel plates [43]. The accuracy of these models was evaluated using reference datasets obtained via the Homotopy Analysis Method and numerical methods.…”

Section: Introductionmentioning

confidence: 99%

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights

et al. 2023

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Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have shown active potential as medications against many viruses. Employing molecular dynamics simulations and machine learning (ML) combined with van der Waals density functional theory, we have uncovered the binding characteristics of these two antiviral drugs on a phosphorene nanocarrier. Herein, by using four different machine learning models (i.e., Bagged Trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Regression Trees (RT)), the Hamiltonian and the interaction energy of antiviral molecules in a phosphorene monolayer are trained in an appropriate way. However, training efficient and accurate models for approximating the density functional theory (DFT) is the final step in using ML to aid in the design of new drugs. To improve the prediction accuracy, the Bayesian optimization approach has been employed to optimize the GPR, SVR, RT, and BT models. Results revealed that the GPR model obtained superior prediction performance with an R2 of 0.9649, indicating that it can explain 96.49% of the data’s variability. Then, by means of DFT calculations, we examine the interaction characteristics and thermodynamic properties in a vacuum and a continuum solvent interface. These results illustrate that the hybrid drug is an enabled, functionalized 2D complex with vigorous thermostability. The change in Gibbs free energy at different surface charges and temperatures implies that the FP and EB molecules are allowed to adsorb from the gas phase onto the 2D monolayer at different pH conditions and high temperatures. The results reveal a valuable antiviral drug therapy loaded by 2D biomaterials that may possibly open a new way of auto-treating different diseases, such as SARS-CoV, in primary terms.

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Neural artificial networking for nonlinear Darcy–Forchheimer nanofluidic slip flow

Cited by 21 publications

References 45 publications

Analyzing magnetic dipole impact in fluid flow with endothermic/exothermic reactions: neural network simulation

Analyzing magnetic dipole impact in fluid flow with endothermic/exothermic reactions: neural network simulation

Radiative Impact on Jeffery Trihybrid Convective Nanoflow over an Extensible Riga Plate: Multiple Linear Regression Analysis

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights

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