Develop the artificial neural network approach to predict thermal transport analysis of nanofluid inside a porous enclosure.

This study explores the impacts of heat transportation on hybrid (Ag + MgO) nanofluid flow in a porous cavity using artificial neural networks (Bayesian regularization approach (BRT-ANN) neural networks technique). The cavity considered in this analysis is a semicircular shape with a heated and a cooled wall. The dynamics of flow and energy transmission in the cavity are influenced by various features such as the effect of magnetize field, porosity and volume fraction of nanoparticles. To explore the outcomes of these features on hybrid nanofluid thermal and flow transport, a BRT-ANN model is developed. The ANN model is trained using a dataset generated through numerical scheme. The trained ANN model is then used to predict the heat and flow transport characteristics for various input parameters. The accuracy of the ANN simulation is confirmed through comparison of the predicted results with the results obtained through numerical simulations. By maintaining the corrugated wall uniformly heated, we inspected the levels of isotherms, streamlines and heat transfer distribution. A graphical illustration highlights the characteristics of the Hartmann and Rayleigh numbers, permeability component in porous material, drag force and rate of energy transport. According to the percentage analysis, nanofluids (Ag + MgO/H2O) are prominent to enhance the thermal distribution of traditional fluids. The study demonstrates the potential of ANNs in predicting the impacts of various factors on hybrid nanofluid flow and heat transport, which can be useful in designing and optimizing heat transfer systems.

Energy transfer through third-grade fluid flow across an inclined stretching sheet subject to thermal radiation and Lorentz force.

The heat and mass transfer through the third grade fluid (TGF) flow over an inclined elongating sheet with the consequences of magnetic field and chemical reaction is reported. The impact of activation energy, heat source/sink, and thermal radiation is considered on the TGF flow. Fluid that demonstrate non-Newtonian (NN) properties such as shear thickening, shear thinning, and normal stresses despite the fact that the boundary is inflexible is known as TGF. It also has viscous elastic fluid properties. In the proposed model, the TGF model is designed in form of nonlinear coupled partial differential equations (PDEs). Before employing the numerical package bvp4c, the system of coupled equations are reduced into non-dimensional form. The finite-difference code bvp4c, in particular, executes the Lobatto three-stage IIIa formula. The impacts of flow constraints on velocity field, energy profile, Nusselt number and skin friction are displayed through Tables and Figures. For validity of the results, the numerical comparison with the published study is performed through Table. From graphical results, it can be perceived that the fluid velocity enriches with the variation of TGF factor and Richardson number. The heat source parameter operational as a heating mediator for the flow system, its influence enhances the fluid temperature.

Stagnation point flow of hybrid nanofluid flow passing over a rotating sphere subjected to thermophoretic diffusion and thermal radiation.

The effects of thermal radiation and thermophoretic particles deposition (TPD) on the hybrid nanofluid (HNF) flow across a circling sphere have momentous roles in research and engineering. Such as electrical devices, projectiles, thermal conveyance, sheet production, renewable energy, and nuclear-powered plants. Therefore, the current study presents the stagnation point flow of HNF flows about an orbiting sphere. The HNF is organized with the accumulation of aluminum alloys (AA70772 and AA7075) nanoparticles in the water. The HNF flow model equations are changed into the non-dimensional form of ODEs through the similarity variables and then numerically solved through the parametric simulation. It has been perceived that the significance of the rotation factor boosts the velocity curve, while the flow motion drops with the increasing numbers of AA7072 and AA7075 nanoparticles. Furthermore, the addition of AA7072 and AA70775 nano particulates in water lessens with the temperature profile. The energy distribution rate in case of hybrid nanoliquid enhances from 3.87 to 13.79%, whereas the mass dissemination rate enhances from 4.35 to 11.24% as the nanoparticles concentration varies from 0.01 to 0.03.

Numerical solution of an electrically conducting spinning flow of hybrid nanofluid comprised of silver and gold nanoparticles across two parallel surfaces.

The analysis of the energy transport mechanism received much attention from scientists and researchers. Conventional fluids like vegetable oils, water, ethylene glycol, and transformer oil play a vital role in numerous industrial activities. In certain industrial operations, the low heat conductivity of base fluids causes significant difficulties. This inevitably led to the advancement of critical aspects of nanotechnology. The tremendous significance of nanoscience is in improving the thermal transfer process in different heating transmitting equipment. Therefore, the MHD spinning flow of hybrid nanofluid (HNF) across two permeable surfaces is reviewed. The HNF is made of silver (Ag) and gold (Au) nanoparticles (NPs) in the ethylene glycol (EG). The modeled equations are non-dimensionalized and degraded to a set of ODEs through similarity substitution. The numerical procedure parametric continuation method (PCM) is used to estimate the 1st order set of differential equations. The significances of velocity and energy curves are derived versus several physical parameters. The results are revealed through Tables and Figures. It has been determined that the radial velocity curve declines with the varying values of the stretching parameter, Reynold number, and rotation factor while improving with the influence of the suction factor. Furthermore, the energy profile enhances with the rising number of Au and Ag-NPs in the base fluid.

Energy transmission through carreau yasuda fluid influenced by ethylene glycol with activation energy and ternary hybrid nanocomposites by using a mathematical model.

The current study aims to assess the augmentation of energy transmission in the presence of magnetic dipole through trihybrid Carreau Yasuda nanofluid flow across a vertical sheet. The rheological properties and thermal conductivity of the based fluids are improved by framing an accurate combination of nanoparticles (NPs). The trihybrid nanofluid (Thnf) has been synthesized by the addition of ternary nanocomposites (MWCNTs, Zn, Cu) to the ethylene glycol. The energy and velocity conveyance has been observed in the context of the Darcy Forchhemier effect, chemical reaction, heat source/sink, and activation energy. The trihybrid nanofluid flow across a vertical sheet has been accurately calculated for velocity, concentration, and thermal energy in the form of a system of nonlinear PDEs. The set of PDEs is reduced to dimensionless ODEs by using suitable similarity replacements. The obtained set of non-dimensional differential equations is numerically computed through the Matlab package bvp4c. It has been perceived that the energy curve enhances by the influence of heat generation factor and viscous dissipation. It is also noted that the magnetic dipole has a momentous contribution to raising the transmission of thermal energy of trihybrid nanofluid and declines the velocity curve. The inclusion of multi-wall carbon nanotubes (MWCNTs), zinc (Zn), and copper (Cu) nano particulates to the base fluid "ethylene glycol", augments the energy and velocity outlines.

A memory effect model to predict COVID-19: analysis and simulation.

On 19 September 2020, the Centers for Disease Control and Prevention (CDC) recommended that asymptomatic individuals, those who have close contact with infected person, be tested. Also, American society for biological clinical comments on testing of asymptomatic individuals. So, we proposed a new mathematical model for evaluating the population-level impact of contact rates (social-distancing) and the rate at which asymptomatic people are hospitalized (isolated) following testing due to close contact with documented infected people. The model is a deterministic system of nonlinear differential equations that is fitted and parameterized by least square curve fitting using COVID-19 pandemic data of Pakistan from 1 October 2020 to 30 April 2021. The fractional derivative is used to understand the biological process with crossover behavior and memory effect. The reproduction number and conditions for asymptotic stability are derived diligently. The most common non-integer Caputo derivative is used for deeper analysis and transmission dynamics of COVID-19 infection. The fractional-order Adams-Bashforth method is used for the solution of the model. In light of the dynamics of the COVID-19 outbreak in Pakistan, non-pharmaceutical interventions (NPIs) in terms of social distancing and isolation are being investigated. The reduction in the baseline value of contact rates and enhancement in hospitalization rate of symptomatic can lead the elimination of the pandemic.

Numerical Study of the Electromagnetohydrodynamic Bioconvection Flow of Micropolar Nanofluid through a Stretching Sheet with Thermal Radiation and Stratification.

The current work explores the bioconvection micropolar nanofluid through a stretching surface subjected to thermal radiation, stratification, and heat and mass transmission. Bioconvection contains the gyrotactic (random movement of microorganism in the direction of gravity with weak horizontal verticity) unicellular microorganism in aqueous environments. Heat and mass transfer assists the bioconvection to occur. The aim of this research is to evaluate the heat transfer rate of nanofluid in the presence of a unicellular microorganism. Self-similar variables are induced to reduce the governing equations into a non-linear differential system which is further solved via the bvp4c algorithm to tackle the fluid problem. Using visual representations, the effects of a number of dimensional less factors arising from the dimensional less differential system are determined. For a range of limiting conditions, the obtained results of this model correspond precisely to those in the literature. This study's findings are highly regarded in the evaluation of the impact of key design factors on heat transfer and, therefore, in the optimization of industrial processes. Skin friction, local Nusselt number, Sherwood number, and density of microorganism concentrations are also studied for various parameters. Buoyancy ratio factor supports skin friction and density of microorganism profile to increase. Local Nusselt number drops due to the thermal radiation factor. Brownian motion speeds up the Sherwood number.

Mixed Convection Nanofluid Flow with Heat Source and Chemical Reaction over an Inclined Irregular Surface.

Two-dimensional mixed convection radiative nanofluid flow along with the non-Darcy permeable medium across a wavy inclined surface are observed in the present analysis. The transformation of the plane surface from the wavy irregular surface is executed via coordinate alteration. The fluid flow has been evaluated under the outcomes of heat source, thermal radiation, and chemical reaction rate. The nonlinear system of partial differential equations is simplified into a class of dimensionless set of ordinary differential equations (ODEs) through a similarity framework, where the obtained set of ODEs are further determined by employing the computational technique parametric continuation method (PCM) via MATLAB software. The comparative assessment of the current outcomes with the earlier existing literature studies confirmed that the present findings are quite reliable, and the PCM technique is satisfactory. The effect of appropriate dimensionless flow constraints is studied versus energy, mass, and velocity profiles and listed in the form of tables and figures. It is perceived that the inclination angle and wavy surface assist to improve the flow velocity by lowering the concentration and temperature. The velocity profile enhances with the variation of the inclination angle of the wavy surface, non-Darcian term, and wavy surface term. Furthermore, the rising value of Brownian motion and thermophoresis effect diminishes the heat-transfer rate.

The impact of vaccination on the modeling of COVID-19 dynamics: a fractional order model.

The coronavirus disease 2019 (COVID-19) is a recent outbreak of respiratory infections that have affected millions of humans all around the world. Initially, the major intervention strategies used to combat the infection were the basic public health measure, nevertheless, vaccination is an effective strategy and has been used to control the incidence of many infectious diseases. Currently, few safe and effective vaccines have been approved to control the inadvertent transmission of COVID-19. In this paper, the modeling approach is adopted to investigate the impact of currently available anti-COVID vaccines on the dynamics of COVID-19. A new fractional-order epidemic model by incorporating the vaccination class is presented. The fractional derivative is considered in the well-known Caputo sense. Initially, the proposed vaccine model for the dynamics of COVID-19 is developed via integer-order differential equations and then the Caputo-type derivative is applied to extend the model to a fractional case. By applying the least square method, the model is fitted to the reported cases in Pakistan and some of the parameters involved in the models are estimated from the actual data. The threshold quantity ( R 0 ) is computed by the Next-generation method. A detailed analysis of the fractional model, such as positivity of model solution, equilibrium points, and stabilities on both disease-free and endemic states are discussed comprehensively. An efficient iterative method is utilized for the numerical solution of the proposed model and the model is then simulated in the light of vaccination. The impact of important influential parameters on the pandemic dynamics is shown graphically. Moreover, the impact of different intervention scenarios on the disease incidence is depicted and it is found that the reduction in the effective contact rate (up to 30%) and enhancement in vaccination rate (up to 50%) to the current baseline values significantly reduced the disease new infected cases.

Non-Fourier energy transmission in power-law hybrid nanofluid flow over a moving sheet.

Ethylene glycol is commonly used as a cooling agent in the engine, therefore the study associated with EG has great importance in engineering and mechanical fields. The hybrid nanofluid has been synthesized by adding copper and graphene nanoparticles into the Ethylene glycol, which obeys the power-law rheological model and exhibits shear rate-dependent viscosity. As a result of these features, the power-law model is utilized in conjunction with thermophysical characteristics and basic rules of heat transport in the fluid to simulate the physical situations under consideration. The Darcy Forchhemier hybrid nanofluid flow has been studied under the influence of heat source and magnetic field over a two-dimensionally stretchable moving permeable surface. The phenomena are characterized as a nonlinear system of PDEs. Using resemblance replacement, the modeled equations are simplified to a nondimensional set of ODEs. The Parametric Continuation Method has been used to simulate the resulting sets of nonlinear differential equations. Figures and tables depict the effects of physical constraints on energy, velocity and concentration profiles. It has been noted that the dispersion of copper and graphene nanoparticulate to the base fluid ethylene glycol significantly improves velocity and heat conduction rate over a stretching surface.

A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load.

COVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained R 0 ≈ 1.50 . Finally, an efficient numerical scheme of Adams-Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.

Dynamics of fractional order COVID-19 model with a case study of Saudi Arabia.

The novel coronavirus disease or COVID-19 is still posing an alarming situation around the globe. The whole world is facing the second wave of this novel pandemic. Recently, the researchers are focused to study the complex dynamics and possible control of this global infection. Mathematical modeling is a useful tool and gains much interest in this regard. In this paper, a fractional-order transmission model is considered to study its dynamical behavior using the real cases reported in Saudia Arabia. The classical Caputo type derivative of fractional order is used in order to formulate the model. The transmission of the infection through the environment is taken into consideration. The documented data since March 02, 2020 up to July 31, 2020 are considered for estimation of parameters of system. We have the estimated basic reproduction number ( R 0 ) for the data is 1.2937 . The Banach fixed point analysis has been used for the existence and uniqueness of the solution. The stability analysis at infection free equilibrium and at the endemic state are presented in details via a nonlinear Lyapunov function in conjunction with LaSalle Invariance Principle. An efficient numerical scheme of Adams-Molten type is implemented for the iterative solution of the model, which plays an important role in determining the impact of control measures and also sensitive parameters that can reduce the infection in the general public and thereby reduce the spread of pandemic as shown graphically. We present some graphical results for the model and the effect of the important sensitive parameters for possible infection minimization in the population.

Modeling and analysis of the dynamics of novel coronavirus (COVID-19) with Caputo fractional derivative.

The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused million of infected cases globally and still pose an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 200,000. Recently, many mathematical models have been considered to better understand the coronavirus infection. Most of these models are based on classical integer-order derivative which can not capture the fading memory and crossover behavior found in many biological phenomena. Therefore, we study the coronavirus disease in this paper by exploring the dynamics of COVID-19 infection using the non-integer Caputo derivative. In the absence of vaccine or therapy, the role of non-pharmaceutical interventions (NPIs) is examined on the dynamics of theCOVID-19 outbreak in Pakistan. First, we construct the model in integer sense and then apply the fractional operator to have a generalized model. The generalized model is then used to present the detailed theoretical results. We investigate the stability of the model for the case of fractional model using a nonlinear fractional Lyapunov function of Goh-Voltera type. Furthermore, we estimate the values of parameters with the help of least square curve fitting tool for the COVID-19 data recorded in Pakistan since March 1 till June 30, 2020 and show that our considered model give an accurate prediction to the real COVID-19 statistical cases. Finally, numerical simulations are presented using estimated parameters for various values of the fractional order of the Caputo derivative. From the simulation results it is found that the fractional order provides more insights about the disease dynamics.