Home Physical Sciences Investigating effects of Lorentz forces and convective heating on ternary hybrid nanofluid flow over a curved surface using homotopy analysis method
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Investigating effects of Lorentz forces and convective heating on ternary hybrid nanofluid flow over a curved surface using homotopy analysis method

  • Showkat Ahmad Lone , F. M. Allehiany , Sadia Anwar , Sana Shahab , Anwar Saeed EMAIL logo and Sayed M. Eldin EMAIL logo
Published/Copyright: October 18, 2023
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Abstract

This work investigates the ternary hybrid nanofluid flow over an extending curved surface. The surface is impermeable and convective with hot working fluid water. Additionally, TiO 2 , CoFe 2 O 4 , and MgO nanoparticles are suspended with water to form a tri-hybrid nanofluid. The modeled equations are presented in the partial differential equation form and are then converted to ordinary differential equations with appropriate similarity variables. The semi-analytical solution is determined by homotopy analysis method. The impacts of magnetic field, Joule heating, chemical reaction, Brownian motion, and thermophoresis on flow profiles, Nusselt number, and Sherwood number are determined using tables and figures. The findings of this study demonstrated that as the magnetic parameter upsurges, the velocity distribution shrinkages, while the temperature distribution escalates. The greater curvature factor boots the velocity, thermal, and volumetric fraction distribution. The thermal and volumetric fraction distributions are the increasing functions of thermophoresis factor. The higher magnetic factor, Eckert number, and thermal Biot number increase the Nusselt number, while they reduce the higher Brownian and thermophoretic factors. The higher thermophoresis and Brownian motion factors heighten the Sherwood number. Furthermore, it has been noted that using nanofluid (TiO2-water) and hybrid nanoliquid (TiO2-CoFe2O4/water), the transfer of energy rate increases by up to 17.31 and 31.72% as the nanoparticle parameter increases from 0.01 to 0.04, respectively. However, the energy transference rate in case of ternary hybrid nanoliquid (TiO2-MgO-CoFe2O4/water) is 47.972%.

Nomenclature

( r , s )

curvilinear coordinates ( m )

TiO2

titanium dioxide

b

positive fixed number

T f

surface temperature ( K )

C p

specific heat capacity ( J K 1 kg 1 )

σ

electrical conductivity ( S m 1 )

Rc

curvature factor

Ec

Eckert number

γ

chemical reaction factor

Nb

Brownian motion factor

f ( η )

velocity field

MgO

magnesium oxide

u w = b s

stretching velocity ( m s 1 )

CoFe2O4

cobalt ferrite

ρ

density ( kg m 3 )

C w

surface volumetric fraction distribution ( mol m 3 )

k

thermal diffusivity ( W K 1 m 1 )

M

magnetic factor

Bi

thermal Biot number

Q

heat source parameter

Sc

Schmidt number

Nt

thermophoresis factor

1 Introduction

Titanium dioxide (TiO2) is an inorganic substance that is used extensively as a white pigment. Compared with many other inorganic pigments, TiO2 has the advantages such as high stability, being non-toxic, and low cost. TiO2 has three polymorphs: anatase, rutile, and brookite, but only anatase and rutile crystal forms are used as pigments. Both anatase and rutile crystals have very high refractive indices, and their particles can scatter visible light almost completely. In the early application of TiO2 as pigment, it was found that paint faded more rapidly than others when painted films were exposed to the Sun and ultraviolet light. Coating with inorganic compounds such as alumina or silica suppressed the catalytic activity on the surface and improved the weather resistance. Hence, titanium dioxide is used in a wide range of applications such as white pigment.

Modifications of metal oxide nanoparticles with metallic element are the ideal choice for heterogeneous photocatalysis to eliminate different kinds of contamination from wastewater. Among the metal and metal oxide nanoparticles, MgO nanoparticles played a significant role in antimicrobial activity, dye degradation, and phenol degradation activity. Ag nanoparticles are, without a doubt, the most widely used inorganic nanoparticles with huge applications. MgO nanoparticles acquired additional consideration due to their vast applications in medical, catalysis, as absorbents, etc.

The reason for cobalt spinel ferrites (CoFe2O4) gaining so much attention is because of their excellent chemical and good mechanical stabilities at room temperature, their moderate saturation magnetization, high coercivity, and their strong anisotropy. The chemical and mechanical stabilities of cobalt spinel ferrites are very different from their bulk counterparts. Due to all of these properties, cobalt ferrites have been broadly used in biotechnology, catalysis, biomedical, magnetic drug delivery, solar cells, magnetic cards, recording devices, and sensors. CoFe2O4 nanoparticles should have high magnetization values and narrow-size distribution for catalysis applications.

Nanofluid flow refers to the movement of a fluid that contains suspended tiny particles with diameters typically fluctuating from 1 to 100 nm. The flow behavior of nanofluids is different from that of pure fluids, as the nanoparticles can affect the viscosity, thermal flow, and flow characteristics of the fluid. Nanofluids have been extensively used recently due to their potential applications in areas such as energy storage, electronic cooling, and biomedical engineering. The idea of such fluids was floated first by Choi and Eastman [1] to augment the thermal behavior of normal fluids. The flow behavior of nanofluids is influenced by numerous factors, comprising the size and shape of nanoparticles, the volumetric fraction distribution and type of the nanoparticles, the flow rate and temperature of the fluid, and the geometry of the system. Mourad et al. [2] executed a numerical analysis of the entropy production and magnetohydrodynamics (MHD) natural convection of a Cu-water nanofluid that was enclosed within a wavy cylinder and heated Koch snowflake. Acharya et al. [3] deliberated on nanofluid flow on a gyratory disk using the influences of nanolayer and diameter of nanoparticles and determined that temperature has increased for progression in nanolayer ratio as well as thermal radiation but has declined with upsurge in diameter of nanoparticles. Shahid et al. [4] conducted numerical experiment for bioconvective nanofluid flow on a porous sheet. Bhatti et al. [5] scrutinized MHD Williamson nanofluid flow through a spinning circular sheet with influence of microorganisms and proved that the liquid velocity has weakened with progression in nanoparticles’ volumetric fraction distribution and magnetic factor, while, on the other side, the thermal distribution has augmented. Wijayanta [6,7] reported on the thermal evaluation of natural convection in an opening using the numerical approach to estimate the laminar natural convective flow along with heat exchange in triangle-shaped cavities. Makarim et al. [8] investigated numerically the Marangoni convection that occurs when steam is absorbed into a water-soluble lithium bromide solution. Kumar et al. [9] investigated theoretically the mathematical model of nanoliquid flow past a sheet with the impact of magnetic factor and chemically reactive effects.

Hybrid nanofluid flow is fluid flow that contains a mixture of two or more dissimilar nature of nanoparticles, such as metal oxide and carbon-based nanoparticles. The combination of nanoparticles can lead to synergistic effects, where the resulting nanofluid has enhanced thermal and/or mechanical properties compared to a single-component nanofluid. The flow behavior of hybrid nanofluids can be more complex than that of single-component nanofluids, as the different types of nanoparticles can interact with each other and with the base fluid in various ways. The properties of hybrid nanofluids can be influenced by factors such as the nature of the particles, particle–fluid interactions, and the flow conditions. The aim of investigating hybrid nanofluid is to gain a better idea about the flow behavior and properties of hybrid nanofluids, as well as to develop methods for optimizing their performance in various applications such as thermal management in electronic devices and energy storage systems. Zhang et al. [10] discussed hybrid nanofluid flow on a surface using nickel and tantalum nanoparticles using the effects of magnetic field and have concluded that flow has propagated faster in case of tantalum nanoparticles and Darcy number, while it has declined with progression in magnetic effects. Khan et al. [11] inspected bioconvective nanofluid flow with impact of binary chemical reactions, viscous dissipation, and activated energy on a heated needle. Chu et al. [12] examined MHD hybrid nanofluid flow through a channel using the impact of nanoparticles’ shape. Ojjela [13] discussed numerically the thermal transportation for silica–alumina hybrid nanofluid flow using modeling simulation. Gumber et al. [14] discussed the thermal flow for hybrid nanofluid flow on a surface using impacts of suction as well as injection and thermal radiations. Slimani et al. [15] discussed the MHD hybrid nanofluid’s flow with heat distribution in a trimmed cone with open domains that were stimulated by a magnetic flux. The study reveals that the rate of Nusselt number enhances with an upshot of magnetic flux.

Ternary hybrid nanofluid flow states the movement of a fluid that contains three different types of nanoparticles suspended in it. As discussed earlier, nanofluids are made by dispersing tiny particles, typically less than 100 nm in size, into a pure fluid. These particles are often metallic, ceramic, or carbon-based, and they can considerably alter the thermal and rheological properties of the fluid. A ternary hybrid nanofluid flow would contain three different types of nanoparticles, which could potentially lead to unique properties such as improved thermal conductivity or enhanced heat transfer characteristics. Arif et al. [16] analyzed the heat transmission of fractal model for ternary hybrid nanofluid flow in a conduit using different shapes of nanoparticles. Shahzad et al. [17] inspected the influences of pressure variation on ternary hybrid nanofluid flow through a circular conduit. When three unlike forms of nanoparticles are dispersed into a base fluid, they can considerably alter the heat and rheological characteristics of liquid, leading to unique behavior. Sudharani et al. [18] assessed computationally the ternary hybrid nanofluid flow affected by linearly radiated effects and slip flow and have deduced that the thermal flow performance has amplified more in case of ternary hybrid nanoparticles in comparison with other two fluids. The study of ternary hybrid nanofluid flow involves both computational modeling and experimental studies to better understand the behavior and potential applications of these complex fluids. Guedri et al. [19] inspected the thermally radiative flow of ternary hybrid nanofluid on a nonlinearly elongating surface using the Darcy–Forchheimer model.

Convective heat transfer refers to the transfer of heat through fluid and solid surface, typically a gas or a liquid that is in motion. The heat is transferred due to the movement of the fluid, which carries thermal energy away from the surface and transports it to another location. Convective heat transfer is an important mechanism in different phenomena, like cooling of electronic devices, the transfer of heat in a car’s radiator, and the circulation of air in a room. The analysis and prediction of convective heat transfer is an important area of study in many fields, including engineering, physics, and materials science. Shevchuk [20] inspected convective heat transportation for geometry of disk and cone by using the advanced asymptotic expansion method and turning effects of the cone on flow model. Olayemi et al. [21] inspected the parametric convective thermal transportation for fluid flow on a cylinder placed in a square conduit and deduced that the rate of heat transmission has been observed to rise with increasing aspect ratio and Rayleigh number. Manna et al. [22] discussed MHD fluid flow and production of entropy on a circular surface subject to the impact of convective thermal transportation. Wang and Wang [23] simulated computationally the convective heat transportation through a packed bed and have proved that the fluid flow and thermal transportation are affected by the porosity factor. Shi et al. [24] discussed the convective thermal transportation properties for fluid flow on a cylinder through a confined space.

Joule heating is a phenomenon in which electrical energy is changed to thermal energy. This heat is generated as a consequence of the collisions among the electrons and the atoms or molecules in the material. Abbas et al. [25] inspected the Darcy–Forchheimer phenomenon for fluid flow on a porous sheet subject to the impacts of Joule heating. Naseem et al. [26] deliberated the effects of Joule heating on MHD fluid flow using variable temperature and deduced that thermal transportation has amplified with progression in the Eckert number and weakened with upsurge in the Prandtl number. Rahseed et al. [27] treated analytically the MHD fluid flow with impact of Joule heating, viscous variable forces, and chemically reactive effects and have deduced that velocity distribution weakened, while thermal panels have expanded with growth in magnetic factor. Mahdy et al. [28] inspected numerically the effects of convective thermal conditions and Joule heating on a stretched sheet and have solved their modeled equations effectively using HPM. Waqas et al. [29] discussed bioconvective MHD fluid flow on a radiating stretchable sheet using viscously dissipative and Joule heating effects. Mahanthesh et al. [30] examined the production of entropy radiative thermal transportation for fluid flow in a channel subject to Joule heating and nonlinear mixed convection effects. Gangadhar et al. [31] deliberated on MHD bioconvective fluid flow with influences of microorganisms and Joule heating.

Curved surfaces are used in many fields, including engineering, architecture, art, and design. They can provide strength and stability to structures, as well as aesthetic applications. In addition, curved surfaces can be used to optimize airflow, improve acoustics, and reduce drag in vehicles and aircrafts. There are many different types of curved surfaces, each with their own properties and applications. Examples include spheres, cylinders, cones, tori (donut-shaped surfaces), and more complex shapes such as hyperboloids and ellipsoids. Curved surfaces are common in everyday life and can be found in a variety of objects, including the human body, cars, airplanes, and buildings. They can be used for both aesthetic and functional purposes. For example, the curved surface of a car’s body can improve its aerodynamics and reduce drag, while the curved surface of a building can improve its structural stability and provide an interesting architectural design. Mishra and Upreti [32] conducted a comparative analysis for two different types of hybrid nanoparticles on a curved sheet using the Buongiorno model and have proved that mass transportation has augmented with progression in thermophoretic and chemically reactive factors. Maity and Kundu [33] examined chemically reactive and magnetically driven flow nanofluid on a curved surface with effects of radiation and have noted that growth in mass and temperature and Biot numbers have supported, respectively, the transportation of volumetric fraction distribution and temperature. Khan et al. [34] investigated the gold nanoparticle carrier for blood flow on a curved sheet using thermal radiation, suction, and MHD effects. Sun et al. [35] investigated experimentally the control flow on a curved surface of jet at Mach 5 hypersonic flow of fluid. Fuzhang et al. [36] discussed the time-based nanofluid flow on an exponentially elongating curved surface using chemically reactive effects.

Keeping in mind the aforementioned literature, the authors decided to present the homotopic solution of the ternary hybrid nanofluid flow on an extending curved surface. The curved surface is considered to be impermeable and convectively heat with a hot working fluid. Three different types of nanoparticles ( TiO 2 , CoFe 2 O 4 , and MgO ) are mixed with water (base fluid) to form ternary nanofluid. The purpose of this investigation is to answer the following research questions:

  • What are the impacts of the embedded factors on velocity, temperature, and volumetric fraction distribution profiles of the ternary hybrid nanofluid flow?

  • How were the flow profiles (i.e., velocity, temperature, and volumetric fraction distribution profiles) if we increase the radius of curvature parameter of the curved surface?

  • How do the skin friction, local Nusselt number, and Sherwood number behave for the increasing embedded factors?

Thus to answer these questions, we have presented this study in the following sections. Section 2 depicts the leading equations. Similarity variables are used for the transformation of partial differential equations into ordinary differential equations (ODEs). The application of the homotopy analysis method (HAM) technique for solving transformed ODEs is discussed in Section 3. The convergence of the HAM method is presented in Section 4. The results obtained using HAM, along with figures and a table, are showcased in Section 5. Finally, the conclusive outcomes are summarized in Section 6.

2 Problem statement

We have assumed the ternary hybrid nanofluid flow encompassing TiO 2 , CoFe 2 O 4 , and MgO nanoparticles over a curved sheet. The curved surface has curvilinear coordinates ( r , s ). The direction of the flow is denoted by s , while r represents the direction perpendicular to the flow. The stretching velocity of sheet is represented by u w = b s , where b is the positive fixed number. The magnetic field B 0 is used along r -direction. For the convectively heat surface with a hot fluid and temperature T f such that T f > T w , where T w is the surface temperature and T is its ambient value, the corresponding values for volumetric fraction distribution are C w and C . Figure 1 shows the geometrical explanation of problem. Additionally, the following suppositions have made as:

  1. The flow is affected by magnetic, Joule heating, and chemical reaction effects.

  2. The surface is assumed to be impermeable.

  3. The temperature and volumetric fraction distribution equations account for the effects of Brownian motion and thermophoresis.

Figure 1 
               Curved surface representation.
Figure 1

Curved surface representation.

Based on the aforementioned assumptions, the model equations can be expressed as follows [37,38,39]:

(1) ( ( r + R ) u r ) r + R u s s = 0 ,

(2) u s 2 r + R = 1 ρ T H p r ,

(3) u r u s r + R r + R u s u s s + u s u r r + R = 1 ρ T H R r + R p s + μ T H ρ T H 2 u s r 2 + 1 r + R u s r u s ( r + R ) 2 σ T H B 0 2 ρ T H u s ,

(4) u r T r + R r + R u s T s = k T H ( ρ C p ) T H 2 T r 2 + 1 r + R T r + σ T H ( ρ C p ) T H B 0 2 u s 2 + Q 0 ( ρ C p ) T H ( T T ) + ( ρ C p ) p ( ρ C p ) T H D B C r T r + D T T T r 2 ,

(5) u r C r + R r + R u s C s = D B 2 C r 2 + 1 r + R C r + D T T 2 T r 2 + 1 r + R T r K r ( C C ) .

The constraints at boundaries are as follows [40,41]:

(6) u s = u w = b s , u r = 0 , k T H T r = h f ( T f T ) , C = C w at r = 0 u s 0 , u s r 0 , T T , C C as r .

The thermophysical properties of the ternary hybrid nanofluids are characterized as follows [19,42,43]:

(7) μ TH μ f = 1 ( 1 χ 1 ) 2.5 ( 1 χ 2 ) 2.5 ( 1 χ 3 ) 2.5 , ρ TH ρ f = ( 1 χ 1 ) ( 1 χ 2 ) ( 1 χ 3 ) + χ 3 ρ S 3 ρ f + χ 2 ρ S 2 ρ f + χ 1 ρ S 1 ρ f ρ f , ( ρ C p ) TH ( ρ C p ) f = ( 1 χ 1 ) ( 1 χ 2 ) ( 1 χ 3 ) + χ 3 ( ρ C p ) S 3 ( ρ C p ) f + χ 2 ( ρ C p ) S 2 ( ρ C p ) f + χ 1 ( ρ C p ) S 1 ( ρ C p ) f ( ρ C p ) f , k NF k f = k S 1 + 2 k f 2 χ 1 ( k f k S 1 ) k S 1 + 2 k f + χ 1 ( k f k S 1 ) , k HF k NF = k S 2 + 2 k f 2 χ 2 ( k f k S 2 ) k S 2 + 2 k f + χ 2 ( k f k S 2 ) , k TH k HF = k S 3 + 2 k f 2 χ 3 ( k f k S 3 ) k S 3 + 2 k f + χ 3 ( k f k S 3 ) σ NF σ f = σ S 1 + 2 σ f 2 χ 1 ( σ f σ S 1 ) σ S 1 + 2 σ f + χ 1 ( σ f σ S 1 ) , σ HF σ NF = σ S 2 + 2 σ f 2 χ 2 ( σ f σ S 2 ) σ S 2 + 2 σ f + χ 2 ( σ f σ S 2 ) , σ TH σ HF = σ S 3 + 2 σ f 2 χ 3 ( σ f σ S 3 ) σ S 3 + 2 σ f + χ 3 ( σ f σ S 3 )

In the aforementioned properties, χ 1 , χ 2 , and χ 3 represent the TiO 2 , CoFe 2 O 4 , and MgO nanoparticle volume fractions, S 1 denotes the TiO 2 nanoparticle, S 2 represents the CoFe 2 O 4 nanoparticle, and S 3 denotes the MgO nanoparticle. The thermophysical characteristics of nanoparticles and water are presented in Table 1.

Table 1

Experimental values of H 2 O and nanoparticles [44,45]

Properties ρ C p k σ
H 2 O 997.1 4,179 0.613 0.05
TiO 2 4,250 686.2 8.9538 2.38 × 106
CoFe 2 O 4 4,907 700 3.7 5.51 × 109
MgO 45 955 45 1.42 × 10-3

To transform the aforementioned equations, the similarity variables are defined as follows [40,46,47,48]:

(8) η = r b ν f , u s = b s f ( η ) , u r = R r + R b ν f f ( η ) , p = ρ f ( b s ) 2 P ( η ) , θ ( η ) = T T T f T , φ ( η ) = C C C f C .

Using the aforementioned similarity variables, equation (1) becomes apparent, and equation (2) can be simplified as follows:

(9) ρ f ρ T H P ( η ) = 1 η + R c f 2 ( η ) .

Eq. (3) can be reduced as:

(10) ρ f ρ T H 2 Rc η + Rc P ( η ) = μ T H / μ f ρ T H / ρ f f ( η ) + 1 η + Rc f ( η ) 1 ( η + Rc ) 2 f ( η ) Rc η + Rc f 2 ( η ) σ T H / σ f ρ T H / ρ f M f ( η ) + Rc η + Rc f ( η ) f ( η ) + Rc ( η + Rc ) 2 f ( η ) f ( η ) .

After differentiating equation (10) with respect to η and subsequently substituting it into equation (9), we obtain the following:

(11) μ T H / μ f ρ T H / ρ f f i v ( η ) + 2 ( η + Rc ) f ( η ) 1 ( η + Rc ) 2 f ( η ) + 1 ( η + Rc ) 3 f ( η ) σ T H / σ f ρ T H / ρ f M f ( η ) + 1 ( η + Rc ) f ( η ) + Rc ( η + Rc ) [ f ( η ) f ( η ) f ( η ) f ( η ) ] + Rc ( η + Rc ) 2 [ f ( η ) f ( η ) f 2 ( η ) ] Rc ( η + Rc ) 3 f ( η ) f ( η ) = 0 .

Likewise, the remaining equations are transformed as:

(12) 1 Pr k T H / k f ( ρ C p ) T H / ( ρ C p ) f θ ( η ) + 1 ( η + Rc ) θ ( ξ ) + Rc ( η + Rc ) θ ( η ) f ( η ) + Nt θ 2 ( η ) + Nb φ ( η ) θ ( η ) + σ T H / σ f ( ρ C p ) T H / ( ρ C p ) f M Ec f 2 ( η ) + 1 ( ρ C p ) T H / ( ρ C p ) f Q θ ( η ) = 0 .

(13) ϕ ( η ) + 1 ( η + Rc ) ϕ ( η ) + Nt Nb ( η + Rc ) θ ( η ) + η Sc ( η + Rc ) ϕ ( η ) f ( η ) + Nt Nb θ ( η ) Sc γ ϕ ( η ) = 0 .

(14) f ( 0 ) = 1 , f ( 0 ) = 0 , θ ( 0 ) = Bi k T H / k f ( θ ( 0 ) 1 ) , ϕ ( 0 ) = 1 , f ( ) 0 , f ( ) 0 , θ ( ) 0 , ϕ ( ) 0 .

Pressure can be reduced as:

(15) P ( η ) = η + Rc 2 Rc μ T H μ f f ( η ) + 1 η + Rc f ( η ) 1 ( η + Rc ) 2 f ( η ) σ T H σ f M f ( η ) + ρ T H ρ f Rc η + Rc f ( η ) f ( η ) + ρ T H ρ f Rc ( η + Rc ) 2 f ( η ) f ( η ) ρ T H ρ f Rc η + Rc f 2 ( η ) ,

where R c = b ν f R is the curvature factor, M = σ f B 0 2 ρ f b is the magnetic parameter, Bi = h f k f ν f b is the thermal Biot number, E c = b 2 s 2 ( C p ) f ( T f T ) is the Eckert number, Q = Q 0 b ( ρ C p ) f is the heat source parameter, γ = K r b is the chemical reaction factor, Nb = ( C f C ) D B ( ρ C p ) n p ν f ( ρ C p ) f is the Brownian motion factor, Nt = D T ( T f T ) ( ρ C p ) p ν f T ( ρ C p ) f is the thermophoresis factor, Sc = ν f D B is the Schmidt number, and Pr = ( ρ C p ) f ν f k f is the Prandtl number.

The quantities of interest are defined as follows:

(16) C f = τ r s ρ f s 2 b 2 , where τ r s = μ T H u r u r + R r = 0 , Nu s = s q w k f ( T f T ) , where q w = k T H T r r = 0 , Sh s = s q m D B ( C f C ) , where q m = D B C r r = 0 .

Using the similarity variables, the reduced form of equation (16) can be written as:

(17) Re s C f s = μ T H μ f f ( 0 ) 1 Rc f ( 0 ) , Nu s Re s = k T H k f θ ( 0 ) , Sh = Sh s Re s = ϕ ( 0 ) ,

where Re s = b s 2 ν f is the local Reynolds number.

3 HAM solution

To apply HAM, we introduced the initial guesses and linear operators as follows:

(18) f 0 ( η ) = 1 exp ( η ) , θ 0 ( η ) = Bi ( k T H / k f ) + Bi exp ( η ) , ϕ 0 ( η ) = exp ( η ) ,

(19) L f ( η ) = f f , L θ ( η ) = θ θ , L ϕ ( η ) = ϕ ϕ ,

with properties:

(20) L f ( x 1 + x 2 η + x 3 exp ( η ) + x 4 exp ( η ) ) = 0 , L θ ( x 5 exp ( η ) + x 6 exp ( η ) ) = 0 , L ϕ ( x 7 exp ( η ) + x 8 exp ( η ) ) = 0 ,

where x 1 x 8 are the constants of general solution.

3.1 Advantages of HAM

HAM has several advantages over many traditional analytical methods. It is a series expansion method that does not directly depend on small or large physical parameters. Thus, it is applicable not only for weakly but also strongly nonlinear problems. HAM is a unified method for the Lyapunov artificial small parameter method, Delta expansion method, Adomian decomposition method, and homotopy perturbation method. It gives excellent flexibility to the expression of the solution and how the solution is explicitly obtained, and it provides great freedom in choosing the base functions of the desired solution and the corresponding auxiliary linear operator of homotopy. HAM provides a simple way to ensure the convergence of the solution series and is able to combine with other techniques used in nonlinear differential equations such as spectral methods and Padé approximants.

4 Validation

To confirm the validation of the coded problem in Mathematica 12.0 software, Tables 2 and 3 are shown. The validation is confirmed under some special conditions when K varies and χ 1 = χ 2 = χ 3 = M = 0.0 . Table 2 shows the skin friction ( Re s C f s ) against different values of K . Upon comparing the present findings with the published results, it can be observed that the current results exhibit a high degree of similarity to the published results. Also, Table 3 displays the comparison of our results for θ ( 0 ) with established results for variations in Pr , while other parameters are taken as Nb = Nt = Q = M = Ec = χ 1 = χ 2 = χ 3 = 0 , β , and Rc . In this table, the problem is converted to a flat sheet by taking the assumption when Rc . In the flat surface problem, the current results and also close to the published results of the previously published works.

Table 2

Comparing the values of Re s C fs for variations in K and χ 1 = χ 2 = χ 3 = M = 0.0

R c 5 10 20 30 40 50 100 200 1000
Present outcomes 1.157565 1.072534 1.035464 1.023463 1.017346 1.013995 1.006963 1.003501 1.000798
Roşca and Pop [49] 1.15076 1.07172 1.03501 1.02315 1.01729 1.01380 1.00687 1.00342 1.00068
Afridi et al. [50] 1.1576312 1.0734886 1.0356098 1.0235310 1.0175866 1.0140492 1.0070384 1.0035641 1.0007993
Abbas et al. [51] 1.15763 1.07349 1.03561 1.02353 1.01759 1.01405 1.00704 1.00356 1.00079
Ahmad et al. [52] 1.157630 1.073490 1.023530 1.014050 1.003560
1.000790
Dey et al. [53] 1.157630 1.073490 1.0223
Table 3

Comparison of the values of θ ( 0 ) for different estimations of Pr , when Nb = Nt = Q = M = Ec = χ 1 = χ 2 = χ 3 = 0 and Rc

Pr 0.01 0.72 1.0 2.0 7.0 10
Present outcomes 0.015664 0.463144 0.581976 0.911361 1.895420 2.308034
Khan and Pop [54] 0.9114 1.8954
Wang [55] 0.9114 1.8954
Gorla and Sidawi [56] 0.9114 1.8905
Devi and Devi [57] 0.91135 1.89540
Grubka and Bobba [58] 0.0099 0.4631 0.5820 2.3080
Gowda et al. [59] 0.00978 0.46273 0.58193 2.30793

5 HAM convergence

The homotopic series solution contains the auxiliary factor . This parameter has the ability to control and adjust the series solution convergence. At 19th order of approximations, as given in Figure 2, the suitable range of velocity, temperature, and volumetric fraction distribution profiles have been obtained. From this figure, we can see that the velocity profile has the convergence area 1.0 f 1.2 , temperature profile has the convergence area 1.0 θ 0.9 , and the convergence area of the volumetric fraction distribution profile is 1.0 ϕ 0.9 .

Figure 2 
               
                  
                     
                        
                        
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                                 f
                              
                              ‴
                           
                           
                              
                                 (
                                 
                                    0
                                 
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                        f\prime\prime\prime (0)
                     
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                              θ
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                                 (
                                 
                                    0
                                 
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                        \theta ^{\prime} (0)
                     
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Figure 2

curves for f ( 0 ) , θ ( 0 ) and ϕ ( 0 ) .

6 Discussion of results

Next, the variations in different profiles (such as f ( η ) , θ ( η ) , and ϕ ( η ) ) , heat transfer rate Nu s Re s , and mass transfer rate Sh s Re s via embedded factors are discussed. Therefore, we have displayed Figures 311 and Tables 4 and 5 in this regard. The default values for the embedded factors are selected as Rc = 5.0 , Ec = 0.5 , M = 1.0 , Bi = 0.5 , Pr = 6.0 , Q = 0.2 , γ = 0.1 , χ 1 = χ 2 = χ 3 = 0.04 , Nt = 0.1 , Nb = 0.1 , and Sc = 1.0 . Figure 3(a) and (b) shows the impression of χ 1 = χ 2 = χ 3 on velocity ( f ( η ) ) and temperature ( θ ( η ) ) distributions, respectively. From both the figures, we see that the higher χ 1 = χ 2 = χ 3 reduces f ( η ) while increasing θ ( η ) . The reason is that the higher values of χ 1 = χ 2 = χ 3 increase the density of the ternary hybrid nanofluid flow due to which the fluid becomes denser, and thus, the momentum boundary layer thickness and velocity profile reduce. On the other hand, the higher values of χ 1 = χ 2 = χ 3 increase the thermal conductivity of the ternary hybrid nanofluid flow, which, as a result, increases the thermal boundary layer thickness and temperature profile. The effects of magnetic factor ( M ) on velocity ( f ( η ) ) and temperature ( θ ( η ) ) distributions are revealed in Figure 4(a) and (b). The velocity distribution reduces, while the thermal panels upsurge with growth in M . The greater M produces the Lorentz force that counteracts the flow of nanoparticles, and hence, the movement of nanoparticles reduces. Therefore, the greater M declines the velocity distribution. On the other hand, the opposite effect of M on temperature distribution is observed. This effect is for reducing the movement of nanoparticles because the greater Lorentz force opposes the flow motion, which results in a growth in θ ( η ) so upsurge in M boosts the temperature distribution. The effects of curvature factor ( R c ) on f ( η ) , θ ( η ) , and ϕ ( η ) are displayed in Figure 5(a)–(c). The greater R c increases f ( η ) , θ ( η ) , and ϕ ( η ) . In fact, “ R c ” represents the radius of the curved surface. As we increase R c , the curved surface transforms to become flat. So by this fact, as Rc (say Rc = 1 , 000 ), the surface is not curved anymore. It is a fact that the velocity, temperature, and volumetric fraction distributions are higher on the flat surface compared to those on the curved surface. Thus, increasing R c increases the panels f ( η ) , θ ( η ) , and ϕ ( η ) . The effect of heat source ( Q ) on θ ( η ) is portrayed in Figure 6 with a booting behavior in θ ( η ) . Actually, the increasing heat source means that we have provided extra heat to the nanofluid flow, which ultimately increases the internal heat of fluid flow. This internal temperature heightens the temperature at the boundary and so are the panels θ ( η ) . Therefore, the higher Q augments θ ( η ) . Figure 7(a) and (b) shows the effects of thermophoresis factor Nt on θ ( η ) and ϕ ( η ) . These graphs show that for larger values of Nt , both the thermal and volumetric fraction distributions increase in boundary layer area. This is attributed to the phenomenon that particles near a heated surface generate a thermophoretic force, aiding in the dispersion of particles away from the fluid region. Consequently, this leads to an increase in the thickness of the temperature and volumetric fraction distribution boundary layers. Thus, increasing Nt heightens both θ ( η ) and ϕ ( η ) . Figure 8(a) and (b) shows the impact of Nb on θ ( η ) and ϕ ( η ) . Upsurge in N b increases the thermal distribution and reduces the volumetric fraction distribution. The Brownian motion occurs when suspended nanoparticles move randomly in the base fluid caused by fast-moving molecules or atoms. It should be noted that the Brownian motion is related to nanoparticle size and usually occurs as aggregates or agglomerates. A large particle will have a very weak Brownian motion, and the factor ( Nb ) will have the smallest value. Boost in N b increases the boundary layer region, and boosting temperature distribution is depicted in Figure 8(a). Figure 8(b) shows, however, that a higher ( N b ) value results in a slower growth in nanoparticle volumetric fraction distribution. Figure 9 illustrates the effects of the Eckert number ( Ec ) on θ ( η ) with a growing behavior of θ ( η ) with variations in Ec . As E c grows, frictional heating produces heat in the fluid. The Eckert number is a physical parameter that signifies the ratio of kinetic energy to the specific enthalpy difference between a wall and a fluid. Consequently, as the Eckert number increases, there is a conversion of kinetic energy into internal energy through the work performed against viscous fluid forces. As a result, rising E c raises the values of θ ( η ) . Figure 10 shows the impact of Biot number ( Bi ) on θ ( η ) . Based on this figure, it can be observed that the temperature distribution increases with higher values of Bi . In fact, higher values of Bi enhance the heat transfer rate at the curved surface of the sheet. This increased rate of heat transfer results in the expansion of the temperature boundary layer thickness and subsequently raises the overall temperature. Consequently, higher values of Bi lead to an increased temperature distribution. Figure 11 displays the effect of the Schmidt number ( Sc ) on ϕ ( η ) , where an upsurge is noted in ϕ ( η ) for variations in S c . Actually, mass diffusivity and Schmidt number are inversely proportional to each other. So from here, as S c increases, the mass diffusivity increases that results in a reduction in ϕ ( η ) . Table 4 shows the variation in the Nusselt number via variations in M , Ec , Nb , Nt , Bi , and χ 1 = χ 2 = χ 3 . The increasing χ 1 = χ 2 = χ 3 shows the increasing variation in the Nusselt number. The reason is that the higher values χ 1 = χ 2 = χ 3 increase the thermal conductivity of the ternary hybrid nanofluid flow, which results in an augmenting variation in the Nusselt number. The higher magnetic factor increases the Nusselt number. The rationale behind this is that a higher magnetic factor diminishes the particle motion, thereby resulting in an increase in temperature and heat transfer rate at the curved surface. Consequently, the Nusselt number rises as the magnetic factor increases. Furthermore, higher values of the Eckert number and thermal Biot number also contribute to an increased Nusselt number. Conversely, the Nusselt number decreases with higher thermophoresis and Brownian motion factors. Similarly, the higher thermophoresis and Brownian motion factors heighten the Sherwood number, as shown in Table 5.

Figure 3 
               (a) Impression of 
                     
                        
                        
                           
                              
                                 χ
                              
                              
                                 1
                              
                           
                           =
                           
                              
                                 χ
                              
                              
                                 2
                              
                           
                           =
                           
                              
                                 χ
                              
                              
                                 3
                              
                           
                        
                        {\chi }_{1}={\chi }_{2}={\chi }_{3}
                     
                   on 
                     
                        
                        
                           
                              f
                              ′
                           
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        f^{\prime} (\eta )
                     
                   and (b) impression of 
                     
                        
                        
                           
                              
                                 χ
                              
                              
                                 1
                              
                           
                           =
                           
                              
                                 χ
                              
                              
                                 2
                              
                           
                           =
                           
                              
                                 χ
                              
                              
                                 3
                              
                           
                        
                        {\chi }_{1}={\chi }_{2}={\chi }_{3}
                     
                   on 
                     
                        
                        
                           θ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \theta (\eta )
                     
                  .
Figure 3

(a) Impression of χ 1 = χ 2 = χ 3 on f ( η ) and (b) impression of χ 1 = χ 2 = χ 3 on θ ( η ) .

Figure 4 
               (a) impression of 
                     
                        
                        
                           M
                        
                        M
                     
                   on 
                     
                        
                        
                           
                              f
                              ′
                           
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        f^{\prime} (\eta )
                     
                   and (b) impression of 
                     
                        
                        
                           M
                        
                        M
                     
                   on 
                     
                        
                        
                           θ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \theta (\eta )
                     
                  .
Figure 4

(a) impression of M on f ( η ) and (b) impression of M on θ ( η ) .

Figure 5 
               (a) Impression of 
                     
                        
                        
                           Rc
                        
                        \text{Rc}
                     
                   on 
                     
                        
                        
                           
                              f
                              ′
                           
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        f^{\prime} (\eta )
                     
                  , (b) impression of 
                     
                        
                        
                           Rc
                        
                        \text{Rc}
                     
                   on 
                     
                        
                        
                           θ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \theta (\eta )
                     
                  , and (c) impression of 
                     
                        
                        
                           Rc
                        
                        \text{Rc}
                     
                   on 
                     
                        
                        
                           ϕ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \phi (\eta )
                     
                  .
Figure 5

(a) Impression of Rc on f ( η ) , (b) impression of Rc on θ ( η ) , and (c) impression of Rc on ϕ ( η ) .

Figure 6 
               Impression of 
                     
                        
                        
                           Q
                        
                        Q
                     
                   on 
                     
                        
                        
                           θ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \theta (\eta )
                     
                  .
Figure 6

Impression of Q on θ ( η ) .

Figure 7 
               (a) Impression of 
                     
                        
                        
                           Nt
                        
                        \text{Nt}
                     
                   on 
                     
                        
                        
                           θ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \theta (\eta )
                     
                   and (b) impression of 
                     
                        
                        
                           Nt
                        
                        \text{Nt}
                     
                   on 
                     
                        
                        
                           ϕ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \phi (\eta )
                     
                  .
Figure 7

(a) Impression of Nt on θ ( η ) and (b) impression of Nt on ϕ ( η ) .

Figure 8 
               (a) Impression of 
                     
                        
                        
                           Nb
                        
                        \text{Nb}
                     
                   on 
                     
                        
                        
                           θ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \theta (\eta )
                     
                   and (b) impression of 
                     
                        
                        
                           Nb
                        
                        \text{Nb}
                     
                   on 
                     
                        
                        
                           ϕ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \phi (\eta )
                     
                  .
Figure 8

(a) Impression of Nb on θ ( η ) and (b) impression of Nb on ϕ ( η ) .

Figure 9 
               Impression of 
                     
                        
                        
                           Ec
                        
                        \text{Ec}
                     
                   on 
                     
                        
                        
                           θ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \theta (\eta )
                     
                  .
Figure 9

Impression of Ec on θ ( η ) .

Figure 10 
               Impression of 
                     
                        
                        
                           Bi
                        
                        \text{Bi}
                     
                   on 
                     
                        
                        
                           θ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \theta (\eta )
                     
                  .
Figure 10

Impression of Bi on θ ( η ) .

Figure 11 
               Impression of 
                     
                        
                        
                           Sc
                        
                        \text{Sc}
                     
                   on 
                     
                        
                        
                           ϕ
                           
                              
                                 (
                                 
                                    η
                                 
                                 )
                              
                           
                        
                        \phi (\eta )
                     
                  .
Figure 11

Impression of Sc on ϕ ( η ) .

Table 4

Nusselt number for different values of M , Ec , Nb , Nt , Bi , and χ 1 = χ 2 = χ 3

χ 1 = χ 2 = χ 3 M Ec Nb Nt Bi Nu s Re s
0.01 0.986684
0.02 1.243256
0.03 1.486474
0.04 1.625784
0.1 0.486546
1.2 0.497549
1.9 0.510753
0.3 0.609543
0.6 0.658074
0.9 0.697544
0.5 0.508538
0.7 0.497538
0.9 0.480746
0.1 0.696743
0.5 0.666436
0.9 0.620753
0.1 0.597438
0.3 0.637906
0.5 0.675274
Table 5

Sherwood number for different values of Nb and Nt

Nb Nt Sh s Re s
0.5 0.864753
0.7 0.896447
0.9 0.925789
0.1 0.596537
0.5 0.608637
0.9 0.612483

7 Conclusion

A ternary hybrid nanofluid flow with TiO 2 , CoFe 2 O 4 , and MgO nanoparticles over a curved sheet has been examined in this effort. The modeled equations are converted to ODEs using suitable similarity variables. The semi-analytical solution is determined by HAM. The impacts of magnetic field, Joule heating, chemical reaction, and other factors have been discussed on flow profiles and Nusselt, Sherwood numbers using figures and tables. By completing this theoretical analysis, the following concluding points are extracted:

  1. The velocity distribution exhibits a decreasing trend with respect to the magnetic parameter, while the temperature distribution demonstrates an increasing trend with respect to the magnetic parameter.

  2. The greater curvature parameter boots the flow, thermal, and volumetric fraction distributions.

  3. Higher heat source factor, the Eckert and thermal Biot numbers increase the thermal distribution.

  4. Thermal and volumetric fraction distributions are the growing functions of the thermophoresis factor.

  5. Thermal distribution has booted up with upsurge in the Brownian factor, while the volumetric fraction distribution panels have retarded with growth in the Brownian factor and the Schmidt number.

  6. The higher magnetic parameter, Eckert and thermal Biot numbers increase the Nusselt number while reduces with the higher Brownian and thermophoresis factors.

  7. Higher Brownian and thermophoresis factors heighten the Sherwood number.

Acknowledgments

This research was supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R259), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

  1. Funding information: This research was supported by the Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R259), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Conflict of interest: The authors state no conflict of interest.

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Received: 2023-05-16
Revised: 2023-08-11
Accepted: 2023-09-05
Published Online: 2023-10-18

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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