Home Thermal valuation and entropy inspection of second-grade nanoscale fluid flow over a stretching surface by applying Koo–Kleinstreuer–Li relation
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Thermal valuation and entropy inspection of second-grade nanoscale fluid flow over a stretching surface by applying Koo–Kleinstreuer–Li relation

  • Faisal Shahzad EMAIL logo , Wasim Jamshed , Rabia Safdar , Nor Ain Azeany Mohd Nasir , Mohamed R. Eid , Meznah M. Alanazi and Heba Y. Zahran
Published/Copyright: June 1, 2022
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Abstract

There are flow research centers on magnetohydrodynamic (MHD) emission of auxiliary liquid in an extended region. The prevailing model is constrained by attractions/infusion and gooey release. The administering model is based on the Koo–Kleinstreuer–Li nanofluid model in the existence of entropy generation. Final requirements of this model are addressed by implementing the shooting strategy, which incorporates a fourth approach for the Runge–Kutta strategy. Into the bargain, the last adds (in standard ordinary differential equations (ODE) divisions) are obtained from the measurable controls partial differential equations, which were represented toward the start of the overseeing model. The varieties for all boundaries are exhibited through graphical arrangements. It is noticed that expanding the substantial volume portion diminishes speed but builds nuclear power dispersion. Likewise, the classification of mathematical qualities on divider heat move rate and skin contact is introduced. Both Reynolds and Brinkman numbers improve the entropy rate of the thermal system resulting in the growth effects of inertial forces and the surface heat dissipation, respectively.

1 Introduction

The limit stream is distinguished as the district of the most extensive stream field nearby, with colossal divider crashes. Since the area of interest is near the surface and it is expected to stream, the speed is roughly equivalent to the surface. We will not analyze the information related to limit layer in-stream regions impacted by the current tension angle [1]. Prandtl proposed the idea of a limit layer in 1904. This idea has considered the expectation of skin pressure, divider heat moves, and limit partition, which works with the plan of aeroplanes, ships, and different vehicles. The century of the Prandtl proposition is commended in different ways [2]. The limit layer is a layer of fluid near the limiting region where the thickness impacts are enormous. In fluid mechanics, a slender layer of streaming gas or liquid contacts an area like the wing of a plane or within a line. The fluid in the limit layer is underneath the shear strength. The speed range is across the limit layer from the top to zero, as long as the fluid contacts the surface. The limit layers are flimsy at the front edges of the aeroplane wing and thicker at the later edges [3]. The limit layer is a tiny district close to the divider, where the speed shifts from 0% to almost 100% of the all-out free-streaming velocity. This district is significant because it can influence stream conduct by influencing stream boundaries, for example, speed and strain decrease [4]. Many cross-line stream applications, particularly in the development of different materials, need to defeat the progression of liquid to work appropriately. A vehicle with an improved body works superior to a body that does not work as expected. A more coordinated body implies that the limit layer of wind current will not sever the body surface, and in this way, there will be less drag on the structure. Carriers, airfoil configuration, is fundamental for an aeroplane. Airfoils are planned by the requirement for a progression of the limit layer to be beneficial, as indicated by the need of the aircraft. What makes a difference is the approach of the airfoil. So airfoils are planned with the goal that the plane can take off and land under typical conditions. While removing the plane requirements to defeat the air opposition, so the airfoil is appropriately planned, no division of the limit layer except for a similar air obstruction is utilized to advantage when it shows up because the aeroplane needs to land. The airfoil assault point along these lines changes what breaks the limit layer, and for that reason, air drag is made, which assists with dealing back [5]. Subsequently, the idea of a limit layer recommends that the progression of Reynolds big numbers can be isolated into two significant imbalances areas. The consistency can be disregarded with the most incredible stream rate, and an inviscid limit arrangement joins the stream. The subsequent district is the littlest limit of the divider where the consistency ought to be considered [6].

Heat transfer is a warm designing discipline that includes assembling, using, changing, and trading hotness energy between versatile frameworks. Heat move is isolated into different cycles, like warm conduction, warm convection, warm radiation, and energy move through stage changes. To undertake heat move, designers likewise consider moving a broad scope of synthetic mixtures (shift in weather conditions mass exchange), either cold or blistering. Albeit these strategies have various qualities, they, as a rule, happen all the while in a similar framework. Heat trade occurs when the progression of a lot of fluid (gas or fluid) conveys its hotness in a fluid. All convective cycles also send halfway hotness to the flow [7]. Heat move is quite possibly the main modern cycle. All through the modern field, heat should be added, deducted, or eliminated from the conveyance of one cycle to another. In principle, the hotness that is disseminated by a hot fluid is never precisely equivalent to the hotness that is acquired by a chilly fluid because of the deficiency of normal hotness [8]. Application for heat move in modern creation, that is, almost 100% of creation, utilizes a particular interaction to move heat. Drying processes are altogether types of hotness move. The modern employments of hotness move liquids from straightforward, dry plans to cutting-edge measured frameworks that fill numerous roles in the creative interaction. As there are numerous varieties in the plan and use of cycles in the utilization of hotness move liquids, the number of businesses that utilized this strategy is additionally huge [9]. Scaling down gigantically affects the innovation of hotness exchangers and transforms heat exchangers into more minimized and more effective. The productivity of the hotness exchanger significantly affects the general proficiency and strength of the nuclear power framework. A miniature channel heat sink is another instrument in heat trade innovation. The benefits of an enormous hotness move region and the high cohesiveness of a little channel heat sink make it an extremely effective hotness exchanger for the utilization of electronic cooling [10].

Nanoliquids seem to be liquid–solid mixtures made up of solid particles as small as a nanometer in diameter, fibers, shafts, or chambers suspended in various basic liquids [11]. Nanoliquids also offer potential mechanical solutions for facilitating additional heat transfer due to their numerous advantages and lack of common high-temperature instabilities. Due to the size impact and Brownian progression of nanoparticles in liquids, nanofluids address further produced dependability that differs from conventional liquids enhanced with solid particles of micrometer or millimeter size. It is possible to stream nanofluid endlessly into a microchannel, and the heat transfer structure can be simplified to use nanofluids for high-efficiency heat transfer. It was a few years ago that various overview articles [12,13,14,15,16,17,18] on nanofluids that came out. This includes metals, nonmetal particles, and carbon nanotubes. All of these materials have been rigorously analyzed by a range of highly qualified experts. Nanofluid diffusion by nanodroplets has recently been described [19]. Because of their fragility, nanoemulsion liquids are well-suited for bulk delivery. However, what if the nanodroplets’ ability to deal with nanofluids’ heated conductivity could be improved? Nanoemulsion fluids must be developed for the research of heated fluids in this scenario. When it comes to warm conductivity, direct heat, consistency, and thickness, the nanofluid’s convective heat transfer coefficients are crucial in determining the fluid’s adequacy for heat transfer. Yang and Han [20] advanced a similar method based on nanoparticles and liquid metal. The importance of nanofluids should be altered in light of the advancement of nanofluid structures. At the moment, assessments of novel nanofluid structures are in the testing stage, and statements regarding the planned usage of an unprecedented nanofluid system are uncommon. The nanoparticles that are frequently employed to organize nanofluids are as follows: (i) metal particles (Cu, Al, Fe, Au, and Ag); (ii) non-metallic particles (Al2O3, CuO, Fe3O4, TiO2, and SiC); (iii) carbon and tuberculosis; and (iv) nanodroplet. Water, oil, (CH3)2CO, decene, and ethylene glycol are all examples of fundamental fluids [21]. Since the turn of the century, the speculation of as far as the feasible layer has demonstrated astounding significance and offered unimaginable power to the assessment of liquid equipment. One of the primary professions of small speculation is the evaluation of pulling bodies on the stream, for example, drawing a level plate into the zero position, this way and that, an airfoil, a plane body, or a turbine edge [22]. The current study determined where imbuement and ingestion occurred by determining the hotness motion and stream of pseudo-plastic non-Newtonian nanofluid over the entry surface. By combining Newtonian and non-Newtonian nanofluids, non-Newtonian nanofluids exhibit superior hotness move execution as compared to Newtonian nanofluids. In any event, changing the kind of nanoparticles has a significant effect on the heat transfer process during maintenance [23].

Second-grade fluids are a subtype of non-Newtonian fluids in which the speed field has up to two auxiliaries in the pressure strain tensor connection. In contrast, Newtonian fluids have up to two subordinates. The investigators evaluated the stream of second-grade fluid augmentations in various breaking point layer streams and found them to be viable in a variety of streams. Nowadays, researchers are particularly interested in the movement of heat in non-Newtonian fluids. The properties of a second-grade fluid are altered by the impact of temperature-dependent thickness. When the temperature increases, the thickness of gaseous substances increases, while the thickness of liquids decreases. Thus, many researchers focused their attention on the impact of variable consistency models. A study by Fetecau [24,25,26] focused on two winding movements for a subsequent grade and Oldroyd-B fluids in tube modeled areas. Real restrictions were explored to see how they affected smooth growth by introducing a time-dependent demand at a certain moment in time. Additionally, Jamil and Khan [27] used a comparative approach to determine Burger’s fluid speed and sheer pressure. Considering the fluid stream’s meaning in barrel modeled space, specialists concentrated on the smooth development of chambers by considering various fluids and their breaking point circumstances. In an indirect part, Barnes et al. [28,29] used pulsatile Air Plethysmography to evaluate the polymer stream, while Davies et al. [30] and Lin et al. [31] focused on a comparative problem for White-Metzner fluid. The formation of a fractionalized second-grade fluid as a result of longitudinal and torsional motions of an infinite round chamber governed by Laplace and restricted Hankel changes was discovered by Jamil et al. [32]. A study by Fetecau et al. [33] found that, in the presence of longitudinal movements of an infinite indirect chamber and those connected to an influencing pressure point, the solutions to the faltering development of a summarized Burgers fluid are set up as Fourier–Bessel series to the extent that some proper eigenfunctions are present. An incompressible second-grade fluid was studied by Fetecau et al. [34] to create a heatwave inside the limits of a possible vortex. One of the streams studied by Hayat et al. [35] was a second-grade fluid stream formed by a non-coaxial rotation of a penetrable plate. Using a two-chamber rheometer, Huang et al. [36] established a numerical method in the middle of the range for the constitutive relationship model of second-grade fluid.

Various examinations have discovered a propensity for the investigation of the magnetoconvection stream in contemporary centuries attributable to its many solicitations in design fields, for example, the cooling of atomic reactors, electronic bundles, microelectronic gadgets, and solar-centered innovation [37,38,39,40]. Numerous studies have explored the effect of attractive fields on convective hotness movement in 2D and 3D cavities. Thermal convection in a moving square enclosure was studied by Pirmohammadi and Ghassemi [41]. They found that the attractive field and the propensity point had a significant impact on the hotness movement system and stream features within the walled-in zone. In a pit filled with alumina–water nanofluid, Malvandi and Ganji [42] evaluated the influence of an attractive field on free convection. As a result, the speed angle at the divider increases and the rate of heat transmission reduces. According to Chamkha et al. [43], regarding the vented cavities exposed to attractive fields, heat transfer was focused in a cover-driven permeable nook with Cu–water nanofluid when MHD consolidated convection heat transfer was applied. The results showed that raising Hartman number improves the neighborhood Nusselt number while increasing the nanofluid volume part reduces the normal Nusselt number for all Richardson number ups. Attractive fields were utilized in a vented T-shaped pit to drive a convection flow of Cu–water nanofluid via adiabatically warmed base dividers in Kasaeipoor et al. [44] study. The low-temperature nanofluid enters the bottom and exits at the top of the chamber. With a rise in Hartmann numbers, they found that the pace at which hotness moves rises marginally. In any event, the rate increases as the Hartmann number rises, despite the substantial advantages of the Reynolds number [45]. It was found that the outer attractive field of three bearings affects the stream design and regular convection heat transfer in a cubic depression at various Rayleigh and Hartmann values. When comparing the appealing field’s upward heading to the corresponding dynamic divider bearing, the findings show that this is the most incorrect. An attractive field-induced normal convection of water–Al2O3 in a cubic cavity was studied by Sheikholeslami and Ellahi [46] using the Lattice Boltzmann Method (LBM) and the benefits of Hartmann number, nanoparticle volume fraction, and Rayleigh number were investigated. Successful thermal conductivity and nanofluid thickness are modeled using the Koo–Kleinstreuer–Li (KKL) model for a 4% concentration of the KKL model. However, they observed that the Nusselt number boosts Rayleigh’s number capacity but reduces Hartmann’s number capacity. Al2O3–water nanofluid was studied Zhou et al. [47] using the LBM in a variably warmed cubical nook with attractive powers. This study found a strong correlation between the smothering hotness move and the Nusselt number, with the normal Nusselt number showing the most severe effects of an attracting field with a flat bearing.

Slips fall into two main categories. First, the no-slip limit condition and then the slip limit condition are alluded to. One definition of the no-slip boundary condition (BC) is that it is one in which the liquid layer that is directly in contact with the limit travels at a constant pace with the boundary. There is no slip since there is no overall movement between them [48]. Limit slip situations are created when there is an overlap between the limit and the layer; as a result, slip is present. The occurrence of a slip limit in microfluidics is exceedingly unusual [49]. The equilibrium of surface energy can attain the convection limit condition, which is linked to convection warming. An incompressible and sticky liquid stream was tested for the influence of the divider’s slip condition on a slope divider temperature and hotness move [50]. Using convective limit conditions and inclining and topsy-turvy passages, Sayed et al. [51] studied the peristaltic movement of an exaggerated digression nanofluid. When it comes to incompressible liquids, speed slip and convective heating have been shown to affect the development of an early polar age [52]. Acharya et al. [53] used a penetrable expanding surface to study the effect of a second request slip mechanism on the development of a nanofluid.

The introduction of entropy establishes the first and second laws of thermodynamics to work on thermodynamics. The investigation of entropy idea has not introduced the examinations in designing; however, it is demonstrated in other fields like physical science, software engineering, and insights. The warm exchange emerges through vanishing energy and its development. With such realities, the entropy idea is focused on outlining such frameworks. The entropy idea characterizes the measures of energy misfortunes. The hypothesis and the properties of entropy can advance the show of assembling game plans. This increase and minimization are known as limited-time thermodynamics. The assets of various elements on entropy are researched to resistor the entropy in various media. Digression exaggerated crossover nanofluid is assessed by entropy [54]. A mathematical review for peristalsis of Sisko nanomaterial with entropy companions is discussed in ref. [55], and the entropy of non-Newtonian material introduces advancement which can be found in ref. [56]. Streamlining by the entropy for pressing progression of gooey liquid is given in ref. [57]. Nonlinear radiation and slip conditions by entropy are found in ref. [58]. Following that, a slew of other studies begins to emerge, each focusing on a different aspect of the problem [59,60,61,62,63,64,65,66], to name a few.

To the best of creators’ information, no mathematical or exploratory examination is directed to concentrate on the joined impacts of second-grade nanofluid stream, hotness move over a level moving surface with Lorentz powers warm radiation and entropy rate analysis utilizing the KKL nanofluid model. This examination work aims to explore the elements of entropy age and improvement of hotness move rate in warm frameworks by portraying the progression of nanofluids through a non-Newtonian numerical model.

2 Mathematical formulation

The flat horizontal stretching sheet’s linear velocity U w and temperature T w are stated as follows:

(2.1) U w ( x , 0 ) = b x ,

(2.2) T w ( x , 0 ) = T + b x ,

where b is a positive constant that is only true when x > 0 , and T is the temperature at a distance from the sheet.

2.1 The graphical model of fluid flow

Figure 1 shows a model of fluid flow. The following is a list of the model’s limitations:

  • steady-state and laminar flow in two-dimensional Cartesian coordinates;

  • nanofluid of second grade with a KKL model;

  • slip BCs;

  • Joule heating, thermal radiation, viscous dissipation, and MHDs, are all concepts that may be applied to a porous sheet;

  • copper oxide (CuO) nanoparticles;

  • engine oil (EO) as a base fluid.

Figure 1 
                  The visual depiction of a fluid flow model.
Figure 1

The visual depiction of a fluid flow model.

2.2 Stress tensor in a second-grade fluid

Second-grade fluid’s Cauchy stress tensor [67] is cited by Shah et al.

S = μ A ς 1 + α 1 A ς 2 + α 1 A ς 1 2 p I ,

A ς 1 = ( grad V ) + ( grad V ) T ,

A ς 2 =   d A ς 1 d t + A ς 1 ( grad V ) + A ς 1 ( grad V ) T ,

where α 1 and α 2   are the substance parameters, A ς 1 and A ς 2 are the Rivlin–Ericksen tensors previously designated, dynamic viscosity μ , identity tensor I , pressure p , fluid velocity V , and the substance time derivative d/dt are all present. This model supports the Clausius–Duhem inequality. In addition, Helmholtz’s free energy is at its lowest point at equilibrium.

μ 0 , α 1 0 , α 1 + α 2 = 0 .

In the second-grade fluid situation, if α 1 + α 2 = 0 , we are left with only the viscous fluid.

2.3 Model equations

Viscoelastic nanofluid, second-grade nanofluid, joule heating, and MHD impact are only some of the improvements to ref. [67] that have been included:

(2.3) u x + v y = 0 ,

(2.4) u u x + v u y = α 1 ρ n f [ u x u y y + u u x y y + u y v y y + v u y y y ] + μ n f ρ n f u y y σ n f B 2 u ρ n f ,

(2.5) u T x + v T y = k n f ( ρ C p ) n f T y y 1 ( ρ C p ) n f ( q r ) y + μ n f ( ρ C p ) n f ( u y ) 2 + σ n f B 2 u 2 ( ρ C p ) n f .

Boundary circumstances identical to those described in control – (2.3)–(2.5):

(2.6) u ( x , 0 ) = U w + N w ( u y ) , v ( x , 0 ) = V w , k 0 ( T y ) = h f ( T w T ) ,

(2.7) u 0 , u y 0 , T T as y .

Vector of flow velocity is defined as v = [ u ( x , y , 0 ) , v ( x , y , 0 ) , 0 ] . T presents the temperature of the fluid. B and N w are the magnetic field and slip length, respectively. V w is representing the porosity of the extending plate, while k symbolizes the porousness of material.

2.4 Primary properties of the KKL nanofluid model

To assess the impact of nanoparticles in the field of velocity and temperature profiles, nanofluid properties must be identified. The frequency, dilute suspensions, certain thermal conductivity, and electrical flow are as follows [68]:

(2.8) ρ n f = ( 1 ϕ ) ρ f + ϕ ρ s , ( ρ C p ) n f = ( 1 ϕ ) ( ρ C p ) f + ϕ ( ρ C p ) s , σ n f σ f = 1 + 3 σ s σ f 1 ϕ σ s σ f + 2 σ s σ f 1 ϕ .

Here, κ n f and μ n f can be estimated via the KKL model [69]:

(2.9) κ n f = κ static + κ Brownian , μ n f = μ static + μ Brownian κ n f = 1 + 3 κ s κ f 1 ϕ κ s κ f + 2 κ s κ f 1 ϕ static + 5 × 10 4 g ( T , ϕ , d p ) ρ f ( C p ) f k b T ρ p d p ϕ , Brownian g ( T , ϕ , d p ) = ln ( T ) ( a 1 + a 2 ln ( d p ) + a 3 ln ( ϕ ) + a 4 ln ( d p ) ln( ϕ ) + a 5 ln ( d p ) 2 ) + ( a 6 + a 7 ln ( d p ) + a 8 ln ( ϕ ) + a 9 ln ( d p ) ln ( ϕ ) + a 10 ln ( d p ) 2 ) μ n f = μ f ( 1 ϕ ) 2.5 + κ Brownian μ f κ f ( Pr ) f , 300 K T 325 K .

ϕ is the coefficient of the fraction of the volumetric nanoparticles. μ f , ρ f , ( C p ) f , and κ f are flexible viscosity, density, active heat capacity, and thermal conductivity of the base fluid, respectively. Other properties ρ s , ( C p ) s , κ s , κ b , and d p are densities, active heat dissipation, the thermal conductivity of nanoparticles, a constant Boltzmann, and a wide range of nanoparticles, respectively.

2.5 Nanoparticles and base fluid features

Basic EO materials and nanoparticles used in the current study are given in Table 1, and coefficient values of CuO and aluminum oxide are shown in Table 2.

Table 1

Factual possessions [70] of nanoparticles at 293 K and base fluid

Property ρ ( kg / m 3 ) C p ( J / kgK ) k ( W/mK ) σ ( S/m ) d p ( nm )
CuO 6,500 540.0 18.00 1 × 10−10 47
EO 884 1910 0.144 5.5 × 10−6
Table 2

Values of coefficients [70] for CuO nanoparticle

Coefficient values CuO
a 1 −26.593310846
a 2 −0.403818333
a 3 −33.3516805
a 4 −1.915825591
a 5 6.42185 × 10−2
a 6 48.40336955
a 7 −9.787756683
a 8 190.245610009
a 9 10.9285386565
a 10 −0.7200998366

2.6 Rosseland approximations

The Rosseland approximation [71,72,73,74,75] is utilized to incorporate the thermal radiation impact into the current regulating equations (2.3)–(2.5).

(2.10) q r = 4 σ 3 k T 4 y .

The Stefan–Boltzmann number is denoted by σ*, while the absorption coefficient is denoted by k*.

3 Final equations

Converting equations (2.3)–(2.5) into the new form solves the mathematical model presented in this article (ODEs). Equation (2.3) ought to be fulfilled by stream functions before moving on to the next phase (3.1). Using the formula (3.1) and similarity variables, the conversion step is completed (3.2) (for instance, refs. [76,77,78,79]) (Figure 2)

(3.1) u = ψ y , v = ψ x .

(3.2) η ( x , y ) = b ν f y , ψ ( x , y ) = ν f b x f ( χ ) , θ ( η ) = T T T w T .

Figure 2 
               Mathematical model’s flowchart structure.
Figure 2

Mathematical model’s flowchart structure.

The following ODEs are obtained by substituting (3.1)–(3.2) into each of the governing equations (2.3)–(2.5).

(3.3) f + ϕ 1 ϕ 2 ( f f f 2 ) + α ( 2 f f f 2 f f iv ) ϕ 4 ϕ 2 M f = 0 ,

(3.4) θ 1 + 1 ϕ 5 PrNr + Pr ϕ 3 ϕ 5 f θ f θ + E c ϕ 1 ϕ 3 f 2 + ϕ 4 ϕ 3 MEc f 2 = 0 ,

through

(3.5) f ( 0 ) = S , f ( 0 ) = 1 + Λ f ( 0 ) ,      θ ( 0 ) = B s ( 1 θ ( 0 ) ) f ( η ) 0 , f ( η ) 0 , θ ( η ) 0 ,    as    η .

In the following equations (3.3)–(3.4), the nanofluid’s thermophysical characteristics are represented by ϕ ' i s , which is 1 i 5 [80,81,82,83]:

(3.6) ϕ 1 = ( 1 ϕ ) 2.5 , ϕ 2 = 1 ϕ + ϕ ρ s ρ f , ϕ 3 = 1 ϕ + ϕ ( ρ C p ) s ( ρ C p ) f , ϕ 4 = 1 + 3 σ s σ f 1 ϕ σ s σ f + 2 σ s σ f 1 ϕ , ϕ 5 = ( k s + 2 k f ) 2 ϕ ( k f k s ) ( k s + 2 k f ) + ϕ ( k f k s ) .

3.1 Parameter expression

ODE parameters and their associated BCs are listed together in Table 3.

Table 3

The existing ODEs and their governing parameter BCs

Governing parameters Appearance
Non-Newtonian second grade α = α 1 b μ f
MHD M = σ f B 0 2 b ρ f
Prandtl number Pr = ν f α f
Thermal diffusivity α f = κ f ( ρ C p ) f
Suction/injection S = V w 1 ν f   b
Thermal radiation Nr = 16 3 σ T 3 κ ν f ( ρ C p ) f
Velocity slip Λ = b ν f N w
Eckert number Ec = U w 2 ( C p ) f ( T w T )
Biot number B s = h f k 0 ν f b

3.2 Quantities for engineering interest

(C f ) and (Nu x ) are the physical quantities that have been measured in this present model [84,85].

(3.7) C f = τ w ρ f U w 2 , Nu x = x q w k f ( T w T ) .

The symbols for τ w and q w are shown below:

(3.8) τ w = [ μ n f u y + α 1 ( u u x y + 2 u y u x + v u y y ) ] y = 0 , q w = k n f 1 + 16 3 σ T 3 κ ν f ( ρ C p ) f ( T y ) y = 0 .

It is possible to get the following result by substituting equations (3.2) and (3.8):

(3.9) C f Re x 1 2 = f ( 0 ) ( 1 ϕ ) 2.5 + α [ 3 f ( 0 ) f ( 0 ) f ( 0 ) f ( 0 ) ] , Nu x Re x 1 2 = k n f k f ( 1 + Nr ) θ ( 0 ) .

Using equation (3.9), the Nusselt number Nu x and the decreased skin friction C f may be found. Meanwhile, Re x = U w x ν f is used to express the local Rayleigh number.

3.3 Entropy generation analysis

The entropy generation for the above mentioned suppositions is [76]:

(3.10) E G = k n f T 2 ( T y ) 2 + 16 3 σ T 3 κ ν f ( ρ C p ) f ( T y ) 2 + μ n f T ( u y ) 2 + σ n f B 2 u 2 T .

The dimensionless form of entropy equation is acquired as follows:

(3.11) N G = T 2 b 2 E G k f ( T w T ) 2 .

From equation (3.10), the dimensionless entropy equation is as follows:

(3.12) N G = Re ϕ 4 ( 1 + Nr ) θ 2 + 1 ϕ 1 Br ω ( f 2 + ϕ 1 ϕ 4 M f 2 f 2 ) .

Here, Re  is the Reynolds number, Br is the Brinkman number, and ω is the dimensionless temperature gradient.

4 Numerical procedure: Shooting method

The shooting method [86] is used to compute the numerical solutions, which adapts the fourth-order Runge–Kutta (RK4) technique. The shooting method’s mathematical modeling (3.33.5) is being disclosed and adorned in Figure 3.

Figure 3 
               The shooting method’s methodological framework.
Figure 3

The shooting method’s methodological framework.

The subsequent phases of the shooting technique need the conversion of (3.3)–(3.5) into a first-order system. The results of the first-order system are displayed in the following way:

(4.1) z 1 = f ,

(4.2) z 2 = z 1 ,

(4.3) z 3 = z 2 ,

(4.4) z 4 = θ ,

(4.5) z 2 + ϕ 1 ϕ 2 ( f z 2 z 1 2 ) + α ( 2 z 1 z 2 z 2 2 f z 3 ) ϕ 4 ϕ 2 M z 1 = 0 ,

(4.6) z 4 1 + 1 ϕ 4 PrNr + Pr ϕ 3 ϕ 5 f z 4 z 1 θ + Ec ϕ 1 ϕ 3 z 2 2 + + ϕ 4 ϕ 3 MEc z 1 2 = 0 ,

(4.7) f ( 0 ) = S , z 1 ( 0 ) = 1 + Λ z 2 ( 0 ) , z 3 ( 0 ) = B s ( 1 θ ( 0 ) ) , z 1 ( ) 0 , z 2 ( ) 0 , θ ( ) 0 .

5 Validity of code

The computed outcomes are verified by comparing with the previous proclaimed results in the literature [87,88]. Every value in Table 4 has been validated for consistency. By contrast, the present results are in good agreement and accepted.

Table 4

Comparison of θ ( 0 ) for various Pr , where ϕ = 0 , Λ = 0 , Nr = 0 , M = 0 , Ec = 0, S = 0 , and B s 0

Prandtl number Pr Ref. [87] Ref. [88] Present results
0.20 0.1691 0.1691 0.1691
0.70 0.4539 0.4537 0.4537
2.00 0.9114 0.9114 0.9114
7.00 1.8954 1.8958 1.8958

6 Numerical results and discussion

Viscosity dissipation and shifting BCs were studied in MHD radiation-level nanofluid experiments. The mathematical modeling is done using the KKL model. Table 1 lists the thermophysical properties of one nanoparticle (CuO) and the principal liquid (EO). Table 2 illustrates the coefficient value of CuO for KKL model, and Table 3 demonstrates the parameters of the nanoparticles and the primary fluid, respectively. In this experiment, nanoparticles and a base liquid are employed. Non-dimensional components will be discussed in this section (Table 4). Consequently, several features are taken into account, such as second-grade, nanoparticle volume friction and magnetic, Eckert number, thermal radiation, Biot number, velocity slip, suction/injection, and velocity slip. The skin friction and Nusselt numbers CuO–EO nanofluids have also been computed numerically.

Figure 4 shows the velocity distribution as the second-grade parameter α augmented. Fluid velocity is shown to have risen significantly on this graph. This will result in an upsurge in momentum boundary layer thickness. Growth causes viscosity to fall owing to the fluid escalation speed and the height at which it is disturbed, according to a statistical representation. The second-grade parameter is also well-known for revamping fluid behavior to reduce fluid viscosity and hence improve liquid velocity.

Figure 4 
               Nature of 
                     
                        
                        
                           f
                           ′
                        
                        f^{\prime} 
                     
                   over 
                     
                        
                        
                           α
                        
                        \alpha 
                     
                  .
Figure 4

Nature of f over α .

The difference in heat distribution across α is shown in Figure 5. According to this graph, thermal distribution is decreasing as a result of rising α . As a result, the thermal boundary layer’s thickness has dwindled. The results showed the heat from the surface is being actively transferred to fluid and released to the far-field. The phenomenon happens due to the velocity of the liquid is enlarged with increasing α in the system.

Figure 5 
               Nature of 
                     
                        
                        
                           θ
                        
                        \theta 
                     
                   over 
                     
                        
                        
                           α
                        
                        \alpha 
                     
                  .
Figure 5

Nature of θ over α .

According to Figure 6, M is growing with f ( η ) diminished. When contrast to the high M values, the movement of the liquid particles is negligible. The occurrence is due to M generating Lorentz force, acting as retarded force in the flow. The Lorentz force will be forcing the molecules of the fluid to flow oppositely from the original flow. It has the opposite impact on particle mobility if a constant magnetic field is provided in the normal flow direction across a vast area.

Figure 6 
               Nature of 
                     
                        
                        
                           f
                           ′
                        
                        f^{\prime} 
                     
                   over 
                     
                        
                        
                           M
                        
                        M
                     
                  .
Figure 6

Nature of f over M .

The heat boundary layer thickness intensifies, leading to an escalation in thermal dispersion, as shown in Figure 7. The system releases thermal energy by exerting more effort than necessary to bring a nanofluid closer to a magnetic field. This occurrence will cause increases in the temperature of the fluid. M instantly amplified the temperature of the surface as it acted on the system. There will be more heat transferred to flow since there are more molecules in the fluid, and this will result in a higher temperature for the flow.

Figure 7 
               Nature of 
                     
                        
                        
                           θ
                        
                        \theta 
                     
                   over 
                     
                        
                        
                           M
                        
                        M
                     
                  .
Figure 7

Nature of θ over M .

Figure 8 depicts the change f ( η ) across various solid volume fractions ϕ . Because of this consequence, Figure 8 shows that the rising ϕ causes fluid particles to slow down. This occurrence is due to the particles occupied at each phase having less space between each particle in the flow. Hence, the molecules of the fluid have difficulties moving in the flow and the liquid velocity is diminished.

Figure 8 
               Nature of 
                     
                        
                        
                           f
                           ′
                        
                        f^{\prime} 
                     
                   over 
                     
                        
                        
                           ϕ
                        
                        \phi 
                     
                  .
Figure 8

Nature of f over ϕ .

In addition, the improved thermal distribution may be noticed through the action of the heat-injection system (Figure 9). The physical definition of the phrase “thermal boundary layer” shows that the solid volume percentage increases the fluid concentration, tumbling momentum thickness, and the thermal boundary layer thickness. This phenomenon has a similar physical explanation as in the discussion of Figure 8.

Figure 9 
               Nature of 
                     
                        
                        
                           θ
                        
                        \theta 
                     
                   over 
                     
                        
                        
                           ϕ
                        
                        \phi 
                     
                  .
Figure 9

Nature of θ over ϕ .

Figure 10 shows a growth in the velocity slip parameter Λ . The diminution f ( η ) is the result of velocity slip Λ . The flow slows down as a result of a higher acceleration element height, which creates a friction force that allows more fluid to flow through the sheet. On the other hand, the slip parameter indicates a different distribution of temperatures.

Figure 10 
               Nature of 
                     
                        
                        
                           f
                           ′
                        
                        f^{\prime} 
                     
                   over 
                     
                        
                        
                           Λ
                        
                        \text{Λ}
                     
                  .
Figure 10

Nature of f over Λ .

When the slip parameter improves, the thermal distribution diminishes at the beginning (for η < 4 ), but opposite observation is observed when the data are η > 4 , as shown in Figure 11. Since there is friction between the flow and the sheet’s surface, the heat will be created on the surface and transported to the fluid flow. Then, the temperature of the flow will consequently double up.

Figure 11 
               Nature of 
                     
                        
                        
                           θ
                        
                        \theta 
                     
                   over 
                     
                        
                        
                           Λ
                        
                        \text{&#x039B;}
                     
                  .
Figure 11

Nature of θ over Λ .

An illustration of the relationship between thermal distribution and Biot number B s is shown in Figure 12. The plotted figure makes it clear that intensifying the amount of heat transferred affects B s. This incident is due to the creation of a thicker layer of the thermal boundary layer in the region as a result of varying temperature changes in the surrounding area. Compared to the permanent circumstances of water temperature, a nanofluid with a convective BC is a more accurate model.

Figure 12 
               Nature of 
                     
                        
                        
                           θ
                        
                        \theta 
                     
                   over B
                  s.
Figure 12

Nature of θ over B s.

Figure 13 examines θ ( η ) over η changes as a result of Nr . Figure 13 shows that heat transfer is abridged without the use of a heat shield. In addition, the heat distribution improves Nr as the temperature rises. As Rosseland radiation absorption decreases, so does the luminous temperature change variability. As a result, the liquid’s radiation temperature rises, helping to raise its temperature.

Figure 13 
               Nature of 
                     
                        
                        
                           θ
                        
                        \theta 
                     
                   over 
                     
                        
                        
                           Nr
                        
                        \text{Nr}
                     
                  .
Figure 13

Nature of θ over Nr .

Figure 14 shows the temperature distribution that is affected by the Eckert number. The rise in thermal variation is shown in Figure 14. Liquids generate heat as the value Ec grows owing to temperature collisions. When it comes to the physical aspects of the Eckert number, it measures the amount of energy that may be transferred from one object to another. As a result, increasing the Eckert numbers leads to the conversion of kinetic energy into internal energy through effort against viscous fluid pressures. As a result, a rise in Ec raises the liquid’s temperature.

Figure 14 
               Nature of 
                     
                        
                        
                           θ
                        
                        \theta 
                     
                   over 
                     
                        
                        
                           Ec
                        
                        \text{Ec}
                     
                  .
Figure 14

Nature of θ over Ec .

Figures 15 and 16 illustrate the suction/injection increment for the velocity and temperature distributions separately. When the injection rate becomes hikes, as S negative, but when the suction rate becomes favorable, as S escalations. While a minor proliferation in the suction parameter declines the fluid velocity, the injection parameter contributes the opposite behavior (Figure 15). Figure 16 illustrates the thermal distribution variance for different. When the injection parameter is improved, the thermal distribution grows; the thermal dispersion shrinks when the suction parameter has dwindled. Because vigorous ventilation surges the temperature of the hot border, and with the proper liquid flow to the wall, absorption may be employed as a surface scraper.

Figure 15 
               Nature of 
                     
                        
                        
                           f
                           ′
                        
                        f^{\prime} 
                     
                   over 
                     
                        
                        
                           S
                        
                        S
                     
                  .
Figure 15

Nature of f over S .

Figure 16 
               Nature of 
                     
                        
                        
                           θ
                        
                        \theta 
                     
                   over 
                     
                        
                        
                           S
                        
                        S
                     
                  .
Figure 16

Nature of θ over S .

In the stretched boundary layer regime, the response in Reynolds number Re on the entropy generation profiles is shown in Figure 17. It can be shown that the higher the Reynolds number Re , the stronger the entropy impact. Due to higher values of Reynolds number Re , retrogression of frictional forces tends to gasify entropy profiles. Figure 18 shows the change in entropy generation number N G versus Brinkman number Br values, indicating that increasing Brinkman number Br increases entropy generation. Br investigates the fluid’s viscous influence because of reality and such behavior. As a result, large Br values indicate that fluid friction is the major cause of entropy formation (Table 5).

Figure 17 
               Nature of 
                     
                        
                        
                           
                              
                                 N
                              
                              
                                 G
                              
                           
                        
                        {N}_{G}
                     
                   over 
                     
                        
                        
                           Re
                        
                        \text{Re}
                     
                  .
Figure 17

Nature of N G over Re .

Figure 18 
               Nature of 
                     
                        
                        
                           
                              
                                 N
                              
                              
                                 G
                              
                           
                        
                        {N}_{G}
                     
                   over 
                     
                        
                        
                           Br
                        
                        \text{Br}
                     
                  .
Figure 18

Nature of N G over Br .

Table 5

Values of skin friction = C f Re x 1 2 and Nusselt number = Nu Re x 1 2 for Pr = 6450

α M ϕ Λ B s Nr Ec S C f Re x 1 2 NuRe x 1 / 2
0.1 0.5 0.01 0.5 0.5 2 3 0.5 0.86494 0.46768
0.5 1.36079 0.49521
1.1 1.92112 0.51589
0.5 0.86494 0.46768
1.5 0.94812 0.35546
2.5 1.0417 0.23367
0.01 0.86494 0.46768
0.03 0.85894 0.46291
0.05 0.81491 0.42592
0.1 0.86494 0.46768
0.5 0.33894 0.62242
1.1 0.07528 0.69001
0.1 0.86494 0.46768
0.5 0.86494 0.64776
1.1 0.86494 0.70595
0.1 0.86494 0.46768
1.5 0.86494 0.50131
2.5 0.86494 0.53138
1 0.86494 0.46768
3 0.86494 0.38086
5 0.86494 0.29403
−0.6 0.42107 0.12815
−0.4 0.44588 0.17523
0.4 0.85029 0.43435
0.6 0.87869 0.50026

7 Final remarks

Using a liquid model, the effects of MHD in a second-grade flow of nanoliquid along with heat transfer and entropy generation are investigated in this study. The model is based on the KKL framework. The RKF45 scheme and shooting approach are used to solve the ODEs. The graphs show second-grade nanofluid velocity and temperature, while the comparative values for physical parameters are shown in the table (Nusselt number and skin friction coefficient). A variety of non-dimensional parameter values are used to measure these representations (graphs and tables). Its best results may be found in the following summary:

  1. α intensification velocity while declining heat distribution and local Nusselt numbers (in both nanofluids). Nanofluid CuO–EO has a higher skin friction coefficient as a result of this characteristic.

  2. Solid volume fraction and the MHD parameter escalation heat distribution, while velocity shrinkages; MHD parameter amplifications of the skin friction coefficient and reductions in the Nusselt number. On the other hand, the solid volume fraction is responsible for the decrease in physical characteristics.

  3. Temperature distribution growing with Nr and Ec augmented Nusselt number. This decrease in velocity and temperature is accompanied by an increase in the Nusselt number of other physical measures (temperature is reduced only at the location close to the sheet).

  4. Skin friction coefficient can be reduced by using Λ . However, the Nusselt number rises as a result of Λ .

  5. Biot number amplified the temperature distributions in the flow due to the thickness of the boundary layer getting swollen.

  6. Entropy rate of the thermal system is enhanced with the boost in the values of Reynolds number Re due to the effect of dominant inertial forces.

  7. System entropy rate is boosted with Brinkman number Br increment results in the growth in the surface heat dissipation.

  8. Injection parameter affects temperature and velocity enhancements, as well as Nusselt number and temperature rise. When suction rises, they become repressed. Physical properties can be improved by increasing the suction rate. In the meantime, both physical characteristics decrease when the injection rate grows.

Acknowledgments

The authors express their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through a research groups program under grant number R.G.P.2/43/40. Also, the authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education, in Saudi Arabia, for funding this research work through the project no.IFP-KKU-2020/9. Also, this study was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R132), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

  1. Funding information: This work received funding from King Khalid University, Ministry of Education, Saudi Arabia. Also, this study was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R132), 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: 2021-12-18
Revised: 2022-02-23
Accepted: 2022-04-27
Published Online: 2022-06-01

© 2022 Faisal Shahzad et al., published by De Gruyter

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

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