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Research article

Heat transport and magnetohydrodynamic hybrid micropolar ferrofluid flow over a non-linearly stretching sheet

  • Received: 24 July 2022 Revised: 28 August 2022 Accepted: 15 September 2022 Published: 27 September 2022
  • MSC : 76D05, 76A02, 65L10

  • A stable colloid called ferrofluid is made up of tiny magnetic particles, often magnetite (Fe3O4), that have been bonded with an amphiphilic dispersion layer and are then suspended in a suitable liquid solvent carrier. Current industrial uses for ferrofluid include dynamic sealing, inertial and viscous damping, magnetic drug targeting, liquid microrobots, etc. In this article, we studied the heat transfer and MHD micropolar ferrofluid flow caused by non-linearly stretching surface. The results are presented for hybrid alumina- copper/ethylene glycol (Al2O3-Cu/EG) nanofluid. The governing non-linear equations describing flow are transformed into a system of ordinary differential equations using similarity transformations. Using the BVp4c method, the microstructure and inertial properties of a magnetite ferrofluid across a non-linear stretched sheet are studied. The influence of relevant parameters on stream function, velocity, micro-rotation velocity, and temperature are obtained and represented graphically. The computed results are original, and it has been observed that if we increase the magnetic parameter, the stream function and the velocity decrease, while the temperature and micro-rotation velocity increase. As the Prandtl number increases, the temperature profile decreases. It has been observed that the Nusselt number or heat transfer rate of hybrid nanofluid is better as compared to nanofluid flow.

    Citation: Abdul Rauf, Nehad Ali Shah, Aqsa Mushtaq, Thongchai Botmart. Heat transport and magnetohydrodynamic hybrid micropolar ferrofluid flow over a non-linearly stretching sheet[J]. AIMS Mathematics, 2023, 8(1): 164-193. doi: 10.3934/math.2023008

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  • A stable colloid called ferrofluid is made up of tiny magnetic particles, often magnetite (Fe3O4), that have been bonded with an amphiphilic dispersion layer and are then suspended in a suitable liquid solvent carrier. Current industrial uses for ferrofluid include dynamic sealing, inertial and viscous damping, magnetic drug targeting, liquid microrobots, etc. In this article, we studied the heat transfer and MHD micropolar ferrofluid flow caused by non-linearly stretching surface. The results are presented for hybrid alumina- copper/ethylene glycol (Al2O3-Cu/EG) nanofluid. The governing non-linear equations describing flow are transformed into a system of ordinary differential equations using similarity transformations. Using the BVp4c method, the microstructure and inertial properties of a magnetite ferrofluid across a non-linear stretched sheet are studied. The influence of relevant parameters on stream function, velocity, micro-rotation velocity, and temperature are obtained and represented graphically. The computed results are original, and it has been observed that if we increase the magnetic parameter, the stream function and the velocity decrease, while the temperature and micro-rotation velocity increase. As the Prandtl number increases, the temperature profile decreases. It has been observed that the Nusselt number or heat transfer rate of hybrid nanofluid is better as compared to nanofluid flow.



    Research has shown that fractional calculus is highly effective for capturing complex dynamics and accurately modeling real-life problems [9,40]. Fractional calculus extends integer-order derivatives and integrals, also known as differentiation and integration, to arbitrary orders [32], proving to be a powerful tool for understanding intricate systems. Numerous researchers have utilized the Lyapunov second method, or Lyapunov direct method, to analyze the qualitative and quantitative characteristics of dynamical systems. The Lyapunov direct method is particularly beneficial because it does not require knowledge of the differential equation's solution [35]. In [1,2,3,4], various fractional derivatives (FrDs) of Lyapunov functions (LF) were used in stability investigations, including the Caputo FrD, Dini FrD, and Caputo fractional Dini derivative. The most preferred is the Caputo FrD:

    Ct0DαtL(t,ν(t))=1Γ(1α)tt0(ts)αdds(L(s,ν(s)))ds,t[t0,T),α(0,1).

    This derivative is easier to handle and more applicable, though it requires the function L(t,ν(t))[R×Rn,R+] to be continuously differentiable, which can be challenging. Other LF derivatives do not have this limitation, allowing sufficient conditions for these derivatives using a continuous LF that does not need to be continuously differentiable. The Dini FrD

    Dα+L(t,ν;t0)=lim supκ0+1κα{L(t,ν)[tt0κ]r=1(1)r+1αCrL(trκ,νκαf(t,ν))},

    where L:R×RnR+, is continuous, fC[R×Rn,Rn], κ is a positive number and αCr=α(α1)...(αr+1)r!, maintains the concept of FrDs, depending on both the present point (t) and the initial point (t0) but not on the initial state L(t0,ν0). This led to a more suitable definition

    Ct0Dα+L(t,ν(t))=lim supκ0+1κα{L(t,ν(t))L(t0,ν(t0))[tt0κ]r=1(1)r+1αCr[L(trκ,ν(t)καf(t,ν(t)))L(t0,ν(t0))]}, (1.1)

    to be considered.

    Several forms of stability for Caputo fractional differential equations (FrDE) with continuous domain have been investigated using this Caputo fractional Dini derivative (1.1) [1]. As noted in [7] and [25], a more holistic examination of stability can be achieved across time domains. In [1,2,3,12,13,18], stability results were obtained for continuous time, ignoring discrete details, while in [8,23,26,28], discrete domains were considered. However, some systems undergo smooth and abrupt changes almost simultaneously, with multiple time scales or frequencies. Modeling such phenomena is better represented as dynamic systems that include continuous and discrete times, known as time scales or measure chains, denoted by T [14,17]. Dynamic equations on time scales, defined on discrete, continuous (connected), or combined domains, provide a broader analysis of difference and differential systems [16]. In order to extend stability properties from the classical to the fractional-order sense, we focus on the Lyapunov stability analysis of the Caputo fractional dynamic equations on time scale (FrDET) using a novel definition for the delta derivative of a LF, known as the Caputo FrΔD on a time scale.

    The study of fractional dynamic systems on time scales is recent and ongoing due to its advantages in modeling, mechanics, and population dynamics (see [39]). Recent literature on fractional dynamic systems on time scales focuses on the existence and uniqueness of solutions of FrDET, with Caputo-type derivatives being given more recognition ([10,11,20,29,30,33]). However, in [24], the stability of fractional dynamic systems on time scales with applications to population dynamics were examined. Although the stability results were interesting, they applied Hyers-Ulam type stability, which is restrictive compared to Lyapunov stability, which has a broader application scope ([19,27,37]). In [36,38], methods for solving discrete time scales in Caputo FrDET were developed.

    Building on the existence and uniqueness results for Caputo-type FrDET established in [4], we extend the stability results in [21] to fractional order and the Lyapunov stability results for Caputo FrDE in [1] to a more generalized domain (time scale). This unification of continuous and discrete calculus gives rise to fractional difference equations (FrDfE) in discrete time, FrDE in continuous time, and fractional calculus on time scales in combined continuous and discrete time.

    For this work, we consider the Caputo fractional dynamic system of order α, with 0<α<1

    CTDανΔ=Υ(t,ν),tT,ν(t0)=ν0,t00, (1.2)

    where ΥCrd[T×Rn,Rn], Υ(t,0)0, and CTDανΔ is the Caputo FrΔD of νRn of order α with respect to tT. Let ν(t)=ν(t,t0,ν0)Cαrd[T,Rn] be a solution of (1.2), assuming the solution exists and is unique ([4,24]), this work aims to investigate the stability and asymptotic stability of the system (1.2).

    To do this, we shall use the dynamic system of the form

    CTDαχΔ=Ξ(t,χ),χ(t0)=χ00, (1.3)

    where χR+, Ξ:T×R+R+ and Ξ(t,0)0. System (1.3) is called the comparison system. For this work, we will assume that the system (1.3) with χ(t0)=χ0 has a solution χ(t)=χ(t;t0,χ0)Cαrd[T,R+] which is unique ([4]).

    In the next section (Section 2), we examine key terminologies, remarks, and a fundamental lemma laying the groundwork for the subsequent contributions. New definitions and crucial remarks are also introduced. In Section 3, we present Lemmas 3.1 and 3.2, which are essential components for proving the major results. In Section 4, practical examples demonstrate the significance and applicability of the newly introduced definitions and the established stability and asymptotic stability theorem. In Section 5, we provide the conclusion, summarizing the major findings and implications of the investigation.

    The foundational principles of dynamic equations, encompassing derivatives and integrals, can be extended to non-integer orders by applying fractional calculus. This generalization to non-integer orders becomes particularly relevant when exploring dynamic equations on time scales, allowing for a versatile and comprehensive analysis of system behavior across continuous and discrete time domains. See [5,24,31,34]. In this section, we set the foundation, introduce notations, and definitions.

    Definition 2.1. [7] For tT, the forward jump operator σ:TT is defined by

    σ(t)=inf{sT:s>t},

    and the backward jump operator ρ:TT is defined by

    ρ(t)=sup{sT:s<t}.

    The following conditions hold:

    (i) If σ(t)>t, then t is termed right-scattered (rs).

    (ii) If ρ(t)<t, then t is termed left-scattered (ls).

    (iii) If t<maxT and σ(t)=t, then t is called right-dense (rd).

    (iv) If t>minT and ρ(t)=t, then t is called left-dense (ld).

    Definition 2.2. [7] The graininess function μ:T[0,) for tT is defined by

    μ(t)=σ(t)t,

    where σ(t) is the forward jump operator.

    Definition 2.3. [7] Let p:TR and tTk. The delta derivative pΔ also known as the Hilger derivative is defined as:

    pΔ(t)=limstp(σ(t))p(s)σ(t)s,sσ(t),

    provided the limit exist.

    If t is rd, the delta derivative of p, becomes

    pΔ(t)=limstp(t)p(s)ts,

    and if t is rs, the Delta derivative becomes

    pΔ(t)=pσ(t)p(t)μ(t),

    where pσ denotes p(σ(t)).

    Definition 2.4. [15] p:TR is said to be rd-continuous if it remains continuous at each rd point within T and possesses finite left-hand limits at ld points in T. The collection of all such rd-continuous functions is denoted as

    Crd=Crd(T).

    Definition 2.5. [7] Let a,bT and pCrd, then, the integration on the time scale T is defined as:

    (i)

    bap(t)Δt=bap(t)dt,

    if T=R.

    (ii) If the interval [a,b]T contains only isolated points, then

    bap(t)Δt={t[a,b)μ(t)p(t)ifa<b0ifa=bt[a,b)μ(t)p(t)ifa>b.

    (iii) If there exists a point σ(t)>t, then

    σ(t)tp(s)Δs=μ(t)p(t).

    Definition 2.6. [15] A function ϕ:[0,r][0,) is of class K if it is continuous, and strictly increasing on [0,r] with ϕ(0)=0.

    Definition 2.7. [15] LC[Rn,R] with L(0)=0 is called positive definite(negative definite) on the domain D if a function ϕK : ϕ(|χ|)L(χ)(ϕ(|χ|)L(χ)) for χD.

    Definition 2.8. [15] LC[Rn,R] with L(0)=0 is called positive semidefinite (negative semi-definite) on D if L(χ)0(L(χ)0) χD and it can also vanish for some χ0.

    Definition 2.9. [21] Assume LCrd[T×Rn,R+], ΥCrd[T×Rn,Rn] and μ(t) is the graininess function, then the dini derivative of L(t,ν) is defined as:

    DLΔ(t,ν)=lim infμ(t)0L(t,ν)L(tμ(t),νμ(t)Υ(t,ν))μ(t), (2.1)
    D+LΔ(t,ν)=lim supμ(t)0L(t+μ(t),ν+μ(t)Υ(t,ν))L(t,ν)μ(t). (2.2)

    If L is differentiable, then DLΔ(t,ν)=D+LΔ(t,ν)=LΔ(t,ν).

    Definition 2.10. [6] Consider α(0,1), with [a,b] being an interval on T, and let Ξ be a function that is integrable over [a,b]. The fractional integral of Ξ, w.r.t the order α, is expressed as follows:

    TaIαtΞΔ(t)=ta(ts)α1Γ(α)Ξ(s)Δs.

    Definition 2.11. [4] Let tT,0<α<1, and Ξ:TR. The Caputo FrD of order α of Ξ is expressed as follows:

    TaDαtΞΔ(t)=1Γ(1α)ta(ts)αΞΔn(s)Δs.

    Lemma 2.1. [22] Let T represent a time scale with a minimal element t00. Assume that for each tT, there is a proposition S(t) such that the following conditions are satisfied:

    (i) S(t0) holds;

    (ii) if t is rs and S(t) holds, then S(σ(t)) also holds;

    (iii) for any rd t, there is a neighborhood U such that if S(t) is true, then S(t) is true for every tU with tt;

    (iv) for left-dense t, if S(t) holds for all t[t0,t), then S(t) also holds.

    Therefore, S(t) is true for all tT.

    Remark 2.1. When T=N, Lemma 2.1 simplifies to the principle of mathematical induction. Specifically:

    (1) S(t0) being true corresponds to the statement holding for n=1;

    (2) S(t)S(σ(t)) corresponds to: if the statement holds for n=k, then it also holds for n=k+1.

    Now, we give the following definitions and remarks.

    Definition 2.12. Let hCαrd[T,Rn], the G-L FrΔD is given by

    GLTDα0hΔ(t)=limμ0+1μα[(tt0)μ]r=0(1)rαCr[h(σ(t)rμ)],tt0, (2.3)

    and the G-L FrΔDiD is given by

    GLTDα0+hΔ(t)=lim supμ0+1μα[(tt0)μ]r=0(1)rαCr[h(σ(t)rμ)],tt0, (2.4)

    where 0<α<1, αCr=α(α1)...(αr+1)r!, and [(tt0)μ] represents the integer part of the fraction (tt0)μ.

    Observe that if the domain is R, then (2.4) becomes

    GLTDα0+hΔ(t)=lim supκ0+1κα[(tt0)κ]r=0(1)rαCr[h(trκ)],tt0.

    Remark 2.2. It is necessary to note that the relationship between the Caputo FrΔD and the G-L FrΔD is given by

    CTDα0hΔ(t)=GLTDα0[h(t)h(t0)]Δ, (2.5)

    substituting (2.3) into (3.11) we have that the Caputo FrΔD becomes

    CTDα0hΔ(t)=limμ0+1μα[(tt0)μ]r=0(1)rαCr[h(σ(t)rμ)h(t0)],tt0,CTDα0hΔ(t)=limμ0+1μα{h(σ(t))h(t0)+[(tt0)μ]r=1(1)rαCr[h(σ(t)rμ)h(t0)]}, (2.6)

    and the Caputo FrΔDiD becomes

    CTDα0+hΔ(t)=lim supμ0+1μα[(tt0)μ]r=0(1)rαCr[h(σ(t)rμ)h(t0)],tt0, (2.7)

    which is equivalent to

    CTDα0+hΔ(t)=lim supμ0+1μα{h(σ(t))h(t0)+[(tt0)μ]r=1(1)rαCr[h(σ(t)rμ)h(t0)]}. (2.8)

    For notation simplicity, we represent the Caputo FrΔD of order α as CTDα and the Caputo FrΔDiD of order α as CTDα+.

    Definition 2.13. The trivial solution of (1.2) is called:

    S1 Stable if for every ϵ>0 and t0T, δ=δ(ϵ,t0)>0 : for any ν0Rn, ν0δ ν(t;t0,ν0)<ϵ for tt0.

    S2 Asymptotically stable if it is stable and locally attractive, that is a δ0=δ0(t0)>0 : ν(t0)<δ0 limtν(t)=0 for t0,tT.

    We now give the definition of the derivative of LF using the FrΔDiD of h(t), as provided in Eq (2.7).

    Definition 2.14. The Caputo FrΔDiD of the Lyapunov function L(t,ν)Crd[T×Rn,R+] (which is locally Lipschitzian with respect to its second argument and L(t,0)0) along the trajectories of solutions of system (1.2) is defined as:

    CTDα+LΔ(t,ν)=lim supμ0+1μα[[tt0μ]r=0(1)r(αCr)[L(σ(t)rμ,ν(σ(t))μαΥ(t,ν(t)))L(t0,ν0)]],

    and can be expanded as

    CTDα+LΔ(t,ν)=lim supμ0+1μα{L(σ(t),ν(σ(t))L(t0,ν0)[tt0μ]r=1(1)r+1(αCr)[L(σ(t)rμ,ν(σ(t))μαΥ(t,ν(t)))L(t0,ν0)]}, (2.9)

    where tT, and ν,ν0Rn, μ=σ(t)t and ν(σ(t))μαΥ(t,ν)Rn.

    If T represents a discrete time scale and L(t,ν(t)) remains continuous at t, the Caputo FrΔDiD of the LF for discrete times is expressed as:

    CTDα+LΔ(t,ν)=1μα[[tt0μ]r=0(1)r(αCr)(L(σ(t),ν(σ(t)))L(t0,ν0))], (2.10)

    and if T is continuous, that is T=R, and L(t,ν(t)) is continuous at t, we have that

    CTDα+LΔ(t,ν)=lim supκ0+1κα{L(t,ν(t))L(t0,ν0)[tt0κ]r=1(1)r+1(αCr)[L(trκ,ν(t))καΥ(t,ν(t))L(t0,ν0)]}. (2.11)

    Notice that (2.11) is the same in [1] where κ>0.

    Given that limNNr=0(1)rαCr=0 where α(0,1), and limμ0+[(tt0)μ]= therefore it is evident that

    limμ0+[(tt0)μ]r=1(1)rαCr=1, (2.12)

    Also, based on Eq (2.7) and given that the Caputo and R-L definitions are equivalent when h(t0)=0 (see [1]), we can conclude that:

    CTDα+hΔ(t)=RLTDα+hΔ(t)=lim supμ0+1μα[(tt0)μ]r=0(1)rαCr[h(σ(t)rμ)],tt0, (2.13)

    setting h(σ(t)rμ=1 we obtain

    CTDα+hΔ(t)=lim supμ0+1μα[(tt0)μ]r=0(1)rαCr=RLTDα(1)=(tt0)αΓ(1α),tt0. (2.14)

    Lemma 3.1. Assume h and mCrd(T,R). Suppose t1>t0, where t1T, : h(t1)=m(t1) and h(t)<m(t) for t0t<t1. Then, if the Caputo FrΔDiD of h and m exist at t1, the inequality CTDα+hΔ(t1)>CTDα+mΔ(t1) holds.

    Proof. Applying (2.7), we have

    CTDα+(h(t)m(t))Δ=lim supμ0+1μα{[tt0μ]r=0(1)rαCr[h(σ(t)rμ)m(σ(t)rμ)][h(t0)m(t0)]},CTDα+hΔ(t)CTDα+mΔ(t)=lim supμ0+1μα{[tt0μ]r=0(1)rαCr[h(σ(t)rμ)m(σ(t)rμ)][h(t0)m(t0)]},

    at t1, we have

    CTDα+hΔ(t1)=lim supμ0+1μα{[t1t0μ]r=0(1)rαCr[h(t0)m(t0)]}+CTDα+mΔ(t1). (3.1)

    Applying (2.14) to (3.1), we have

    CTDα+hΔ(t1)=(t1t0)αΓ(1α)[h(t0)m(t0)]+CTDα+mΔ(t1),

    however, based on the Lemma's statement, we know that

    h(t)<m(t),fort0t<t1,h(t)m(t)<0,fort0t<t1,

    then, we obtain

    (t1t0)αΓ(1α)[h(t0)m(t0)]>0,

    implying

    CTDα+hΔ(t1)>CTDα+mΔ(t1).

    Lemma 3.2. Assume that:

    (1) ν(t;t0,ν0), with νCαrd([t0,T]T,Rn), represents a solution to the system (1.2).

    (2) L(t,ν)Crd[T×Rn,R+] and for any t[t0,)T,νRn,

    CTDα+LΔ(t,ν)ϕ(ν(t)), (3.2)

    where ϕK.

    Then for t[t0,T]T, the inequality

    L(t,ν(t))L(t0,ν0)1Γ(α)tt0(ts)α1ϕ(ν(t))Δs

    holds.

    Proof. Let

    p(t)=L(t,ν(t)),withp(t0)=L(t0,ν0), (3.3)

    and

    W(t)=ϕ(ν(t)). (3.4)

    Then, from (3.2) we have

    CTDα+pΔ(t)=CTDα+LΔ(t,ν(t))ϕ(ν(t))=W(t),fort[t0,T]T. (3.5)

    Consider the system

    CTDαχΔ(t)=W(t),χ(t0)=pω(t0),where pω(t0)=p(t0)+ω. (3.6)

    A solution χ(t)=χ(t,t0,χ0) of (3.6) will also satisfy the Volterra delta integral equation

    χ(t)=pω(t0)1Γ(α)tt0(ts)α1W(s)Δs. (3.7)

    We claim that

    p(t)<χ(t),t[t0,T]T. (3.8)

    In the event that this claim is untrue, there is a time t1(t0,T]T:

    p(t1)=χ(t1)andp(t)<χ(t)fort[t0,t1)T. (3.9)

    Lemma 3.1 is applied to (3.9) to obtain

    CTDα+pΔ(t1)>CTDα+χΔ(t1)=CTDαχΔ(t1)=W(t1),CTDα+pΔ(t1)>W(t1). (3.10)

    Thus, based on (3.10) and for t[t0,T]T, we obtain:

    CTDα+pΔ(t)>W(t). (3.11)

    Clearly, (3.11) contradicts (3.5), so we conclude that the claim in (3.8) holds.

    Combining (3.3), (3.4), (3.7) and (3.8) we get

    L(t,ν(t))=p(t)<pω(t0)1Γ(α)tt0(ts)α1ϕ(ν(s))Δs, (3.12)

    L(t,ν(t))L(t0,ν0)1Γ(α)tt0(ts)α1ϕ(ν(s))Δs. (3.13)

    If T contains right scattered points, and L(t,ν) is continuous, then (3.13) becomes

    L(t,ν(t))L(t0,ν0)1Γ(α)σ(t)t0(ts)α1ϕ(ν(s))ds.

    Theorem 3.1. Assume that:

    (i) ΞCrd[T×R+,R+] and Ξ(t,χ)μα is non-decreasing in χ.

    (ii) LCrd[T×Rn,R+] be locally Lipschitz in the second variable such that

    CTDα+LΔ(t,ν)Ξ(t,L(t,ν)),(t,ν)T×Rn. (3.14)

    (iii) z(t)=z(t;t0,χ0) existing on T is the maximal solution of (1.3).

    Then,

    L(t,ν(t))z(t),tt0, (3.15)

    provided that

    L(t0,ν0)χ0, (3.16)

    where ν(t)=ν(t;t0,ν0) is any solution of (1.2), tT, tt0.

    Proof. Utilizing the principle of induction as outlined in Lemma 2.1 for the assertion

    S(t):L(t,ν(t))z(t),tT,tt0,

    (ⅰ) S(t0) is true since L(t0,ν0)χ0;

    (ⅱ) Let t be rs and S(t) be true. We need to show that S(σ(t)) is true; that is

    L(σ(t),ν(σ(t)))z(σ(t)), (3.17)

    set p(t)=L(t,ν(t)), then, p(σ(t))=L(σ(t),ν(σ(t))), but from (2.7), we have

    CTDα+pΔ(t)=lim supμ0+1μα[(tt0)μ]r=0(1)rαCr[p(σ(t)rμ)p(t0)],tt0.

    Also,

    CTDα+zΔ(t)=lim supμ0+1μα[(tt0)μ]r=0(1)rαCr[z(σ(t)rμ)z(t0)],tt0,

    so that,

    CTDα+zΔ(t)CTDα+pΔ(t)=lim supμ0+1μα[(tt0)μ]r=0(1)rαCr[z(σ(t)rμ)z(t0)]lim supμ0+1μα[(tt0)μ]r=0(1)rαCr[p(σ(t)rμ)p(t0)],CTDα+zΔ(t)CTDα+pΔ(t)=lim supμ0+1μα[(tt0)μ]r=0(1)rαCr[[z(σ(t)rμ)z(t0)][p(σ(t)rμ)p(t0)]],(CTDα+zΔ(t)CTDα+pΔ(t))μα=lim supμ0+[(tt0)μ]r=0(1)rαCr[[z(σ(t)rμ)z(t0)][p(σ(t)rμ)p(t0)]],(CTDα+zΔ(t)CTDα+pΔ(t))μα[z(σ(t))p(σ(t))][z(t0)p(t0)][p(σ(t))z(σ(t))](CTDα+pΔ(t)CTDα+zΔ(t))μα+[p(t0)z(t0)](Ξ(t,p(t))Ξ(t,z(t)))μα+[p(t0)z(t0)].

    Given that Ξ(t,χ)μα is non-decreasing in u and S(t) holds, it follows that p(σ(t))z(σ(t))0, ensuring that (3.17) is satisfied.

    (ⅲ) Let t be rd and N denote the right neighborhood of tT. We demonstrate that S(t) holds for tN. This can be established by applying the comparison theorem for Caputo FrDEs, since at every rd-point tN, σ(t)=t.

    We shall make this proof in 3 parts. In Part 1, we show that the LF, L(t,ν(t)), is maximized by a solution of the comparison system; in Part 2, we show that the family of solutions of the comparison system is uniformly bounded and equi-continuous and therefore by the Arzela-Ascoli theorem, there would exist a sub-sequence that converges uniformly to a function z(t); in Part 3, we show that this function z(t) is indeed the maximal solution. This three parts put together concludes that L(t,ν(t))z(t) for tN.

    Let ω be a small enough arbitrary positive number such that ωBT (where BT is a small enough number), for the IVP

    CTDαχΔ=Ξ(t,χ)+ω,χ(t0)=χ0+ω, (3.18)

    where tN, the function χω(t)=χ(t)+ω is a solution of (3.18) if and only if it satisfies:

    χω(t)=χ0+ω+1Γ(α)tt0(ts)α1(Ξ(s,χω(s))+ω)Δs,t,sN. (3.19)
    Part1

    Let p(t)Crd(T,R+) be such that p(t)=L(t,ν(t)).

    We show that

    p(t)<χω(t),fortN, (3.20)

    the inequality (3.20) holds for t=t0 since

    p(t0)=L(t0,ν0)χ0<χα(t0).

    Assuming that the inequality (3.20) is false, then, a point t1>t0 :

    p(t1)=χω(t1)andp(t)<χω(t),fort0t<t1.

    From Lemma (3.1) it follows that

    CTDα+pΔ(t1)>CTDα+χΔω(t1),

    so,

    CTDα+LΔ(t1,ν(t1))>CTDα+χΔω(t1),

    and using (3.18), we arrive at

    CTDα+LΔ(t1,ν(t1))>Ξ(t1,χω(t1))+ω)>Ξ(t1,p(t1)).

    Therefore,

    CTDα+pΔ(t1)>Ξ(t1,p(t1)). (3.21)

    Now, for tN.

    CTDα+pΔ(t)=lim supμ0+1μα{p(t)p(t0)[tt0μ]r=1(1)r+1(αCr)[p(trμ)p(t0)]}=lim supμ0+1μα{L(t,ν(t))L(t0,ν0)[tt0μ]r=1(1)r+1(αCr)[L(trμ,ν(trμ))L(t0,ν0)]}=lim supμ0+1μα{L(t,ν(t))L(t0,ν0)[tt0μ]r=1(1)r+1(αCr)[[L(trμ,ν(t)μαΥ(t,ν(t)))L(t0,ν0)][L(trμ,ν(t)μαΥ(t,ν(t)))L(t0,ν0)]+[L(trμ,ν(trμ))L(t0,ν0)]]}=lim supμ0+1μα{L(t,ν(t))L(t0,ν0)[tt0μ]r=1(1)r+1(αCr)[[L(trμ,ν(t)μαΥ(t,ν(t)))L(t0,ν0)][L(trμ,ν(t)μαΥ(t,ν(t)))]+L(trμ,ν(trμ))]}=lim supμ0+1μα{L(t,ν(t))L(t0,ν0)[tt0μ]r=1(1)r+1(αCr)[[L(trμ,ν(t)μαΥ(t,ν(t)))L(t0,ν0)]+[L(trμ,ν(trμ))[L(trμ,ν(t)μαΥ(t,ν(t)))]]}=lim supμ0+1μα{L(t,ν(t))L(t0,ν0)[tt0μ]r=1(1)r+1(αCr[L(trμ,ν(t)μαΥ(t,ν(t)))L(t0,ν0)]}lim supμ0+1μα{[tt0μ]r=1(1)r+1(αCr)[L(trμ,ν(trμ))L(trμ,ν(t)μαΥ(t,ν(t)))]},

    but L(t,ν) is Lipschitz in the second variable, so,

    CTDα+pΔ(t)CTDα+LΔ(t,ν(t))+Llim supμ0+1μα[tt0μ]r=1(1)r(αCr)ν(trμ)(ν(t)μαΥ(t,ν(t))),

    where L>0 is the Lipschitz constant.

    As μ0, ν(trμ)(ν(t)μαΥ(t,ν(t)))0, so that from (3.14) we have

    CTDα+pΔ(t)=CTDα+LΔ(t,ν(t))Ξ(t,L(t,ν(t)))=Ξ(t,p(t)). (3.22)

    Now, (3.22) with t=t1 contradicts (3.21), hence (3.20) is true.

    Part2

    For tN, we now show that whenever ω1<ω2, then

    χω1(t)<χω2(t). (3.23)

    Notice that (3.23) holds for t=t0 since χ(t0)+ω1<χ(t0)+ω2 ω1<ω2. If the inequality (3.23) is false, there would exist a time t1 such that χω1(t1)=χω2(t1) and χω1(t)<χω2(t) for t0t<t1, tN.

    From Lemma (3.1), it follows that

    CTDα+χΔω1(t1)>CTDα+χΔω2(t1).

    However,

    CTDα+χΔω1(t1)CTDα+χΔω2(t1)=Ξ(t1,χω1(t1))+ω1[Ξ(t1,χω2(t1))+ω2]=ω1ω2<0,

    which is a contradiction, and so (3.23) is true. Now, from (3.23), and since ωBT, we determine that

    χω1(t)<χω2(t)<<χ(t)+ωi|χ(t)+BT|M,

    and therefore we can say that the family of solutions {χωi(t)} is uniformly bounded with bound M>0 on T. This means that |χωi(t)|M for tN and ω(0,BT].

    We will now demonstrate that the family {χωi(t)} is equicontinuous on T. Suppose S=sup{|Ξ(t,ν)|:(t,ν)N×[M,M]}. Next, consider {ωi}i=1(t) as a decreasing sequence with limiωi=0, and a sequence of functions χωi(t). Let t1,t2N with t1<t2, then the estimation that follows is valid:

    |χωi(t2)χωi(t1)|=|χ0+ωi+1Γ(α)t2t0(t2s)α1(Ξ(s,χωi(s))+αi)Δs(χ0+ωi+1Γ(α)t1t0(t1s)α1(Ξ(s,χωi(s))+ωi))Δs|=1Γ(α)|t2t0(t2s)α1(Ξ(s,χωi(s)))Δst1t0(t1s)α1(Ξ(s,χωi(s)))Δs|1Γ(α)[|t2t0(t2s)α1||(Ξ(s,χωi(s)))|Δs+|t1t0(t1s)α1||(Ξ(s,χωi(s)))|Δs]SΓ(α)[|t2t0(t2s)α1Δs|+|t1t0(t1s)α1Δs|]=SΓ(α)[|t1t0(t2s)α1Δs+t2t1(t2s)α1Δs|+|t1t0(t1s)α1Δs|]=SΓ(α)[|(t2t1)αα+(t2t0)αα+(t2t1)αα|+|(t1t0)αα|]=SΓ(α)[|(t2t0)αα|+|(t1t0)αα|]=SΓ(α+1)[(t2t0)α+(t1t0)α]2SΓ(α+1)[(t2t0)α].

    A family of solutions {χωi(t)} is said to be equicontinuous if given ϵ>0, we can find δ>0 such that |χωi(t2)χωi(t1)|<ϵ whenever |t2t1|<δ,

    implying that |χωi(t2)χωi(t1)|2SΓ(α+1)[(t2t0)α]<ϵ provided |t2t1|<δ.

    Now, we choose δ=(ϵΓ(α+1)2S)1α, (ϵΓ(α+1)2S)1α>(2S(t2t0)αΓ(α+1)×Γ(α+1)2S)1α=(t2t0) but (t2t0)>|t2t1| so since (t2t0)<δ, then |t2t1|<δ. Proving that the family of solutions {χω(t)} is equicontinuous. According to the Arzelà-Ascoli theorem, the family {χωi(t)} contains a subsequence {χωij(t)} that converges uniformly to a function z(t) on T.

    Part3

    We then show that z(t) is a solution of (1.3). Equation (3.19) becomes

    χωij(t)=χ0+ωij+1Γ(α)tt0(ts)α1(Ξ(s,χωij(s))+ωij)Δs. (3.24)

    Taking the limit as ij, then χωij(t)z(t) on T. Now (3.24) yields

    z(t)=χ0+1Γ(α)tt0(ts)α1(Ξ(s,z(t)))Δs. (3.25)

    Thus, z(t) is a solution of (1.3) on T. Since limjχωij(t)=z(t) exists, then for any χωi that satisfies the dynamic equation (1.3), χω(t)z(t). So from (3.20), we have that p(t)<χω(t)z(t) on T. Therefore by induction principle, the statement S(t) is true. Completing the proof.

    Remark 3.1. Although comparison theorems for FrDE, FrDfE, and FrDET focus on understanding the behavior of solutions using a simpler comparison system, they differ in their time domains: FrDE has a continuous time domain, FrDfE has a discrete domain, and FrDET combines both. FrDE applies to continuous time systems, while FrDfE applies to discrete time systems. Theorem 3.1 examines the behavior of the LF concerning the maximal solution of the comparison system (1.3), considering an arbitrary time domain with a jump operator σ(t) that can be discrete or continuous. This is illustrated in conditions (ii) and (iii) of Lemma 2.1 in the proof of Theorem 3.1. This suggests that the comparison theorems found in the literature [1,2] address only a specific case (case iii) of Theorem 3.1, particularly condition (iii), when T=R.

    Theorem 3.2. (Stability) Assume that:

    (1) L(t,ν)Crd[T×Rn,R+], L(t,ν(t)) is locally Lipschitz with respect to ν, L(t,0)0, and

    ϕ(ν)L(t,ν(t)) (3.26)

    holds (t,ν)T×Rn and ϕK.

    (2) ΞCrd[T×R+,R+] is nondecreasing with respect to the second variable at all tT, Ξ(t,0)0, and

    CTDα+LΔ(t,ν(t))Ξ(t,L(t,ν(t))).

    (3) The zero solution of the comparison equation (1.3) is stable.

    Then, the zero solution of the system (1.2) is stable.

    Proof. By the assumption of stability of the zero solution of (1.3), let ϵ>0 be given, and for ϕ(ϵ) and t0T, there exists λ=λ(t0,ϵ)>0 :

    z(t)<ϕ(ϵ)for alltt0, (3.27)

    whenever χ0<λ, where z(t)=z(t,t0,χ0) is the maximal solution of (1.3).

    Given that L(t,0)=0 and LCrd, which implies continuity of L at the origin, then given λ>0, we can find a δ=δ(t0,λ)>0 : for ν0Rn, if ν0<δ, then L(t0,ν0)<λ.

    Claim that ν0<δ implies ν(t)<ϵ, tT, where ν(t)=ν(t,t0,ν0) is any solution of (1.2). If this claim is incorrect, then there would exist a time t1T, t1>t0 : the solution ν(t) of the dynamic system (1.2) at the instant time t1 leaves the ϵneighborhood of the zero solution. That is ν(t)<ϵ at t0t<t1 and

    ν(t1)ϵ. (3.28)

    However, we know from Theorem 3.1 that

    L(t,ν(t))z(t),t0tt1, (3.29)

    provided L(t0,ν0)χ0, where z(t) is the maximal solution of system (1.3).

    Combining (3.26), (3.27), (3.29), and (3.28) for t=t1, we obtain

    ϕ(ν(t1))L(t1,ν(t1))z(t1)<ϕ(ϵ)ϕ(ν(t1)),ϕ(ν(t1))<ϕ(ν(t1)). (3.30)

    The contradiction (3.30) shows that t1T and therefore ν(t)<ϵ tT whenever ν0<δ, and so the zero solution (1.2) is stable.

    Theorem 3.3 (Asymptotic Stability). Assume the following:

    (1) L(t,ν)Crd[R+×Rn,R+] is locally Lipschitz in ν for each tT and L(t,0)0.

    (2) For tt0,

    b(ν(t))L(t,ν),wherebK.

    (3) The inequality

    CTDα+LΔ(t,ν)ϕ(ν(t))holdsfor(t,ν)T×RnandϕK.

    Then, the zero solution ν=0 of the fractional dynamic system (1.2) is asymptotically stable.

    Proof. By Theorem 3.2, the zero solution ν=0 of (1.2) is stable. It remains to show that

    limtν(t)=0. (3.31)

    We shall make this proof in two phases.

    Phase 1. Assuming (3.31) is not true, such that lim inftν(t)0, then there would exist T>0 such that for a given ϵ>0,

    ν(t)ϵ,fort=σ(T)>T. (3.32)

    By condition 3, we deduce that L(ν(t)) is monotone decreasing and

    limtL(t,ν(t))=L00, (3.33)

    since L(t,ν(t)) is positive definite and only 0 at ν=0.

    By utilizing Lemma 3.2 in relation to condition 3, we get

    L(t,ν(t))L(t0,ν0)1Γ(α)tT(ts)α1ϕ(ν(s))Δs,fort>T,L(t,ν(t))L(t0,ν0)ϕ(ϵ)αΓ(α)(tT)α,0L(t0,ν0)ϕ(ϵ)αΓ(α)(tT)α. (3.34)

    As t, the RHS of (3.34) approaches . This is a contradiction, so lim inftν(t)=0.

    Phase 2. If lim suptν(t)0, then given η>0, there is a divergent sequence {tk}, : ν(tk)η.Each tkT could potentially be related to one of the following:

    (ⅰ) tk is rs and ls (isolated points).

    (ⅱ) tk is rs and ld.

    (ⅲ) tk is rd and ls.

    (ⅳ) tk is rd and ld (dense points).

    Suppose a divergent subsequence {ti} of {tk}, where each ti falls into one of the four cases mentioned above. Then, by Lemma 3.2 and Definition 2.5, we have that

    for case (ⅰ),

    L(ti,ν(ti))L(t0,ν0)1Γ(α)σ(t)tr(si)ϕ(ν(si))Δs,
    0L(ti,ν(ti))L(t0,ν0)ϕ(η)Γ(α)ij=1μ(tj)r(tj),

    for all ti,tjT and r(t)=(ts)α1.

    This leads to a contradiction as i, since μ(ti) remains constant for each i. Therefore, lim suptν(t)=0.

    For case (ⅱ),

    L(ti,ν(ti))L(t0,ν0)1Γ(α)σ(t)tr(si)ϕ(ν(si))Δs,
    0L(ti,ν(ti))L(t0,ν0)ϕ(η)Γ(α)μ(ti)r(tj),

    for r(t)=(ts)α1. This results in a contradiction as i, since μ(ti) is a constant for each i. So lim suptν(t)=0.

    For cases (ⅲ) and (ⅳ)

    L(ti,ν(ti))L(t0,χ0)1Γ(α)tit0(ts)α1ϕ(ν(si))Δs,0L(ti,ν(ti))L(t0,ν0)σ(η)(tit0)ααΓ(α). (3.35)

    As ti, the right-hand side of (3.35) approaches also contradicting the definition of L(t,ν(t)); lim suptν(t)=0.

    Since lim inftν(t)=lim suptν(t)=0, so, (3.31) holds. Then the zero solution ν=0 of (1.2) is asymptotically stable.

    Remark 3.2. Theorems 3.2 and 3.3 represent a significant advancement in fractional calculus and stability analysis, building on research in FrDfE, FrDE, and integer-order dynamic equations. Although similar to the stability analysis in [1,2,3,23,24,26,28,33,35,36,38], which addresses the stability of the zero solution of fractional dynamic systems, they differ by generalizing the time domain of system (1.2). This allows for stability analysis on various time scales, including discrete, continuous, and mixed. Moreover, the results extend to non-integer orders, enabling a more comprehensive analysis of system (1.2)'s behavior. Theorems 3.2 and 3.3 are better suited for analyzing the behavior of solutions in complex systems with multiple time scales and non-uniform grids, which can be challenging with FrDE and FrDfE.

    Let us examine the dynamic system

    νΔ1(t)=ν1cos2t(ν2+ν1)sin2tcos2t+ν2cos2tsin2t,νΔ2(t)=2(ν1ν2)+ν2sin2t2ν1cos2t, (4.1)

    for tt0, with initial conditions

    ν1(t0)=ν10andν2(t0)=ν20,

    where ν=(ν1,ν2) and L=(L1,L2).

    Consider L(t,ν1,ν2)=|ν1|+|ν2|, for tT and (ν1,ν2)R2. Then we compute the Dini derivative for L(t,ν1,ν2)=|ν1|+|ν2| as follows; from (2.2), we have that

    D+LΔ(t,ν)=lim supμ(t)0L(t+μ(t),ν+μ(t)Υ(t,ν))L(t,ν)μ(t)=lim supμ(t)0|ν1+μ(t)Υ1(t,ν)|+|ν2+μ(t)Υ2(t,ν)|[|ν1|+|ν2|]μ(t)lim supμ(t)0|ν1|+|μ(t)Υ1(t,ν)|+|ν2|+|μ(t)Υ2(t,ν)||ν1||ν2|μ(t)=lim supμ(t)0μ(t)[|Υ1(t,ν)|+|Υ2(t,ν)|]μ(t)|Υ1(t,ν)|+|Υ2(t,ν)|=|ν1cos2t(ν2+ν1)sin2tcos2t+ν2cos2tsin2t|+|2(ν1ν2)+ν2sin2t2ν1cos2t|=|ν1(1cos2tsin2tcos2t)ν2(sin2tcos2tcos2tsin2t)|+|2ν1(1cos2t)ν2(21sin2t)||ν1(cos2tcos2t)ν2(sin2tcos2t)(sin2t+cos2t)cos2tsin2t)|+2|ν1|+3|ν2||ν1|+|ν2||(sin2tcos2tcos2tsin2t)|+2|ν1|+3|ν2|=|ν1|+|ν2||(1cos2t1sin2t)|+2|ν1|+3|ν2|3|ν1|+|ν2|(|1cos2t|+|1sin2t|)+3|ν2|3|ν1|+5|ν2|5[|ν1|+|ν2|]D+LΔ(t,ν)5L(t,ν1,ν2)=Ξ(t,L).

    Now, consider the consider the comparison equation

    D+χΔ=5χ>0,χ(0)=χ0, (4.2)

    with solution

    χ(t)=χ0e5t. (4.3)

    Even though conditions (ⅰ)–(ⅲ) of [21] are satisfied that is LCrd[T×Rn,R+], D+LΔ(t,ν1,ν2)Ξ(t,L(t,ν)) and ν21+ν22|ν1|+|ν2|2(ν21+ν22), for b(ν)=r and a(ν)=2r2, it is obvious to see that the solution (4.3) of the comparison system (4.2) is not stable, so we can not deduce the stability properties of the system (4.1) by applying the basic definition of the Dini-derivative to the LF L(t,ν1,ν2)=|ν1|+|ν2|.

    Now, we will apply our new definition on the same system but as a Caputo fractional dynamic system

    CTDανΔ1(t)=ν1cos2t(ν2+ν1)sin2tcos2t+ν2cos2tsin2t,CTDανΔ2(t)=2(ν1ν2)+ν2sin2t2ν1cos2t, (4.4)

    for tt0, with initial conditions

    ν1(t0)=ν10andν2(t0)=ν20,

    where ν=(ν1,ν2) and Υ=(Υ1,Υ2).

    Consider L(t,ν1,ν2)=|ν1|+|ν2|, for tT and (ν1,ν2)R2. Then condition 1 of Theorem (3.2) is satisfied, for ϕ=12r, where ϕK, with ν=(ν1,ν2)R2, so that the associated norm ν=ν21+ν22.

    Since,

    \begin{equation*} \mathcal{L}(t,\nu_1,\nu_2) = |\nu_1|+|\nu_2|, \end{equation*}

    then, \phi(\|\nu\|)\leq \mathcal{L}(t, \nu_1, \nu_2) . From (2.9), we compute the Caputo Fr \Delta DiD for \mathcal{L}(t, \nu_1, \nu_2) = |\nu_1|+|\nu_2| as follows:

    \begin{eqnarray*} ^{C\mathbb{T}}D^\alpha_+\mathcal{L}^\Delta(t,\nu) \nonumber& = &\limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\bigg\{\mathcal{L}(\sigma(t),\nu(\sigma(t))-\mathcal{L}(t_0,\nu_0) \\\nonumber&&-\sum\limits_{r = 1}^{[\frac{t-t_0}{\mu}]}(-1)^{r+1}(^\alpha C_r)[\mathcal{L}(\sigma(t)-r\mu,\nu(\sigma(t))-\mu^\alpha \Upsilon(t,\nu(t)))-\mathcal{L}(t_0,\nu_0)]\bigg\} \\ \nonumber & = &\limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\bigg\{(|\nu_1(\sigma(t))|+|\nu_2(\sigma(t))|)-(|\nu_{10}|+|\nu_{20}|)\\ &&+\sum\limits_{r = 1}^{[\frac{t-t_0}{\mu}]}(-1)^r{(^\alpha C_r)}[|\nu_1(\sigma(t))-\mu^\alpha \Upsilon_1(t,\nu_1)|+|\nu_2(\sigma(t))-\mu^\alpha \Upsilon_2(t,\nu_2)|-(|\nu_{10}|+|\nu_{10}|)]\bigg\}\\ \nonumber&\leq& \limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\bigg\{(|\nu_1(\sigma(t))|+|\nu_2(\sigma(t))|)-(|\nu_{10}|+|\nu_{20}|)\\ &&+\sum\limits_{r = 1}^{[\frac{t-t_0}{\mu}]}(-1)^r{(^\alpha C_r)}[|\nu_1(\sigma(t))|+|\mu^\alpha \Upsilon_1(t;\nu_1)|+|\nu_2(\sigma(t))|+|\mu^\alpha \Upsilon_2(t;\nu_2)|-(|\nu_{10}|+|\nu_{10}|)]\bigg\}\\ \nonumber&\leq& \limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\bigg\{(|\nu_1(\sigma(t))|+|\nu_2(\sigma(t))|)-(|\nu_{10}|+|\nu_{20}|) \\ &&+\sum\limits_{r = 1}^{[\frac{t-t_0}{\mu}]}(-1)^r{(^\alpha C_r)} \bigg[|\nu_1(\sigma(t))|+|\nu_2(\sigma(t))|\bigg] +\sum\limits_{r = 1}^{[\frac{t-t_0}{\mu}]}(-1)^r{(^\alpha C_r)}|\bigg[|\mu^\alpha \Upsilon_1(t;\nu_1)|+|\mu^\alpha \Upsilon_2(t;\nu_2)|\bigg] \\&&-\sum\limits_{r = 1}^{[\frac{t-t_0}{\mu}]}(-1)^r{(^\alpha C_r)}\bigg[|\nu_{10}|+|\nu_{10}|\bigg]\bigg\}\\ &\leq& \limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\bigg\{\sum\limits_{r = 0}^{[\frac{t-t_0}{\mu}]}(-1)^r{(^\alpha C_r)} \bigg[|\nu_1(\sigma(t))|+|\nu_2(\sigma(t))|\bigg] -\sum\limits_{r = 0}^{[\frac{t-t_0}{\mu}]}(-1)^r{(^\alpha C_r)}\bigg[|\nu_{10}|+|\nu_{10}|\bigg] \bigg\} \\ &&+\limsup\limits_{\mu\rightarrow 0^+}\sum\limits_{r = 1}^{[\frac{t-t_0}{\mu}]}(-1)^r{(^\alpha C_r)}|\bigg[|\Upsilon_1(t;\nu_1)|+|\Upsilon_2(t;\nu_2)|\bigg]. \end{eqnarray*}

    Applying (2.12) and (2.14) we obtain

    \begin{eqnarray*} & = &\frac{(t-t_0)^{-\alpha}}{\Gamma(1-\alpha)} (\left|\nu_1(\sigma(t))\right|+\left|\nu_2(\sigma(t))\right|)- \frac{(t-t_0)^{-\alpha}}{\Gamma(1-\alpha)}(\left|\nu_{10}\right|+\left|\nu_{10}\right|)-\bigg[\left|\Upsilon_1(t;\nu_1)\right|+\left|\Upsilon_2(t;\nu_2)\right|\bigg]\\ &\leq&\frac{(t-t_0)^{-\alpha}}{\Gamma(1-\alpha)} (\left|\nu_1(\sigma(t))\right|+\left|\nu_2(\sigma(t))\right|)-\bigg[\left|\Upsilon_1(t;\nu_1)\right|+\left|\Upsilon_2(t;\nu_2)\right|\bigg]. \end{eqnarray*}

    As t\rightarrow \infty, \frac{(t-t_0)^{-\alpha}}{\Gamma(1-\alpha)} (\left|\nu_1(\sigma(t))\right|+\left|\nu_2(\sigma(t))\right|)\to 0 , then

    \begin{eqnarray*} &&^{C\mathbb{T}}D^\alpha_+\mathcal{L}^\Delta(t;\nu_1,\nu_2)\\&\leq&-\bigg[\left|\chi_1(t;\nu_1)\right|+\left|\chi_2(t;\nu_2)\right|\bigg]\\ & = &-\left[\left|\frac{\nu_1}{\cos^2t}-(\nu_2+\nu_1)\frac{\sin^2t}{\cos^2t}+\nu_2\frac{\cos^2t}{\sin^2t}\right|+\left|2(\nu_1-\nu_2)+\frac{\nu_2}{\sin^2t}-2\nu_1\cos^2t\right|\right]\\ & = &-\bigg[\left|\left(\frac{\nu_1}{\cos^2t}-\nu_1\frac{\sin^2t}{\cos^2t}\right)-\nu_2\left(\frac{\sin^2t}{\cos^2t}-\frac{\cos^2t}{\sin^2t}\right)\right|+\left|2\nu_1(1-\cos^2t)-\nu_2\left(2-\frac{1}{\sin^2t}\right)\right|\bigg]\\ & = &-\bigg[\left|\nu_1\left(\frac{1}{\cos^2t}-\frac{\sin^2t}{\cos^2t}\right)-\nu_2\left(\frac{\sin^2t}{\cos^2t}-\frac{\cos^2t}{\sin^2t}\right)\right|+\left|2\nu_1(\sin^2t)-\nu_2\left(2-\frac{1}{\sin^2t}\right)\right|\bigg]\\ &\leq&-\left[\left|\nu_1\left(\frac{\cos^2t}{\cos^2t}\right)-\nu_2\left(\frac{(\sin^2t-\cos^2t)(\sin^2t+\cos^2t)}{\cos^2t\sin^2t}\right)\right|+2\left|\nu_1\right|+3\left|\nu_2\right|\right]\\ &\leq&-\left[\left|\nu_1\right|+\left|\nu_2\right|\left|\left(\frac{\sin^2t-\cos^2t}{\cos^2t\sin^2t}\right)\right|+2\left|\nu_1\right|+3\left|\nu_2\right|\right]\\ &\leq&-3\left|\nu_1\right|-5\left|\nu_2\right|\leq-3\left[\left|\nu_1\right|+\left|\nu_2\right|\right]. \end{eqnarray*}

    Therefore,

    \begin{equation} ^{C\mathbb{T}}D^\alpha_+\mathcal{L}^\Delta(t;\nu_1,\nu_2)\leq-3\mathcal{L}(t,\nu_1,\nu_2). \end{equation} (4.5)

    Consider the comparison system

    \begin{equation} ^{C\mathbb{T}}D^\alpha_+\chi^\Delta = \Xi(t,\chi)\leq-3\chi, \end{equation} (4.6)

    using the Laplace transform method

    \begin{equation*} \label{ec} ^{C\mathbb{T}}D^\alpha_+\chi^\Delta+3\chi = 0. \end{equation*}
    \begin{eqnarray*} X(s) = \frac{\chi_0S^{\alpha-1}}{S^\alpha+3} \end{eqnarray*}

    taking the inverse Laplace transform we have

    \begin{eqnarray} \chi(t) = \chi_0E_{\alpha,1}(-3t^\alpha),\quad \text{for}\; \alpha\in(0,1), \end{eqnarray} (4.7)

    where E_{\alpha, 1}(z) is the Mittag-Leffler function, which can be approximated as:

    \begin{equation*} E_{\alpha,1}(-t^\alpha) = \sum\limits_{n = 0}^{\infty}(-1)^n\frac{t^{n\alpha}}{\Gamma(\alpha k+1)} = 1-\frac{t^\alpha}{\Gamma(1+\alpha)}+...\approx \exp\left[-\frac{t^\alpha}{\Gamma(1+\alpha)}\right]. \end{equation*}

    Now, let \left|\chi_0\right| < \delta , then from (4.11), we have \left|\chi(t)\right| = \left|3\chi_0 E_{\alpha, 1}(-t^\alpha)\right| = \left|3\chi_0\exp\left[-\frac{t^\alpha}{\Gamma(1+\alpha)}\right]\right| < 3\left|\exp\left[-\frac{t^\alpha}{\Gamma(1+\alpha)}\right]\right|\delta < \epsilon whenever \left|\chi_0\right| < \delta = \frac{\epsilon}{3\left|\exp\left[-\frac{t^\alpha}{\Gamma(1+\alpha)}\right]\right|} .

    Therefore given \epsilon > 0 , we can find a \delta > 0 such that \left|\chi(t)\right| < \epsilon whenever \left|\chi_0\right| < \delta .

    We conclude that the trivial solution of system (4.4) is stable as it satisfies all the conditions of Theorem 3.2 and the trivial solution of the comparison system (4.10) is stable.

    Figure 1 below is the graphical representation of E_{\alpha, 1}(-3t^\alpha) . The behavior of the curve shows stability of the solution \chi(t) over time.

    Figure 1.  Graph of \chi(t) = \chi_0E_{\alpha, 1}(-3t^\alpha) , for \alpha\in(0, 1) against t .

    Consider the Caputo fractional dynamic system

    \begin{eqnarray} \begin{split} ^{C\mathbb{T}}D^\alpha \upsilon^\Delta_1(t) & = & \upsilon_1 + \upsilon_2 - 3\upsilon_3, \\ ^{C\mathbb{T}}D^\alpha \upsilon^\Delta_2(t) & = & -\upsilon_1 + \upsilon_2 + \upsilon_2 \upsilon_3^2, \\ ^{C\mathbb{T}}D^\alpha \upsilon^\Delta_3(t) & = & 3\upsilon_1 + \upsilon_1^2 \upsilon^2_2 \upsilon_3 + \upsilon_3, \end{split} \end{eqnarray} (4.8)

    for t \geq t_0 , with initial conditions

    \begin{equation*} \upsilon_1(t_0) = \upsilon_{10},\quad \upsilon_2(t_0) = \upsilon_{20}, \quad \text{and} \quad \upsilon_3(t_0) = \upsilon_{30} \end{equation*}

    where \upsilon = (\upsilon_1, \upsilon_2, \upsilon_3) , \Upsilon = (\Upsilon_1, \Upsilon_2, \Upsilon_3) .

    Consider \mathcal{L}(t, \upsilon_1, \upsilon_2, \upsilon_3) = \upsilon_1^2 + \upsilon_2^2 + \upsilon_3^2 , for t \in \mathbb{T} and (\upsilon_1, \upsilon_2, \upsilon_3) \in \mathbb{R}^3 . Then, condition 1 of Theorem (3.3) is satisfied, for b(\|\upsilon\|) \leq \mathcal{L}(t, \upsilon) \leq a(\|\upsilon\|) , with b(r) = r , a(r) = 2r^2 , a, b \in \mathcal{K} , where the associated norm \|\upsilon\| = \sqrt{\upsilon_1^2 + \upsilon_2^2 + \upsilon_3^2} .

    Since

    \begin{equation*} \mathcal{L}(t, \upsilon_1, \upsilon_2, \upsilon_3) = \upsilon_1^2 + \upsilon_2^2 + \upsilon_3^2, \end{equation*}

    then, \sqrt{\upsilon_1^2 + \upsilon_2^2 + \upsilon_3^2} \leq \upsilon_1^2 + \upsilon_2^2 + \upsilon_3^2 \leq 2(\upsilon_1^2 + \upsilon_2^2 + \upsilon_3^2) . From (2.9), we compute the Caputo Fr \Delta DiD for \mathcal{L}(t, \upsilon_1, \upsilon_2, \upsilon_3) = \upsilon_1^2 + \upsilon_2^2 + \upsilon_3^2 as follows:

    \begin{eqnarray*} ^{C\mathbb{T}}D^\alpha_+\mathcal{L}^\Delta(t, \upsilon) & = & \limsup\limits_{\mu \rightarrow 0^+} \frac{1}{\mu^\alpha} \bigg\{ \left[(\upsilon_1(\sigma(t)))^2 + (\upsilon^2_2(\sigma(t)))^2 + (\upsilon_3(\sigma(t)))^2 \right] - \left[(\upsilon_{10})^2 + (\upsilon_{20})^2 + (\upsilon_{30})^2 \right] \\ &&+\sum\limits_{r = 1}^{\left[\frac{t-t_0}{\mu}\right]}(-1)^r \left( ^\alpha C_r \right) [(\upsilon_1(\sigma(t))-\mu^\alpha \Upsilon_1(t, \upsilon_1, \upsilon_2, \upsilon_3))^2 + (\upsilon_2(\sigma(t))\\&&-\mu^\alpha \Upsilon_2(t, \upsilon_1, \upsilon_2, \upsilon_3))^2] +[(\upsilon_3(\sigma(t))-\mu^\alpha \Upsilon_3(t, \upsilon_1, \upsilon_2, \upsilon_3))^2 - ((\upsilon_{10})^2 + (\upsilon_{20})^2 + (\upsilon_{30})^2)] \bigg\}\\ \nonumber & = &\limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\bigg\{\left[(\upsilon_1(\sigma(t)))^2 + (\upsilon^2_2(\sigma(t)))^2 + (\upsilon_3(\sigma(t)))^2\right]- \left[(\upsilon_{10})^2 + (\upsilon_{20})^2 + (\upsilon_{30})^2\right] \\ &&+\sum\limits_{r = 1}^{\left[\frac{t-t_0}{\mu}\right]}(-1)^r \left( ^\alpha C_r \right) \left[(\upsilon_1(\sigma(t)))^2 - 2\upsilon_1(\sigma(t))\mu^\alpha \Upsilon_1(t,\upsilon_1,\upsilon_2,\upsilon_3) + \mu^{2\alpha} (\Upsilon_1(t,\upsilon_1,\upsilon_2,\upsilon_3))^2 \right] \\ &&+ (\upsilon_2(\sigma(t)))^2 - 2\upsilon_2(\sigma(t))\mu^\alpha \Upsilon_2(t,\upsilon_1,\upsilon_2,\upsilon_3) + \mu^{2\alpha} (\Upsilon_2(t,\upsilon_1,\upsilon_2,\upsilon_3))^2\\ &&+ (\upsilon_3(\sigma(t)))^2 - 2\upsilon_3(\sigma(t))\mu^\alpha \Upsilon_3(t,\upsilon_1,\upsilon_2,\upsilon_3) + \mu^{2\alpha} (\Upsilon_3(t,\upsilon_1,\upsilon_2,\upsilon_3))^2 \\&&- \left[(\upsilon_{10})^2 + (\upsilon_{20})^2 + (\upsilon_{30})^2\right] \bigg\}\\ \nonumber & = &\limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\bigg\{\left[(\upsilon_1(\sigma(t)))^2 + (\upsilon^2_2(\sigma(t)))^2 + (\upsilon_3(\sigma(t)))^2\right]- \left[(\upsilon_{10})^2 + (\upsilon_{20})^2 + (\upsilon_{30})^2\right] \\ &&+\sum\limits_{r = 1}^{\left[\frac{t-t_0}{\mu}\right]}(-1)^r \left( ^\alpha C_r \right) \left[(\upsilon_1(\sigma(t)))^2 - 2\upsilon_1(\sigma(t))\mu^\alpha \Upsilon_1(t,\upsilon_1,\upsilon_2,\upsilon_3)+ \mu^{2\alpha} (\Upsilon_1(t,\upsilon_1,\upsilon_2,\upsilon_3))^2\right] \\ &&+\sum\limits_{r = 1}^{\left[\frac{t-t_0}{\mu}\right]}(-1)^r \left( ^\alpha C_r \right) \left[(\upsilon_2(\sigma(t)))^2 - 2\upsilon_2(\sigma(t))\mu^\alpha \Upsilon_2(t,\upsilon_1,\upsilon_2,\upsilon_3)+ \mu^{2\alpha} (\Upsilon_2(t,\upsilon_1,\upsilon_2,\upsilon_3))^2\right] \\ &&+\sum\limits_{r = 1}^{\left[\frac{t-t_0}{\mu}\right]}(-1)^r \left( ^\alpha C_r \right) [(\upsilon_3(\sigma(t)))^2 - 2\upsilon_3(\sigma(t))\mu^\alpha \Upsilon_3(t,\upsilon_1,\upsilon_2,\upsilon_3) \\&&+ \mu^{2\alpha} (\Upsilon_3(t,\upsilon_1,\upsilon_2,\upsilon_3))^2 - \left[(\upsilon_{10})^2 + (\upsilon_{20})^2 + (\upsilon_{30})^2\right] \bigg\}\\ \nonumber & = &-\limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\left\{\sum\limits_{r = 0}^{\left[\frac{t-t_0}{\mu}\right]}(-1)^r \left( ^\alpha C_r \right) \left[(\upsilon_{10})^2 + (\upsilon_{20})^2 + (\upsilon_{30})^2\right]\right\}\\ &&+\limsup\limits_{\mu\rightarrow 0^+}\frac{1}{\mu^\alpha}\left\{\sum\limits_{r = 0}^{\left[\frac{t-t_0}{\mu}\right]}(-1)^r \left( ^\alpha C_r \right) \left[(\upsilon_1(\sigma(t)))^2 + (\upsilon_2(\sigma(t)))^2 + (\upsilon_3(\sigma(t)))^2\right]\right\} \\ &&-\limsup\limits_{\mu\rightarrow 0^+}\bigg\{\sum\limits_{r = 1}^{\left[\frac{t-t_0}{\mu}\right]}(-1)^r \left( ^\alpha C_r \right) [2\upsilon_1(\sigma(t))\mu^\alpha \Upsilon_1(t,\upsilon_1,\upsilon_2,\upsilon_3) \\&&+ 2\upsilon_2(\sigma(t))\mu^\alpha \Upsilon_2(t,\upsilon_1,\upsilon_2,\upsilon_3) + 2\upsilon_3(\sigma(t))\mu^\alpha \Upsilon_3(t,\upsilon_1,\upsilon_2,\upsilon_3)]\bigg\}. \end{eqnarray*}

    Applying (2.12) and (2.14) we obtain

    \begin{eqnarray*} &\leq& \frac{(t-t_0)^{-\alpha}}{\Gamma(1-\alpha)} \left[(\upsilon_1(\sigma(t)))^2 + (\upsilon_2(\sigma(t)))^2 + (\upsilon_3(\sigma(t)))^2\right] \\ &&-[2\upsilon_1(\sigma(t)) \Upsilon_1(t,\upsilon_1,\upsilon_2,\upsilon_3) + 2\upsilon_2(\sigma(t)) \Upsilon_2(t,\upsilon_1,\upsilon_2,\upsilon_3) + 2\upsilon_3(\sigma(t)) \Upsilon_3(t,\upsilon_1,\upsilon_2,\upsilon_3)]. \end{eqnarray*}

    As t\rightarrow \infty , \frac{(t-t_0)^{-\alpha}}{\Gamma(1-\alpha)} \left[(\upsilon_1(\sigma(t)))^2 + (\upsilon_2(\sigma(t)))^2 + (\upsilon_3(\sigma(t)))^2\right] \to 0 , then

    \begin{eqnarray*} &\leq& -2[\upsilon_1(\sigma(t)) \Upsilon_1(t,\upsilon_1,\upsilon_2,\upsilon_3) + \upsilon_2(\sigma(t)) \Upsilon_2(t,\upsilon_1,\upsilon_2,\upsilon_3)+ \upsilon_3(\sigma(t)) \Upsilon_3(t,\upsilon_1,\upsilon_2,\upsilon_3)], \end{eqnarray*}

    applying \upsilon(\sigma(t))\leq\mu^{C\mathbb{T}}D^\alpha \upsilon(t) + \upsilon(t)

    \begin{eqnarray} & = &-2\bigg[\mu(t) \Upsilon^2_1(t,\upsilon_1,\upsilon_2,\upsilon_3) + \upsilon_1(t) \Upsilon_1(t,\upsilon_1,\upsilon_2,\upsilon_3) + \mu(t) \Upsilon^2_2(t,\upsilon_1,\upsilon_2,\upsilon_3) \\&&+ \upsilon_2(t) \Upsilon_2(t,\upsilon_1,\upsilon_2,\upsilon_3)+\mu(t) \Upsilon^2_3(t,\upsilon_1,\upsilon_2,\upsilon_3) + \upsilon_3(t) \Upsilon_3(t,\upsilon_1,\upsilon_2,\upsilon_3)\bigg]\\ & = &-2\bigg[\mu(t)(\upsilon_1 + \upsilon_2 - 3\upsilon_3)^2 + \upsilon_1(\upsilon_1 + \upsilon_2 - 3\upsilon_3) + \mu(t)(-\upsilon_1 + \upsilon_2 + \upsilon_2\upsilon_3^2)^2 \\&&+ \upsilon_2(-\upsilon_1 + \upsilon_2 + \upsilon_2\upsilon_3^2)+\mu(t)(3\upsilon_1 + \upsilon_1^2\upsilon^2_2\upsilon_3 + \upsilon_3)^2 + \upsilon_3(3\upsilon_1 + \upsilon_1^2\upsilon^2_2\upsilon_3 + \upsilon_3)\bigg]\\ & = &-2\bigg[\upsilon^2_1 + \upsilon^2_2 + \upsilon^2_3 + \mu(t)(\upsilon_1 + \upsilon_2 - 3\upsilon_3)^2 + \mu(t)(-\upsilon_1 + \upsilon_2 + \upsilon_2\upsilon_3^2)^2 +\mu(t)(3\upsilon_1 + \upsilon_1^2\upsilon^2_2\upsilon_3 + \upsilon_3)^2\bigg] \\ &&- 2\bigg[\upsilon^2_2\upsilon_3^2 + \upsilon_1^2\upsilon^2_2\upsilon^2_3\bigg] \\ &\leq& -2\bigg[\upsilon^2_1 + \upsilon^2_2 + \upsilon^2_3 + \mu(t)(\upsilon_1 + \upsilon_2 - 3\upsilon_3)^2 + \mu(t)(-\upsilon_1 + \upsilon_2 + \upsilon_2\upsilon_3^2)^2 +\mu(t)(3\upsilon_1 + \upsilon_1^2\upsilon^2_2\upsilon_3 + \upsilon_3)^2\bigg]\\ & = &-2\bigg[\upsilon^2_1 + \upsilon^2_2 + \upsilon^2_3\bigg] - 2\mu(t)\bigg[(\upsilon_1 + \upsilon_2 - 3\upsilon_3)^2 + (-\upsilon_1 + \upsilon_2 + \upsilon_2\upsilon_3^2)^2 + (3\upsilon_1 + \upsilon_1^2\upsilon^2_2\upsilon_3 + \upsilon_3)^2\bigg]. \end{eqnarray} (4.9)

    If \mathbb{T} = \mathbb{R} we have that \mu = 0 , so that (4.9) becomes;

    \begin{eqnarray*} ^{C\mathbb{T}}D^\alpha_+\mathcal{L}^\Delta(t;\upsilon_1,\upsilon_2)\leq-2\bigg[\upsilon^2_1+\upsilon^2_2+\upsilon^2_3\bigg]. \end{eqnarray*}

    Therefore,

    \begin{eqnarray*} ^{C\mathbb{T}}D^\alpha_+\mathcal{L}^\Delta(t;\upsilon_1,\upsilon_2)\leq-2\mathcal{L}(t,\upsilon_1,\upsilon_2,\upsilon_3). \end{eqnarray*}

    Consider the comparison system

    \begin{equation} ^{C\mathbb{T}}D^\alpha_+\chi^\Delta = \Xi(t,\chi)\leq-2\chi, \end{equation} (4.10)
    \begin{equation*} ^{C\mathbb{T}}D^\alpha_+\chi^\Delta+2\chi = 0. \end{equation*}

    Applying the Laplace transform method, we obtain

    \begin{eqnarray} \chi(t) = \chi_0E_{\alpha,1}(-2t^\alpha),\quad \text{for}\; \alpha\in(0,1). \end{eqnarray} (4.11)

    Now, let \chi_0 < \delta , then from (4.11), we have \chi(t) = 2\chi_0E_{\alpha, 1}(-t^\alpha) < 2E_{\alpha, 1}(-t^\alpha) < \epsilon whenever \chi_0 < \delta = \frac{\epsilon}{2E_{\alpha, 1}(-t^\alpha)}

    Therefore given \epsilon > 0 , we can find a \delta(\epsilon) > 0 (independent of t ) : \chi(t) < \epsilon whenever \chi_0 < \delta If \mathbb{T} = \mathbb{N}_0 we have that \mu = 1 , so that (4.9) becomes;

    \begin{eqnarray*} & = &-2\bigg[\upsilon^2_1+\upsilon^2_2+\upsilon^2_3\bigg]-2\bigg[(\upsilon_1+\upsilon_2-3\upsilon_3)^2 +(-\upsilon_1+\upsilon_2+\upsilon_2\upsilon_3^2)^2+(3\upsilon_1+\upsilon_1^2\upsilon^2_2\upsilon_3+\upsilon_3)^2\bigg],\\ &&^{C\mathbb{T}}D^\alpha_+\mathcal{L}^\Delta(t;\upsilon_1,\upsilon_2)\leq-2\bigg[\upsilon^2_1+\upsilon^2_2+\upsilon^2_3\bigg]; \end{eqnarray*}

    considering the same comparison system as (4.10), we also arrive at the same conclusion as (4.11). Since all the conditions of Theorem 3.3 are satisfied, and zero solution of the comparison system (4.10) is stable, then we conclude that the zero solution of system (4.8) is stable and also asymptotically stable.

    Figure 2 below is the graphical representation of \chi(t) = E_{\alpha, 1}(-2t^\alpha) . The behavior of the curves further buttresses the stability of \chi(t) over time for \alpha\in(0, 1) .

    Figure 2.  Graph of \chi(t) = E_{\alpha, 1}(-2t^\alpha) against t .

    In conclusion, our study significantly advances the understanding of Lyapunov stability for Caputo FrDET. The novelty of our work is in the development of a new Dini derivative (the Caputo Fr \Delta DiD) for a Lyapunov function, which preserves the properties of FrD, requires only right dense continuity of the function and depends on the initial data \mathcal{L}(t_0, \nu_0) . Our novel definition generalizes existing definitions because it unifies the continuous ( \sigma(t) = t ) and discrete ( \sigma(t) > t ) time domain, as can be observed in Eqs (2.10) and (2.11). We have also shown the theoretical applicability of this definition in Theorems 3.1, 3.2, and 3.3 and the practical applicability as well as effectiveness in systems (4.4) and (4.8). Also, Figures 1 and 2 show a consistent behavior of the curves (a downward trend towards the trivial solution). This behavior reinforces the stability of the solutions obtained for systems (4.4) and (4.8), providing visual confirmation of the theoretical results. The new concept developed in this work successfully contributes to the advancement of fractional calculus in general and stability theory in particular from a continuous domain to a unified continuous and discrete domain, which is a breakthrough for modeling and other practical applications. By establishing comparison results and stability criteria, we have provided a solid theoretical foundation for analyzing the stability properties of these equations across time scales.

    Michael Precious Ineh: Conceptualization, Methodology, Software, Investigation, Writing original draft; Edet Peter Akpan: Conceptualization, Methodology, Investigation, Supervision; Hossam A. Nabwey: Conceptualization, Software, Investigation, Validation, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

    The authors extend their appreciation to Prince Sattam Bin Abdulaziz University for funding this research through project number (PSAU/2024/01/ 921606).

    The authors declare that they have no conflicts of interest.

    Table 1.  Abbreviation key.
    Abbreviation Definition
    FrDE Fractional Differential Equations
    FrDfE Fractional Difference Equations
    FrDET Fractional Dynamic Equations on Time scale
    FrD Fractional Derivative
    Fr \Delta D Fractional Delta Derivative
    Fr \Delta DiD Fractional Delta Dini Derivative
    G-L Grunwald-Letnikov
    IVP Initial Value Problem
    LF Lyapunov Function
    rd right dense
    rs right scattered
    ls left scattered
    ld left dense

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