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Design of an off-grid hybrid PV/wind power system for remote mobile base station: A case study

  • There is a clear challenge to provide reliable cellular mobile service at remote locations where a reliable power supply is not available. So, the existing Mobile towers or Base Transceiver Station (BTSs) uses a conventional diesel generator with backup battery banks. This paper presents the solution to utilizing a hybrid of photovoltaic (PV) solar and wind power system with a backup battery bank to provide feasibility and reliable electric power for a specific remote mobile base station located at west arise, Oromia. All the necessary modeling, simulation, and techno-economic evaluation are carried out using Hybrid Optimization Model for Electric Renewable (HOMER) software. The best optimal system configurations namely PV/Battery and PV/Wind/Battery hybrid systems are compared with the conventional stand-alone diesel generator (DG) system. Findings indicated that PV array and battery is the most economically viable option with the total net present cost (NPC) of $57,508 and per unit cost of electricity (COE) of $0.355. Simulation results show that the hybrid energy systems can minimize the power generation cost significantly and can decrease CO2 emissions as compared to the traditional diesel generator only. The sensitivity analysis is also carried out to analysis the effects of probable variation in solar radiation, wind speed, diesel price and average annual energy usage of the system load in the optimal system configurations.

    Citation: Mulualem T. Yeshalem, Baseem Khan. Design of an off-grid hybrid PV/wind power system for remote mobile base station: A case study[J]. AIMS Energy, 2017, 5(1): 96-112. doi: 10.3934/energy.2017.1.96

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  • There is a clear challenge to provide reliable cellular mobile service at remote locations where a reliable power supply is not available. So, the existing Mobile towers or Base Transceiver Station (BTSs) uses a conventional diesel generator with backup battery banks. This paper presents the solution to utilizing a hybrid of photovoltaic (PV) solar and wind power system with a backup battery bank to provide feasibility and reliable electric power for a specific remote mobile base station located at west arise, Oromia. All the necessary modeling, simulation, and techno-economic evaluation are carried out using Hybrid Optimization Model for Electric Renewable (HOMER) software. The best optimal system configurations namely PV/Battery and PV/Wind/Battery hybrid systems are compared with the conventional stand-alone diesel generator (DG) system. Findings indicated that PV array and battery is the most economically viable option with the total net present cost (NPC) of $57,508 and per unit cost of electricity (COE) of $0.355. Simulation results show that the hybrid energy systems can minimize the power generation cost significantly and can decrease CO2 emissions as compared to the traditional diesel generator only. The sensitivity analysis is also carried out to analysis the effects of probable variation in solar radiation, wind speed, diesel price and average annual energy usage of the system load in the optimal system configurations.


    First, I take this opportunity to express my great admiration toward Professor Goong Chen by dedicating this paper to him on the occasion of his 70th birthday.

    In this paper, we mainly consider the problem of vibrations governed by 1D wave equation wttc2wxx=0, where c denotes wave propagation speed, associated with a generalized boundary condition. Recall the definition of chaos for this kind of system, which is firstly introduced in [1], as below:

    Definition 1.1. Consider an initial-boundary problem (S) governed by 1D wave equation wttc2wxx=0 defined on a segment I, where c>0 denotes the propagation speed of the wave. The system is said to be chaotic if there exists a large class of initial data (w0,w1) such that

    (i) |wx|+|wt| is uniformly bounded,

    (ii) V(t)def=VI(wx(,t))+VI(wt(,t))<+ for all t0,

    (iii) lim inft+lnV(t)t>0.

    Remark 1.1. When chaos occurs, the function x(|wx|+|wt|)(x,t) is uniformly bounded, whereas the length of the curve {(x,(|wx|+|wt|)(x,t)),x[0,1]} grows exponentially w.r.t. time t. Therefore, the system (S) must undergo extremely complex oscillations as time t increasing.

    Remark 1.2. There are lots of work about chaos studies, see e.g., [2,3,4] and references therein. As we know, there is no a common mathematical definition for chaos, which is actually a challenge to give. However, Li-Yorke chaos is probably one of the most popular and acceptable notions of chaos. We will study the relationship between Li-Yorke chaos and chaos in the sense of Definition 1.1 in our future work. Particularly, if chaos happens and the solution (wx,wt) can be represented by two interval maps, denoted by K1 and K2, respectively, then K1 and K2 have positive entropy, which implies K1 and K2 are chaotic in the sense of Li-Yorke.

    Let us take a classical model to introduce the research background and our motivation, as below:

    {wttwxx=0,  x(0,1), t>0,wx(0,t)=ηwt(0,t), η1,  t>0,wx(1,t)=αwt(1,t)βw3t(1,t),  0<α<1, β0, t>0,w(x,0)=w0(x),wt(x,0)=w1(x),0x1, (1.1)

    where α,β, and η are given constants. If η=1, the system (1.1) is not well-posed. Thus throughout this paper we assume η1. The wave equation itself is linear and represents the infinite-dimensional harmonic oscillator. Let

    E(t)=1210|w(x,t)|2dx=1210[w2x(x,t)+w2t(x,t)]dx,

    be the energy function of this system. And assume that (1.1) admits a C2 solution, then the boundary conditions show

    ddtE(t)=ηw2t(0,t)+w2t(1,t)[αβw2t(1,t)].

    The right-handed side boundary condition (at x=1) is nonlinear when β0, which is usually called a van der Pol type boundary condition (see, e.g., [1,5,6,7,8,9]). The left-handed side boundary condition (at x=0) is linear, where η>0 indicates that energy is being injected into the system at x=0. Thus if η>0, the system (1.1) has a self-excited mechanism that supplies energy to the system itself, which induces irregular vibrations [1,6]. In particular, when η=0, the free end at x=0 has no effect to the energy, which is also called an energy-conserving boundary condition.

    The existence and uniqueness of the classical solution of (1.1) can be found in [5,6]. Furthermore, the system (1.1) has a smooth solution wC2 if the initial data satisfy

    w0C20([0,1]),w1C10([0,1]), (1.2)

    where

    Ck0([0,1])={fCk([0,1]) | f(i)(0)=f(i)(1)=0, 0ik }, kN, (1.3)

    see Theorem 6.1 in [6]. The weak solution as well as its numerical approximation are discussed in [9].

    The PDE system (1.1) has received considerable attention since it exhibits many interesting and complicated dynamical phenomena, such as limit cycles and chaotic behavior of (wt,wx) when the parameters α,β and η assume certain values [1,6]. Different from dynamics of a system of ODEs, this is a simple and useful infinite-dimensional model for the study of spatiotemporal behaviors as time evolutes. For instance, the propagation of acoustic waves in a pipe satisfies the linear wave equation: wttwxx=0. As we know, the solution of 1D wave equation describes a superposition of two traveling wave with arbitrary profiles, one propagating with unit speed to the left, the other with unit speed to the right. The boundary conditions appeared in (1.1) can create irregularly acoustical vibrations ([1,6,7]). This type of vibrations, for example, can be generated by noise signals radiated from underwater vehicles, and there are intensive research for the properties of acoustical vibrations in current literature (see e.g., [10] and references therein). Hence the study of this type of vibration is not only important but also may lead to a better understanding of the dynamics of acoustic systems.

    In this paper, we mainly consider the oscillation problems described by the following models:

    {wtt(x,t)c2wxx(x,t)=0,  x(0,1), t>0,wx(0,t)=ηwt(0,t), ηc1,  t>0,wx(1,t)=h(wt(1,t)), t>0,w0(x)=w(x,0),w1(x)=wt(x,0),0x1, (1.4)
    {ztt(x,t)c2zxx(x,t)=0,  x(0,1), t>0,z(0,t)=0,  t>0,zx(1,t)=h(zt(1,t)), t>0,z0(x)=z(x,0),z1(x)=zt(x,0),0x1, (1.5)

    where the function hC0(R) satisfies

    (A1) ϕ:t12(h(t)1ct) strictly monotonically decreases on R,

    (A2) ϕ(R)=R.

    It is clear that ϕ1:RR is well-defined and strictly decreases on R. In fact, the model (1.4) is a generalized case of the van del Pol type boundary condition, h(x)=αxβx3, say. Denote the total energy of system (1.4) as

    E(t)=12c210[c2w2x(x,t)+w2t(x,t)]dx,

    then the boundary conditions show

    ddtE(t)=ηw2t(0,t)+w2t(1,t)h(wt(1,t))wt(1,t).

    Hence η>0 can cause the energy of system to increase. Moreover, the system (1.1) has a generalized self-excited mechanism if we assume that, roughly speaking, h(y)y>0 if |y| is small and h(y)y<0 if |y| is large. Thus if η>0, the system (1.4) has a self-excited mechanism that supplies energy to the system itself, which could induce irregular oscillations. The interesting part is that the system (1.5) may have chaos even though there is no energy supplier at the boundary.

    We can treat the initial-boundary problems (1.4) and (1.5) by using wave propagation method. Let's take (1.4) as a start. It is well-known that w, a solution of 1D wave equation, has the following form:

    w(x,t)=L(x+ct)+R(xct), (1.6)

    where L and R are two C1 functions. Then the gradient of w can be represented as follows:

    wt(x,t)=cL(x+ct)cR(xct),wx(x,t)=L(x+ct)+R(xct), (1.7)

    for x[0,1],t0. Introduce two new variables

    u(x,t)=L(x+ct),v(x,t)=R(xct), (1.8)

    which are called the Riemann invariants. It is evident that u and v keeps constants along the lines x+ct=const. and xct=const., respectively, which are referred to as characteristics.

    When t>0 and x=0, from the boundary condition at the left end x=0,

    v(0,t)=cwx(0,t)wt(0,t)2c=cηwt(0,t)wt(0,t)2c=1+cη2cwt(0,t)=1+cη2(u(0,t)v(0,t)),

    that is

    v(0,t)=cη+1cη1u(0,t)def=γ(η)u(0,t). (1.9)

    Without confusion, we also take γ(η) as a linear map.

    When t>0 and x=1, from the boundary condition at the right end x=1, it follows:

    h(c(u(1,t)v(1,t)))1cc(u(1,t)v(1,t))2v(1,t)=0,

    that is

    v(1,t)=ϕ(c(u(1,t)v(1,t))),

    which determines a reflection relationship between u(1,t) and v(1,t) as follow

    u(1,t)=1cϕ1(v(1,t))+v(1,t)def=φ(v(1,t)), (1.10)

    where

    φ(x)=1cϕ1(x)+x. (1.11)

    When t=0, from the initial conditions,

    u0(x)def=u(x,0)=cw0(x)+w1(x)2c,v0(x)def=v(x,0)=cw0(x)w1(x)2c, (1.12)

    which are referred to as the initial date of (u,v).

    For x[0,1] and τ0, it follows from the boundary reflections (1.9) and (1.10) that

    u(x,τ+2c)=L(x+cτ+2)=u(1,1cx+τ+1c)=φ(v(1,1cx+τ+1c))=φ(R(xcτ))=φ(v(0,1cx+τ))=φ(γ(η)u(x,τ)),

    and

    v(x,τ+2c)=γ(η)φ(v(x,τ));

    inductively,

    u(x,τ+2nc)=(φγ(η))n(u(x,τ)),v(x,τ+2nc)=(γ(η)φ)n(v(x,τ)), (1.13)

    where the superscript nN denotes the nth iteration of a function. Analogously, for x[0,1] and τ[0,2c),

    u(x,τ)={u0(x+cτ),0cτ1x,φ(v0(2xcτ)),1x<cτ2x,φγ(η)(u0(x+cτ2)),2x<cτ<2, (1.14)

    and

    v(x,τ)={v0(xcτ),0cτx,γ(η)(u0(cτx)),x<cτx+1,γ(η)φ(v0(2+xcτ)),x+1<cτ<2. (1.15)

    Equations (1.13)–(1.15) show that the system (1.4) is solvable and the dynamics of the solution (w,wx,wt) to the equation (1.4) can be uniquely determined by the initial data and the following two functions:

    ψη=γ(η)φ,g=φγ(η). (1.16)

    Note that g=γ1(η)ψηγ(η), that is to say there is topological conjugacy between ψη and g. Therefore one only needs to consider one of them, say ψη.

    The paper will be organized as follows. In the next two sections, we will present the necessary and sufficient conditions to cause the onset of chaos, respectively. Section 4 shows, as a special case, in which the wave equation has a fixed end, that chaos can occur with the effects of self-regulations and energy-conservation. In Section 5, there are some applications of the theoretical outcomes. In the last section, it is the numerical simulations.

    In this section, we firstly give a necessary condition for the onset of chaos in the following system:

    {wtt(x,t)c2wxx(x,t)=0,  x(0,1), t>0,wx(0,t)=ηwt(0,t), ηc1,  t>0,wx(1,t)=h(wt(1,t)), t>0,w0(x)=w(x,0),w1(x)=wt(x,0),0x1, (2.1)

    where h satisfies hypotheses (A1) and (A2). In addition, assume the function t(h(t)+1ct) is piecewise monotone.

    Theorem 2.1. Suppose the system (2.1) is chaotic in the sense of Definition 1.1. Then h is not even and η0.

    Proof. It is equivalent to prove that there is no chaos in the system (2.1) if η=0 or h is an even function.

    Firstly, assume h is an even function. Let ηR and ηc1. Recall the function ψη given by (1.16):

    ψη=γ(η)φ,φ(x)=1cϕ1(x)+x.

    Introduce a new map Q from R to R as follow:

    Q(y)=12(h(y)+1cy). (2.2)

    For xR, let y=ϕ1(x), then

    ψη(x)=γ(η)(1cy+ϕ(y))=12γ(η)(1cy+h(y))=γ(η)Qϕ1(x).

    Since ϕ1 is strictly decreasing and γ(η)R, ψ is monotonic if and only if Q is monotonic. Let y1,y2R with y1<y2. From the hypothesis h being an even function, it follows

    Q(y2)Q(y1)=12(h(y2)1c(y2))12(h(y1)1c(y1))=ϕ(y2)ϕ(y1)>0,

    which implies Q strictly monotonically increases. Therefore ψ strictly monotonically increases (decreases) if γ(η)<0(γ(η)>0). It is easily seen that

    wt(x,t)=c[γ1(η)ψn(γ(η)u(x,τ))ψn(v(x,τ))],wx(x,t)=γ1(η)ψn(γ(η)u(x,τ))+ψn(v(x,τ)).

    Hence the dynamics of (wx,wt) is simple, and the chaos doesn't occur in the system.

    Next, assume η=0, in other words, the system is free at the left end. In this case,

    ψ0(x)=xϕ1(x).

    We will show that there is no period point of ψ0 with period 2. Let x0R satisfy x0<ψ0(x0). Define two functions as follows:

    q:xx+ψ0(x0)+x0,k=ψ0q.

    Since ϕ1() strictly monotonically decreases and

    k(x)=xϕ1(x)q(x)=ϕ1(x0)ϕ1(x),

    k strictly monotonically increases in [x0,ψ0(x0)]. Hence

    k(ψ0(x0))=ψ20(x0)x0>k(x0)=0.

    That implies there are no period points of ψ0 with period 2. By Sharkovsky's Theorem, there are no periods of ψ0 with period lager than 2. By virtue of the Main Theorem 6 in [12], there is still no chaos in the system when η=0.

    Next, we consider the following system governed by 1D wave equation with a fixed end:

    {ztt(x,t)c2zxx(x,t)=0,  x(0,1), t>0,z(0,t)=0,  t>0,zx(1,t)=h(zt(1,t)), t>0,z0(x)=z(x,0),z1(x)=zt(x,0),0x1, (2.3)

    where h satisfies hypotheses (A1) and (A2).

    Theorem 2.2. Suppose the system (2.3) is chaotic in the sense of Definition 1.1. Thenh is neither an even function nor an odd function.

    Proof. Put

    ψ(x)=x+ϕ1(x).

    By the analysis in Section 1, it is clear that (zx,zt) can be represented by iterations of ψ and initial data. Hence, the dynamics of (zx,zt) is completely determined by ψ.

    If h is even, ψ is monotonically monotone. If h is odd, ψ has no periodic points of period larger than 2. Therefore, chaos never occurs in the system 2.3 if h is either an even function or an odd function.

    Remark 2.1. Chaos can definitely happen in the system 2.3 for a special kind of h. One can find more details about that in the later section.

    In this section, we mainly try to determine some sufficient conditions to ensure the onset of chaotic oscillations in the following system:

    {wtt(x,t)c2wxx(x,t)=0,  x(0,1), t>0,wx(0,t)=ηwt(0,t), ηc1,  t>0,wx(1,t)=h(wt(1,t)), t>0,w0(x)=w(x,0),w1(x)=wt(x,0),0x1, (3.1)

    where hC0(R) satisfies h(0)=0, hypotheses (A1) and (A2). When wt(1,t)0, there should be no signals feedback to wx(1,t), that's to say the right end should be free. Therefore it is reasonable to let h(0)=0.

    According to the analysis in the first section, we have known that the function ψη given by (1.16) plays a vital role in studying the dynamics of system (3.1). We firstly give two lemmas that are useful in analyzing dynamics of ψη, as follow:

    Lemma 3.1. Let I be a non-degenerate closed interval, JR and F:IJ, G:JR. Assume that VJG<+ and F is piecewise monotone. Then

    VF(I)GVIGF<+.

    Proof. Let I1=[a,b] be a monotone interval of F. Without loss of generality, let F monotonically increase in I1. Let

    P:a=t0t1tn=b,

    be a partition of I1. Then

    VI1[GF,P]=VF(I1)[G,P]VF(I1)G,

    where

    P:F(a)=F(t0)F(t1)F(tn)=F(b),

    is a partition of F(I1). Consequently, VI1GFVF(I1)G. Conversely, if

    P:F(a)=q0q1qn=F(b),

    is a partition of of F(I1), then

    P:a=˜F1(q0)˜F1(q1)˜F1(qn)qn+1=b,

    where ˜F1(q)=min{xI1,F(x)=q}, is a partition of I1. That implies

    VF(I1)[G,P]=VI1[GF,P]VI1GF.

    Therefore, VI1GF=VF(I1)G.

    Let (Ij)1jn be a finite sequence consisting of monotone intervals of F. Assume 1jnIj=I and #(IiIj)1 provided ij. Then,

    VIGF=nj=1VIjGF=nj=1VF(Ij)GV1jnF(Ij)G=VF(I)G.

    Lemma 3.2. Let I be a closed interval and FC0(I,I) be piecewise monotone. If there exist non-degenerate subintervals I1,I2A with Card(I1I2)1 such that I2F(I1) and I1I2F(I2), then

    lim infn+lnVI1I2Fnnln1+52. (3.2)

    Proof. Let two subintervals I1,I2A satisfy the hypothesis. Let nN. Put

    xn=VI2Fn,yn=VI1I2Fn,

    where F0=IdR if n=0. Note that F() is continuous and piecewise monotone, it follows from Lemma 3.1 that

    xn+1=VI2FnFVF(I2)FnVI1I2Fn=yn,

    and

    yn+2=VI1Fn+1F+VI2Fn+1FVF(I1)Fn+1+VF(I2)Fn+1VI2Fn+1+VI1I2Fn+1=xn+1+yn+1yn+yn+1.

    Let (zn)nN be Fibonacci sequence, i.e., zn+2=zn+1+zn, with the initial data z0=0,z1=1. It is well known that

    zn=15[(1+52)n(152)n],nN.

    It is clear that y1>0 and nN,yny1zn. Therefore,

    lim infn+lnVI1I2Fnn=lim infn+lnynnlimn+lny1znn=ln1+52.

    This competes the proof.

    Proposition 3.1. Let hC0(R) satisfy the hypothesises (A1)(A2) and ψη be given by (1.16). In addition, assume h(0)=0 and

    (i) non-constant function Q:t12(h(t)+1ct) is piecewise monotone,

    (ii) there is at least one solution to the equation h(y)+c1y=0 in R.

    Then there exist A>0 and a non-degenerate interval I(0,+){c1} such that for any ηI, [0,A] is an invariant set of ψη and

    lim infn+lnV[0,A]ψnηnln1+52. (3.3)

    Proof. We need to finish the following two steps:

    (1) Determine an invariant interval [0,A] of ψη.

    (2) To make sure the hypothesises of Lemma 3.2 holds.

    It has been known that

    ψη=γ(η)φ=γ(η)Qϕ1.

    Put

    S={y<0Q(y)=0 and  t(y,0),Q(t)0}

    and

    S+={y>0Q(y)=0 and  t(0,y),Q(t)0}.

    By the hypothesises (i)(ii), S or S+. Without loss of generality, assume S. We take

    ˉy=maxS,A=c1ˉy, (3.4)

    then

    ˉy<0, A=ϕ(ˉy)>0, ψη(A)=0. (3.5)

    Moreover, let

    M=max{Q(t)t[ˉy,0]},m=min{Q(t)t[ˉy,0]}.

    By the definition of S, it is evident that M=0 or m=0.

    Case 1: Assume M=0. That implies m<0. Put

    y0=max{t[ˉy,0]Q(t)=m},x0=ϕ(y0).

    For any η[0,c1), it is easily seen that

    ψη(x0)=max{ψη(x)x[0,A]}=mγ(η).

    and ψη(x)<ψη(x0) provided that x[0,x0). We need to prove that mγ(0)<A. Proceed the proof by contradiction. Assume mγ(0)A, which implies

    ψ0([0,x0])ψ0([x0,A])[0,ψ0(x0)][0,A]=[0,x0][x0,A].

    Thus ψ0 is turbulent. According to Lemma 3 in [11], ψ0 has periodic points of all periods. But from the proof of Theorem 2.1, ψ0 has no periodic points whose period is larger than 2. We have thus reached a contradiction. Since that mγ() is strictly increasing in [0,c1) and mγ(η)+ as η(c1), it is reasonable to put

    ˉη=γ1(mA)(0,c1). (3.6)

    Then for η[0,ˉη], we have

    x[0,A],0ψη(x)A,

    that is to say [0,A] is an invariant set of ψη provided η[0,ˉη]. Put

    η_={γ1(mx0), if mx0<1,0,  else, (3.7)

    then η_<ˉη and ψ(x0)>x0 if η>η_. For η[η_,c1), consider the following set:

    S(η,x0)={x(0,x0)γ(η)φ(x)=x0}.

    It is clear that S(η,x0) is closed. Since x0 is uniquely determined by h and h is independent of η, the following function is well-defined:

    α(η)=maxS(η,x0),η[η_,c1). (3.8)

    We first prove that α() is strictly monotonically decreasing in [η_,c1). Let t1,t2(η_,c1) with t1<t2. From the definition of α(), it follows

    γ(t1)φ(α(t1))=x0,γ(t1)φ(x)>x0,x(α(t1),x0],γ(t2)φ(α(t2))=x0,γ(t2)φ(x)>x0,x(α(t2),x0]. (3.9)

    Since that

    γ(t1)φ(α(t2))<γ(t2)φ(α(t2))=x0, γ(t1)φ(x0)>x0,

    by applying the continuity of γ(t1)φ() there exists x(α(t2),x0) such that γ(t1)φ(x)=x0. According to the definition of α(t1), we have

    α(t2)<xα(t1).

    Therefore, α() strictly monotonically decreases in [η_,c1).

    Next, prove that α() is continuous from the left. Let t0(η_,x0) be fixed. Since α is monotone, we have

    α(t0)Ldef=limtt0α(t)<+. (3.10)

    By the continuity of ηγ(η) and xφ(x), we obtain

    limε0+γ(t0ε)=γ(t0),limε0+φ(α(t0ε))=φ(L), (3.11)

    which gives γ(t0)φ(L)=γ(t0)φ(α(t0))=x0. From the definition of α() and (3.10), it follows

    Lα(t0)L=limtt0α(t).

    Therefore α() is continuous from the left.

    In particular, ψˉη(α(η_))=γ(η_)φ(α(ˉη))=x0 and ψˉη(x0)=γ(η_)φ(x0)=A. Define a function as follow:

    K(s)=α(η_s)ψ2ˉηs(x0). (3.12)

    It is easily seen that K() is continuous from right at s=0. Note that

    K(0)=α(η_)ψˉη(ψˉη(x0))=α(η_)ψˉη(A)=α(η_)>0,

    hence there exists ρ0(0,ˉηη_) such that

    s[0,ρ0],K(s)=α(η_s)ψ2ˉηs(x0)>0. (3.13)

    Put

    I=[ˉηρ0,ˉη] (3.14)

    and

    J1=[α(η),x0], J2=[x0,ψη(x0)], ηI. (3.15)

    Let ηI be fixed. By (3.13),

    ψη(J1)[ψη(α(η)),ψη(x0)]=J2,ψη(J2)[ψ2η(x0),ψη(x0)][α(η),ψη(x0)]J1J2,

    which is to say hypotheses of Lemma 3.2 holds. Therefore for any ηI, by applying Lemma 3.2 we have

    lim infn+lnV[0,A]ψnηnlim infn+lnVJ1J2ψnηnln1+52. (3.16)

    For the case m=0, one just needs to consider η>c1. The proof is similar, we omit it.

    Theorem 3.1. Consider the system (3.1) with the hypotheses (A1)(A2). Suppose h(0)=0 and

    (i) non-constant function Q:t12(h(t)+1ct) is piecewise monotone,

    (ii) there is at least one solution to the equation h(y)+c1y=0 in R.

    Then there exists a non-degenerate interval I such that for all ηI the system (3.1) is chaotic in the sense of Definition 1.1.

    Proof. Without loss of generality, assume the equation h(y)+c1y=0 has at least one solution in (,0). We still use the symbols given in the proof of Proposition 3.1, such as A, x0, α(η) and so on. Let I and J1,J2 be given by (3.14) and (3.15), respectively. Take ηI.

    Let (w0,w1) be the initial data of system (3.1) and (w,wx,wt) be a solution of system (3.1). Recall the Riemann invariants as follow

    u(x,t)=cwx(x,t)+wt(x,t)2c, v(x,t)=cwx(x,t)wt(x,t)2c, x[0,1], t0,

    and u0()=u(,0) and v0()=v(,0) are the initial data. Let t>2c1, n=[ct2] and τ=t2nc1 be fixed. It is clear that 0cτ<2. By using (1.13), (1.14) and (1.15), we obtain

    u(x,t)={γ1(η)ψnη(γ(η)u0(x+cτ)),0cτ1x,γ1(η)ψn+1η(v0(2xcτ)),1x<cτ2x,γ1(η)ψnη(γ(η)u0(x+cτ2)),2x<cτ<2, (3.17)

    and

    v(x,t)={ψnη(v0(xcτ)),0cτx,ψnη(γ(η)u0(cτx)),x<cτx+1,ψn+1η(v0(2+xcτ)),x+1<cτ<2. (3.18)

    Since [0,A] is an invariant set of ψη, |u|+|v| is uniformly bounded if

    Range(γ(η)u0)Range(v0)[0,A]. (3.19)

    In addition, assume that u0 and v0 are piecewise monotone and

    [0,α(η)]Range(γ(η)u0)Range(v0). (3.20)

    Consider the total variations of u(,t) and v(,t) on [0,1]. When 0cτ<1, from (1.13)-(1.15) and Lemma 3.1, it follows

    V[0,1]u(,t)=V[0,1cτ]u(,t)+V[1cτ,1]u(,t)=V[cτ,1]γ1(η)ψnηγ(η)u0+V[1cτ,1]γ1(η)ψn+1ηv0=|γ1(η)|(V[cτ,1]ψnηγ(η)u0+V[1cτ,1]ψn+1ηv0),
    V[0,1]v(,t)=V[0,cτ]v(,t)+V[cτ,1]v(,t)=V[0,cτ]ψnηγ(η)u0+V[0,1cτ]ψnηv0.

    Note that 0<|γ1(η)|<1, by Lemma 3.1 and (3.20) we obtain

    V[0,1]u(,t)+V[0,1]v(,t)|γ1(η)|Vγ(η)u0([0,1])ψnη|γ1(η)|V[0,α(η)]ψnη|γ1(η)|Vψ2η[0,α(η)]ψn2η|γ1(η)|VJ1J2ψn2η.

    Then Proposition 3.1 and (3.16) show that

    ln(V[0,1]u(,t)+V[0,1]v(,t))tln|γ1(η)|t+ln(VJ1J2ψn2η)tcln(VJ1J2ψn2η)2n+2+ln|γ1(η)|tc2ln1+52, as t+. (3.21)

    When 1cτ<2, in the same way we can obtain

    ln(V[0,1]u(,t)+V[0,1]v(,t))tcln(VJ1J2ψn1η)2n+2+ln|γ1(η)|tc2ln1+52, as t+. (3.22)

    It is evident that

    V[0,1]u(,t)+V[0,1]v(,t)max{1,c1}(V[0,1]wx(,t)+V[0,1]wt(,t)). (3.23)

    Hence

    lim inft+ln(V[0,1]wx(,t)+V[0,1]wt(,t))tc2ln1+52>0.

    Therefore, the system (3.1) is chaotic in the sense of Definition 1.1.

    In this section, we mainly consider the oscillation problem governed by 1D wave equation with a fixed end. We will show that only the effect of self-regulation effect at one of the two ends can cause the onset of chaos. But if the fixed end is replaced by a free end, the system never has chaos. As we know, both a fixed end and a free end are called the energy-conserving boundary condition. Even though either a fixed end or a free end has the same effect to the energy of the system, the systems may show completely different dynamics: one with chaos, the other without chaos. Thus the relationship between chaos and energy of the system is much more complicated.

    Theorem 4.1. Consider an initial-boundary problem governed by 1D wave equation with a fixed end as follows:

    {ztt(x,t)c2zxx(x,t)=0,  x(0,1), t>0,z(0,t)=0,  t>0,zx(1,t)=h(zt(1,t)), t>0,z0(x)=z(x,0),z1(x)=zt(x,0),0x1. (4.1)

    There exists a piecewise linear function h so that the system is chaotic in the sense of Definition 1.1.

    Proof. Without loss of generality, we take c=1. Put

    hθ(x)={2x+2(θ23),  x[2(θ23),2),(1θ2)x, x[2,0],(θ1)x, x(0,1],2x+(θ+1), x(1,θ+1],x, else, (4.2)

    where θ(0,1) is a fixed parameter. It is clear that hθC0(R) and

    ϕθ:x12(hθ(x)x)

    is strictly monotonically decreasing and ϕθ(R)=R. Then ϕ1θ exists. Let

    Qθ(y)=12(hθ(y)+y),

    then

    ψθ(x)=x+ϕ1θ(x)=Qθϕ1θ(x).

    Let u=zx+zt2 and u=zxzt2 be the Riemann invariants of system 4.1. For some initial date (u0,v0), suppose (u,v) is solved for t2. Then for t=2n+τ with τ[0,2) and nN, we have

    u(x,t)=ψnθ(u(x,τ)),v(x,t)=ψnθ(v(x,τ)), x[0,1]. (4.3)

    Put

    I1=[θ1,12θ1], I2=[12θ1,0], I3=[0,θ2].

    They are monotone intervals of ψθ, respectively. A simple calculation shows that

    ψθ(θ1)=0, ψθ(12θ1)=12θ, ψθ(0)=0, ψθ(θ2)=θ22.

    Let θ(0,1) be sufficiently small such that 12θθ2 and θ22θ1, which implies

    I3ψθ(I1)ψθ(I2), I1I2ψθ(I3).

    Let nN. Put

    xn=VI1ψnθ, yn=VI2ψnθ, zn=VI3ψnθ.

    Lemma 3.2 shows that

    xn+1Vψθ(I1)ψnθVI3ψnθ=zn, yn+1zn, zn+2xn+1+yn+1,

    which implies

    zn+22zn.

    Inductively, we obtain

    z2n+22nz2>0, z2n+12nz1>0.

    Therefore,

    lim infn+lnznn12ln2>0. (4.4)

    A simple calculation shows that

    ψθ(x)=0, x(,θ1][2(3θ2),+].

    By (4.3), |u|+|v| is always uniformly bounded for any initial date. Analogous to the proof of Theorem 3.1, there exists a large class of initial data such that

    lim inft+ln(V[0,1]zx(,t)+V[0,1]zt(,t))t14ln2>0.

    Therefore, the proof completes.

    We first consider a problem about perturbations at boundaries, described by the following model:

    {wtt(x,t)c2wxx(x,t)=0,  x(0,1), t>0,wx(0,t)=ηwt(0,t), ηc1,  t>0,wx(1,t)=c1wt(1,t)+εsin(wt(1,t)), t>0,w0(x)=w(x,0),w1(x)=wt(x,0),0x1. (5.1)

    If ε=0, for any initial data (w0,w1) and ηc1, by using wave propagation method we can obtain

    t>2,x[0,1], wx(x,t)=wt(x,t)=0.

    That shows system (5.1) is of global asymptotical stability.

    Assume ε0 and |ε| is sufficiently small. Put

    hε(y)=c1y+εsin(y)

    It is evident that i)yhε(y)+c1y is piecewise monotone, ii)yhε(y)c1y strictly monotonically decreasing, iii) there are infinite solutions to the equation hε(y)+c1y=0 in R. That shows the hypotheses of Theorem 3.1 hold. Therefore, system (5.1) is chaotic in the sense of Definition 1.1.

    The above analysis shows that this kind of system is no longer stable under small perturbations at one of the boundaries. That is to say this kind of system is not of structural stability.

    As we have stated in the previous sections, if the feedback control law is even chaos never happen. In particular, consider the system described by the following model:

    {ztt(x,t)zxx(x,t)=0,  x(0,1), t>0,zx(0,t)=ηzt(0,t), η1, or z(0,t)=0, t>0,zz(1,t)=cos(zt(1,t))1, t>0,z0(x)=z(x,0),z1(x)=zt(x,0),0x1. (5.2)

    The feedback control law at the right end is periodic and even. Therefore there is no chaos in the system (5.2). Moreover, there should exists a compact attractor in the system 5.2.

    In this section, we will give some numerical simulations to validate the theoretical results of this paper. We first consider the system as follow:

    {ztt(x,t)zxx(x,t)=0,  x(0,1), t>0,z(0,t)=0,  t>0,zx(1,t)=h(zt(1,t)), t>0,z0(x)=z(x,0),z1(x)=zt(x,0),0x1, (6.1)

    where

    h(x)={2x112,  x[112,2),34x, x[2,0],12x, x(0,1],2x+32, x(1,32],x, else. (6.2)

    Choose

    w0=0, w1(x)=14sin4(2πx), x[0,1],

    as the initial data of system (6.1). Put

    ψ(x)={13x116,  x(14,112],7x, x(0,14],13x, x(34,0],13x+12, x[32,34],0, else. (6.3)

    As we show in Section 4, the solution (z,zx,zt) can be represented by the iterations of this ψ and initial data (z0,z1). Note that h and ψ are piecewise linear, thus the feedback control at the boundaries is easy to implement and the solutions of system are much simpler to be solved and represented.

    We present the graphics in some detail, for zx,zt for 98t100. Figure 1 shows that zx,zt are extremely oscillatory in every direction of space and time.

    Figure 1.  The profile of zx (left) zt(right) for t[98,100].

    This work was supported by the National Natural Science Foundation of China under Grant 11671410 and the Natural Science Foundation of Guangdong Province, China (No.2022A1515012153).

    The authors declare there is no conflicts of interest.

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