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Research article Special Issues

Multi-objective crashworthiness design optimization of a rollover protective structure by an improved constraint-handling technique

  • This study proposes a multi-objective optimization (MOO) strategy with an improved constraint-handling technique to improve the crashworthiness of an excavator rollover protective structure (ROPS). First, the experimental test under the ISO 12117 criteria is conducted and the developed numerical model is verified. Then, the amounts of energy absorption and the cross-sectional forces of components in the ROPS are analyzed. The main energy absorbing and load carrying components are identified. Finally, the thicknesses of the identified components are considered as the design variables. A multi-objective crashworthiness optimization process aims at improving the safety distance and reducing the total mass is designed by the finite element analysis-based surrogate model technique and a modified MOO algorithm. The proposed algorithm modifies the objective function values of an individual with its constraint violations and the true objective function values, of which adaptive penalty weights fed back from the constraint violations are used to keep the balance. Compared with the existing methods, it is found that the optimal solutions obtained by the proposed algorithm show superiority on convergence rate and diversity of distribution. The optimal results show that the safety distance is 27.42% higher while the total mass is 7.06% lower than those of the baseline design when it meets the requirements of ISO 12117. This study provides an alternative crashworthiness design route for the ROPS of the construction machines.

    Citation: Chao Ma, Hong Fu, Pengcheng Lu, Hongpeng Lu. Multi-objective crashworthiness design optimization of a rollover protective structure by an improved constraint-handling technique[J]. Electronic Research Archive, 2023, 31(7): 4278-4302. doi: 10.3934/era.2023218

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  • This study proposes a multi-objective optimization (MOO) strategy with an improved constraint-handling technique to improve the crashworthiness of an excavator rollover protective structure (ROPS). First, the experimental test under the ISO 12117 criteria is conducted and the developed numerical model is verified. Then, the amounts of energy absorption and the cross-sectional forces of components in the ROPS are analyzed. The main energy absorbing and load carrying components are identified. Finally, the thicknesses of the identified components are considered as the design variables. A multi-objective crashworthiness optimization process aims at improving the safety distance and reducing the total mass is designed by the finite element analysis-based surrogate model technique and a modified MOO algorithm. The proposed algorithm modifies the objective function values of an individual with its constraint violations and the true objective function values, of which adaptive penalty weights fed back from the constraint violations are used to keep the balance. Compared with the existing methods, it is found that the optimal solutions obtained by the proposed algorithm show superiority on convergence rate and diversity of distribution. The optimal results show that the safety distance is 27.42% higher while the total mass is 7.06% lower than those of the baseline design when it meets the requirements of ISO 12117. This study provides an alternative crashworthiness design route for the ROPS of the construction machines.



    In biological systems, the continuous predator-prey model has been successfully investigated and many interesting results have been obtained (cf. [1,2,3,4,5,6,7,8,9] and the references therein). Moreover, based on the continuous predator-prey model, many human factors, such as time delay [10,11,12], impulsive effect [13,14,15,16,17,18,19,20], Markov Switching [21], are considered. The existing researches mainly focus on stability, periodic solution, persistence, extinction and boundedness [22,23,24,25,26,27,28].

    In 2011, the authors [28] considered the system incorporating a modified version of Leslie-Gower functional response as well as that of the Holling-type Ⅲ:

    {˙x(t)=x(a1bxc1y2x2+k1),˙y(t)=y(a2c2yx+k2). (1)

    With the diffusion of the species being also taken into account, the authors [28] studied a reaction-diffusion predator-prey model, and gave the stability of this model.

    In model (1) x represents a prey population, y represents a predator with population, a1 and a2 represent the growth rate of prey x and predator y respectively, constant b represents the strength of competition among individuals of prey x, c1 measures the maximum value of the per capita reduction rate of prey x due to predator y, k1 and k2 represent the extent to which environment provides protection to x and to y respectively, c2 admits a same meaning as c1. All the constants a1,a2,b,c1,c2,k1,k2 are positive parameters.

    However, provided with experimental and numerical researches, it has been obtained that bifurcation is a widespread phenomenon in biological systems, from simple enzyme reactions to complex ecosystems. In general, the bifurcation may put a population at a risk of extinction and thus hinder reproduction, so the bifurcation has always been regarded as a unfavorable phenomenon in biology [29]. This bifurcation phenomenon has attracted the attention of many mathematicians, so the research on bifurcation problem is more and more abundant [30,31,32,33,34,35,36,37,38,39,40].

    Although the continuous predator-prey model has been successfully applied in many ways, its disadvantages are also obvious. It requires that the species studied should have continuous and overlapping generations. In fact, we have noticed that many species do not have these characteristics, such as salmon, which have an annual spawning season and are born at the same time each year. For the population with non-overlapping generation characteristics, the discrete time model is more practical than the continuous model [38], and discrete models can generate richer and more complex dynamic properties than continuous time models [39]. In addition, since many continuous models cannot be solved by symbolic calculation, people usually use difference equations for approximation and then use numerical methods to solve the continuous model.

    In view of the above discussion, the study of discrete system is paid more and more attention by mathematicians. Many latest research works have focused on flip bifurcation for different models, such as, discrete predator-prey model [41,42]; discrete reduced Lorenz system [43]; coupled thermoacoustic systems [44]; mathematical cardiac system [45]; chemostat model [46], etc.

    For the above reasons, we will study from different perspectives in this paper, focusing on the discrete scheme of Eq (1).

    In order to get a discrete form of Eq (1), we first let

    u=ba1x,v=c1a1y,τ=a1t,

    and rewrite u,v,τ as x,y,t, then (1) changes into:

    {˙x(t)=x(1xβ1y2x2+h1),˙y(t)=αy(1β2yx+h2), (2)

    where β1=b2c1a1,h1=b2k1a21,α=a2c1,β2=c2bc1a2,h2=bk2a1.

    Next, we use Euler approximation method, i.e., let

    dxdtxn+1xnt,dydtyn+1ynt,

    where t denotes a time step, xn,yn and xn+1,yn+1 represent consecutive points. Provided with Euler approximation method with the time step t=1, (2) changes into a two-dimensional discrete dynamical system:

    {xn+1=xn+xn(1xnβ1y2nx2n+h1),yn+1=yn+αyn(1β2ynxn+h2). (3)

    For the sake of analysis, we rewrite (3) in the following map form:

    (xy)(x+x(1xβ1y2x2+h1)y+αy(1β2yx+h2)). (4)

    In this paper, we will consider the effect of the coefficients of map (4) on the dynamic behavior of the map (4). Our goal is to show how a flipped bifurcation of map (4) can appear under some certain conditions.

    The remainder of the present paper is organized as follows. In section 2, we discuss the fixed points of map (4) including existence and stability. In section 3, we investigate the flip bifurcation at equilibria E2 and E. It has been proved that map (4) can undergo the flip bifurcation provided with that some values of parameters be given certain. In section 4, we give an example to support the theoretical results of the present paper. As the conclusion, we make a brief discussion in section 5.

    Obviously, E1(1,0) and E2(0,h2β2) are fixed points of map (4). Given the biological significance of the system, we focus on the existence of an interior fixed point E(x,y), where x>0,y>0 and satisfy

    1x=β1(y)2(x)2+h1,x+h2=β2y,

    i.e., x is the positive root of the following cubic equation:

    β22x3+(β1β22)x2+(β22h1+2β1h2)x+β1h22β22h1=0. (5)

    Based on the relationship between the roots and the coefficients of Eq (5), we have

    Lemma 2.1 Assume that β1h22β22h1<0, then Eq (5) has least one positive root, and in particular

    (ⅰ) a unique positive root, if β1β22;

    (ⅱ) three positive roots, if β1<β22.

    The proof of Lemma 2.1 is easy, and so it is omitted.

    In order to study the stability of equilibria, we first give the Jacobian matrix J(E) of map (4) at any a fixed point E(x,y), which can be written as

    J(E)=(22xβ1y2(h1x2)(x2+h1)22β1xyx2+h1αβ2y2(x+h2)21+α2αβ2yx+h2).

    For equilibria E1, we have

    J(E1)=(0001+α).

    The eigenvalues of J(E1) are λ1=0,λ2=1+α with λ2>1 due to the constant α>0, so E1(1,0) is a saddle.

    For equilibria E2, note that

    J(E2)=(2β1h22β22h10αβ21α),

    then the eigenvalues of J(E2) are λ1=2β1h22β22h1,λ2=1α, and so we get

    Lemma 2.2 The fixed point E2(0,h2β2) is

    (ⅰ) a sink if 1<β1h22β22h1<3 and 0<α<2;

    (ⅱ) a source if β1h22β22h1<1 or β1h22β22h1>3 and α>2;

    (ⅲ) a a saddle if 1<β1h22β22h1<3 and α>2, or, β1h22β22h1<1 or β1h22β22h1>3 and 0<α<2;

    (ⅳ) non-hyperbolic if β1h22β22h1=1 or β1h22β22h1=3 or α=2.

    In this section, we will use the relevant results of literature [38,39,40] to study the flip bifurcation at equilibria E2 and E.

    Based on (ⅲ) in Lemma 2.2, it is known that if α=2, the eigenvalues of J(E2) are: λ1=2β1h22β22h1,λ2=1. Define

    Fl={(β1,β2,h1,h2,α):α=2,β1,β2,h1,h2>0}.

    We conclude that a flip bifurcation at E2(0,h2β2) of map (4) can appear if the parameters vary in a small neighborhood of the set Fl.

    To study the flip bifurcation, we take constant α as the bifurcation parameter, and transform E2(0,h2β2) into the origin. Let e=2β1h22β22h1,α1=α2, and

    u(n)=x(n),v(n)=y(n)h2β2,

    then map (4) can be turned into

    (uv)(euu22β1h2β2h1uv+O((|u|+|v|+|α1|)3)2β2uv2β2h2u22β2h2v2+4h2uv+α1β2uα1vα1β2h2u2α1β2h2v2+2α1h2uv+O((|u|+|v|+|α1|)3)). (6)

    Let

    T1=(1+e02β21),

    then by the following invertible transformation:

    (uv)=T1(sw),

    map (6) turns into

    (sw)(es(1+e)s22β1h2β2h1s(2sβ2+w)+O(|s|+|w|+|α1|)3w+F2(s,w,α1)), (7)

    where

    F2=2β2[(1+e)s2+2β1h2β2h1s(2sβ2+w)]2β2h2(1+e)2s22β2h2(2sβ2+w)2+4(1+e)h2s(2sβ2+w)
    +(1+e)α1β2sα1(2sβ2+w)(1+e)2α1β2h2s2α1β2h2(2sβ2+w)2
    +2(1+e)α1h2s(2sβ2+w)+O(|s|+|w|+|α1|)3.

    Provided with the center manifold theorem (Theorem 7 in [40]), it can be obtained that there will exist a center manifold Wc(0,0) for map (7), and the center manifold Wc(0,0) can be approximated as:

    Wc(0,0)={(w,s,α1)R3:s=aw2+bwα1+c(α1)2+O(|w|+|α1|)3}.

    As the center manifold satisfies:

    s=a(w+F2)2+b(w+F2)α1+c(α1)2=e(aw2+bwα1+c(α1)2)(1+e)(aw2+bwα1+c(α1)2)22β1h2β2h1(aw2+bwα1+c(α1)2)(2β2(aw2+bwα1+c(α1)2)+w)+O(|s|+|w|+|α1|)3,

    it can be obtained by comparing the coefficients of the above equality that a=0,b=0,c=0, so the center manifold of map (7) at E2(0,h2β2) is s=0. Then map (7) restricted to the center manifold turns into

    w(n+1)=w(n)α1w(n)2β2h2w2(n)α1β2h2w2(n)+O(|w(n)|+|α1|)3
    f(w,α1).

    Obviously,

    fw(0,0)=1,fww(0,0)=4β2h2,

    so

    (fww(0,0))22+fwww(0,0)30,fwα1(0,0)=10.

    Therefore, Theorem 4.3 in [38] guarantees that map (3) undergoes a flip bifurcation at E2(0,h2β2).

    Note that

    J(E)=(22xβ1(y)2(h1(x)2)((x)2+h1)22β1xy(x)2+h1αβ21α),

    then the characteristic equation of Jacobian matrix J(E) of map (3) at E(x,y) is:

    λ2(1+α0α)λ+(1α)α0ηα=0, (8)

    where

    α0=22xβ1(y)2(h1(x)2)((x)2+h1)2,η=2β1xyβ2((x)2+h1).

    Firstly, we discuss the stability of the fixed point E(x,y). The stability results can be described as the the following Lemma, which can be easily proved by the relations between roots and coefficients of the characteristic Eq (8), so the proof has been omitted.

    Lemma 3.1 The fixed point E(x,y) is

    (ⅰ) a sink if one of the following conditions holds.

    (ⅰ.1) 0<α0+η<1, and α01α0+η<α<2(1+α0)1+α0+η;

    (ⅰ.2) 1<α0+η<0, and α<min{α01α0+η,2(1+α0)1+α0+η};

    (ⅰ.3) α0+η<1, and α01α0+η>α>2(1+α0)1+α0+η;

    (ⅱ) a source if one of the following conditions holds.

    (ⅱ.1) 0<α0+η<1, and α<min{α01α0+η,2(1+α0)1+α0+η};

    (ⅱ.2) 1<α0+η<0, and α01α0+η<α<2(1+α0)1+α0+η;

    (ⅱ.3) α0+η<1, and α>max{α01α0+η,2(1+α0)1+α0+η};

    (ⅲ) a saddle if one of the following conditions holds.

    (ⅲ.1) 1<α0+η<1, and α>2(1+α0)1+α0+η;

    (ⅲ.2) α0+η<1, and α<2(1+α0)1+α0+η;

    (ⅳ) non-hyperbolic if one of the following conditions holds.

    (ⅳ.1) α0+η=1;

    (ⅳ.2) α0+η1; and α=2(1+α0)1+α0+η;

    (ⅳ.3) α0+η0,α=α01α0+η and (1+α0α)2<4((1α)α0ηα).

    Then based on (ⅳ.2) of Lemma 3.1 and α1+α0,3+α0, we get that one of the eigenvalues at E(x,y) is 1 and the other satisfies |λ|1. For α,β1,β2,h1,h2>0, let us define a set:

    Fl={(β1,β2,h1,h2,α):α=2(1+α0)1+α0+η,α0+η1,α1+α0,3+α0}.

    We assert that a flip bifurcation at E(x,y) of map (3) can appear if the parameters vary in a small neighborhood of the set Fl.

    To discuss flip bifurcation at E(x,y) of map (3), we choose constant α as the bifurcation parameter and adopt the central manifold and bifurcation theory [38,39,40].

    Let parameters (α1,β1,β2,h1,h2)Fl, and consider map (3) with (α1,β1,β2,h1,h2), then map (3) can be described as

    {xn+1=xn+xn(1xnβ1y2nx2n+h1),yn+1=yn+α1yn(1β2ynxn+h2). (9)

    Obviously, map (9) has only a unique positive fixed point E(x,y), and the eigenvalues are λ1=1,λ2=2+α0α, where |λ2|1.

    Note that (α1,β1,β2,h1,h2)Fl, then α1=2(1+α0)1+α0+η. Let |α| small enough, and consider the following perturbation of map (9) described by

    {xn+1=xn+xn(1xnβ1y2nx2n+h1),yn+1=yn+(α1+α)yn(1β2ynxn+h2), (10)

    with α be a perturbation parameter.

    To transform E(x,y) into the origin, we let u=xx,v=yy, then map (10) changes into

    (uv)(a1u+a2v+a3u2+a4uv+a5v2+a6u3+a7u2v+a8uv2+a9v3+O((|u|+|v|)4)b1u+b2v+b3u2+b4uv+b5v2+c1uα+c2vα+c3u2α+c4uvα+c5v2α+b6u3+b7u2v+b8uv2+b9v3+O((|u|+|v|+|α|)4)), (11)

    where

    a1=22xβ1(y)2f(0)β1x(y)2f(0); a2=2β1xyf(0);

    a3=1β1(y)2f(0)12β1x(y)2f

    Now let's construct an matrix

    It's obvious that the matrix is invertible due to and then we use the following invertible translation

    map (11) can be described by

    (12)

    where

    with

    In the following, we will study the center manifold of map (12) at fixed point (0, 0) in a small neighborhood of The well-known center manifold theorem guarantee that a center manifold can exist, and it can be approximated as follows

    which satisfies

    By comparing the coefficients of the above equation, we have

    So, restricted to the center manifold map (12) turns into

    (13)

    where

    with

    To study the flip bifurcation of map (13), we define the following two discriminatory quantities

    and

    which can be showed in [38]. Then provided with Theorem 3.1 in [38], the following result can be given as

    Theorem 3.1. Assume that and are not zero, then a flip bifurcation can occur at of map (3) if the parameter varies in a small neighborhood of origin. And that when the period-2 orbit bifurcated from of map (3) is stable (unstable).

    As application, we now give an example to support the theoretical results of this paper by using MATLAB. Let then we get from (5) that map (3) has only one positive point And we further have which implies that all conditions of Theorem 3.1 hold, a flip bifurcation comes from at the bifurcation parameter , so the flip bifurcation is supercritical, i.e., the period-2 orbit is unstable.

    According to Figures 1 and 2, the positive point is stable for and loses its stability at the bifurcation parameter value Which implies that map (3) has complex dynamical properties.

    Figure 1.  Flip bifurcation diagram of map (3) in the (, x) plane for The initial value is (0.0213, 0.2326).
    Figure 2.  Flip bifurcation diagram of map (3) in the (, y) plane for The initial value is (0.0213, 0.2326).

    In this paper, a predator-prey model with modified Leslie-Gower and Holling-type Ⅲ schemes is considered from another aspect. The complex behavior of the corresponding discrete time dynamic system is investigated. we have obtained that the fixed point of map (4) is a saddle, and the fixed points and of map (4) can undergo flip bifurcation. Moreover, Theorem 3.1 tell us that the period-2 orbit bifurcated from of map (3) is stable under some sufficient conditions, which means that the predator and prey can coexist on the stable period-2 orbit. So, compared with previous studies [28] on the continuous predator-prey model, our discrete model shows more irregular and complex dynamic characteristics. The present research can be regarded as the continuation and development of the former studies in [28].

    This work is supported by the National Natural Science Foundation of China (60672085), Natural Foundation of Shandong Province (ZR2016EEB07) and the Reform of Undergraduate Education in Shandong Province Research Projects (2015M139).

    The authors would like to thank the referee for his/her valuable suggestions and comments which led to improvement of the manuscript.

    The authors declare that they have no competing interests.

    YYL carried out the proofs of main results in the manuscript. FXZ and XLZ participated in the design of the study and drafted the manuscripts. All the authors read and approved the final manuscripts.



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