Mathematical Problems in Engineering

Volume 2014, Article ID 237234, 14 pages

http://dx.doi.org/10.1155/2014/237234

## A Transformation Method for Solving Conservative Nonlinear Two-Degree-of-Freedom Systems

Departamento de Ingeniería Mecánica, Tecnológico de Monterrey, Campus Monterrey, E. Garza Sada 2501 Sur, 64849 Monterrey, N.L., Mexico

Received 9 October 2013; Revised 30 January 2014; Accepted 16 March 2014; Published 7 May 2014

Academic Editor: Michael J. Brennan

Copyright © 2014 Alex Elías-Zúñiga et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

A nonlinear transformation approach based on a cubication method is developed to obtain the equivalent representation form of conservative two-degree-of-freedom nonlinear oscillators. It is shown that this procedure leads to equivalent nonlinear equations that describe well the numerical integration solutions of the original equations of motion.

#### 1. Introduction

Recently, we have introduced in [1, 2] a procedure that transforms the original equation of motion of single degree of freedom systems, having nonlinear restoring forces, into nonlinear ordinary differential equations of the Duffing type. Furthermore, this procedure has been used in conjunction with the Chebyshev polynomials to derive the equivalent representation form of nonlinear system with dissipative and sinusoidal driving forces [2] in which the numerical comparisons between the equivalent and the original equations of motion demonstrate the effectiveness of this method in predicting the corresponding system dynamics response. Motivated by these results, we herein expand the application of this technique to obtain the equivalent representation form of two-degree-of-freedom homogeneous, undamped, and ordinary differential equations of the form that models the dynamics response of systems that arise in physics and engineering applications [3–6], where and are the masses of the system, , , , and are stiffness parameters, and are nonlinear restoring forces, and , , , and are the system initial conditions.

The method is based on replacing first the system restoring forces by polynomial expressions and, then, we use a transformation technique to replace the resulting equations by two uncoupled nonlinear differential equations of the Duffing type whose solutions are based on Jacobi elliptic functions. It is shown that the appropriate derivation of one of these solutions can improve the numerical estimates of the equivalent equations of motion even at larger and feasible nonlinear parameter values.

#### 2. A Nonlinear Transformation Approach

The equivalent representation form of (1) is obtained by replacing it by two uncoupled, nonlinear ordinary differential equations of the Duffing type by following an approach similar to the one described in [1], This procedure yields Here we assume that the coefficients are determined by the following expressions: where and the values of are fitted to satisfy (4) and (5) [7]. Notice, in this case, that the system (2) can be solved in closed form by using Jacobi elliptic functions. This yields [8]

However, an alternative approach to find the equivalent representation form of (1) can be derived if we use the form (2)_{1} or (2)_{2} and then substitute its exact solution into the remaining equation in (1). In this case, the influence of the nonlinear effects of or into the system dynamics response will be taken into account by the resulting coupled equation of motion whose approximate solution could be based on the usage of Jacobi elliptic functions.

Other equivalent representation forms of (1) are possible since these depend on the polynomial order use to write the equivalent form of the system restoring forces [9–11]. For instance, let us suppose that the nonlinear conservative forces of (1) are of the cubic type and rewrite (1) in its canonical representation form [12]: with initial conditions given as , , , and . Here the dots denote the time derivative, and are known as the system normal coordinates, is a nonlinear parameter, and , , and through are corresponding system parameters that are defined in accordance with the physics of the system. Now, let us replace (7) by their corresponding equivalent equations of motion of the Duffing type which are valid for the complete range of oscillation amplitudes [1], by using the relations where , , , and are fitted constants that satisfy (8), and the parameters are determined by solving the equations

This yields Then, (7) can be rewritten in equivalent form as whose exact solutions are similar to those given by (6).

We shall next explore the applicability of the nonlinear transformation method in obtaining the equivalent representation form of (1) and, then, we will compare the numerical integration solutions of five dynamics systems, having nonlinear restoring forces with rational or irrational terms, with respect to their equivalent representation forms [13–20]. First, let us consider the case for which the restoring forces are of the cubic type.

##### 2.1. Example 1: A Cubic Nonlinear System

Let us consider the conservative system built with a nonlinear spring of the cubic type. The corresponding equations of motion are given as [13] with initial conditions , , , and . If we introduce the transformations and so that , , , , thus (12) can be written in normalized form as If we use our proposed nonlinear transformation approach then the equivalent representation form of (13) is given as where the , determined from (4) and (5), are given as and , , , and are the corresponding fitting parameters described earlier.

To apply our nonlinear transformation approach to the canonical representation form of (13), we first use the following linear coordinate transformation [12]: which yields the canonical form (7). Here, the system parameters of (7) are described by the relations Thus, the approximate solutions of (13), by considering their canonical representation form, are given by expressions similar to those of (6).

Tables 1 and 2 show the root-mean-square error (RMSE) values computed by comparing the numerical integration solutions of the original equations of motion (13), with respect to numerical results computed from their derived equivalent representation forms by considering the initial conditions values of , , , and , the parameter values of , , and , and different values of . In the second and third columns of Table 1 are tabulated the RMSE values computed by using the equivalent representation forms of (13) given by (14), while the fourth and the fifth columns provide the RMSE values obtained by substituting (14)_{1} into (13)_{2}. The sixth and the seventh columns of Table 1 contain the RMSE values computed by substituting (14)_{2} into (13)_{1}. As we can see from Table 1, the lowest RMSE values are tabulated in the fifth and the sixth columns which implies that the most accurate solutions of (13) are obtained when the equivalent representation form (14)_{1} is used to derive the approximate solution of . Also notice from Table 1, that the computed values of the RMSE become smaller for increasing values of the nonlinear parameter which shows that our solution procedure can be used to obtain solutions that follow the numerical integration solutions of the original equations of motion even at larger values of . For illustrative purposes, we have plotted in Figure 1 the amplitude-time response curves obtained from (13), and those provided by substituting (14)_{1} into (13)_{2}. We have selected the value of which provides a highly nonlinear dynamic system response. Notice from Figure 1 that both solutions are almost the same. In fact, the RMSE values do not exceed , for , and , for . In Figure 1, the gray solid lines describe the numerical integration solutions of (13), while the blue dashed lines represent the numerical solutions obtained from our derived equivalent nonlinear equations of motion.

Table 2 shows the RMSE values obtained by using the canonical representation form of (13). One can notice from Table 2 that the smallest RMSE values were computed at decreasing values of the nonlinear parameter when the equivalent form (11)_{1} was substituted into (7)_{2} (the fifth and the sixth columns). Also, one can notice that when , (7) describes the oscillatory behavior of the resulting linear dynamics system; however, this is not the case when we use the equivalent representation form of (13) given by (14) because the terms and are different from zero. This situation increases the RMSE errors as listed in Table 1. In an attempt to further quantify the accuracy of our proposed procedure, we next introduce the global error estimation based on a technique similar to the ones developed in [14–16] in which the system global error, GE, can be determined from the following expression:
where and represent the values obtained by numerically integrating the equivalent equations of motion and and are the numerical values computed by using the original system equations of motion. Figure 2 displays the global error curve plotted versus time by considering the numerical integration solutions of both, the resulting equation obtained by substituting (14)_{1} into (13)_{2} and the original equations of motion (13). One can notice from Figure 2, that the global error tends to grow linearly at increasing time values, as predicted by Calvo and Hairer in [14].

To further assess the accuracy of our proposed nonlinearization method, we will next examine a hyperelastic shear suspension system.

##### 2.2. Example 2: Hyperelastic Shear Suspension System

Here the equations of motion of a linear absorber attached to a rigid body that is supported symmetrically by incompressible, homogeneous, and isotropic hyperelastic shear blocks are given as
with initial conditions given as , , , and . Here and denote, respectively, the simple shear deformation of the load and the motion of the linear absorber system, describes the nonlinear material response effects, is a tuning system parameter, and , , and are defined as
The details of the derivation of the equations of motion given by (19) are provided in [2]. Next, we set and and rewrite (12) in its normalized form
with , , , and . Notice that the canonical representation form of (21) can be obtained by using the transformation (16); this yields exactly (7), where the system parameters are given by the following relations:
Since (21)_{2} is only coupled with through the linear term , we slightly modify our procedure and assume that the equivalent representation form of (21) is given as
where the parameters , computed from (4), are given as
and , , , and are the corresponding fitting parameters. The assumption that (21)_{2} can be transformed into a linear equation of the form (23)_{2} is based on the fact that in (21)_{2}, there is not terms of the cubic type.

Next, if the canonical representation form of (21) is considered, their approximate solutions, that can be obtained from (7), are given by (6). Tables 3 and 4 show the RMSE values computed from the numerical integration solutions of (21) and from the derived equivalent representation forms. Here, we have used as initial conditions the values of , , , and , with and . One can notice from Table 3 that the lowest RMSE values are tabulated in the fifth and the sixth columns at values of . In fact, as the value of tends to increase, the RMSE values tend to decrease. Figure 3 shows the amplitude-time response curves computed by substituting (23)_{1} into (21)_{2}. In this case, we have considered that the nonlinear parameter has the value of . Notice that the numerical integration solutions of (21) are almost indistinguishable from those computed by using the equivalent transformation forms. Here the gray solid lines represent the numerical integration solutions of (21), while the blue dashed lines described the numerical integration solutions obtained from the equivalent equations of motion. Therefore, we can conclude that our equivalent transformation approach provides accurate solutions to (21) if the form (23)_{1} is used to derive the approximate solution of .

Also notice from Table 4 that the smallest RMSE values, computed when the canonical form (7) of (19) is used to derive its equivalent representation form, are on , that is, when the equivalent form (11)_{1} is substituted into (7)_{2}. Therefore, if one wants to have solutions that describe well the numerical integration solutions of (21), we need to use their canonical representation form at values of on the interval . For larger nonlinear system parameter values, good predictions of the original equation of motion are obtained when the equivalent form (23)_{1} is substituted into (21)_{2}.

##### 2.3. Example 3: A System with Three Nonlinear Springs

We now derive the equivalent representation form of two-mass system with three nonlinear springs introduced in [17] and whose differential equation of motion is given by
with initial conditions , , , and . If we introduce the transformations and , thus (25) can be written as
with initial conditions given as , , , and . Notice that (26) has the same form of (7) with system parameters given as
Therefore and in accordance with our transformation approach, (26) are transformed into (11) in which
To assess the accuracy of the equivalent equations of motion when compared to the original ones (25), let us consider the parameter values of , , and and initial conditions , , , and . Figure 4 shows the amplitude-time response curves obtained by numerical integration of the corresponding equations of motion by using the Runge-Kutta method. One can notice from Figure 4 that the amplitude-time response curves obtained from (11) provide periodic solutions that cannot capture the high frequency components exhibited by the numerical amplitude-time curves plotted from (25). However, if we substitute the exact solution of (11)_{1} into (7)_{2} and plot its corresponding numerical integration solution, the high frequency components are captured as illustrated in Figure 5. Therefore, we can conclude that if during the dynamic analysis processes the motion of the coordinate exhibits harmonic behavior at lower angular frequency values, then the dynamics response behavior of this coordinate solution can be used to obtain the high frequency components exhibited by the remaining coordinate. In fact and for the nonlinear parameter value of , the RMSE values, for the time interval shown in Figure 5, do not exceed and for and , respectively.

We next increase the system nonlinearity at the value of and plot the corresponding amplitude versus time response curves. Notice from Figure 6 that when , the numerical integration solution of the equations of motion (25) obtained by using the Runge-Kutta numerical method fails in spite of using the several solver options provided by Mathematica 9.0 or the MATLAB V.2012a computer packages. In an attempt to capture the dynamics system response with this nonlinear value, we have numerically solved (25) by using the Enhanced Multistage homotopy perturbation method (EMHPM) introduced in [18]. This technique also fails at values of , as illustrated in Figure 6 by the black and the purple dots. However, the predicted amplitude versus time response curves computed by substituting (11)_{2} into (7)_{1} exhibits periodic behavior for the time interval shown in Figure 6. We can conclude that this transformation approach can help to obtain the numerical integration solutions of nonlinear dynamics systems in which the numerical integrations of the original equations of motion could fail because of the convergence of the corresponding numerical method.

##### 2.4. Example 4: Finite Extensibility Nonlinear Elastic Absorber

We now focus our attention on finding the approximate solution of a finite extensibility nonlinear elastic (FENE) absorber attached to a nonlinear primary system [19], whose equations of motions are of the form
and the nonlinear restoring forces are given by
with initial conditions , , , and . Here, is the mass of the primary system, represents the mass of the FENO system, and are, respectively, the linear and nonlinear stiffness parameters of the main system, and and represent the coupling stiffness and the maximum possible extension of the FENO system, respectively. By introducing the transformations and into (29), we get that
with initial conditions given as , , , and , and , , , and . We next use the Chebyshev polynomials of the first kind [9, 11] to derive the cubic-like representation form of the nonlinear rational restoring force of (31). This procedure yields
where , and by definition
and are the Chebyshev polynomials of the first kind defined as
By using (33), we obtain that
Thus, the FENO nonlinear restoring force can be written in equivalent form as
in which
Based on (37), it is clear that the equivalent representation form (36) of the restoring force (32) holds if and only if . Thus, (31) can be written as
We next apply the aforementioned nonlinear transformation approach to (38), to obtain that
where the are given as
To assess the degree of accuracy attaining by (39), let us consider the parameter values of , , , , , and , with , , , and . Figure 7 shows the amplitude-time response curves of (31) compared to those obtained by substituting (39)_{1} into (38)_{2}. Here, the computed parameter values are , , , , , and , with , , , and . In this case, the computed RMSE values for and were, respectively, and . Table 5 shows the RMSE computed at different values of . Notice that when tends to , the RMSE values tend in general, to decrease, while the global error exhibits a linear increasing behavior, as illustrated in Figure 8.

##### 2.5. Example 5: A Two-Degree-of-Freedom System with Irrational Restoring Force

As a final example, let us consider the system shown in Figure 9 having two masses, and which are connected by four elastic springs of stiffness and having an undeformed length, . Furthermore, the element with mass is moving on a smooth horizontal bearing surface whose motion is restricted by an elastic linear spring of stiffness . By using Newton second law, it is easy to show that the equations of motion are where the irrational restoring forces are given by Here , , , and . The system (41) can be cast in different form by setting and , and after some algebraic manipulations, we get that with initial conditions , , , and . If we set and , then (43) becomes where with , , , and . By using the Chebyshev polynomials of the first kind, the cubic-like representation form of the irrational restoring force becomes where Here and are the complete elliptic integral of the first and second kind for the modulus , respectively. Thus, (44) can be rewritten as Application of the proposed nonlinear transformation approach to (48) yields where the are given as

Figure 10 illustrates the numerical predictions obtained by substituting (49)_{1} into (48)_{2}, as well as those computed from the numerical integration solution of (48). In Example 5, we have used the following system parameter values: , , , , , and , with initial conditions given as , , , and . The predicted parameter values are , , , , , and , with , , , and . Notice from Figure 10 that our numerical integration predictions computed from (49)_{1} and (48)_{2} follow the amplitude-time response curves computed from (48) with RMSE values of for and for , respectively. The system global error as illustrated in Figure 11 tends to linearly grow as time increases. Of course, other values of the system initial conditions can be used to study the quantitative and qualitative system dynamics response; however, when the system parameter changes, it could be necessary to adjust the values of and in order to satisfy (8).

#### 3. Conclusions

We have introduced a nonlinear transformation approach to determine the equivalent representation form of two degree-of-freedom oscillatory systems with nonlinear restoring forces. It has been shown that this nonlinear transformation approach decoupled the system equation into two Duffing equations. It has been found that the numerical integration of the equivalent equations of motion, of the five dynamic systems examined here, follows well the computed numerical values obtained from the original equations of motion. Furthermore, it has been shown that by substituting the decouple equation related to the () coordinate system into the original equation of motion described by (), it provides the lower RMSE values. However, when the canonical representation form of the original equations of motion is used to decouple the corresponding equations, the smallest RMSE values were found at small values of the nonlinear parameter .

We also have found in Example 3 that our equivalent representation form can capture high frequency system components if the exact solution of the cubic-like representation form of the coordinate is used to find the dynamics response of the coordinate. Besides, the corresponding equivalent equations of motion can be numerically integrated at the value of while the numerical integration solution of the original equations of motion (25), by using the Runge-Kutta, fails in spite of using the several solver options provided by Mathematica 9.0 or the MATLAB V.2012a computer packages. We next applied the EMHPM to find the numerical solution of (25) but it was found that this technique provides numerical results that diverge when . However, the numerical integration of our equivalent representation form provides solutions that describe the system dynamics periodic behavior.

In the case of Examples 4 and 5, we believe that the RMSE and the global error magnitudes are mainly due to two factors: (a) the Chebyshev polynomial representation of the system restoring forces and (b) the equivalent representation form of the original equations of motion that are based on cubic-like Duffing’s equations. These two factors affect the accuracy achieved during the solution process; however, the quantitative and qualitative system response are captured with good degree of accuracy.

Therefore, it can be concluded, in accordance with some of the systems studied here, that if one wants to have the smallest RMSE values when using the proposed nonlinear transformation approach, the canonical representation form has to be used for weak nonlinear system; however, for larger values of , the accurate results are obtained when the original equations of motion were decoupled. Based on the numerical simulation results, it is clear that our method is a promising technique that could provide equivalent representation forms of dynamic systems with two or more degrees of freedom that can follow the numerical integration solutions of the original equations of motion, at larger nonlinear parameter values.

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

This work was funded by Tecnológico de Monterrey-Campus Monterrey, through the Research Chair in Nanomaterials for Medical Devices and the Research Chair in Intelligent Machines. Additional support was provided from the European Commission Project, IREBID (FP7-PEOPLE-2009-IRSES-247476), and from Consejo Nacional de Ciencia y Tecnología (Conacyt), México.

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