Journal of Applied Mathematics

Volume 2018 (2018), Article ID 7487851, 15 pages

https://doi.org/10.1155/2018/7487851

## The Equivalent Linearization Method with a Weighted Averaging for Solving Undamped Nonlinear Oscillators

^{1}Thai Nguyen University of Technology, Thai Nguyen, Vietnam^{2}Hanoi Architectural University, Ha Noi, Vietnam

Correspondence should be addressed to D. V. Hieu; nv.ude.tunt@vdueih

Received 27 June 2017; Accepted 5 March 2018; Published 18 April 2018

Academic Editor: Peter G. L. Leach

Copyright © 2018 D. V. Hieu 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

The Equivalent Linearization Method (ELM) with a weighted averaging is applied to analyze five undamped oscillator systems with nonlinearities. The results obtained via this method are compared with the ones achieved by Parameterized Perturbation Method (PPM), Min–Max Approach (MMA), Variational Iteration Method (VIM), Homotopy Perturbation Method (HPM), Energy Balance Method (EBM), Harmonic Balance Method (HBM), 4th-Order Runge-Kutta Method, and the exact ones. The obtained results demonstrate that this method is very convenient for solving nonlinear equations and also can be successfully applied to a lot of practical engineering and physical problems.

#### 1. Introduction

Nonlinear oscillation problem is very important in the physical science, mechanical structures, and other kinds of mathematical sciences. Most of real systems are modeled by nonlinear differential equations which are important issues in mechanical structures, mathematical physics, and engineering. In most cases, it is difficult to solve such equations, especially analytically; and in addition, the most important information, such as the natural circular frequency of a nonlinear oscillation which depends on the initial conditions, will be lost during the procedure of numerical simulation.

Recently, nonlinear oscillator models have been widely considered in physics and engineering. It is obvious that there are many nonlinear equations in the study of different branches of science which do not have analytical solutions. Due to the limitation of existing exact solutions, many analytical approaches have been investigated. Many researchers have been working on various analytical methods for solving nonlinear oscillation systems in the last decades, such as Homotopy Perturbation Method (HPM) [1–5], Max–Min Approach (MMA) [5–10], Variational Iteration Method (VIM) [2, 5], Energy Balance Method (EBM) [5, 11–16], Amplitude-Frequency Formulation (AFF) [5–7, 15, 17, 18], Improved Amplitude-Frequency Formulation (IAFF) [5], Parameter Expansion Method (PEM) [5, 7, 18–20], Homotopy Analysis Method (HAM) [5, 21], Modified Homotopy Perturbation Method (MHPM) [5, 6], Modified Lindstedt-Poincare Method [22], Harmonic Balance Method [23, 24], and combined Newton’s Method with the Harmonic Balance Method [25].

The Equivalent Linearization Method is one of the common approaches to approximate analysis of dynamical systems. The original linearization for deterministic systems was proposed by Krylov and Bogoliuboff [26]. Then Caughey expanded the method for stochastic systems [27]. Thenceforward, there have been some extended versions of the Equivalent Linearization Method [28–32]. It has been shown that the Equivalent Linearization Method is presently the simplest tool widely used for analyzing nonlinear stochastic problems. Nevertheless, the accuracy of the Equivalent Linearization Method with conventional averaging normally reduces for middle or strong nonlinear systems. A reason is that some terms will vanish in the averaging process; for example, the averaging value of the functions and over one period is equal to zero. Recently, Anh proposed a new way for determining averaging values: instead of using conventional averaging process the author introduced weighted coefficient functions [31]; by this manner, the averaging value is calculated in a new way called the weighted averaging value. This proposed method has been applied effectively to analyze some strongly nonlinear oscillations such as the nonlinear Duffing oscillator with third, fifth, and seventh powers of the amplitude, the strongly nonlinear oscillators in forms and , and the cubic Duffing with discontinuity [33].

In this paper, the Equivalent Linearization Method with a weighted averaging continues to be applied to analyze five undamped nonlinear oscillators: the Duffing oscillator with cubic nonlinearity (Example 1); the motion of simple pendulum attached to a rotating rigid frame (Example 2); the motion of a mass attached to the center of a stretched elastic wire (Example 3); the Duffing oscillator with discontinuity (Example 4); and the nonlinear oscillator with fractional elastic force (Example 5). It should be emphasized that the generalized forms of the Duffing equation (in Example 1) and the oscillator (in Example 3) have been solved by Energy Balance Method [12, 16], and the generalized form of the nonlinear oscillator with fractional elastic force (in Example 5) has been solved by Energy Balance Method and Variational Approach [14]; these solutions are achieved by Younesian et al. [12, 14, 16]. The frequency-amplitude relationship is analytically obtained by using this proposed method. Accuracy and validity of results are then presented by comparing the results with the ones obtained by the other well-known techniques and the exact and numerical ones.

#### 2. The Equivalent Linearization Method with a Weighted Averaging

##### 2.1. The Equivalent Linearization Method

In order to introduce the general idea of the Equivalent Linearization Method, we consider a nonlinear oscillator governed by the following equation [33]:where is a nonlinear function only depending on two variables of velocity and displacement and and are constants. The equivalent linear oscillator is described by the equation as follows:

The equation error between the two oscillators is taken as

The coefficients of linearization in the linearized equation (2) are found from an optimal criterion. There are some criteria for determining these coefficients. The most common criterion is the mean square error criterion which requires that the mean square of equation error be minimum:

Thus,yields

In the formulations in (4), (6a), and (6b), the symbol denotes the time-averaging operator in classical meaning [26]:

For a -frequency function , the averaging process is taken during one period ; that is,

In this technique, the importance of the attended terms is considered to be the same on time scale. In fact, their roles generally differ from time to time. That may be one of the reasons causing the classical equivalent replacement to be effective only for oscillators with weak nonlinearity but normally not being good for ones with strong nonlinearity. In order to improve this shortcoming, the averaging operation with weighting functions is proposed as in the next section. This idea is introduced by Anh [31].

##### 2.2. The Weighted Averaging

The conventional averaging value of an integrable deterministic function on a domain : is a constant value defined by

In many cases when the function is periodic with period *,* the value is taken as and it leads to the averaging value of over one period:where . Definition (9) has some deficiencies; for example, if (9) or (10) are equal to zero, the information about* x(t)* will be lost. For all harmonic functions such as and *,* this observation is true. In [31], Anh suggested replacing the constant coefficient in (9) by a weighted coefficient function . Thus one gets the so-called weighted average value:with the condition

There are three basic weighted coefficients:(i)*Basic Optimistic Weighted Coefficients*. They are increasing functions of and denoted as* O(t)*. Examples are and ,* α, β > 0*.(ii)

*Basic Pessimistic Weighted Coefficients*. They are decreasing functions of and denoted as . Examples are and ; or .(iii)

*Neutral Weighted Coefficients*. They are denoted as and are constants.

An arbitrary weighted coefficient can be obtained as summation and/or product of basic weighted coefficients. Example iswhere are constant.

In this paper, a special form of weighted coefficient is introduced as follows [31, 33]:where is constant.

The properties of the weighted function in (14) can be viewed in [31, 33].

Based on the weighted coefficient (14), a new weighted averaging value is proposed:which is a linear operator. From Laplace transform, we get, for example,

As -periodic functions can be expanded into Fourier series; hence we can easily calculate (15) by using (16).

In this paper, for the sake of comparison, the parameter is chosen equal to 2. In the next section, some practical examples for some strongly nonlinear vibration systems are illustrated to show the applicability, accuracy, and effectiveness of the present method.

#### 3. Some Examples and Discussions

##### 3.1. Example 1

Consider the following Duffing equation with cubic nonlinearity:

The linearized equation of (17) is

The equation error between (17) and (18) iswhere is determined by using the mean square criterion, as follows:

The periodic solution of the linearized equation (18) is

Using definition (11) and weighted coefficient (14), we can calculate averaging operators in (20):

Substituting (22) into (20), we get the approximate frequency of this oscillator:

With chosen equal to 2, we have

From (21), the approximate solution of this oscillator is

Accuracy of this method for this example is shown in Table 1 and Figures 1 and 2. Table 1 shows the comparison between the approximate frequencies obtained by this method, the ones obtained by Parameterized Perturbation Method (PPM) [5], and the exact ones ; the relative error is again obtained for small and large values of the oscillation amplitude . Table 1 shows that the maximum relative error is less than 0.4733 for this method and 2.2135 for Parameterized Perturbation Method. The numerical results obtained by three different methods are illustrated in Figures 1 and 2. Numerical results validate again accuracy of this method. As it is shown in Figures 3 and 4, validity of the solution technique is guaranteed for stronger nonlinearities. Relative error versus the oscillation amplitude is illustrated in Figure 3.