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For the given system, (a) Use Theorem \(6.2\) to show that the system is a Hamiltonian system. (b) Find a Hamiltonian function for the system. (c) Use computational software to graph the phase-plane trajectory passing through \((1,1)\). Also, indicate the direction of motion for the solution point. $$ \begin{aligned} &x^{\prime}=2 y \cos x \\ &y^{\prime}=y^{2} \sin x \end{aligned} $$

Short Answer

Expert verified
#Answer# (a) The given system is Hamiltonian. (b) The Hamiltonian function is \(H(x, y) = y^2 \cos x\). (c) Use computational software to graph the phase-plane trajectory and determine the direction of motion for the solution point \((1,1)\).

Step by step solution

01

(a) Applying Theorem 6.2 #

Theorem 6.2 states that a planar system is Hamiltonian if it has the following form: $$ \begin{aligned} x' &= \frac{\partial H}{\partial y} \\ y' &= -\frac{\partial H}{\partial x} \end{aligned} $$ where \(H(x, y)\) is a scalar function, called the Hamiltonian function. Compare this with the given system: $$ \begin{aligned} x' &= 2y\cos x \\ y' &= y^2\sin x \end{aligned} $$ To see if the given system has the form specified by Theorem 6.2, we can set: $$ \begin{aligned} \frac{\partial H}{\partial y} &= 2y\cos x \\ -\frac{\partial H}{\partial x} &= y^2\sin x \end{aligned} $$ Now, we need to find the Hamiltonian function \(H(x, y)\). To do this, we can integrate both equations with respect to \(y\) and \(x\), respectively.
02

(b) Find the Hamiltonian function #

Integrate with respect to \(y\) and \(x\): $$ \begin{aligned} H(x, y) &= \int 2y\cos x \, dy \\ H(x, y) &= y^2 \cos x + C(x) \\ \end{aligned} $$ Now differentiate \(H(x, y)\) with respect to \(x\) and equate it to \(-y^2 \sin x\): $$ \begin{aligned} -\frac{\partial H}{\partial x} &= -y^2\sin x \\ -y^2 \sin x &= -y^2\sin x \\ \end{aligned} $$ The equation holds true meaning we found the correct form of the Hamiltonian function. So the Hamiltonian function is: $$ H(x, y) = y^2 \cos x + C(x) $$ However, \(C(x)\) doesn't contribute when computing \(x'\) and \(y'\) so we can drop it for the sake of simplicity, and obtain: $$ H(x, y) = y^2 \cos x $$
03

(c) Graph the phase-plane trajectory and determine the direction of motion #

You will need to use computational software to graph the phase-plane trajectory of the given system. Plot the trajectory passing through point \((1,1)\) and observe its direction. Some popular software options are MATLAB, Mathematica, or Python with the matplotlib library.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Hamiltonian Function
A fundamental concept in classical mechanics and mathematical physics is the Hamiltonian function, which is instrumental in describing a system's total energy—combining both kinetic and potential energy. Think of it like a mathematical recipe that provides a snapshot of the entire system's energy landscape.

In the given exercise, the system's Hamiltonian function serves as a bridge to transforming a set of differential equations into a form that reveals the underlying mechanics. The integration step showed us that the Hamiltonian function for our system is \(H(x, y) = y^2 \times \text{cos}(x)\), which represents the energy of the system at any given point \((x, y)\) in space.

This perspective isn't just academic; by understanding the system through its Hamiltonian function, we gain powerful analytical tools. For instance, we can predict the possible states and evolution of the system over time without needing to solve the equations explicitly. It's like having a map of all the possible paths a system could take, just based on the energy terrain provided by the Hamiltonian.
Phase-Plane Trajectory
Phase-plane trajectory is a core concept used to visualize the behavior of dynamical systems in two dimensions. Imagine charting a course for a ship at sea: the phase-plane trajectory tells you not just where the ship is, but also where it's likely headed.

In our exercise, the phase-plane trajectory is the path traced out by the solution of the system of differential equations, which includes points like \((1, 1)\). By graphing these trajectories using computational software, we can see a picture of how the system evolves over time.

Understanding Directions

To gauge the direction of motion along these trajectories, pay attention to the arrows drawn on them in the graph. These arrows act like a compass, showing the direction in which the system will evolve from any given starting point. It's about dynamic forecasting, knowing the future state based on present conditions. And much like weather predictions, the phase-plane trajectories provide a forecast, not of weather, but of the system's states as they change through time.
Differential Equations
Differential equations are pivotal in translating real-world phenomena into the language of mathematics. They are to dynamics what grammar is to language: a set of rules explaining how things change.

In our context, the coupled differential equations \(x' = 2y\times\text{cos}(x)\) and \(y' = y^2\times\text{sin}(x)\) describe the rate of change of the variables 'x' and 'y' with respect to time. Solving these equations isn't just about finding 'x' and 'y'; it's about comprehending how the system's state (represented by 'x' and 'y') evolves as time marches on.

By following the step-by-step solution, we apply Theorem 6.2, matching the structure of our differential equations with the pattern of the Hamiltonian system. This allows us to unlock an analytical way to study our system's behavior, zooming out from the minutiae of calculation and diving into the broader narrative of the system's motion and equilibrium.

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Most popular questions from this chapter

Consider the system \(x^{\prime}=y+\alpha x\left(x^{2}+y^{2}\right), y^{\prime}=-x+\alpha y\left(x^{2}+y^{2}\right) .\) Introduce polar coordinates and use the results of Exercises 25 and 26 to derive differential equations for \(r(t)\) and \(\theta(t)\). Solve these differential equations, and then form \(x(t)\) and \(y(t)\).

Each exercise lists a nonlinear system \(\mathbf{z}^{\prime}=A \mathbf{z}+\mathbf{g}(\mathbf{z})\), where \(A\) is a constant ( \(2 \times 2\) ) invertible matrix and \(\mathbf{g}(\mathbf{z})\) is a \((2 \times 1)\) vector function. In each of the exercises, \(\mathbf{z}=\mathbf{0}\) is an equilibrium point of the nonlinear system. (a) Identify \(A\) and \(\mathbf{g}(\mathbf{z})\). (b) Calculate \(\|\mathbf{g}(\mathbf{z})\|\). (c) Is \(\lim _{\mid \mathbf{z} \| \rightarrow 0}\|\mathbf{g}(\mathbf{z})\| /\|\mathbf{z}\|=0\) ? Is \(\mathbf{z}^{\prime}=A \mathbf{z}+\mathbf{g}(\mathbf{z})\) an almost linear system at \(\mathbf{z}=\mathbf{0}\) ? (d) If the system is almost linear, use Theorem \(6.4\) to choose one of the three statements: (i) \(\mathbf{z}=\mathbf{0}\) is an asymptotically stable equilibrium point. (ii) \(\mathbf{z}=\mathbf{0}\) is an unstable equilibrium point. (iii) No conclusion can be drawn by using Theorem \(6.4\). $$ \begin{aligned} &z_{1}^{\prime}=-3 z_{1}+z_{2}+z_{1}^{2}+z_{2}^{2} \\ &z_{2}^{\prime}=2 z_{1}-2 z_{2}+\left(z_{1}^{2}+z_{2}^{2}\right)^{1 / 3} \end{aligned} $$

In each exercise, the given system is an almost linear system at each of its equilibrium points. (a) Find the (real) equilibrium points of the given system. (b) As in Example 2, find the corresponding linearized system \(\mathbf{z}^{\prime}=A \mathbf{z}\) at each equilibrium point. (c) What, if anything, can be inferred about the stability properties of the equilibrium point(s) by using Theorem \(6.4\) ? $$ \begin{aligned} &x^{\prime}=(x-2)(y-3) \\ &y^{\prime}=(x+2 y)(y-1) \end{aligned} $$

In each exercise, the given system is an almost linear system at each of its equilibrium points. (a) Find the (real) equilibrium points of the given system. (b) As in Example 2, find the corresponding linearized system \(\mathbf{z}^{\prime}=A \mathbf{z}\) at each equilibrium point. (c) What, if anything, can be inferred about the stability properties of the equilibrium point(s) by using Theorem \(6.4\) ? $$ \begin{aligned} &x^{\prime}=1-x^{2} \\ &y^{\prime}=x^{2}+y^{2}-2 \end{aligned} $$

Each exercise lists a nonlinear system \(\mathbf{z}^{\prime}=A \mathbf{z}+\mathbf{g}(\mathbf{z})\), where \(A\) is a constant ( \(2 \times 2\) ) invertible matrix and \(\mathbf{g}(\mathbf{z})\) is a \((2 \times 1)\) vector function. In each of the exercises, \(\mathbf{z}=\mathbf{0}\) is an equilibrium point of the nonlinear system. (a) Identify \(A\) and \(\mathbf{g}(\mathbf{z})\). (b) Calculate \(\|\mathbf{g}(\mathbf{z})\|\). (c) Is \(\lim _{\mid \mathbf{z} \| \rightarrow 0}\|\mathbf{g}(\mathbf{z})\| /\|\mathbf{z}\|=0\) ? Is \(\mathbf{z}^{\prime}=A \mathbf{z}+\mathbf{g}(\mathbf{z})\) an almost linear system at \(\mathbf{z}=\mathbf{0}\) ? (d) If the system is almost linear, use Theorem \(6.4\) to choose one of the three statements: (i) \(\mathbf{z}=\mathbf{0}\) is an asymptotically stable equilibrium point. (ii) \(\mathbf{z}=\mathbf{0}\) is an unstable equilibrium point. (iii) No conclusion can be drawn by using Theorem \(6.4\). $$ \begin{aligned} &z_{1}^{\prime}=2 z_{2}+z_{2}^{2} \\ &z_{2}^{\prime}=-2 z_{1}+z_{1} z_{2} \end{aligned} $$

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