Skip to main content

Calculus III - 3-Dimensional Space: Equations of Lines

In this section we need to take a look at the equation of a line in  R 3 R 3 . As we saw in the previous section the equation  y = m x + b y = m x + b  does not describe a line in  R 3 R 3 , instead it describes a plane. This doesn’t mean however that we can’t write down an equation for a line in 3-D space. We’re just going to need a new way of writing down the equation of a curve. So, before we get into the equations of lines we first need to briefly look at vector functions. We’re going to take a more in depth look at vector functions later. At this point all that we need to worry about is notational issues and how they can be used to give the equation of a curve. The best way to get an idea of what a vector function is and what its graph looks like is to look at an example. So, consider the following vector function. → r ( t ) = ⟨ t , 1 ⟩ r → ( t ) = ⟨ t , 1 ⟩ A vector function is a function that takes one or more variables, one in this case, and returns a vector. Note as we

Differential Equations - Second Order: Complex Roots




In this section we will be looking at solutions to the differential equation
ay+by+cy=0
in which roots of the characteristic equation,
ar2+br+c=0
are complex roots in the form r1,2=λ±μi.
Now, recall that we arrived at the characteristic equation by assuming that all solutions to the differential equation will be of the form
y(t)=ert
Plugging our two roots into the general form of the solution gives the following solutions to the differential equation.
y1(t)=e(λ+μi)tandy2(t)=e(λμi)t
Now, these two functions are “nice enough” (there’s those words again… we’ll get around to defining them eventually) to form the general solution. We do have a problem however. Since we started with only real numbers in our differential equation we would like our solution to only involve real numbers. The two solutions above are complex and so we would like to get our hands on a couple of solutions (“nice enough” of course…) that are real.
To do this we’ll need Euler’s Formula.
eiθ=cosθ+isinθ
A nice variant of Euler’s Formula that we’ll need is.
eiθ=cos(θ)+isin(θ)=cosθisinθ
Now, split up our two solutions into exponentials that only have real exponents and exponentials that only have imaginary exponents. Then use Euler’s formula, or its variant, to rewrite the second exponential.
y1(t)=eλteiμt=eλt(cos(μt)+isin(μt))y2(t)=eλteiμt=eλt(cos(μt)isin(μt))
This doesn’t eliminate the complex nature of the solutions, but it does put the two solutions into a form that we can eliminate the complex parts.
Recall from the basics section that if two solutions are “nice enough” then any solution can be written as a combination of the two solutions. In other words,
y(t)=c1y1(t)+c2y2(t)
will also be a solution.
Using this let’s notice that if we add the two solutions together we will arrive at.
y1(t)+y2(t)=2eλtcos(μt)
This is a real solution and just to eliminate the extraneous 2 let’s divide everything by a 2. This gives the first real solution that we’re after.
u(t)=12y1(t)+12y2(t)=eλtcos(μt)
Note that this is just equivalent to taking
c1=c2=12
Now, we can arrive at a second solution in a similar manner. This time let’s subtract the two original solutions to arrive at.
y1(t)y2(t)=2ieλtsin(μt)
On the surface this doesn’t appear to fix the problem as the solution is still complex. However, upon learning that the two constants, c1 and c2 can be complex numbers we can arrive at a real solution by dividing this by 2i. This is equivalent to taking
c1=12iandc2=12i
Our second solution will then be
v(t)=12iy1(t)12iy2(t)=eλtsin(μt)
We now have two solutions (we’ll leave it to you to check that they are in fact solutions) to the differential equation.
u(t)=eλtcos(μt)andv(t)=eλtsin(μt)
It also turns out that these two solutions are “nice enough” to form a general solution.
So, if the roots of the characteristic equation happen to be r1,2=λ±μi the general solution to the differential equation is.
y(t)=c1eλtcos(μt)+c2eλtsin(μt)
Let’s take a look at a couple of examples now.


Example 1 Solve the following IVP.y4y+9y=0y(0)=0y(0)=8

The characteristic equation for this differential equation is.
r24r+9=0The roots of this equation are r1,2=2±5i. The general solution to the differential equation is then.
y(t)=c1e2tcos(5t)+c2e2tsin(5t)Now, you’ll note that we didn’t differentiate this right away as we did in the last section. The reason for this is simple. While the differentiation is not terribly difficult, it can get a little messy. So, first looking at the initial conditions we can see from the first one that if we just applied it we would get the following.
0=y(0)=c1In other words, the first term will drop out in order to meet the first condition. This makes the solution, along with its derivative
y(t)=c2e2tsin(5t)y(t)=2c2e2tsin(5t)+5c2e2tcos(5t)A much nicer derivative than if we’d done the original solution. Now, apply the second initial condition to the derivative to get.
8=y(0)=5c2c2=85The actual solution is then.
y(t)=85e2tsin(5t)



Example 2 Solve the following IVP.y8y+17y=0y(0)=4y(0)=1

The characteristic equation this time is.
r28r+17=0The roots of this are r1,2=4±i. The general solution as well as its derivative is
y(t)=c1e4tcos(t)+c2e4tsin(t)y(t)=4c1e4tcos(t)c1e4tsin(t)+4c2e4tsin(t)+c2e4tcos(t)Notice that this time we will need the derivative from the start as we won’t be having one of the terms drop out. Applying the initial conditions gives the following system.
4=y(0)=c11=y(0)=4c1+c2Solving this system gives c1=4 and c2=15. The actual solution to the IVP is then.
y(t)=4e4tcos(t)+15e4tsin(t)



Example 3 Solve the following IVP.4y+24y+37y=0y(π)=1y(π)=0

The characteristic equation this time is.
4r2+24r+37=0The roots of this are r1,2=3±12i. The general solution as well as its derivative is
y(t)=c1e3tcos(t2)+c2e3tsin(t2)y(t)=3c1e3tcos(t2)c12e3tsin(t2)3c2e3tsin(t2)+c22e3tcos(t2)Applying the initial conditions gives the following system.
1=y(π)=c1e3πcos(π2)+c2e3πsin(π2)=c2e3π0=y(π)=c12e3π3c2e3πDo not forget to plug the t=π into the exponential! This is one of the more common mistakes that students make on these problems. Also, make sure that you evaluate the trig functions as much as possible in these cases. It will only make your life simpler. Solving this system gives
c1=6e3πc2=e3πThe actual solution to the IVP is then.
y(t)=6e3πe3tcos(t2)+e3πe3tsin(t2)y(t)=6e3(tπ)cos(t2)+e3(tπ)sin(t2)
Let’s do one final example before moving on to the next topic.




Example 4 Solve the following IVP.y+16y=0y(π2)=10y(π2)=3


The characteristic equation for this differential equation and its roots are.
r2+16=0r=±4iBe careful with this characteristic polynomial. One of the biggest mistakes students make here is to write it as,
r2+16r=0
The problem is that the second term will only have an r if the second term in the differential equation has a y in it and this one clearly does not. Students however, tend to just start at r2 and write times down until they run out of terms in the differential equation. That can, and often does mean, they write down the wrong characteristic polynomial so be careful.
Okay, back to the problem.
The general solution to this differential equation and its derivative is.
y(t)=c1cos(4t)+c2sin(4t)y(t)=4c1sin(4t)+4c2cos(4t)Plugging in the initial conditions gives the following system.
10=y(π2)=c1c1=103=y(π2)=4c2c2=34So, the constants drop right out with this system and the actual solution is.

Comments

Popular posts from this blog

Digital Signal Processing - Basic Continuous Time Signals

To test a system, generally, standard or basic signals are used. These signals are the basic building blocks for many complex signals. Hence, they play a very important role in the study of signals and systems. Unit Impulse or Delta Function A signal, which satisfies the condition,   δ ( t ) = lim ϵ → ∞ x ( t ) δ ( t ) = lim ϵ → ∞ x ( t )   is known as unit impulse signal. This signal tends to infinity when t = 0 and tends to zero when t ≠ 0 such that the area under its curve is always equals to one. The delta function has zero amplitude everywhere except at t = 0. Properties of Unit Impulse Signal δ(t) is an even signal. δ(t) is an example of neither energy nor power (NENP) signal. Area of unit impulse signal can be written as; A = ∫ ∞ − ∞ δ ( t ) d t = ∫ ∞ − ∞ lim ϵ → 0 x ( t ) d t = lim ϵ → 0 ∫ ∞ − ∞ [ x ( t ) d t ] = 1 Weight or strength of the signal can be written as; y ( t ) = A δ ( t ) y ( t ) = A δ ( t ) Area of the weighted impulse signal can

Differential Equations - First Order: Bernoulli

In this section we are going to take a look at differential equations in the form, y ′ + p ( x ) y = q ( x ) y n y ′ + p ( x ) y = q ( x ) y n where  p ( x ) p ( x )  and  q ( x ) q ( x )  are continuous functions on the interval we’re working on and  n n  is a real number. Differential equations in this form are called  Bernoulli Equations . First notice that if  n = 0 n = 0  or  n = 1 n = 1  then the equation is linear and we already know how to solve it in these cases. Therefore, in this section we’re going to be looking at solutions for values of  n n  other than these two. In order to solve these we’ll first divide the differential equation by  y n y n  to get, y − n y ′ + p ( x ) y 1 − n = q ( x ) y − n y ′ + p ( x ) y 1 − n = q ( x ) We are now going to use the substitution  v = y 1 − n v = y 1 − n  to convert this into a differential equation in terms of  v v . As we’ll see this will lead to a differential equation that we can solve. We are going to have to be c

Differential Equations - Systems: Solutions

Now that we’ve got some of the basics out of the way for systems of differential equations it’s time to start thinking about how to solve a system of differential equations. We will start with the homogeneous system written in matrix form, → x ′ = A → x (1) (1) x → ′ = A x → where,  A A  is an  n × n n × n  matrix and  → x x →  is a vector whose components are the unknown functions in the system. Now, if we start with  n = 1 n = 1 then the system reduces to a fairly simple linear (or separable) first order differential equation. x ′ = a x x ′ = a x and this has the following solution, x ( t ) = c e a t x ( t ) = c e a t So, let’s use this as a guide and for a general  n n  let’s see if → x ( t ) = → η e r t (2) (2) x → ( t ) = η → e r t will be a solution. Note that the only real difference here is that we let the constant in front of the exponential be a vector. All we need to do then is plug this into the differential equation and see what we get. First notice that

Calculus III - 3-Dimensional Space: Equations of Lines

In this section we need to take a look at the equation of a line in  R 3 R 3 . As we saw in the previous section the equation  y = m x + b y = m x + b  does not describe a line in  R 3 R 3 , instead it describes a plane. This doesn’t mean however that we can’t write down an equation for a line in 3-D space. We’re just going to need a new way of writing down the equation of a curve. So, before we get into the equations of lines we first need to briefly look at vector functions. We’re going to take a more in depth look at vector functions later. At this point all that we need to worry about is notational issues and how they can be used to give the equation of a curve. The best way to get an idea of what a vector function is and what its graph looks like is to look at an example. So, consider the following vector function. → r ( t ) = ⟨ t , 1 ⟩ r → ( t ) = ⟨ t , 1 ⟩ A vector function is a function that takes one or more variables, one in this case, and returns a vector. Note as we

Digital Signal Processing - Miscellaneous Signals

There are other signals, which are a result of operation performed on them. Some common type of signals are discussed below. Conjugate Signals Signals, which satisfies the condition  x ( t ) = x ∗ ( − t ) are called conjugate signals. Let  x ( t ) = a ( t ) + j b ( t ) So,  x ( − t ) = a ( − t ) + j b ( − t ) And  x ∗ ( − t ) = a ( − t ) − j b ( − t ) By Condition,  x ( t ) = x ∗ ( − t ) If we compare both the derived equations 1 and 2, we can see that the real part is even, whereas the imaginary part is odd. This is the condition for a signal to be a conjugate type. Conjugate Anti-Symmetric Signals Signals, which satisfy the condition  x ( t ) = − x ∗ ( − t ) are called conjugate anti-symmetric signal Let  x ( t ) = a ( t ) + j b ( t ) So  x ( − t ) = a ( − t ) + j b ( − t ) And  x ∗ ( − t ) = a ( − t ) − j b ( − t ) − x ∗ ( − t ) = − a ( − t ) + j b ( − t ) By Condition  x ( t ) = − x ∗ ( − t ) Now, again compare, both the equations just as w

Differential Equations - First Order: Modeling - i

We now move into one of the main applications of differential equations both in this class and in general. Modeling is the process of writing a differential equation to describe a physical situation. Almost all of the differential equations that you will use in your job (for the engineers out there in the audience) are there because somebody, at some time, modeled a situation to come up with the differential equation that you are using. This section is not intended to completely teach you how to go about modeling all physical situations. A whole course could be devoted to the subject of modeling and still not cover everything! This section is designed to introduce you to the process of modeling and show you what is involved in modeling. We will look at three different situations in this section : Mixing Problems, Population Problems, and Falling Objects. In all of these situations we will be forced to make assumptions that do not accurately depict reality in most cases, but wi

Differential Equations - Basic Concepts: Definitions

Differential Equation The first definition that we should cover should be that of  differential equation . A differential equation is any equation which contains derivatives, either ordinary derivatives or partial derivatives. There is one differential equation that everybody probably knows, that is Newton’s Second Law of Motion. If an object of mass  m m  is moving with acceleration  a a  and being acted on with force  F F  then Newton’s Second Law tells us. F = m a (1) (1) F = m a To see that this is in fact a differential equation we need to rewrite it a little. First, remember that we can rewrite the acceleration,  a a , in one of two ways. a = d v d t OR a = d 2 u d t 2 (2) (2) a = d v d t OR a = d 2 u d t 2 Where  v v  is the velocity of the object and  u u  is the position function of the object at any time  t t . We should also remember at this point that the force,  F F  may also be a function of time, velocity, and/or position. So, with all these things in

Differential Equations - Partial: Summary of Separation of Variables

Throughout this chapter we’ve been talking about and solving partial differential equations using the method of separation of variables. However, the one thing that we’ve not really done is completely work an example from start to finish showing each and every step. Each partial differential equation that we solved made use somewhere of the fact that we’d done at least part of the problem in another section and so it makes some sense to have a quick summary of the method here. Also note that each of the partial differential equations only involved two variables. The method can often be extended out to more than two variables, but the work in those problems can be quite involved and so we didn’t cover any of that here. So with all of that out of the way here is a quick summary of the method of separation of variables for partial differential equations in two variables. Verify that the partial differential equation is linear and homogeneous. Verify that the boundary condi

Differential Equations - Systems: Repeated Eigenvalues - i

This is the final case that we need to take a look at. In this section we are going to look at solutions to the system, → x ′ = A → x x → ′ = A x → where the eigenvalues are repeated eigenvalues. Since we are going to be working with systems in which  A A  is a  2 × 2 2 × 2  matrix we will make that assumption from the start. So, the system will have a double eigenvalue,  λ λ . This presents us with a problem. We want two linearly independent solutions so that we can form a general solution. However, with a double eigenvalue we will have only one, → x 1 = → η e λ t x → 1 = η → e λ t So, we need to come up with a second solution. Recall that when we looked at the double root case with the second order differential equations we ran into a similar problem. In that section we simply added a  t t  to the solution and were able to get a second solution. Let’s see if the same thing will work in this case as well. We’ll see if → x = t e λ t → η x → = t e λ t η → will also be a

Differential Equations - First Order: Modeling - ii

Example 4  A 50 kg object is shot from a cannon straight up with an initial velocity of 10m/s off a bridge that is 100 meters above the ground. If air resistance is given by 5 v v  determine the velocity of the mass when it hits the ground. First, notice that when we say straight up, we really mean straight up, but in such a way that it will miss the bridge on the way back down. Here is a sketch of the situation. Notice the conventions that we set up for this problem. Since the vast majority of the motion will be in the downward direction we decided to assume that everything acting in the downward direction should be positive. Note that we also defined the “zero position” as the bridge, which makes the ground have a “position” of 100. Okay, if you think about it we actually have two situations here. The initial phase in which the mass is rising in the air and the second phase when the mass is on its way down. We will need to examine both situations and set up an IVP for