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...

Differential Equations - Higher Order: Series Solutions



The purpose of this section is not to do anything new with a series solution problem. Instead it is here to illustrate that moving into a higher order differential equation does not really change the process outside of making it a little longer.
Back in the Series Solution chapter we only looked at 2nd order differential equations so we’re going to do a quick example here involving a 3rd order differential equation so we can make sure and say that we’ve done at least one example with an order larger than 2.


Example 1 Find the series solution around x0=0 for the following differential equation.y+x2y+xy=0


Recall that we can only find a series solution about x0=0 if this point is an ordinary point, or in other words, if the coefficient of the highest derivative term is not zero at x0=0. That is clearly the case here so let’s start with the form of the solutions as well as the derivatives that we’ll need for this solution.
y(x)=n=0anxny(x)=n=1nanxn1y(x)=n=3n(n1)(n2)anxn3Plugging into the differential equation gives,
n=3n(n1)(n2)anxn3+x2n=1nanxn1+xn=0anxn=0Now, move all the coefficients into the series and do appropriate shifts so that all the series are in terms of xn.
n=3n(n1)(n2)anxn3+n=1nanxn+1+n=0anxn+1=0n=0(n+3)(n+2)(n+1)an+3xn+n=2(n1)an1xn+n=1an1xn=0Next, let’s notice that we can start the second series at n=1 since that term will be zero. So let’s do that and then we can combine the second and third terms to get,
n=0(n+3)(n+2)(n+1)an+3xn+n=1[(n1)+1]an1xn=0n=0(n+3)(n+2)(n+1)an+3xn+n=1nan1xn=0So, we got a nice simplification in the new series that will help with some further simplification. The new second series can now be started at n=0 and then combined with the first series to get,
n=0[(n+3)(n+2)(n+1)an+3+nan1]xn=0We can now set the coefficients equal to get a fairly simply recurrence relation.
(n+3)(n+2)(n+1)an+3+nan1=0n=0,1,2,Solving the recurrence relation gives,
an+3=nan1(n+1)(n+2)(n+3)n=0,1,2,Now we need to start plugging in values of n and this will be one of the main areas where we can see a somewhat significant increase in the amount of work required when moving into a higher order differential equation.
n=0:a3=0

n=1:a4=a0(2)(3)(4)

n=2:a5=2a1(3)(4)(5)

n=3:a6=3a2(4)(5)(6)

n=4:a7=4a3(5)(6)(7)=0

n=5:a8=5a4(6)(7)(8)=5a0(2)(3)(4)(6)(7)(8)

n=6:a9=6a5(7)(8)(9)=(2)(6)a1(3)(4)(5)(7)(8)(9)

n=7:a10=7a6(8)(9)(10)=(3)(7)a2(4)(5)(6)(8)(9)(10)

n=8:a11=8a7(9)(10)(11)=0

n=9:a12=9a8(10)(11)(12)=(5)(9)a0(2)(3)(4)(6)(7)(8)(10)(11)(12)

n=10:a13=10a9(11)(12)(13)=(2)(6)(10)a1(3)(4)(5)(7)(8)(9)(11)(12)(13)

n=11:a14=11a10(12)(13)(14)=(3)(7)(11)a2(4)(5)(6)(8)(9)(10)(12)(13)(14)



Okay, we can now break the coefficients down into 4 sub cases given by a4ka4k+1a4k+2 and a4k+3 for k=0,1,2,3, We’ll give a semi-detailed derivation for a4k and then leave the rest to you with only couple of comments as they are nearly identical derivations.

First notice that all the a4k terms have a0 in them and they will alternate in sign. Next notice that we can turn the denominator into a factorial, (4k)! to be exact, if we multiply top and bottom by the numbers that are already in the numerator and so this will turn these numbers into squares. Next notice that the product in the top will start at 1 and increase by 4 until we reach 4k3. So, taking all of this into account we get,
a4k=(1)k(1)2(5)2(4k3)2a0(4k)!k=1,2,3,and notice that this will only work starting with k=1 as we won’t get a0 out of this formula as we should by plugging in k=0.
Now, for a4k+1 the derivation is almost identical and so the formula is,
a4k+1=(1)k(2)2(6)2(4k2)2a1(4k+1)!k=1,2,3,and again notice that this won’t work for k=0
The formula for a4k+2 is again nearly identical except for this one note that we also need to multiply top and bottom by a 2 in order to get the factorial to appear in the denominator and so the formula here is,
a4k+2=2(1)k(3)2(7)2(4k1)2a2(4k+2)!k=1,2,3,noticing yet one more time that this won’t work for k=0.
Finally, we have a4k+3=0 for k=0,1,2,3,
Now that we have all the coefficients let’s get the solution,
y(x)=a0+a1x+a2x2+a3x3++a4kx4k+a4k+1x4k+1+a4k+3x4k+3+a4k+3x4k+3+=a0+a1x+a2x2+(1)k(1)2(5)2(4k3)2a0(4k)!x4k+(1)k(2)2(6)2(4k2)2a1(4k+1)!x4k+1+2(1)k(3)2(7)2(4k1)2a2(4k+2)!x4k+2+Collecting up the terms that contain the same coefficient (except for the first one in each case since the formula won’t work for those) and writing everything as a set of series gives us our solution,
y(x)=a0{1+k=1(1)k(1)2(5)2(4k3)2x4k(4k)!}+a1{x+k=1(1)k(2)2(6)2(4k2)2x4k+1(4k+1)!}+a2{x2+k=12(1)k(3)2(7)2(4k1)2x4k+2(4k+2)!}


So, there we have it. As we can see the work in getting formulas for the coefficients was a little messy because we had three formulas to get, but individually they were not as bad as even some of them could be when dealing with 2nd order differential equations. Also note that while we got lucky with this problem and we were able to get general formulas for the terms the higher the order the less likely this will become.

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 s...

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 th...

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 d...

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...

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...

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 ) ...

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...

Differential Equations - Systems: Repeated Eigenvalues - ii

Example 3  Solve the following IVP. → x ′ = ( − 1 3 2 − 1 6 − 2 ) → x → x ( 2 ) = ( 1 0 ) x → ′ = ( − 1 3 2 − 1 6 − 2 ) x → x → ( 2 ) = ( 1 0 ) First the eigenvalue for the system. det ( A − λ I ) = ∣ ∣ ∣ ∣ − 1 − λ 3 2 − 1 6 − 2 − λ ∣ ∣ ∣ ∣ = λ 2 + 3 λ + 9 4 = ( λ + 3 2 ) 2 ⇒ λ 1 , 2 = − 3 2 det ( A − λ I ) = | − 1 − λ 3 2 − 1 6 − 2 − λ | = λ 2 + 3 λ + 9 4 = ( λ + 3 2 ) 2 ⇒ λ 1 , 2 = − 3 2 Now let’s get the eigenvector. ( 1 2 3 2 − 1 6 − 1 2 ) ( η 1 η 2 ) = ( 0 0 ) ⇒ 1 2 η 1 + 3 2 η 2 = 0 η 1 = − 3 η 2 ( 1 2 3 2 − 1 6 − 1 2 ) ( η 1 η 2 ) = ( 0 0 ) ⇒ 1 2 η 1 + 3 2 η 2 = 0 η 1 = − 3 η 2 → η = ( − 3 η 2 η 2 ) η 2 ≠ 0 → η ( 1 ) = ( − 3 1 ) η 2 = 1 η → = ( − 3 η 2 η 2 ) η 2 ≠ 0 η → ( 1 ) = ( − 3 1 ) η 2 = 1 Now find  → ρ ρ → , ( 1 2 3 2 − 1 6 − 1 2 ) ( ρ 1 ρ 2 ) = ( − 3 1 ) ⇒ 1 2 ρ 1 + 3 2 ρ 2 = − 3 ρ 1 = − 6 − 3 ρ 2 ( 1 2 3 2 − 1 6 − 1 2 ) ( ρ 1 ρ 2 ) = ( − 3 1 ) ⇒ 1 2 ρ 1 + 3 2 ρ 2 = − 3 ρ 1 = − 6 − 3 ρ 2 → ρ = ( − 6 − 3 ρ 2 ρ 2 ) ⇒ → ρ = ( − 6 0 ) if  ρ 2 = 0 ρ → ...

Differential Equations - Second Order: Repeated Roots

In this section we will be looking at the last case for the constant coefficient, linear, homogeneous second order differential equations. In this case we want solutions to a y ′′ + b y ′ + c y = 0 a y ″ + b y ′ + c y = 0 where solutions to the characteristic equation a r 2 + b r + c = 0 a r 2 + b r + c = 0 are double roots  r 1 = r 2 = r r 1 = r 2 = r . This leads to a problem however. Recall that the solutions are y 1 ( t ) = e r 1 t = e r t y 2 ( t ) = e r 2 t = e r t y 1 ( t ) = e r 1 t = e r t y 2 ( t ) = e r 2 t = e r t These are the same solution and will NOT be “nice enough” to form a general solution. We do promise that we’ll define “nice enough” eventually! So, we can use the first solution, but we’re going to need a second solution. Before finding this second solution let’s take a little side trip. The reason for the side trip will be clear eventually. From the quadratic formula we know that the roots to the characteristic equation are, r 1 , 2 = ...

Differential Equations - Laplace Transforms: Table

f ( t ) = L − 1 { F ( s ) } f ( t ) = L − 1 { F ( s ) } F ( s ) = L { f ( t ) } F ( s ) = L { f ( t ) }  1 1 s 1 s e a t e a t 1 s − a 1 s − a t n , n = 1 , 2 , 3 , … t n , n = 1 , 2 , 3 , … n ! s n + 1 n ! s n + 1 t p t p ,  p > − 1 p > − 1 Γ ( p + 1 ) s p + 1 Γ ( p + 1 ) s p + 1 √ t t √ π 2 s 3 2 π 2 s 3 2 t n − 1 2 , n = 1 , 2 , 3 , … t n − 1 2 , n = 1 , 2 , 3 , … 1 ⋅ 3 ⋅ 5 ⋯ ( 2 n − 1 ) √ π 2 n s n + 1 2 1 ⋅ 3 ⋅ 5 ⋯ ( 2 n − 1 ) π 2 n s n + 1 2 sin ( a t ) sin ⁡ ( a t ) a s 2 + a 2 a s 2 + a 2 cos ( a t ) cos ⁡ ( a t ) s s 2 + a 2 s s 2 + a 2 t sin ( a t ) t sin ⁡ ( a t ) 2 a s ( s 2 + a 2 ) 2 2 a s ( s 2 + a 2 ) 2 t cos ( a t ) t cos ⁡ ( a t ) s 2 − a 2 ( s 2 + a 2 ) 2 s 2 − a 2 ( s 2 + a 2 ) 2 sin ( a t ) − a t cos ( a t ) sin ⁡ ( a t ) − a t cos ⁡ ( a t ) 2 a 3 ( s 2 + a 2 ) 2 2 a 3 ( s 2 + a 2 ) 2 sin ( a t ) + a t cos ( a t ) sin ⁡ ( a t ) + a t cos ⁡ ( a t ) 2 a s 2 ( s 2 + a 2 ) 2 2 a s 2 ( s 2 + a 2 ) 2 cos ( a t ) − a t sin ( a t ) cos ⁡ (...