4th Order Runge-Kutta method and over/under estimates

In summary, the conversation discusses using Excel and the Runge-Kutta method to approximate a solution to the equation dF/dt=-0.1F+70. The conversation includes generating approximations for F at different times and determining whether these approximations are greater or less than the exact values. The conversation also explores the shape of the solution curve and the effect of weight on certain terms in the approximation. Ultimately, the conversation concludes with discussing the error in the RK4 method and its relationship to a specific 4th order polynomial.
  • #1
dwdoyle8854
16
0

Homework Statement



"Use Excel to approximate dF/dt=-0.1F+70, F(0)=0 to generate approximations for F at t=1,2 and 4 using step size 0.1. Explain whether these approximation are greater than or less than the exact values. Determine whether the shape of the solution curve is increasing, decreasing, concave up or concave down based on the data alone. Explain."

Homework Equations



ynext = ynow + (1/6)(k1 +2k2 + 2k3 + k4)
k1= Δx*f'(xnow, ynow)
k2= Δx*f'(xnow+.5Δx,ynow +.5k1)
k3= Δx*f'(xnow+.5Δx,ynow +.5k2)
k4= Δx*f'(xnow+Δx,ynow+k3)

I found the exact solution to be F(t)=700-700*exp(-.1t)

The Attempt at a Solution



i've attached my excel file.

Since Runge-Kutta is al inear technique and I observed all the slopes in RK to be decreasing I predicted that the method would give an over estimate. However, comparing my estimate with the exact solution shows that I infact get an under-approximation. I am completely lost as to why this occurs and am looking for some explanation. I do not feel i am able to say anything about the concavity given my results that the decreasing rates (f'1, f'2 et cetera on my excel) gave an underestimation.

futher, how exactly does putting weight on the k2 and k3 terms affect the approximation?




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Attachments

  • RK approximation problems.xlsx
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  • #2
RK4 is not a linear technique. Where did you get the idea that it is?
 
  • #3
doesnt it assume that rate of change is constant over an interval of time? that to me says linear. but nonetheless still don't know what to say about the concavity or the reason why I got an underestimation.
 
  • #4
dwdoyle8854 said:
doesnt it assume that rate of change is constant over an interval of time? that to me says linear.
No. That's the whole point of evaluating the derivative four times during one step. The "4" in RK4 is short for fourth order. RK4 essentially comes up with a fourth order polynomial for each step.
 
  • #5
okay, so since its a polynomial, is there anything that can be said about whether we expect it to over/under approximate?
 
  • #6
It's not some random 4th order polynomial. It's a very specific 4th order polynomial. Which is it? What does that tell you about the error? This is homework, so at this stage I'm leaving the rest up to you.
 
  • #7
to be honest, i have no idea what you are hinting at with the whole 4th order polynomial thing. I don't know what that has to do with error, or what precisely you mean by a 4th order polynomial.

this is more of a discovery project than a homework. Its graded sure, but its purpose is to explore.
 

FAQ: 4th Order Runge-Kutta method and over/under estimates

What is the 4th Order Runge-Kutta method?

The 4th Order Runge-Kutta method is a numerical method used to solve differential equations. It is an iterative process that calculates the value of a function at a given point by taking into account the slope at multiple points within a small interval. This method is commonly used in scientific and engineering applications.

How does the 4th Order Runge-Kutta method work?

The 4th Order Runge-Kutta method works by approximating the solution of a differential equation at a given point using four intermediate values. These values are calculated using a weighted average of the slope at the beginning of the interval, the slope at the midpoint, and the slopes at two other points within the interval. The final value is then calculated using a weighted average of these four intermediate values.

What are the benefits of using the 4th Order Runge-Kutta method?

The 4th Order Runge-Kutta method is a highly accurate and efficient method for solving differential equations. It is a higher-order method, meaning that it has a smaller error compared to lower-order methods such as Euler's method. It also allows for larger step sizes, reducing the computational time required to solve a problem.

How does the 4th Order Runge-Kutta method handle over/under estimates?

The 4th Order Runge-Kutta method can sometimes overestimate or underestimate the solution of a differential equation. This can happen when the function being approximated has a steep slope or a large change in value within a small interval. To address this, the method uses a weighted average of the slopes at multiple points within the interval, resulting in a more accurate approximation of the solution.

Can the 4th Order Runge-Kutta method be applied to any type of differential equation?

The 4th Order Runge-Kutta method can be applied to most types of differential equations, including first-order and higher-order differential equations. However, for certain types of equations, such as stiff equations, other methods may be more suitable. It is important to consider the properties of the equation and the desired level of accuracy when choosing a numerical method to solve a differential equation.

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