Equation for 2D Dose Distribution: Solving for Any Point on the Surface

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In summary, the conversation discusses the need for a formula to calculate any point on a 2d dose distribution surface, similar to determining the equation of a straight line using a few points. Linear interpolation and other functions are mentioned as potential methods, with a Wikipedia article suggested for further information. The distribution is described as representing a dose distribution around a radioactive source, with the possibility of using a polynomial fit for accuracy.
  • #1
wopp
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Hi there,

I have a table with two variables that relates to a 2d dose distribution and need to determine a formula that will solve for any point on that surface. Similar to determining the equation of a straight line with a few points (y = mx +c) to then be able to calculate any point on the line.

For example, what i need to calculate: The dose at a specific angle and distance from a point is...
(each parameter is in relation to a single point).

It will save me a LOT of time and effort looking up values and interpolating between them in work, so any help would be appreciated! Even to point me to a resource that explains the method.

Thank you in advance!
 
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  • #2
How does that distribution look like? You can use a linear interpolation there as well: z=mx+ny+c.
Other functions might be better, and this Wikipedia article could be interesting.
 
  • #3
mfb said:
How does that distribution look like? You can use a linear interpolation there as well: z=mx+ny+c.
Other functions might be better, and this Wikipedia article could be interesting.

Thank you for the quick reply, its much appreciated!

The distribution represents a dose distribution around a radioactive source so its similar to an exponential fall-off. The wiki link you sent me has distributions similar to what I am looking at actually!

Would a type of polynomial fit work for something of that nature?
 
  • #4
If the grid is fine enough, polynomials will always fit to any reasonable distribution.
You could try to include your physics model (exponential, 1/r^2-law, ...) in the functions to get more accuracy even with less grid cells.
 

FAQ: Equation for 2D Dose Distribution: Solving for Any Point on the Surface

What is an equation with two variables?

An equation with two variables is a mathematical expression that contains two unknown quantities or variables. These variables are usually represented by x and y and can take on different values, resulting in different solutions for the equation.

How do you solve an equation with two variables?

To solve an equation with two variables, you need to find a value for each variable that makes the equation true. This can be done by using algebraic methods such as substitution, elimination, or graphing. By finding the intersection point of the two lines on a graph, you can determine the values for both variables.

Can an equation with two variables have more than one solution?

Yes, an equation with two variables can have infinitely many solutions. This is because there are an infinite number of possible values for each variable that can make the equation true. However, there are also cases where an equation with two variables has no solution, meaning that there is no possible value for either variable that makes the equation true.

What is the purpose of using two variables in an equation?

The use of two variables in an equation allows us to represent relationships between two quantities. By manipulating the equation and solving for one variable in terms of the other, we can better understand the relationship between the two variables and make predictions about their values.

Can an equation with two variables be graphed?

Yes, an equation with two variables can be graphed on a coordinate plane. The x and y values of each solution can be plotted as points, and a line can be drawn through these points to represent the relationship between the two variables. This can help us visualize the solutions and understand the behavior of the variables in the equation.

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