Buckingham-Pi for "algorithmic" non-dimensionalization

In summary, the conversation discusses using the Buckingham-Pi theorem for "algorithmifying" non-dimensionalization of equations and the challenges the speaker is facing with a more complex problem. They also mention seeking help on a forum and the possibility of using a different method involving defining dimensionless parameters for independent variables. They also provide an example of how to apply this method to a problem and mention how the choice of parameters can be based on boundary conditions.
  • #1
sysidentify
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I would like to use the Buckingham-Pi theorem in order to "algorithmify" non-dimensionalization of existing equations. I can get things to work for very simple problems, but am running into issues with a harder example. I posted my question on physics.stackexchange.com the day before yesterday, but it has received little attention.

Could someone here comment, either here or there?

Alternatively, do you know of existing ways to "algorithmify" non-dimensionalization?
 
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  • #2
The way I learned to do this kind of thing is not by use of the Buckingham Pi Theorem. The method I learned always works great. For each of the independent variables (in this case only the time t), define a dimensionless parameter such as τ = t / t0, where t0 is a characteristic time to be defined in terms of the other parameters in the problem. If there are spatial variables, such as x, define X = x/x0.

In your problem, for example, substitute t = t0τ into your differential equation, and reduce all the terms to dimensionless form (by multiplying or dividing the entire equation by whatever parameters necessary to do this). You will end up with groups involving t0. Select one of the groups involving t0, and choose t0 such that that group is equal to unity. Substitute this expression for t0 into all the other groups involving t0. This will automatically generate the other dimensionless groups involved.

The choice of t0 or x0 can also come out of the boundary conditions of the problem, if that choice makes better sense.

In your problem, the dependent variable is already dimensionless. However, in most problems that is not the case. If the dependent variable is not dimensionless, you can do the same trick with that variable.

Chet
 
  • #3
This gives:
$$τ=\frac{mgLt}{b}$$
$$ε=\frac{m^2gL^3}{b^2}$$

Chet
 

FAQ: Buckingham-Pi for "algorithmic" non-dimensionalization

What is Buckingham-Pi for "algorithmic" non-dimensionalization?

Buckingham-Pi for "algorithmic" non-dimensionalization is a method used to reduce the number of variables in a mathematical model by grouping them into dimensionless groups. This allows for easier analysis and comparison of different systems or processes.

How does Buckingham-Pi for "algorithmic" non-dimensionalization work?

This method uses the principle of dimensional homogeneity, which states that all terms in a mathematical equation must have the same dimensions. By selecting a set of fundamental dimensions (such as length, time, and mass), the remaining variables can be combined into dimensionless groups using the Pi theorem.

What are the benefits of using Buckingham-Pi for "algorithmic" non-dimensionalization?

Using this method can simplify complex equations and make them easier to understand and solve. It also allows for easier comparison of different systems or processes, as the dimensionless groups are independent of the units used to measure the variables.

Are there any limitations to using Buckingham-Pi for "algorithmic" non-dimensionalization?

While this method is useful for reducing the number of variables in a model, it does not always capture all the important physical relationships between the variables. It is important to carefully consider which variables to group together and how to interpret the resulting dimensionless groups.

In what fields of science is Buckingham-Pi for "algorithmic" non-dimensionalization commonly used?

This method is commonly used in physics, engineering, and other fields of science where mathematical models are used to describe physical systems. It is particularly useful in fluid dynamics, heat transfer, and other areas where there are many variables with different units of measurement.

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