Consequences of Choosing Incorrect Variables in BuckinghamPi

In summary, the Buckingham Pi Theorem states that for n variables with k dimensions, the number of dimensionless quantities is n-k. This can simplify experiments when the relationship between variables is unknown. However, it is important to correctly identify relevant variables as any mistakes can change the number of dimensionless quantities and affect modeling. The theorem does not require all dimensionless combinations to be used in the function, it simply states that any physical relationship can be written as a function of n-k dimensionless parameters.
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This question arose as I was studying mathematical modeling in fluid mechanics. It was posted to math.stackexchange, but there a was a lack of response, probably due to the applied nature of the problem.

One form of the Buckingham Pi Theorem says that for nn variables with kk dimensions, the number of dimensionless quantities (or pi groups) is n−kn−k. This theorem is often applied when the relationship between the set of variables are unknown, and by using the fewer dimensionless quantities, experiments can be simplified to determine the relationship between the set of variables.

However, the first step is to determine which variables are relevant (and hence the number of variables nn). But what if we make a mistake and identify an extra variable that is not relevant? Or if we mistakenly miss variables that are relevant? Then, n-k will be different and the number of dimensionless numbers will change, which should not be the case in modelling.

For example, if we wish to relate the time traveled by a car (https://en.wikipedia.org/wiki/Buckingham_π_theorem#Speed) to some other variables. If in addition to the velocity of the car and distance traveled by the car, we also identify the distance a squirrel has traveled, we will get:
n=4n=4
k=2k=2
Number of dimensionless variables = 4−2=24−2=2, which is not the case.

Does the Pi Theorem require us to identify the correct variables, or does it indicate somehow we have misidentified the relevant variables?
 
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I think you are over-interpreting the theorem. It states that any physical relationship between n physical quantities and k physical dimensions may be written as a dimensionless function of n-k dimensionless parameter combinations. This does not imply that the function must depend on all of the dimensionless combinations, a constant function is also a function.
 

1. What is BuckinghamPi and why is it important in scientific research?

BuckinghamPi, also known as the Buckingham Pi theorem, is a mathematical concept that helps scientists understand relationships between physical variables in a system. It is important in scientific research because it allows scientists to identify and eliminate redundant variables, simplifying complex systems and making it easier to analyze and understand experimental data.

2. How do incorrect variables affect the accuracy of a BuckinghamPi analysis?

Incorrect variables can significantly impact the accuracy of a BuckinghamPi analysis. If incorrect variables are included in the analysis, it can lead to incorrect relationships and conclusions being drawn from the data. This can ultimately result in flawed scientific theories or models.

3. What are some common consequences of choosing incorrect variables in a BuckinghamPi analysis?

Some potential consequences of choosing incorrect variables in a BuckinghamPi analysis include inaccurate predictions, incorrect relationships between variables, and the inability to fully understand the system being studied. This can also lead to wasted time and resources in the research process.

4. How can scientists prevent or minimize the impact of incorrect variables in a BuckinghamPi analysis?

To prevent or minimize the impact of incorrect variables in a BuckinghamPi analysis, scientists should thoroughly understand the system being studied and carefully select variables based on their physical significance and relevance to the research question. It is also important to validate the chosen variables using experimental data and to constantly evaluate and adjust the variables as needed.

5. Are there any limitations to the BuckinghamPi theorem?

While the BuckinghamPi theorem is a valuable tool in scientific research, it does have its limitations. It assumes that the variables being analyzed are dimensionally homogeneous, which may not always be the case. It also cannot account for nonlinear relationships between variables. Therefore, scientists should use the BuckinghamPi theorem with caution and consider other methods in cases where these limitations may affect the accuracy of the analysis.

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