Comparing theory predictions with practical results of mass/spring/damper system

[Jowin86]

Homework Statement

Compare theoretical predictions for frequency response of a mass spring damper system with analytical test data.

1.) Carry out free response tests and use data to calculate damping ratio, damped and natural frequencies and damping coefficient and spring stiffness.

2.) model the system and derive equation of motion, put into standard form and use solution to show which equation to use for magnification factor.

3.) plot theoretical frequency response graphs

4.) carry out forced response tests and use data to calculate magnification factor, frequency ratio and phase lag.

5.) plot the forced tests on the same graph as theoretical.

6.) compare the two

Homework Equations

lots...

The Attempt at a Solution

I have completed all of the above. My query is that when plotting the two sets of data on my graphs i find that the magnification factor values for the test data are a LOT lower than the theory. I was expecting a bit lower because the theory doesn't account for friction etc but not this much lower.

So my question i guess is: When comparing practical with theory is it usual for the two to differ greatly or just marginally?

 

[anathema]

it is important to always compare theoretical predictions with experimental data in order to validate the accuracy of the theory. In the case of a mass spring damper system, it is expected that there will be some differences between the theoretical and experimental results, as the theoretical model does not always account for all real-world factors such as friction and other sources of energy loss.

However, the extent of the differences between the two sets of data should not be significant. If the magnification factor values for the test data are a lot lower than the theoretical values, it is important to carefully examine the experimental setup and data collection methods to ensure that there are no errors or sources of variability that could be skewing the results.

It is also important to consider the limitations of the theoretical model and whether it accurately represents the real-world system being studied. If the theoretical model is not a good fit for the experimental data, it may be necessary to revise the model or consider alternative explanations.

Ultimately, the goal is to have the theoretical and experimental results closely match each other, within a reasonable margin of error. Any significant discrepancies should be carefully investigated and addressed in order to improve the accuracy and validity of the theoretical model.