Model order in curve fitting
Model order in curve fitting
(OP)
Hi,
Can someone suggest how to decide the model order (or size) while curve fitting in modal analysis? As the curve fitting model order is increased, number of stable poles are increasing. But as some of these are computational modes, I want to know if there are any thumb rules or guidelines in selecting model order for a given type of structure - say lightly damped, heavily damped, etc. What is the minimum order one usually starts with ?
Thank you.
Kind regards
Geoff
Can someone suggest how to decide the model order (or size) while curve fitting in modal analysis? As the curve fitting model order is increased, number of stable poles are increasing. But as some of these are computational modes, I want to know if there are any thumb rules or guidelines in selecting model order for a given type of structure - say lightly damped, heavily damped, etc. What is the minimum order one usually starts with ?
Thank you.
Kind regards
Geoff





RE: Model order in curve fitting
A typical approach 20 years ago would have been to calculate the mode indicator function, and use roughly twice the number of modes it suggested.
LMS has a nice plot of modal frequencies vs number of modes which is handy.
Way back when I would page through every response, identify the frequency of every peak, and use that table.
Cheers
Greg Locock
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RE: Model order in curve fitting
I am using LMS only. Can you please be a bit more descriptive about the twice the number of modes concept?
I am attaching here a file with two stabilisation diagrams with 3 zones marked. As you can see in zone 1 it shows stable mode (from lower order of modes) though there is no visible droop in mode indicator function curves (top two curves). So should I select it or not?
And in zone 2 mode gets stabilised only after model size of 72.
In zone 3, even though there is a clear peak, the mode gets stabilised only after 80 modes.
This makes me think if there is an optimum or rule of thumb on number of modes (model size in curve fitting) which we should restrict ourselves.
Thank you.
Geoff
RE: Model order in curve fitting
can you do a plot of the whole frequency range, and 0-50 modes, and MI2 and MI3 (imganariness and gross amplitude respectively) all overlaid.
Cheers
Greg Locock
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RE: Model order in curve fitting
Thank you. I think I didn't understand exactly what you asked for. However I am attaching a picture with frequency range up to 6000Hz and model size 50 modes. And Imaginary MIFs overlaid. And bottom curve is the amplitude of one of the drive point FRF. Please note that I acquired FRFs up to 16kHz to find certain very high frequency modes of interest. But at the moment I am concentrating only up to 6 kHz.
Is this what you asked ? If not, please tell me what you meant.
As you can see, with model size 50 only a few are picked as stable modes though visible you can see many peaks in FRF.
Kind regards
Geoff
RE: Model order in curve fitting
Cheers
Greg Locock
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RE: Model order in curve fitting
Thank you for this. Any hint how you estimated that 90-120 ?
Kind regards
Geoff
RE: Model order in curve fitting
It's not very clear what is the benefit to use an algorithm to detect the modes, because you see them!
Why don't you use a pick peak method to find the modal parameters, then as Greg said, you can tune this parameters by matching the experimental curve with the resynthetised one.
I use two very powerful Excel files to do this :
Pick peak method
Fitting method
You can try them if you want, they are free.
Regards
Amanuensis
RE: Model order in curve fitting
As I understand, peak picking is a SDOF (Single Degree of Freedom) method. You can do that if you have few response curves with few modes and that to widely separated. In addition, you will not be able to capture all modes of a structure in every FRF. Reasons are many - the accelerometer might be situated at a nodal point for a particular mode, participation factor may be very weak for some modes, etc.
MDOF (Multi DOF) methods are powerful in the sense that they fit many FRFs at a time. Even with MDOF methods, you need assistance of tools such as MIF (Mode Indicator Function) to be certainly able to tell if there is a genuine mode or it is a computational mode. Or repeated roots (symmetric modes), etc.
Peak picking is ok for initial checks during running the test for having a quick look at the mode shapes which makes you ascertain if your set up is right or not like your accelerometer direction is ok,etc.
Geoff
RE: Model order in curve fitting
Actually, till today, I had never heard about the MIF. Yet, it seems necessary to know linear algebra to understand the meaning of this indicator...
RE: Model order in curve fitting
How many points are in your survey? It might be worth putting together a subset of the interesting and important ones, and just using those to identify frequencies.
Cheers
Greg Locock
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RE: Model order in curve fitting
There are total 144 points! How to identify subset of important ones ?
For e.g. please find attached picture of casing which you might have seen earlier in two previous posts. Now looking at the picture, which points do you want me to create subset? I want to capture axially 2nd order modes and circumferentially up to 16th order modes (due to some interest in frequency). That is circumferential profile with 16 petals. On safer side I made it 18. Hence total 36 points. So highest frequency worst case would be like \/\/\/ shape along the circumference with one point at each extreme.
Kind regards
Geoff
RE: Model order in curve fitting
In this case I'd just take 10 points randomly scattered over the body, prepare modal indicator functions from them, and then choose another 10 and repeat.
If they throw up common frequencies, great, if not then you just have to start overlaying each response and seeing what is going on (which is the best way anyway)
The way I'd prefer to examine the high frequency modes of that thing is with a remote sensing technique such as laser doppler, and a sine sweep.
Cheers
Greg Locock
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RE: Model order in curve fitting
In my opinion, a test with the structure with free-free boundary conditions would be more appropriaite. Indeed,the structure is so simple that it's easy to calculate its modes by finite elements method. Then you can compare the both.
RE: Model order in curve fitting
Cheers
Greg Locock
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RE: Model order in curve fitting
I'm french, so please forgive the English mistakes I can make.
RE: Model order in curve fitting
RE: Model order in curve fitting
To be honest I haven't run a large scale modal in 9 years or more, so I'm certainly not up on current techniques.
Cheers
Greg Locock
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RE: Model order in curve fitting
Thank you for your inputs. I did FE modelling as well simulating the boundary condition with some approximation which would affect damping mostly like bolted joints replaced by rigid node connections. But the thing is FE predicts 106 modes within the frequency range of interest and I am curve fitting clear stable modes more than 400 within the same range. I am really wondering where from these many modes are coming, all seems to be genuinely stable.
I don't know that GA on MIFs as well. I will look at it sometime. Sounds interesting.
Kind regards
Geoff
RE: Model order in curve fitting
I notice you haven't mentioned much what is happening below 800 hz, if you don't get good correlation at low frequencies then it is unlikely you'll get good correlation at high frequency.
Cheers
Greg Locock
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RE: Model order in curve fitting
Though I didn't make a plot, I checked in FE and EMA. In the range 1200-2200 Hz, FE predicts 23 modes, EMA fits 95 modes. All seem to be genuinely stable. I used model size of 90. Considering that I am using POLYMAX routine in LMS to fit the modes, there is very less chance of computational modes as it automatically excludes them.
The fundamental mode is at 1290Hz. So I am not looking at below 1200. Of course I can't see any experimental modes also.
Thank you.
Kind regards
Geoff
RE: Model order in curve fitting
Cheers
Greg Locock
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RE: Model order in curve fitting
N=(L/2k)*(f/fr)*{1+[(pi/2)/((f/fr)^0.5+0.5*(f/fr)^3.5)]^4}^1/4
where
L: length of cylinder
k=h/(12)^0.5 is radius of gyration and h is thickness of cylinder
f is the frequency range
fr = c/(2*pi*r) is the ring frequency, c is longitudinal wavespeed and r is radius of cylinder.
pi = 3.14159...
RE: Model order in curve fitting
Amanuensis, Now what are the boundary conditions which apply to the equation. I mean free-fre or clamped free, etc.
And what value to input for f, if I want frequencies from say 1200 - 2200 Hz? Is it 1000? But then this can be 1000 Hz anywhere. Or do you mean the starting frequency of the band is fr ? Please elaborate a bit, if it's not a problem.
Thank you.
Kind regards
Geoff
RE: Model order in curve fitting
It's a good point you raise here. Indeed, there are several limitations in the use of this formula.
Only radial modes on a simply-supported, thin-walled cylinder are considered here (no in-plane compressional modes, no torsional modes, and so on).
But the advantage of an analytical model (compared to a numerical model) is to give you information about the behavior of the structure: namely, the cylinder mode count changes character around the ring frequency.
It might be interesting to see whether or not this ring frequency can be identified on both your experimental and numerical results.
It's certainly more efficient to try to find out the ring frequency (a property of this kind of structure) than applying foolishly this formula.
RE: Model order in curve fitting
Thank you for your response. I am really curious to know what you are saying. I really didn't get it right. I made the calculation as per the formula you gave. I got ring frequency 4477.56 Hz and mode count 60.86. Now what does these figures mean I have no idea.
As I asked earlier where does this mode count apply? I mean I chose 1000 Hz as frequency range. Does my above result mean that there are 60 modes around 4477 Hz within 1000Hz frequency range? Can you please give the reference of the book where this formula is discussed?
Thank you.
Kind regards
Geoff
RE: Model order in curve fitting
Theory and application of Statistical Energy Analysis by Richard LYON(page 143 of the second edition of the book).
Actually, this theory (S.E.A.) uses the modal density as an input parameter for estimating the energy response of complex structure.
So in this book, mode counts and modal density are extensively discussed.
For my understanding, the ring frequency means that :
At greater frequency than the ring frequency, the mode count of a cylinder is the same as the one of a flat plate of equal surface area : the waves don't see the curvature of the cylinder.
Conversely, at lower frequency than the ring frequency, the mode count of a cylinder is very different from the one of a flat plate.
Last point. For the frequency range, it can be relevant to use the 1/3 octave frequency bands. Then you can plot the mode counts versus 1/3 octave frequency bands.
RE: Model order in curve fitting
This is a diagnostic, not an absolute aid.
Cheers
Greg Locock
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RE: Model order in curve fitting
Thanks for your replies. I was hoping to get hold of that Lyon's book before responding. But I couldn't. Once I get it I will do what you suggested and if something interesting I will post it here.
Kind regards
Geoff
RE: Model order in curve fitting
Lyon found out an amazing result in the 1960's!
He revealed a hidden relation between the lack of information and the irreversibility between two coupled modes!
His result is comparable to the Boltzmann's H-theorem which explains the irreversibility through the assumption of molecular chaos.