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MSC Software Confidential MSC Software Confidential
Building Good ADAMS Models
From Not-So-Good Data
Dr. Andrew Elliott
MSC Software Services Group
7 May 2013
MSC Software Confidential
• USAF veteran - fixed and
rotary wing pilot, flight test
engineer.
• ADAMS user since 1988!
• Began at MDI in 1990 in
Solver PD group.
• Joined Consulting Services
in 1994; there ever since.
• Still think ADAMS is almost
magic!
Introduction
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MSC Software Confidential
• Problematic Interesting Modeling Jobs
• Design Data, Test Data, Computational “Data”
• Method 1 – Data Fitting
• Method 2 – Model Parameterization & Tuning
• Conceptual Example
• Summary
• Q & A
Outline
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MSC Software Confidential
• Often for a project, you will be very familiar with the operation of
the system being modeled, and may have a good understanding
of all the salient underlying physics.
– In these cases, direct modeling of the important features can be
done by adding the required modeling elements and simplified
equations of that system to the ADAMS model.
• But sometimes, you may not be very familiar with the operation
of the system, and/or may not have a complete understanding of
the salient underlying physics.
– In all cases, useful DATA are the things you need the most.
“Interesting” Modeling Jobs
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• Current trend in structures and fluids is to use defined geometry
and material properties to build massive models, and then let the
code grind through the constitutive equations to an answer.
– Appears to give results from “first principles”, although in fact many
assumptions are hidden in the codes.
• The ADAMS modeling paradigm is rather different.
– No assumptions are made with respect to mechanical dynamics,
– But most of the ancillary process physics are missing, and must be
added by the modeler.
– Requires intelligent abstraction and appropriate simplification of
sometimes very complex phenomena.
Interesting Modeling Jobs
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MSC Software Confidential
• Pneumatics / Hydraulics / Controls
• Surface Contact
– Impact, Friction, Lubrication
• Structural Compliance
– Linear, Nonlinear, Failures
• External Loads
– Environmental (aero/hydrodynamic, electromagnetic, etc.)
– Directly Applied
• Other?
What “Ancillary” Process Physics?
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• In cases where direct modeling is not feasible, or needs some
“tuning”, it is often still possible to build a very useful ADAMS
model by one of two useful methods:
1. If there are “good” data describing the process, which span the
operating envelope of the model, we can use table look-up or
multi-dimensional spline fitting methods.
2. If there are not good data, or not enough of it, we can make
some submodel(s) of the process(es), based on what part of the
physics we do understand, and parameterize the various
contributing factors. Then we make best fits for those
parameters using whatever data we can get.
Data, Data, Data!
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What are good Data?
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• Design Data
– Geometry data from CAD, drawings or sketches
– Specification data from handbooks, vendors, etc.
• Experimental Data (in order of value)
– Testing you observed or was done by someone you know
– Testing that is well documented, whose data are well preserved
– Test data of unknown provenance and/or quality
• Computational Data
– From other commercial or in-house codes
– Vendor supplied
Types of Good, and Not-so-good Data
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MSC Software Confidential
• This approach has the advantage of being mainly based in
experimental results, which usually gets you a consensus
approval for your model.
– It is up to the analyst, however, to determine the whether the data are
“good” or not.
– “No one believes the analysis, except the analyst. Everyone believes the
test data, except the test engineer!”
• Data quality determination is based on:
– Your experience with the laboratory, the testing methods employed, and
the data acquisition and reduction methods used.
– You must also determine if, and how far, the data can be extrapolated if it
does not cover the operating space.
– Engineering judgment is required.
#1 – Fitting Experimental Data
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• Subject of a whole branch of mathematics on its own!
• Various toolboxes in Matlab, both algebraic and dynamic
• Regressions even available in Excel (least squares direct)
• Minimization methods
• Dynamic filtering
Direct Data Fitting Methods
11
Belong more in next section
MSC Software Confidential
• This approach can work very well, or not so well.
• The success you have with modeling a poorly-understood
phenomenon depends directly on the
– the applicability and correctness of the simplifications you make
– on the quality and quantity of the data you do have or can get.
• The success depends inversely on
– the number of parameters you use, and
– on the number of unaccounted-for or unknown parameters left out.
• Engineering judgment is required.
#2 Model Parameterizing & Tuning
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• When using this approach, and planning for new testing to support it,
you can improve your chances of success by reducing, as much as
possible, the complexity of the experiments that get the data.
• Each test should be carefully designed to eliminate as many
unaccounted-for variables as possible, and to reduce the number of
parameters that will be defined by that test.
• Many simple subsystem tests are much more useful than full-up
systems tests.
• Increasing both the quality and the quantity of the data is always
good. For this you must work closely with the test engineers, not just
send over a test plan and hope!
Successful Parameter Identification
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MSC Software Confidential
• Algebraic Methods (off-line)
– Brute Force
– Simplex
– Gauss-Newton
– Levenberg-Marquardt
Parameter Identification Methods
14
• Dynamic Methods (on-line)
– “parameter estimation”
– part of signal processing
and statistics
– can work surprisingly well!
– Kalman-type filters
– sequential Monte Carlo
– hybrid Monte Carlo
MSC Software Confidential
Parameter Identification Methods
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Characterizing Friction An Example
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• Imagine we have two parts in
contact and need to charac-
terize the stiction & friction
forces between them.
• The 1st-principles approach
would be a difficult MARC
model with sliding contact.
– Getting this to work
dynamically over a range of
loads and velocities is not
trivial.
– Surface characteristics are
still just a guess.
Characterizing Friction for ADAMS
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MSC Software Confidential
• We could cosimulate our ADAMS model with MARC (very slowly),
but depending on the application, we might instead make some
reasonable, time-saving approximations.
– We could use the ADAMS surface-to-surface CONTACT element
based on the given geometry. Very simple to implement. Much
faster. Requires friction coefficients and transition velocities.
– We could take advantage of the simple geometry of the problem
and use a 4-corner approach with VFORCEs for both the impact
and the friction/stiction. Still requires values for friction coefficients
and transition velocities. Super fast.
• There is a wide range of other possibilities, too.
Contact Modeling Options
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• In either case, we will have a simplified model compared to the
complete MARC solution, but it will run orders of magnitude
faster and, if properly tuned, may be more accurate.
• These approximations have 4 parameters that have to be fit
– static , dynamic ,Vstiction ,Vfriction
– These can be fit to experimental data if available, or
– They can be fit to “calculated data” from a numerical solution.
• In any case, we will end up with a semi-empirical model.
• Reasonable applicability, i.e. capabilities and limitations, of this
must be determined by the analyst.
• Methods for determining parameter values have to be chosen.
Choosing an Appropriate Model
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Setting Up a Test
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Material
#1
Material
#2
Forcer
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• We ask the lab guys to set up this test, with
– Identical tribological conditions to the real device
– 200 N forcing at 1 Hz
– Measure these quantities:
1. Input Force
2. Box Velocity
3. Box Position
• They say that they have a good forcer, and can get #1 and
#2 pretty easily, but that their string pots don’t have the
reach and response for #3.
• They offer to integrate #2 to get us #3.
Test Set-up
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Comparing Model to Test Data
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Velocity
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Comparing Model to Test Data
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Load
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Comparing Model to Test Data
24
Displacement
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• Method should be appropriate for extracting parameter values
from time history data.
– Kalman filtering?
– Ask test guys to run more test cycles for averaging?
• Be sure to put the final tuned values using the test data
into the model and check that they give good results.
– RMS error is usually a good comparison measure.
– Little DOE of ±5-10% is also a good idea!
• What is a “good fit”?
Parameter Tuning
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After Tuning
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Displacement
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After Tuning
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Displacement
MSC Software Confidential
• In a very similar approach, you can imagine incorporating a
structural beam into an ADAMS model at different levels of
approximation.
– Euler-Bernoulli analytical
– Euler-Bernoulli with shear corrections
– Lumped parameter models
– Flex-imported FEA linear beam elements
– Flex-imported FEA beams with shear/warping corrections
– Flex-imported FEA shell or solid elements
– Cosimulated Nonlinear FEA
• For each case, we will need more parameters defined, more test
data, and applicability still must be determined by the analyst.
A Structural Example
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• The same approach can be applied to many different kinds of
processes or subprocesses.
• The process model chosen should be appropriate for, and
consistent with, the available supporting data, whether generated
from testing or some other more detailed analysis.
• Sometimes to adjust the parameters for best fit, you may need to
use some fancy nonlinear optimization algorithm instead of a
simpler least-squares curve fitting method.
• Methods you develop for this approach will be generally useful
across a range of problems. Specific models – not so much.
• Engineering judgment will always be required!
Summary
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Q & A
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