building adams models with less-than-optimal...

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

2


• 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

3


• 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

4


• 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

5


• 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?

6


• 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!

7


What are good Data?

8


• 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

9


• 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

10


• 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


• 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

12


• 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

13


• 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


Parameter Identification Methods

15


Characterizing Friction An Example

16


• 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

17


• 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

18


• 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

19


Setting Up a Test

20

Material

#1

Material

#2

Forcer


• 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

21


Comparing Model to Test Data

22

Velocity



23

Load



24

Displacement


• 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

25


After Tuning

26

Displacement


After Tuning

27

Displacement


• 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

28


• 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

29


Q & A

30

MSC Software Confidential 31

building adams models with less-than-optimal...

Documents