Altair's Student Guides - Instructor's Manual - CAE and Design Optimization - Advanced

Student Project Summaries CAE and Design Optimization - Advanced

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Contents

Introduction.............................................................................................2

Installation Instructions: .......................................................................2 Sensitivity To Support Stiffness .................................................................3

Description of the Problem ....................................................................3 The Analysis Model ...............................................................................4 Design of Experiment............................................................................5 Further Work........................................................................................6 Summary .............................................................................................6

Multi-objective Optimization – Battery Tray................................................8 Description of the Problem ....................................................................8 The Analysis Model ...............................................................................9 Design of Experiment............................................................................9 Further Work...................................................................................... 11 Summary ........................................................................................... 11

Optimization With Load-Redistribution ..................................................... 13 Description of the Problem .................................................................. 13 The Analysis Model ............................................................................. 14 Design of Experiment.......................................................................... 14 Further Work...................................................................................... 16 Summary ........................................................................................... 17

Geneva Mechanism – Effect Of Friction.................................................... 18 Description of the Problem .................................................................. 18 The Analysis Model ............................................................................. 19 Design of Experiment.......................................................................... 19 Further Work...................................................................................... 21 Summary ........................................................................................... 21


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Introduction This material is best used after reading the book CAE And Design Optimization - Advanced.

Access to HyperWorks software is not essential for you, the instructor. Of

course, if you choose to solve the problem yourself before working with your students, you will need HyperStudy, HyperMesh and OptiStruct.

This book describes 4 assignment problems that highlight different applications of HyperWorks. Each problem is independent, and is complete

in itself. Students may choose to do more than one, depending on their

interest.

To make best use of this material you will need a computer with a sound-

card and speakers. Your computer should have a media-player programme

(such as Windows Media Player) and an Internet Browser that supports JavaScript. The material can be copied to a server and accessed by clients.

You can customize the HTML files to suit your

requirements. After opening the file, double-

click on any text to edit it. Use the save changes link on the left of your Browser window when

you are finished.

Installation Instructions: 1. Copy the folders to your computer or to your server. If you are

working on a server, it is a good idea to set the folders to “read only” to prevent inadvertent modifications.

2. The videos are best played in full-screen at a resolution of 1024 x

768. You may need to install the CamStudio Codec to view video on your computer. To do this, right-click on the file camcodec.inf and

choose Install from the popup menu. You may need administrator privileges to do this.

3. Ensure that JavaScript is enabled on your browser.

4. Each folder contains one HTML file. Double-click on it to open the instructions.

5. Data files are provided as relevant – IGES files, HM files, etc.

6. In case you need support, contact your distributor or email

[email protected]


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Sensitivity To Support Stiffness Areas covered:

• Construction of an FE Model for thermo-mechanical analysis

• Creation of a Template for Study-construction • Design Of Experiment • Design Space Exploration

Software used: • HyperStudy • HyperMesh • OptiStruct/Analysis

Description of the Problem A design proposal has been received for a subassembly, and the task is to simulate its

performance. The screw-shaft subassembly,

which fits into a housing, is manufactured to

very close tolerances. The dimensional

accuracy is measured in 10s of microns.

In the test chamber, the complete assembly is cooled to -40 degrees Kelvin and tested. It is

subsequently raised to 135 degrees Kelvin and

tested. The designer needs to know whether the sub-assembly will function correctly.

A standard Finite Element approach is to

analyze the component assuming the

surrounding housing is rigid. How reasonable is this assumption? Can we gain an insight into

the impact of the housing's flexibility, without modeling the housing itself?

If the behavior of the shaft and plugs is not

sensitive to the stiffness of the supports, then

the analyst can repose a high degree of confidence in the FE model. If this were not

the case, it would be prudent to suggest that the analysis would be more reliable if the

housing too were supplied for analysis.


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The Analysis Model A numerical experiment can be quite

expensive. A good high-fidelity model is

always preferable to ensure that returns justify

the investment. Refer to the the assignment

that accompanies A Designer's Guide To Finite Element Analysis for a detailed

description on how to create a hexahedral

mesh of the shaft and the plugs, and apply loads and restraints.

In this assignment, our focus is not on

creation of the high-fidelity model: our focus is

on setting up and interpreting the results of the experiment.

This assignment starts with an IGES file

containing the quarter-model of the shaft. A relatively coarse FE mesh that uses the less

accurate tetrahedral elements is constructed.

Also, the plug is omitted in this coarse model.

A local cylindrical coordinate system is used to generate spring supports on the circumference

of the mating face, and one end of each

spring is “grounded”. The stiffness assigned to the springs is a part of the experiment,

since we want to study the impact of varying the support stiffness.

Remember that computer models treat "zero"

and "infinity" differently because of finite

precision. Since computers use finite precision arithmetic, we vary the levels between a small

value and a large value, rather than using zero and infinite stiffnesses.

The restraints, temperatures, loads and subcase definition are applied, as in the

assignment named above – Thermo-Mechanical Analysis Of Screw Shaft.

An analysis is performed and deformation


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viewed, to ensure that the behavior of the model with spring supports is

correct.

Design of Experiment This assignment involves exploration of the Design Space. The FE model addresses "ideal" conditions - where the supports are infinitely rigid. Our

task is to gain in insight into how the component will behave if the housing is not

infinitely rigid.

Since the data for the housing is not available,

the analyst has no way to include it in the analysis. Instead of glossing over the

ambiguity, we devise an experiment that

allows us to investigate the impact on the model.

Since the Study requires data that cannot be

accessed using the ready-made template, we use the Templex programming language to

extract data we need. In this approach, we

use an initial "master" data set - in this case

the "fem" file generated by HyperMesh - to

define the changes we want for each evaluation required by the experiment. Once

the changes are defined, HyperStudy

generates a different data set (or "deck") for each set of levels that we choose for the

factors. In this case, we vary the stiffness of the springs - we define the upper and lower

bounds for the stiffnesses. The levels are set when we choose the experiment design.

Once the nominal run has been executed, we

use the results-translators to extract results

from the binary files generated by OptiStruct / Analysis. The Expression Builder gives us ways

to build a wide range of responses.

Since each spring element has its stiffness

assigned individually, we link the design variables. This way, when the levels are varied

as a part of the experiment, the springs all


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change together - reflecting the radial symmetry.

There are no black-and-white rules that define how many levels to use for each factor, or to decide which type of experiment to use. We use the

Fractional Factorial design for the controlled factors and the Central Composite design for the uncontrolled factors.

After performing the analyses, the results must be extracted from the results files. That is, the output files should be examined for the values of the

responses, and these should then be tabulated and graphed. The tedium of this task is eliminated by the elegant results-translation performed by

HyperStudy. This leaves the designer free to focus on interpreting the

results.

Since this is an experiment intended to explore the Design Space, the main focus is on understanding the "Main" effects - that is, the variation of

responses with the controlled parameters.

Further Work The experiment offers an excellent way to tackle, in a rigorous fashion, the

many assumptions implicit in an FE analysis.

You may choose to assign further investigations to your students based on their level of proficiency on statistics and design, the time available, etc.

Some of the areas include • the use of an approximation for the contact analysis

• a study of possible alternate steel-alloys for the plugs, to see how a different thermal expansion coefficient affects the design

• use of a Monte Carlo method to build a confidence level

Summary By the end of this assignment, the student will know how to

• import IGES files • use the Model Browser

• zoom, pan and rotate

• create collectors for materials, elements, forces and restraints

• use consistent units

• build hexahedral meshes

• use local coordinate systems

• create and use spring elements

• check for different types of element-edges - free, shared, etc. • fill and stitch surfaces


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• perform a thermo-mechanical analysis

• plan and setup an experiment for Design-Space Exploration

• create and troubleshoot a Templex template

• link variables in an experiment

• plot stress contours • view deformed shapes

• view the effects of design variables on design responses


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Multi-objective Optimization – Battery Tray Areas covered:

• Construction of FE Models for static and normal-modes analyses

• Creation of a Template for Study-construction • Design Of Experiment • Trade-Off Study


Description of the Problem A design for the tray to hold the battery of an

automobile has been proposed. The basis for the model is a study of earlier designs. The

Instructor's Manual that accompanies the book

CAE And Design Optimization - Basics

addresses optimization for FRF response.

The designer's have another query too: can

they reduce the maximum deformation as much as possible, while raising the minimum

frequency as much as possible?

In other words, both the responses -

maximum-deformation and base-frequency - are to be treated as objectives. One should be

minimized while the other should be

maximized.

We use two different finite element models - one for static analysis, under the dead-weight

of the batteries and the other for the normal-modes calculation. A numerical experiment is

constructed to sample the design space, and

an approximation built from the results of the DOE. A trade-off study is then used to choose

the optimal combination of the design variables for the two objective


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The Analysis Model A numerical experiment can be quite

expensive. A good high-fidelity model is

always preferable to ensure that returns justify

the investment. In this assignment, our focus

is not on creation of the high-fidelity model: our focus is on setting up and interpreting the

results of the experiment and the subsequent

trade-off study.

This assignment starts with an IGES file containing a simplified geometry of the tray,

though you should ask your students to work

with the detailed model to better learn the issues involved in building FE models from

CAD geometries.

Two different models are used for the FEA - one for static analysis and the other for the

normal modes calculation. The first can take

advantage of symmetry, and is likely to require a finer mesh. Before proceeding with

the numerical experiment, your students should perform convergence studies to ensure

that their models are adequate.

The battery is modeled as a dead-weight, that

is, under the affect of gravity. SI units are used to ensure that calculations are

consistent.

Design of Experiment Since we have a Finite Element model

available, we can invoke HyperStudy from

HyperMesh. This gives us access to several model parameters. We can also invoke

HyperStudy as a standalone application. Each has its advantages. If the Study requires data

that cannot be accessed using the ready-made

template, we can use the Templex programming language to extract data we

need.


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In this approach, we use an initial "master"

data set - in this case the "fem" file generated

by HyperMesh - to define the changes we want for each evaluation required by the

experiment.

Once the changes are defined, HyperStudy

generates a different data set (or "deck") for each set of levels that we will choose for the

factors. In this case, we vary the thickness of the sheet-metal. We define the upper and

lower bounds for the thickness. The levels will

be set later when we choose the experiment design. Since sheet-metal comes in standard

thicknesses, it does not make sense to treat the design variables as continuous.

Accordingly, we define the discrete thicknesses that should be used for the

experiment.


use the results-translators to extract results from the files generated by OptiStruct /

Analysis. The Expression Builder can build a

wide range of responses - we are interested in the deformation at selected nodes.

Since the thickness of the sheet-metal must

vary consistently for both analysis models, we link the variables between the two FE models.

There are no black-and-white rules that define

how many levels to use for each factor, or to

decide which type of experiment to use. We use a Central Composite design to study the

behavior. Your students should be encouraged

to evaluate other studies, so that they clearly understand the advantages and disadvantages

of various designs. 11 levels for each factor means 121 runs are required for each FE

model for a full factorial! The rigorous

statistical approach allows the problem to be brought under control in terms of the time

involved.


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After performing the analyses, the output files should be examined for the

values of the responses, and these should then be tabulated and graphed. The tedium of this task is eliminated by the elegant results-translation

performed by HyperStudy. This leaves the designer free to focus on interpreting the results.

Your students should be encouraged to use HyperStudy to perform more than one experiment! The results of the first experiment should help cast

light on the various assumptions implicit in the model. Ensure that your students can enumerate the assumptions - for instance the nodes at which

response values were tracked - and use the software to enhance their

understanding of the behavior of the component.

Recall the importance of a trade-off study - it should be usable in a design review meeting to quickly answer questions from the designers.

HyperStudy provides three different types of approximations. We start with

the Least-Squares Regression (LSR) model, and go on to see how good it is.

We review the scatter plot and conclude that the approximation is not as

good is it should be. Your students should review the relationship between

the number of runs involved in the DOE and the permissible approximations.

The trade-off study is performed in real-time, interactively, illustrating the

impact of variable-changes on the responses. We review the ANOVA plot to understand which design variables have an effect on the responses.

Further Work he assignment illustrates how to use two separate FE models to generate different objectives, and study the impact of the design variables on these.

You may choose to assign further investigations to your students based on

their level of proficiency on statistics and design, the time available, etc.

Some of the areas for deeper research include

• the use of the FRF results to estimate the maximum deformation

• addition of stress as an objective

• an investigation into the use of laminated composites for the battery-tray


• import IGES files


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• use the Model Browser















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Optimization With Load-Redistribution Areas covered:

• Construction of an FE Model with gravity loads • Design Of Experiment • Construction of an Approximate Model • Optimization with change in applied loads


Description of the Problem A housing, used to hold an actuator, is the subject of this optimization study. The

Instructor's Manual that accompanies the book CAE And Design Optimization - Basics

addresses optimization for failure-stress, with

a changed material.

That analysis assumed that the "internal" weights - of the actuator electronics, etc. -

could be taken as "given data". The designers, however, have another query.

Since some leeway is available in designing and mounting the "internals" themselves,

should they redistribute these weights at the same mounting points? Will it reduce stress?

Optimization techniques such as topology optimization cannot change the boundary

conditions - loads and restraints. In this case, we explicitly need to study the effect of

changing the loads. Going one step further, we need to suggest the best distribution. That is,

we need to suggest the "optimum"

distribution.


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The Analysis Model A numerical experiment can be quite expensive.

A good high-fidelity model is always preferable

to ensure that returns justify the investment. In

this assignment, our focus is not on creation of

the high-fidelity model: our focus is on setting up and interpreting the results of the

experiment and the subsequent trade-off study.

This assignment starts with an IGES file

containing a simplified geometry of the housing, though you should ask your students to work

with the detailed model to better learn the

issues involved in building FE models from CAD geometries.

A coarse tetrahedral mesh is constructed, and

concentrated masses created to simulate the weights of the "internals". We choose a point

roughly midway between the "pairs" of bolt

holes to monitor the stress. Your students should use their converged-model to pick the

elements which are better indicative of the effects - remembering, of course, that stress

decays fairly rapidly as you go away from the

point of application of load.

Design of Experiment Since we have a Finite Element model available,

we invoke HyperStudy from HyperMesh. This gives us access to several model parameters.


use the results-translators to extract results

from the binary files generated by OptiStruct / Analysis.

The Expression Builder gives us ways to build

a wide range of responses - for our design,

however, we are interested only in the stress at selected locations. The "pairs" of bolt-holes

should be loaded such that the total weight bolted at the pair is the same. Remember that


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the designers can change the distribution of

weight between the bolt holes, not the actual

weight itself. We link variables to establish this behavior.


how many levels to use for each factor, or to

decide which type of experiment to use. We use a Central Composite design to study the

behavior. Your students should be encouraged to evaluate other studies, so that they clearly

understand the advantages and disadvantages

of various designs.

Your students should appreciate the importance of DOE - the rigorous statistical

approach allows the problem to be brought under control in terms of the time involved.

There are no uncontrolled factors in the

experiment since the factors are all under the

designer's control.

After performing the analyses, the results must

be extracted from the results files. That is, the output files should be examined for the values

of the responses, and these should then be tabulated and graphed.

The tedium of this task is eliminated by the

elegant results-translation performed by

HyperStudy. This leaves the designer free to

focus on interpreting the results.

Your students should be encouraged to use

HyperStudy to perform more than one

experiment! The results of the first experiment should help cast light on the various

assumptions implicit in the model. Ensure that your students can enumerate the assumptions

- for instance the stresses at which response

values were tracked - and use the software to enhance their understanding of the behavior of

the component.


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Ann approximation is not essential for an

optimization since the FE model is well behaved. The main advantage in a scenario

such is this is the reduction in analysis time, allowing more analyses to be performed.

HyperStudy provides three different types of approximations. We start with the Least-

Squares Regression (LSR) model, and go on to see how good it is. Your students should

review the relationship between the number of

runs involved in the DOE and the permissible approximations.

We review the ANOVA plot to understand

which design variables have an effect on the responses, and see the utility of trade-offs

before setting up the optimization.

There are no constraints on the optimization.

Remember that the variables are linked, and bounds have been specified for the variation of

the parameters. The objective is to choose the

proper distribution of weight to achieve the minimum stress.

There are three bolt hole pairs that are

involved in the study. Your students should be encouraged to consider whether a multi-

objective optimization is required, or whether

the objectives can be treated independently.

We review the system identification feature which would be useful if we had a target value

for the stress.

We set the parameters that govern the

behavior of the optimizer and use the approximation to perform the minimization.

Further Work The assignment illustrates how to use two separate FE models to generate

different objectives, and study the impact of the design variables on these.


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You may choose to assign further investigations to your students based on

their level of proficiency on statistics and design, the time available, etc.


• a stochastic study to determine the reliability of the design

• an optimization study that uses the material data as parameters - several alloys can be evaluated and the best one suggested

• an optimization study on the design suggested by the topology optimization

















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Geneva Mechanism – Effect Of Friction Areas covered:

• Use of a Multi-Body-Dynamics (MBD) Model to simulate a mechanism

• Design Of Experiment • Construction of an Approximation • Use of Monte Carlo methods for stochastic analysis

Software used: • HyperStudy • MotionView • MotionSolve

Description of the Problem A Geneva Mechanism is a widely used indexing

mechanism. It relies on friction between the crank and the slotted-disk for the indexing.

Friction is hard to pin down perfectly. At best,

we can estimate the range between which the

friction coefficient can vary.

Further complicating the issue, there is more

than one "kind" of friction coefficient the designer has to grapple with. We have static

friction at the initiation of motion, dynamic

friction when the mechanism is running, and "stiction" or stick-friction, which can be a

nightmare for a designer.

With this, what is the reliability of the design?

There are two areas the designer is interested

in.

First, the forces on the crank during motion. If these can be evaluated, a stress analysis can

be carried out to ensure that the crank is safe

for all ranges of expected forces. That is, at the best and worst case friction-coefficient

scenarios.

Second, which ranges of coefficients are the

worst and best case scenarios?


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We perform a Monte Carlo study to investigate

the behavior of the mechanism given the designer's interest.

The Analysis Model Refer to the assignment that accompanies CAE and Multi-Body Dynamics for a

detailed description on how to create a multi-

body dynamics model using MotionView.

In this assignment, our focus is not on creation of the high-fidelity model: our focus is on

setting up and interpreting the results of the

stochastic study. Accordingly, we start with an existing model of the Geneva Mechanism.

This assignment starts with an "MBD" model -

a Multi-Body-Dynamics model. We review the structure of the model. Our attention is on the

various control elements and the tessellated

geometry that provides both the graphical

display and the surface-definitions for contact.

We also review the contact parameters,

choose the output we want, and plot the

forces on the body.

Design of Experiment Since we have an MBD model available, we

can invoke HyperStudy from MotionView. This gives us access to several model parameters.

We can also invoke HyperStudy as a standalone application. Each has its

advantages, as we will see.


how many levels to use for each factor, or to decide which type of experiment to use.

We use a Central Composite design to study the behavior. Your students should be

encouraged to evaluate other studies, so that


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they clearly understand the advantages and disadvantages of various

designs.

Your students should appreciate the importance of DOE - the rigorous

statistical approach allows the problem to be brought under control in terms of the time involved.

There are no uncontrolled factors in the experiment since the factors are all under the designer's control.

An approximation is almost essential for a stochastic study since it allows us

to perform many more analyses at a marginal increase in computation cost.

HyperStudy provides three different types of approximations. We start with

the Least-Squares Regression (LSR) model, and go on to see how good it is. Your students should review the relationship between the number of runs

involved in the DOE and the permissible approximations.

We review the ANOVA plot to understand which design variables have an

effect on the responses, and see the utility of trade-offs before setting up

the optimization.

The Monte Carlo approach uses a large number of trials to estimate

behavior. We could have skipped the DOE and Approximation and directly

created a Stochastic Study using the high-fidelity model. That is, we could have used MotionSolve to perform each trial.

However, if a MotionSolve analysis takes 60 CPU seconds, and the Monte

Carlo study is to be conducted with as few as 500 analyses, this means each study takes over 6 hours of CPU time.

This can inhibit design-exploration. The use of the DOE to construct the

approximation effectively removes this inhibitor. You should encourage your

students to review the different forms of stochastic studies HyperStudy offers, and to understand the impact of changing the number of trials.

Your student should also be encouraged to understand the various ways to measure reliability and robustness in the context of engineering design.


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Further Work The assignment illustrates how to use Monte

Carlo methods to conduct a stochastic study of a random variable.

You may choose to assign further

investigations to your students based on their

level of proficiency on statistics and design, the time available, etc.


• the generation of nomograms for various material combinations

• use of system-identification to achieve

target values for forces on the components

• the impact of friction on the timing of the device




• create collectors for materials,

elements, forces and restraints • use consistent units




• check for different types of

element-edges - free, shared, etc. • fill and stitch surfaces








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Altair's Student Guides - Instructor's Manual - CAE and Design Optimization - Advanced

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