Lecture 12: Detailed Design: Modeling - University of...

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MECH 350Engineering Design I

University of VictoriaDept. of Mechanical Engineering

Lecture 12: Detailed Design:

Modeling

© N. Dechev, University of Victoria

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INTRODUCTIONTHE MODELING PROCESS8 PRINCIPLES OF MODELINGMODEL VALIDATIONADVANCED MODELING

Outline:


Detailed Design-Detailed Analysis-Simulate & Optimize-Detail Specifications-Drawings, GD&T

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Project Planning within the “General” Design Process


Identify Need-Talk with Client-Project Goals-Information Gathering

Conceptualization-Brainstorming-Drawing/Visualization-Functional Decomp.-Morphologic Chart

Preliminary Design & Planning-Prelim. Specifications-Prelim. Analysis-Decision Making-Gantt Charts & CPM

Report/Deliver-Oral Presentation-Client Feedback-Formal Design Report

Prototyping-Prototype Fabrication-Concept Verification

Testing/Evaluation-Evaluate Performance-Are Objectives Met?-Iterate Process Steps 2 - 7 as needed

Problem Definition-Problem Statement-Information Gathering-Design Objectives(quantifiable/measurable)

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In engineering, “Modeling” is the process of generating a “simplified/abstract” representation of a complex real/physical system.

Models have many uses, and their simplified/abstract representation may be in the form of (but not limited to):

a mathematical model__________________________________a graphical model__________________________________a computer/software model__________________________________a physical model__________________________________

Modeling in Engineering Design


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There are numerous reasons to create “Models” which are representations of real/physical systems. This includes:

Simulation__________________________________Prediction__________________________________Substitute for direct measurement__________________________________Substitute for testing__________________________________Optimization__________________________________



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Modelling is critical for many engineering fields, where working with the real system is otherwise difficult, impractical, or impossible.



SEM image of Micro-machine, a.k.a. Micro-Electromechanical Systems (MEMS) [N. Dechev]

Examples of real systems where modeling is essential:__________________________________________________________________________________________________________________________________________________________________

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Modeling allows engineers to:Expand upon a vision/conceptFormulate an interpretation of that visionOriginate innovationsEnhance the understanding of physical problemsProcess a large number of alternative solutionsOptimize



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Basic Modeling Flow Chart:



Modelling Goal

Real SystemAnalysis

ModelSynthesis

ModelVerification

ModelValidation

Inference(Model Conclusions)

Check on Model Synthesis

Check onReal SystemAnalysis

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The formulation of models is sometimes considered to be an art!

Model formulation consists of the activities shown as “blue blocks” and their iterations, in the basic modelling flow chart of page 8.

Typical questions to answer in formulating a model:What aspects of the real system should be included?What aspects can be ignored?What assumptions can and should be made?

Model Formulation


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Involves a sequence of logical statements, that build upon each other, to form a conclusion. The techniques used will depend on the nature of the model.

It may involve solving equations, running a computer program, expressing a sequence of logical statements – whatever it takes to solve the problem of interest relative to the model.

It should not be subject to differences of opinion, provided that the assumptions are clearly stated and identified.


Model Formulation (Deduction Phase)

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Involves human judgment.

The model conclusions must be translated to the real world conclusions, in full cognizance of possible discrepancies between the model and its real-world referent.

Remember: Ties between model and system are often only at best ties of plausible association, SO BE CAREFUL WITH THE CONCLUSIONS!!!!


Model Formulation (Interpretation Phase)

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A ‘Common Sense’ or ‘Experience Based’ model that represents a system.

Another possible heuristic approach would be ‘trial and error’.

Heuristic models are:Simple to developSimple to understandHelp to learn and ‘rough out’ a problem

Be very careful when using Heuristic modelsModel validity may be limited to small range/scopePersonal ‘perception, bias, etc...’ may distort the model.

Model Types: Heuristic Models


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Mathematical Models:Example: Numerical model of stress around a hole in a plate undergoing uniaxial tension.

Model Types: Engineering Models


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Graphical Models: Example shown in the form of drawings:



11.9

9

6.30

11.99

8.13

0.50

0.75

0.0003

0.00050.0005 B

B

9.02

SCALE: NTS SHEET 1 OF 1

Material: AluminumDimensions are in inches

UVic Aluminum Bracket

REV.Email: [email protected]: 250-514-4810Drawn by: Robert Roulston

SIZEA 2

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Physical Models: Example shown in the form of scale mockups:



[http://www.eyescreamanimation.com]

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1. Do not build a complicated model when a simple one will suffice. This can be contrary to traditional mathematics or when one wants to build a general, strong model. Typically, though, a useful model is a simple model.

2. Beware of molding the problem to fit the technique. This happens if somebody has “tunnel vision” in a specific solution technique.

3. The deduction phase of modeling must be conducted rigorously. If you do this and the conclusions are inconsistent with reality, then the fault lies in the assumptions. (Be extremely careful with computer programs which may contain hidden bugs.)

8 Principles of Modeling


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4. Models should be validated prior to implementation. Some ways to do this:

Retrospective testing against historical data (especially for forecasting models). Before you try to predict with model, see if it can replicate the past!Construct artificial tests, e.g., enter extreme values and see what happens (zero is always an interesting number to try).Systematically vary parameters in the model and real system (if possible) and see whether the changes in behaviour match.



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5. A model should never be taken too literally. This sounds obvious, but is often forgotten as the model grows and is supposed to be more "accurate".

6. A model should neither be pressed to do, nor criticized for failing to do, that for which it was never intended. Always remember and investigate the original context in which a model was made (e.g., a model for predicting the hull fluid resistance of fishing vessels should not be used for aircraft carriers).



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7. A model cannot be better than the information that goes into it.Garbage In, Garbage Out (GIGO) is very applicable to modeling. Models do not create information! They only condense or convert information. In some cases, instead of exerting one's efforts on model construction, one would be better off just gathering more information about the real system. Be careful that you don't put in too much information.



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8. Models cannot replace decision makers!One of the most common misconceptions about the purpose of optimization is that they are supposed to provide "optimal" solutions, free of human subjectivity and error.Don’t oversell your model!There are so many decisions and assumptions to be made in modeling, that only in a restricted and tight context we can speak of optimal solutions.A human decision maker is always necessary, whether you like it or not.



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The process of making sure that a model actually works is commonly called validation.

When people are persuaded or convinced that a model is useful in some basic context, they will speak of it as a valid model.

However, the validity is often restricted to a certain context, and hence, it is vital to know the limitations of the model.

Some people may never accept a model.

Note: Validation is a considerably weaker term than "proof"

Model Validation


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1)Always remember that a model is never perfect.

2)An absolute measure of validity does not exist.

On what basis can we compare the validity of models? Four phases are suggested:

1. An evaluation of model structure.2. An evaluation of model logic.3. An evaluation of design and/or input data. 4. An evaluation of model response.

Model Validation Steps


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Model Validation: Step #1Evaluation of the Model Structure


Keep track of the model structure at all times.

Best way is to start with a preliminary simple model and slowly build on it in a systematic and logical way.

KISS (Keep It Simple and Sensible)

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Model Validation: Step #2Evaluation of the Model Logic


If the model logic truly reflects the system model, then the model will react to a stimulus (or change) in the same way as the actual system would.

Often, the relative difference in the outputs are all that we need to be concerned with (trends), rather than whether the model response and the actual system values are identical.

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Model Validation: Step #3Evaluation of the Input Data


Input Data is defined as:1) the design data or information used to construct the model2) the input data of data used to stimulate the system

Data collection and verification may well be the most overlooked portion of model construction.

The process of data collection often consumes the major amount of time and resources when dealing with actual problems.

Good practice: First decide on the basic form of the model, then identify its specific data needs.

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Model Validation: Step #4Evaluation of the Model Response


True validation is often said to be reflected solely in its ability to predict the behaviour of the system that has been modeled.

The fact that a model is accurate does not mean it is valid!

Look at the response, the trends, and compare with actual data or theoretical information from other sources at hand to validate model response.

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Advanced Modelling Approach


Customer NeedEng. Requirement

Identify DesiredInputs and Outputs

Identify Physical Mechanisms

Experiment:Direct Observation,Simple Test

Experience:Analogous Problems, Literature,History

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Modelling Tools:Dynamics,Statics,Kinematics,Thermodynamics,Solid Mechanics,Etc...

Previously ModelledProblems:Standard Solutions:Machine Elements,Dynamics,Beam Theory4-Bar MechanismsHeat Transfer, Etc...

Basic:Spreadsheet,Program, Etc...

Advanced:FEM,Simulators (Matlab...)

Construct the Model

Apply Dimensional Analysis,and Group Variables

Implement Computationally

Define the Precision

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Check Model:Simplifications,Limiting Behaviour,Limits on Geometry,Limits of Validity,Sensitivity Analysis,Compare with Data,Validate Model

Tune Model-Lump Empirical Parameters,-Devise Calibration Procedure,-Calibrate Against Subset of Data,-Validate the Model

Interrogate the Model

Display,Use the Model

Display:Graphics/Plots,Kinematic Motion,Limits on Geometry,Limits of Validity

Use:Design Choices,Optimization,Portfolio Adjustment,Process Control

Iterate!

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