Research Overview

Research Overview Chris Paredis Systems Realization Laboratory Product and Systems Lifecycle Management Center G.W. Woodruff School of Mechanical Engineering Georgia Institute of Technology www.srl.gatech.edu www.pslm.gatech.edu

Presentation Overview • Research Scope, Context, and Foundation • Model-Based Systems Engineering • Reusable Analysis Models Defined in SysML • Predictive Trade-off Models • Variable Fidelity Analysis • Capturing Synthesis Knowledge as Graph Transformations • Risk Management in Systems Engineering • Summary

Research Scope and Context • Research Area • Modeling and Simulation in Design • Application Focus: Systems engineering • Fluid power systems • Mechatronic systems • Research Focus • Decision theory • Modeling and Simulation • Information technology

PortfolioPlanning Concept Development Design Production & Testing Sales & Distribution Maintenance & Support Product Development: A Decision-Based Perspective Decisions Modeling and Simulation Provides Informationin Support of Decisions GenerateAlternatives Evaluate Alternatives Select Alternative GenericDecisionProcess Information Knowledge

A Generic Design Decision Alternatives Outcomes(Attributes) Preferences

Why are Design Decisions Difficult? Uncertainty Uncertainty Infinite number of alternatives Alternatives Outcomes(Attributes) Preferences Limited Resources

Goal: maximize overallutility Must consider process in utility assessment Value trade-offs between product and process Decrease expected utility of product to reduce expected cost of process "Optimal" solution is guaranteed not to be optimal from a pure product perspective Information Economics Meta-level decision:How to frame your design problem? Integration of Design Product & Design Process

The systems approach provides a balanced trade-off between product and process objectives Systematic decomposition Large, flexible, yet manageable design space Decoupling Limit the risk due to unexpected interactions Enable collaborative engineering Helps us deal with complexity Systems Approach to Product Development

Model-based Systems Engineering (MBSE) • Effective and Efficient Analysisof Alternatives • Model from different perspectives • Model at different levels of abstraction • Model reuse & modularity • Optimization with grid-computing • Effective Generationof Alternatives • Graph transformations for generating plausible system architectures • Automated generation of system models MBSE: Model formally all aspects of a systems engineering problem Requirements & Objectives System Alternatives ModelLibraries System Behavior Models Executable Simulations Design Optimization

Given: Primary components Decision objectives / preferences Find: Best system topology Best component parameters Very large search and optimization problem Many competing objectives Many topologies Many component types/sizes Many control strategies engine Excavator pump_vdisp accum v_3way cylinder MBSE: Example Problem How to size and connect these? 10

Research Questions • Which decisions should be made at each point in the product development process? • How should decisions be ordered in a decision sequence? • How should the decisions be framed? Which decision alternatives should one consider? • Which information should be used in support of these decisions? • How accurate should the information be? • How do we trade off accuracy versus cost? • Which tools allow us to obtain this information most cost effectively? • Which Information Technologies can be used to reduce the cost of information? • Is it cost effective to use repositories of reusable models?

Reusable Analysis Models Formal Representation of Engineering Analysis Models in SysML Student: Jonathan Jobe

Components are the reusable elements of design MAsCoMs: Group all models related to single fluid power component Multiple disciplines and levels of abstraction Modular Formal & unambiguous Systems Modeling Language (OMG SysMLTM) Formally model systems design information, from requirements through testing Multi-Aspect Component Models

How to use MAsCoMs? Log Splitter Design Example ISO 1219 Fluid Power Graphics Design Concept Schematics -Hydraulic System

Log Splitter—Model Composition Power Subsystem

Automatic Translationfrom SysML to Modelica Formal Graph Transformations Modelica SysML

Predictive Trade-off Models Reusable Models for Predicting the Performance of Design Concepts Student: Rich Malak

Alternative 1 Concept 3.1 Alternative 2 Decision 3.1 Concept 2.1 Alternative 3 Decision 2.1 Alternative 1 Concept 3.2 Alternative 4 Decision 3.2 Alternative 2 DesignDecision Decision 1 Alternative 3 Concept 3.3 Alternative… Decision 3.3 . . . Concept 2.2 Alternative… Decision 2.2 Alternative n Concept 3.4 Alternative n-1 Decision 3.4 Alternative n One-Shot Decision A Chain of Decisions Decision Chains: From Concept to Detail

Alternative 1 Alternative 1 Concept 3.1 Alternative 2 Alternative 2 Decision 3.1 Concept 2.1 Alternative 3 Alternative 3 Decision 2.1 Decision 2.1 Alternative 1 Concept 3.2 Alternative 4 Alternative 4 Decision 3.2 Alternative 2 DesignDecision Decision 1 Alternative 3 Concept 3.3 Alternative… Decision 3.3 . . . Concept 2.2 Alternative… Decision 2.2 Alternative n Concept 3.4 Alternative n-1 Decision 3.4 Alternative n One-Shot Decision A Chain of Decisions Decision Chains: From Concept to Detail Each Concept = Set of Design Alternatives

Modeling Set-Based Design Concepts • What is the appropriate gear ratiofor the differential? • …it depends • On the other drivetraincomponents • On manufacturing andcost considerations • On the details ofdifferential itself How do we best model a subsystem conceptto support decision making at the systems level?

Functional and Preference Attributes • What is important about the differential? • Gear ratio • Maximum torque • Maximum speed • Size, mass • Cost • What is NOT important about the differential? • Working principle • Detailed dimensions • Manufacturing processes Functional Attributes Preference Attributes Only important to the extent that theyinfluence the preference attributes Model Preference Attributes as Function of Key Functional Attributes

Predictive Tradeoff Model: Hydraulic Cylinder • Key functional attributes • Bore diameter • Stroke length • Predicted preference attributes • Mass • Cost

Taking Designer Preferences into Account • Not all feasible designs are preferred: Pareto optimality Feasible Designs: Planetary Gear Train Feasible, but dominated. No engineer will choose these Non-dominated set(efficient frontier). Every point is optimal under some tradeoff Care only about non-dominated set

Parameterized Efficient Sets Gear Ratio No problem-independent preference for gear ratio.Solution: find efficient set as function of gear ratio.

Combining Multiple Concepts with the same Functionality Blue reverted gear traindominates at gear ratios < 5 Redplanetary gear traindominates at gear ratios > 5 Best concept can be identified after consideringsystems-level tradeoff for gear ratio and other attributes.

Another Example: Small 4-stroke Engines • Some engines are dominated • E.g. same torque, mass, power, but lower price • Not all combinationsof the functional attributes are feasible • Clustering algorithmsare used to define the feasible set(Support Vector Machines)

Often more than technical performance • E.g., price/cost, mass, “-ilities” (reliability, usability,…) Predictive Tradeoff Modeling:Making System-Level Decisions Typical Iterative Loop (optimization / search) Utility Model System Model • Engine Attributes: • max power • max speed • max torque • efficiency • mass • price Other Components … • Pump Attributes: • max displacement • max speed • efficiency • mass • price • Cylinder Attributes: • bore diam. • stroke length • mass • price Attribute values for a given component are not independent How to formalize the dependencies efficiently and effectively? Project 2E Webcast - 23 July 2008

Making System-Level Decisions with Predictive Models

Efficient Design Optimization under Uncertainty Adaptive Kriging Models Students: Alek Kerzhner, Roxanne Moore

Motivating Study:Backhoe Dig-Cycle Optimization under uncertainty LatinHyperCube sampler used to predict expected value Kriging model used in conjunction with sampler to generate response surface to reduce computational cost  optimizer  Latin Hypercube + Kriging response surface • Objectives: • Maximize Efficiency • Minimize CostDesign variables: • bore diameters • pump max disp 32

Adaptive Kriging Models • Why Surrogate Models? • Without Kriging: 50 optimizer iterations*7 evaluations per iteration*1000 LHS samples = 350,000 runs • With Kriging: 2000 seeding runs + 100 extra runs = 2,100 runs! • Adaptive Kriging model • Kriging: computational complexity is O(n4) • Adaptive approach: keep n small • Many localized (small) response surfaces • Automatically requests additional simulations • Automatically regenerates response surface 33

Level of EffortRequired The Information Economic Challenge LevelofFidelity Level of Exploration / Optimization

Obj Feasible solutions x2 A C x1 B x1 Variable Fidelity Modeling • Low fidelity for broad explorationidentify promising regions of design space • High fidelity for detailed optimization

Capturing Synthesis Knowledge as Graph Transformations Formal Representation of Domain-Specific Knowledge Student: Alek Kerzhner

Requirements & Objectives SysML System Alternatives MAsCoMs SysML System Behavior Models SysML Executable Simulations Dymola Design Optimization ModelCenter Most Knowledge Can Be Representedas Graphs or Graph Transformations systemalternative Topology Generation using Graph Transf Model Composition using Graph Transf simulationconfiguration Model Translation using Graph Transf behaviormodel Simulation Configuration using Graph Transf

MOFLON: Meta-Modeling & Graph Xform Tool • Define graph transformationsin Storyboards:activity diagrams + graphs • Easily integrated with SysMLtools(through standardized JMI interface) • Capture domain-specific knowledge

Generative Grammar for Design Synthesis • Graph Transformation rules to generate systems • Generate random system alternatives by applying rules in randomized order • Improve system alternatives through evolutionary search algorithms

Composition of Multi-Aspect Models AutomatedComposition MACM Repository System Models Perspective C Perspective B Perspective A ArchitectureOptimization

Risk Management in Systems Engineering Trading off Product Quality and Risk versus Design Process Costs Student: Stephanie Thompson

Frame of Reference • Maximize expected utility! Of what? • Product (artifact) utility • Product + Design Process utility Instead of “What configuration of the product gives us the best product utility?” we should ask “What can we do now to give us the best balance of product and process utility later?”

Questions To Answer • When is it valuable to perform additional analyses to reduce risk? • When is it valuable to perform detailed uncertainty analysis? • In which order should the analyses be performed? • How many design concepts should one carry forward in parallel? • … • Approach: • Investigate simplified design scenario for which a theoretically optimal design process can be determined • Generalize these theoretical insights toward practical guidelines

Simple Illustration Problem – Problem Definition • Two Concepts • Designer must choose concept A or B • The utilities of concepts A and B are uncertain • Two Analyses • Low quality (LQ) and high quality (HQ) analyses to refine the utility estimates • LQ is cheaper, but the HQ is more precise. • Cost of analyses reduces expected utility.

Simplifying Assumptions • Normal distributions: • The utilities of A and B, and uncertainties of analyses are all distributed normally. • All derived conditional distributions are also Normal • Limited scope of decision tree • The low quality analyses have already been performed for both concepts, reducing the size of the decision tree. • In further work, the decision tree will include branches to perform the low quality analyses.

Decision Tree of Design Process uA-cHQA Select A uA-cHQA-cHQB Select A HQ B HQ A uB-cHQA-cHQB Select B uB-cHQA Select B uA Select A uB Select B uA-cHQB Select A uA-cHQA-cHQB Select A HQ A HQ B uB-cHQA-cHQB Select B uB-cHQB Select B

Probability Distributions for Chance Events • Apply Bayes’ Theoremto derive conditional probabilities • All distributions end up being Normal:

Research Overview