energy efficient propulsion systems optimization execution j. brent staubach & michael winter...

Energy Efficient Propulsion Systems

Optimization

Execution

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ETHERMALS

DRAFTING

MFG

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ETHERMALS

DRAFTING

MFG

Y1

J. Brent Staubach & Michael WinterPratt & WhitneyUnited Technologies Corporation

Energy & ComputationMIT

May 10th 2006

Simulation

CAD/CAM

Computing Power Will Fundamentally Change How We Design Gas Turbines

• Review Current Manual Design Optimization Practices

• Computer Based Design & Key Enablers

• Future New Design Paradigms; Less Time; Fewer People

Computer Speed- 2x Every 18 months -

Network Speed- 2x Every 9 months -

TremendousGrowth

Billion X SpeedBillion X Speed by 2025by 2025

Hardware: 100,00X

Massive Parallel Grid Computing, Assume ~10,000X

IBM & IntelExpect ContinuedGrowth

• Transistor Scaling• Manufacturing: Copper, SiGe,• Parallelization: chip, board, rack• Quantum Computing

Cost of Sending1012 BitsData AcrossCountry HasDropped From$150,000 in 1970To 12 cents In 2000

Cost of aThreeMinutePhone Call FromNew YorkToLondon

# T

ran

sist

ors

(K

s)

RELEASE THIS POWER ON DESIGN PROCESSES

Integrated TEC& Augmentor

Integrated TEC& Augmentor

Variant-common F135Turbomachinery

LO Axi-symmetricNozzle

LO Axi-symmetricNozzle

Lift Fan, Clutch,& Driveshaft

Roll ControlDucts and Nozzles

Roll ControlDucts and Nozzles

3-BearingSwivel Duct3-Bearing

Swivel Duct

Controls & externals,engine gearbox

Pratt & WhitneyPratt & Whitney

Hamilton SundstrandHamilton Sundstrand Rolls-Royce Rolls-Royce

Propulsion System Complexity Driving Need for More Robust Systems Engineering Process and Tools

Propulsion has Become a System of Systems

System Engineering Process Driven by Product Needs

~ 80,000 PARTS

~5000 PART NUMBERS

~ 200 MAJOR PART NUMBERS REQUIRING 3D FEA/CFD ANALYSIS

~ 5000-10,000 PARAMETRIC CAD VARIABLES DEFINE MAJOR PART NUMBERS

~ 200 MAN-YEAR ANALYTICAL DESIGN EFFORT

~ 200 MAN-YEARS DRAFTING / ME EFFORT

Modern Gas Turbine Optimization Is An Exercise In Managing Complexity

• IPTs ARE THE ORGANIZATIONAL MECHANISM THAT ENABLES A BALANCED DESIGN - MANUAL MULTI-DISCIPLINARY DESIGN LOCAL OPTIMIZATION

INTEGRATEDPRODUCT TEAM

BURNERMECHANICAL

COMPRESSORAERO

COMPRESSORMECHANICAL

TURBINESTRUCTURES

COMPRESSORSTRUCTURES

FANSTRUCTURES.

FANMECHANICAL.

TURBINEAEROTURBINEAERO

BURNERSTRUCTURES

BURNERSTRUCTURES

TURBINESTRUCTURES

BURNERAERO

COMPRESSORAERO

TURBINEMECHANICAL

TURBINEMFG.

FANAERO.

Complexity Is Managed By Decomposing The Design, Coordinating & Re-assembling via The SIPT/CIPT/IPT

SYSTEM

CIPT CIPT

CIPTCIPT

Within Modules & Disciplines SophisticatedSimulation Based Design Systems Have Evolved

CADMODEL

PHYSICSMODEL

DECISIONDESIGN

DESIGN SPACE

- NON LINEAR - MULTI MODAL - DISCONTINUOUS - NOISEY - HIGHLY CONSTRAINED

ENGINEER MAY ITERATE 100s TO 1000s OF TIMES TO GETSATISFACTORY RESULTS -- MANUALLY !

Complex Designs Are Inherently & Brutally Iterative, Bounded By Best Practice Rules . . . . . . . .

WORKINSTRUCTIONS

CRITERIA

VALIDATEDANALYSIS

PREFERREDCONFIGURATIONS

STANDARDWORK

. . . and Complex Designs Are Iterated Across Disciplines & Organizations . . .

STAR

PROSTAR 3.00

23-SEP-97VIEW

1.000 1.000 1.000

ANGLE 0.000

DISTANCE 6.549

CENTER 10.138 -0.554 0.776

EHIDDEN PLOT

X

Y Z

CYCLE

1D AERO

COOLINGFLOWS

3D AERO

HEAT XDESIGN

PLATFORM

NECK

ATTACHMENT

DISK & SEALS

FEED FORWARD

FE

ED

BA

CK

IPTPROCESS

. . . And Iterations Can Take Place Across The Globe

• OUTSOURCING

• PARTNERSHIPS

• INTER-DIVISIONAL

• CUSTOMERS

STAFFINGSTAFFING

• STAFFING EXPLODES WITH FIDELITY: ANOTHER “CURSE OF DIMENSIONALITY”

FIDELITY

DESIGNSPACE

NEWENGINE

0D 1D 2D 3D 4D VTE

Iterations Are Simplified By Employing A Range Of Fidelity

KNOWLEDGE INFORMATION DATA

PRODUCT DEVELOPMENT PHASES

ConceptInitiation Detailed Design

PreliminaryDesign

Validation/Certification

ConceptOptimization

AirplaneValidation

Service &Support0 I 2 3 4 5

• REQUIRES EXTENSIVE KNOWLEDGE TO JUDGE LOW FIDELITY MODELS

SYSTEMOPTIMIZATIONENSURE CONFORMANCETO CUSTOMER NEEDS &BUSINESS GOALS

COMPONENTOPTIMIZATIONTEAMS - HUMAN INTERACTIONEXECUTE MANUAL MDO

PARTOPTIMIZATIONISOLATED MULTI-FIDELITY DESIGN SYSTEMS

Labor Intensive, Compartmentalized Design Process That Relies On Teams, Management, & Procedures

Result Is Manual “Human” Based Multidisciplinary Design Optimization – At Best

IPMT

CIPT CIPTCIPT

IPTIPTIPTIPTIPTIPT

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OF

FID

EL

ITY

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MARKETNEEDS

BUSINESSNEEDS

SYSTEM

Gains Can Be Made By Shifting From “Human” To “Computer” Based MDO

AUTOMATE WORKFLOW

AUTOMATE MODEL BUILDING & EXECUTION

AUTOMATE DESIGN EXPLORATION

MANUAL WORK FLOW per PROCESS MAPS

MANUAL CAD/CAE MODEL BUILDING& EXECUTION per STANDARD WORK

MANUAL EXPLORATION TO FIND OPTIMAL DESIGNS THAT MEET TECHNOLOGY LEVELS

“COMPUTER” BASED

WORKFLOW, RULES,WORKFLOW, RULES,And DESIGN ITERATIONSAnd DESIGN ITERATIONSAUTOMATED WITHINAUTOMATED WITHINAnd ACROSS SYSTEMS And ACROSS SYSTEMS & DISCIPLINES & DISCIPLINES

SYSTEM SUB-SYSTEM A SUB-SYSTEM B

Business PlanConcept &

Venture Definition

IntegratedBusiness

& Project Plan

Product/ Industrial Plan Execution & FETT

Validate, Certify, Deliver

EIS, OperationalService

& Support0 I II III IV V

Program Program Standard Work Flow

Business PlanConcept &

Venture Definition

IntegratedBusiness

& Project Plan

Product/ Industrial Plan Execution & FETT

Validate, Certify, Deliver

EIS, OperationalService

& Support0 I II III IV V

Program Program Standard Work Flow

Engineering Standard Work Flow

System

ConceptInitiation

Product Design Procurement & Initial Validation (FETT)

PreliminaryDesign


ConceptOptimization

AirplaneValidation

Service &Support0 I 2 3 4 5

Module

ConceptInitiation


PreliminaryDesign


ConceptOptimization

AirplaneValidation

Service &Support

Part

ConceptInitiation


PreliminaryDesign


ConceptOptimization

AirplaneValidation

Service &Support3I2 4 5I

SelectConcept

ProgramLaunch

After FETT

Release toProduction

I 2 3 4 5

2.52.5

“HUMAN” BASED

IPMT

CIPT CIPTCIPT

IPTIPTIPTIPTIPTIPT

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-- LABOR INTENSIVE --

ASSESSMENT ASSESSMENT MODELINGMODELING

PHYSICS

COST

- SOLVE TURBULENCE- MULTIDISCIPLINARY ANALYSIS (MDA)- MATERIAL PROPERTIES- PROBABILISTICS

- MFG - MAINTENANCE - NEGOTIATED

INTEGRATION FRAMEWORKSINTEGRATION FRAMEWORKSPHYSICSMODELS

CAD/CAMMODELS

COSTMODELS

STANDARDWORK

COMPUTERESOURCESMDO

FIPER

Technologies Are Progressing To Enable Large Scale Computer Based MDO

GRID COMPUTINGGRID COMPUTING

CHALLENGES-SECURITY- POLITE COEXISTENCE- FAULT TOLERANCE

ROBUST ROBUST PARAMETRICPARAMETRIC

MASTER MODELMASTER MODEL

CHALLENGES -LARGE ASSEMBLIES -TOPOLOGICAL -GENERATIVE - DIRECT MFG

SYSTEM ANALYSISSYSTEM ANALYSIS

CHALLENGES-CYCLE BALANCE-2ND FLOWS-TRANSIENTS

COMPUTER BASEDCOMPUTER BASEDMDOMDO

NAVIGATETHE VIRTUAL

DESIGN SPACE

Implementation Path: Electronic Enterprise EngineeringElectronic Enterprise Engineering

LIBRARY OF“WRAPPED” TOOLS

A

B

C

B

CG

VALIDATED

CERTIFIED

EMBEDDEDKNOWLEDGE

CONTROLLED

INTEGRATIONFRAMEWORK

A

E

C

G

B

D

F

H

INTEGRATETHIRD PARTY& LEGACY TOOLS

INDUSTRYACCEPTED

COMMERCIAL

ELECTRONICPROCESS MAPS

A

B

D E

F

C

G

AA

BB

DD EE

FF

CC

GG

TOOLSINTEGRATEDINTO ELECTRONICPROCESS MAPS & ASSOCIATED WITH WORKINSTRUCTIONS

ELECTRONICIPT

A

B

D E

F

C

G

IPT 1

IPT 2

IPT 3

WORK FLOWMANAGEMENT

COLLABORATIVEENGINEERING

SECURE B2B

COMPUTER BASEDOPTIMIZATION

A

B

D E

F

C

G

SYSTEMOPTIMIZER

OP

TIM

IZE

R

OP

TIM

IZE

R

IPT 1&2

IPT 3

AUTOMATEITERATION

SATISFY CRITERIA

GRID COMPUTING

Large Scale Computer Based MDO Is AlreadyPractical

Aero Xsections

0

0.1

0.2

0.3

0.4

0.5

AreaS1 AreaS2 AreaS3 AreaS4 AreaS5

Are

a

Initial

Iteration 515

Aero Xsections

0

0.1

0.2

0.3

0.4

0.5

AreaS1 AreaS2 AreaS3 AreaS4 AreaS5

Are

a

Initial

Iteration 515

AIRFOILSHAPE

OPTIMIZER

PARAMETRICCAD MESH

VIBRATORYANALYSIS

3D AEROCFD

EFFICIENCY

STRESS MODE 1

MODE 2 MODE 3 MODE 4

CAMPBELL DIAGRAM

3D Aero-Vibratory Shape Optimization Of A Cooled Turbine Airfoil(Single Row RANS CFD, Cooled UG Parametric Model, 3D ANSYS Vibes)

Large Scale Computer Based MDO Is AlreadyBecoming Practical

GENERATION0 10 20 30

0.6

0.4

0.2

0.0

DE

LT

A T

UR

BIN

E E

FF

ICIE

NC

Y

150 VARIABLES 15 CONSTRAINTS

LOSS CONTOURS

LOSS CONTOURS

3D Shape Optimization Based On Hybrid Genetic Algorithm & Rule System(3D RANS Multi Row CFD, Population Size 80, Total Runs 2400, Run Time 48 hrs on 40CPUs)

Discovered “bowed” rotorTo control tip leakageVortex

Efficient Scalable System Problem FormulationsAre Becoming Understood For Gas Turbine Design

APPROXIMATIONTO HIGH FIDELITYAPPROXIMATIONTO HIGH FIDELITY

LIFE

WEIGHT

COST

LIFE

WEIGHT

COST

MIN: WEIGHT & COST ST: JOB TCKT & SWVAR: MECHANICALS

LIFE

WEIGHT

COST

LIFE

WEIGHT

COST


LIFE

WEIGHT

COST

LIFE

WEIGHT

COST


HIGH FIDELITY PARTSHIGH FIDELITY PARTS

NUMERICALOPTIMIZATION

x1

x2Xi+1 = Xi - ifi

NUMERICALOPTIMIZATION

x1

x2Xi+1 = Xi - ifi

x1

x2Xi+1 = Xi - ifi

TURBINE (i) MIN: COST LOCAL ST: JOB TCKT & SWVAR: RADI & AIRFOILS

MEANLINE

COMPRESSOR (i)MIN: COST LOCAL ST: JOB TCKT & SWVAR: RADI & AIRFOILS

MEANLINE

BURNER (i) MIN: COST LOCAL ST: JOB TCKT & SWVAR: RADI & LENGTHS

MEANLINE

FLOWPATH


MEANLINE


MEANLINE


MEANLINE


MEANLINE


MEANLINE


MEANLINE

FLOWPATHFLOWPATH

PROBLEM FORMULATION

MIN: MANUFACTURING COSTST: DOC+I, RANGE, NOISE, EMISSIONSVARIABLES: SYSTEM DISCRETE & CONTINUOUS

DISCRETE PARAMETERS,--- Nspool, GEAR, ROTATION, MIXED, MOUNTS, etc.

CONTINUOUS PARAMETERS -- BPR, FPR, OPR, WS, CET, Wc, RPMi, etc

FULLENGINEMODEL(0D, 1D, 2D, 3D) VEHICLE ANALYSIS

REPRESENTATION

CONFIGURATOR

CYCLE

PROBLEM FORMULATION





PROBLEM FORMULATION
















REPRESENTATION

CONFIGURATOR

CYCLE

REPRESENTATION

CONFIGURATOR

CYCLE

CONFIGURATOR

CYCLE

ALGORITHMS

$

WT

SFC

TRIAL A B C D E F G

1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 3 1 2 2 1 1 2 2 4 1 2 2 2 2 1 1 5 2 1 2 1 2 1 2 6 2 1 2 2 1 2 1 7 2 2 1 1 2 2 1 8 2 2 1 2 1 1 2

RESPONSE SURFACEMODELS

Y1

ROBUSTDESIGN

DESIGNOFEXPERIMENT

ALGORITHMS

$

WT

SFC

TRIAL A B C D E F G

1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 3 1 2 2 1 1 2 2 4 1 2 2 2 2 1 1 5 2 1 2 1 2 1 2 6 2 1 2 2 1 2 1 7 2 2 1 1 2 2 1 8 2 2 1 2 1 1 2


Y1

ROBUSTDESIGN

DESIGNOFEXPERIMENT

ALGORITHMS

$

WT

SFC

$$

WTWT

SFCSFC

TRIAL A B C D E F G

1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 3 1 2 2 1 1 2 2 4 1 2 2 2 2 1 1 5 2 1 2 1 2 1 2 6 2 1 2 2 1 2 1 7 2 2 1 1 2 2 1 8 2 2 1 2 1 1 2

TRIAL A B C D E F G

1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 3 1 2 2 1 1 2 2 4 1 2 2 2 2 1 1 5 2 1 2 1 2 1 2 6 2 1 2 2 1 2 1 7 2 2 1 1 2 2 1 8 2 2 1 2 1 1 2


Y1

ROBUSTDESIGN

DESIGNOFEXPERIMENT

LESS TIMELESS TIMEFEWER PEOPLEFEWER PEOPLE

Will Enable New Design ParadigmsNew Design Paradigms

COMPUTER BASED DESIGNCOMPUTER BASED DESIGNRUN 24/7 365 DAYS A YEAR

CONTINUOUS DETAILED DESIGN

SOLVE ALL POSSIBLEAPPLICATIONS @ TECHNOLOGY READINESS LEVEL

CUSTOMER NEEDS

UNDERSTAND THE FUTURE

CREATE TECHNOLOGY

IMPROVE MODELS

RE-FORMULATE PROBLEM

UPGRADE COMPUTER BASED DESIGN “MACHINE”

ENGINEERSENGINEERS

CUSTOMER REQ. EXCEED TECHNOLOGY

Powering Change………….Powering Freedom

energy efficient propulsion systems optimization execution j. brent staubach & michael winter...

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