physically-based modeling in state- awareness monitoring strategies david l. mcdowell 1,2 regents’...

Physically-Based Modeling in State-Awareness Monitoring Strategies

David L. McDowell1,2

Regents’ Professor and Carter N. Paden, Jr. Distinguished Chair in Metals Processing

Director, MPRL1School of Materials Science and Engineering

2GWW School of Mechanical EngineeringGeorgia Institute of Technology, Atlanta, GA 30332

February 19, 2008

The George W. Woodruff School of Mechanical Engineering School of Materials Science and Engineering

Background

• DARPA AIM program (2001-2003, GEAC) – Development of hierarchical multiscale microstructure sensitive crystal plasticity models for Ni-base superalloys to support objectives of modeling strength and fatigue resistance (PI)

• ONR/DARPA Prognosis program (2004-2007, PWA) – Microstructure-sensitive macroscale models for component level design, informed by crystal plasticity calculations for Ni-base and Ti alloys (PI)

Shearing

Looping


Related Technoliges

• ONR MURI on Integrated Diagnostics (1995-2000) GT, NWU, U. Minn

• NSF Center for Computational Materials Design (PSU-GT I/UCRC)


Elements of Next Generation State-Awareness

• Characterization and “fingerprinting” of as-processed materials and components (including secondary processing)

• Modeling of material damage level/state

• Strategy for fusion of sensor-model-decision framework that integrates NDE with systems strategies to define the current state and project future state of the system.

Paraphrased from comments of Thomas A Cruse, DARPA/DSO Consultant, on Prognosis – A Vision for 2030


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Damage State Interrogation

Prognosis System

Life Estimate Models

Uncertain Prognosis Results /

Prediction

Model Uncertainty

Noise, Uncertain Sensor Data

• How should the damage state analysis process be configured? Which models should be employed

for diagnosis? How do we account for process

history and initial conditions?

• Sensors? Number, locations and types Nonunique relation to material state What is uncertainty of representing

state? What is state? Affected by

conception of failure mode – system related coupled


System-Level (Fleet) Decision Support

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Damage State InterrogationRemaining Life Models

Decisions

Uncertain Prognosis Results /

Prediction

Model Uncertainty

Noise, Uncertain Sensor Data

• How should the prognosis results be used for real-time decisions? Appropriately setting the operating

conditions Redesigning critical parts

• System-level Prognosis based on part-level prognosis data


Triad of Technologies Embedded in Decision Support Framework

methods for in situ interrogation of state

coupled state-awareness and life models

physically-based models

Treatment of uncertainty is paramountProbabilistic micromechanics approachesRobust decision-support framework

• Feasibility studies• Justifying impact of prognosis

Premise: This is a system and couplings contribute to uncertainty

Decision-support framework

Materials design for prognosis requirements


System-Based, Concurrent Product and Materials Design

Part

Continuum

Microscale

Molecular

Quantum

Goal-Oriented Design Methods

Cause/Effect Analysis Methods

MaterialSelection

New areaNew area

Design methods are availableDesign methods are available

High Degree of UncertaintyHigh Degree of Uncertainty

Structure

Properties

Performance

Goals/means (in

ductive)

Cause and effect (d

eductive)

Processing

Structure

Properties

Performance

Goals/means (in

ductive)

Cause and effect (d

eductive)

Processing

Limitation in Limitation in Inverse Inverse problemproblem

G.B. Olson, Science, 29 Aug., 1997, Vol. 277

System

Assembly

Top-down design requirements can include design for damage tolerance and probability of detection

CCMD – GT/PSU


Classification of Uncertainty based on Isukapalli’s Definition (Isukapalli, et al., 1998)

• Natural Uncertainty (system variability) Parameterizable: Errors associated with process history, operating

conditions, etc. (noise and control factors) Unparameterizable: random microstructure; randomness of initial conditions

of microstructure state

• Model Parameter Uncertainty (parameter uncertainty) Incomplete knowledge of model parameters due to insufficient or inaccurate

data; material and interrogation scheme

• Model Structural Uncertainty (model uncertainty ) Uncertain structure of a model due to insufficient knowledge (approximations

and simplifications) about a system; NDE interrogation algorithms; definition of what constitutes “state” is a substantial one.

• Propagated Uncertainty in a Process Chain (process uncertainty) Propagation of natural and model uncertainty through a chain of models

(e.g., multiscale materials; sequence of hot spots, etc.)

Uncertainty as a Driver in Hierarchical, Multilevel Decision Framework


Balancing System Uncertainty• Undue emphasis on accuracy and/or fidelity of material structure-

property models may be unwarranted if uncertainty of distribution of initial conditions, residual stresses, secondary processing, etc. is prominent

• Models aimed at producing probabilistic/stochastic information are desirable extreme value prognosis (both hot spots and rogue flaws)

• Multiple models with different potential mechanisms may be preferable to single, complex model for supporting decisions regarding range of remaining system life

• Balanced investment in more comprehensive characterization, monitoring and damage state modeling is warranted

• State-awareness sampling and material modeling are strongly coupled, and assessment of coupling effects should be evaluated at the systems level.

Uncertainty as a Driver in Hierarchical, Multilevel Decision Framework


Microstructure-Sensitive Fatigue Analysis

Controlling microstructure features for crack formation and early growth

Numerical analyses for representative loading cases

Physically Based Crystal Plasticity Models

a[010]

r a3 = a/78 [7 3 5 0]

b2

b1

Cross-slip onto {112}

{211} planes

-ra3 3ra3

-ra3

a3 a3

a/3[1120] a3=

Variation ofMicrostructure

Variation ofFatigue Life

Fatigue indicator parameters

Nonlocal Coffin-Manson relation

2Nf

1e+3 1e+4 1e+5 1e+6

/ re

f

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

' '(2 )cFS f fP N


Crack Initiation Life Distribution - Polycrystals

a y0.5

Distribution of the fraction of cracks as a function of the crack initiation life, Rε=-1,

T=650C.

a y0.8

a y0.5


P

t

P

B

HHot

H - AE, EC

B - AE, UL, DSR

Spot

RogueFlaw

ONR MURI on Integrated Diagnostics (CBM) (1995-00)

GT, NWU, U. Minn.

McDowell, Saxena, Qu, Jacobs, Neu, Johnson,Jarzynski


Ti-6Al-4V

Hamm (1998)

0.0001

0.001

0.01

0.1

1

1 10 100 1000a (m)

da/d

N (

m/c

ycle

)ExperimentWang modelEnhanced Wang

Smax = 405 MPa

R = 0

Kt = 3.2

0.00001

0.0001

0.001

0.01

0.1

1

1 10 100 1000a (m)

da/d

N (

m/c

ycle

)

ExperimentWang modelEnhanced Wang

Smax = 277 MPa R = .4Kt = 3.62

0.0001

0.001

0.01

0.1

1

1 10 100 1000a (m)

da/d

N (

m/c

ycle

)

ExperimentWang modelEnhanced Wang

Smax = 305 MPaR = .4Kt = 3.62

Microstructurally Small Fatigue Crack Growth


Neu and Papp

Microstructurally Small Fatigue Crack Growth


Test System Configuration

1

4

3

2

Transducer output

Compact PZT AE sensor

AE Waveform Acquisition Fracture

Wave Analysis

Digital Wave Signal Conditioning Module

Preamplifier

Monitoring crack to length of 1 mm (SAW)and up to 3 mm (AE)


Needs: Physically Based Models

• Identifying and modeling sources of damage and/or degradation, linking physics-based models to engineering models that have utility in prognosis• Computational micromechanics to model variability of microstructurally small crack behavior (formation, propagation)• Effects of load history on evolving damage state and interpretation of sensor signals• Predicting variability of mechanical properties (strength, ductility, fatigue resistance) to stochastic microstructure, initial conditions, environmental exposure, etc.• Nonlinear acoustics or other means of interpreting material state prior to formation of cracks.• Accounting for process history effects on residual stresses, initial damage and defect density, etc. that affect future evolution in prognosis


ONR D3D Tool Suite

New Material System

Primary Deformation processing

Thermo-Chemo-Mechanical Processing

Surface treatment (e.g. shot or shock

peening)

Microstructure and Inclusion distribution

information

Apply fatigue analysis algorithms

Depth (m)

50 100 150 200 250 300 350

0.0

2.0e-5

4.0e-5

6.0e-5

8.0e-5

1.0e-4

1.2e-4

Case ACase B

Depth (m)

Dri

vin

g f

orc

e

HIPping, altering inclusion orientation,

inclusion modification

S

N

Explore surface vs. subsurface nucleation

Modify process route

Improved fatigue Improved fatigue performance performance

Extreme value

statistics

With QuesTek, LLC


System Level Needs – State Awareness

• Material modeling should not be done in a “vacuum” apart from systems level considerations.

• Methodologies for fingerprinting materials and initial conditions on material state and relation to sensor thresholds/signals• Coupling of models with interrogation schemes via probabilistic, decision-based framework for state awareness• Methods for quantifying level of uncertainty and quantifying propagation of uncertainty (microstructure, model, etc.) in prognosis systems• Shifting the balance of sensing and modeling state via new materials may require materials design

Questions?

physically-based modeling in state- awareness monitoring strategies david l. mcdowell 1,2 regents’...

Documents

processed materials

materials designpartcauseeff

prognosis requirements

current state

based modeling

damage state analysis

component level design

project future state