Predicting Wind Turbine Life with Computational Testing

November 20, 2014

Predicting Wind Turbine Life with

Computational Testing

What Business Challenge Exists?

November 20, 2014

Predicting Wind Turbine Life with Computational Testing

Conceptual Design

Detailed Design Prototype

Physical Testing Launch

Customer Tests Product in the FieldFailure

Could this perform better?

Failure Example #1

White Etching Cracks

- Occurs across wind industry

- Disagreement on root cause

- Causing significant failures

November 20, 2014


Failure Examples #2 & #3

Blades – Composite Delamination

– Premature cracking, delamination

Pitch/Yaw – Lubrication Failures

– Seal/Lubrication Issues

– High Loading Conditions

November 20, 2014


Failure Examples #4

Supplier Selection

– Evaluating Case Carburized vs. Through Hardened

– Determining the best supplier for your application

– Testing quantities and loads

November 20, 2014


Innovation Examples #1

Gearbox Lubrication Testing

– Physical testing is bottleneck for lubrication innovation

– Very expensive to test the same conditions as in the field

– Using wrong lubrication is risky

November 20, 2014


Innovation Examples #2

Gearbox Design Changes

– Larger gearbox desings are under increasing pressure to perform to requirements

– More design ideas and iterations that can be tested

– Most design changes are not physically tested before going out to the field

November 20, 2014


Business Challenge Impact

November 20, 2014


WIND TURBINE

OEMS

High Warranty

Incremental

Design Changes

Limited Supplier

Evaluation

COMPONENT &

SYSTEM

SUPPLIERS

Long Sales

Cycle to Prove

Performance

Long

Development

Time

WIND

TURBINE

OPERATORS

Unexpected

Early Failures

Uncertain

Performance

What Business Challenge Exists?

November 20, 2014


Conceptual Design

Detailed Design

PrototypePhysicalTesting

Launch



Computational Testing – Perform 100’s of tests before prototyping

Computational Testing Applications

November 20, 2014


Component Lifecycle

Prediction

Assembly/System

Lifecycle Prediction

Fleet Analysis,

Monitoring, & Reporting

Manag

ed S

erv

ices

Saa

S/ A

aaS

Materials

Product Lifecycle

RequirementsDesign &

Test

Manufacture

WarrantyOperate &

MaintainReuse/Retire

Materials

DigitalClone Technical Approach

November 20, 2014


1) DigitalCloneSystem™

Loads & Requirements &

System Life

6) DigitalCloneLive™ Output

Predict-Acquire-Cofirm-Control

2) DigitalCloneMaterial™

Characterize & Create

Microstructure Model

3)DigitalCloneComponent™

Friction, & Lubrication

Surface Treatments

5) Predict Component

Failure Mode/Failure Life

4) Simulate Stress in

Microstructure - Predict

Crack Initiation &

Propagation

Superfinish

Ground Finish

Patents Pending

Failure Modes Ready for

Implementation

• Micropitting Fatigue

• Bending Fatigue

• Spalling Fatigue

• Fretting Fatigue

November 20, 2014


Bearing SpallingGear Pitting

Bending Fatigue Spline Fretting

Failure Modes in R&D

Released 2014

• White Etching

• Metal Wear (Abrasion, Adhesion, Scuffing)

• Corrosion Fatigue

• Composite Delamination

• Coating Degradation

November 20, 2014


Corrosion Fatigue and Wear

Composite Laminate

Metal Wear

White Layer Etching

Key Differentiation to Applications

Multi-body Dynamics

• Gearbox Design

Material Microstructure

• Supplier Selection

Mixed EHL Model

• Lubrication Testing

November 20, 2014


SuperfinishGround Finish

November 20, 2014


4 Main

Features

Vertical

ApplicationsOnline

Help

Private

Customer

Libraries

Online

Support

Wind Turbine Design Life

Prediction

November 20, 2014


November 20, 2014


Wind Turbine Fleet Life Prediction

Wind Turbine Fleet Life Extension

November 20, 2014


Wind Turbine Asset Life Prediction

November 20, 2014


B - Failure initiates

C - Failure can be confidently predicted - PROGNOSIS

A – Asset enters service

Gap between current diagnostic state D and future prognostic state C.

D - functional failure occurs

Wind Turbine Asset Life Extension

November 20, 2014


Case Study: First Wind

November 20, 2014


“Predictive maintenance allows us to be able to manage maintenance downtime and costs better than reactive maintenance programs.”

Frank SilvernailVice President of Engineering

http://vimeo.com/71119685

http://vimeo.com/71119685

Business Challenge Solution

November 20, 2014


Conceptual Design

Detailed Design


Launch




Business Challenge Solution

November 20, 2014


Conceptual Design

Detailed Design


Launch

Customer Tests Product in the Field


Business Solution Impact

November 20, 2014


WIND TURBINE

OEMS

Reduce

Warranty

New Designs

Evaluate

Suppliers

COMPONENT &

SYSTEM

SUPPLIERS

Increase Speed

of Confidence

Prove

Competitive

Advantage

WIND

TURBINE

OPERATORS

Root Cause

Analysis

Evaluate Fleet

Reliability

Extend Life

November 20, 2014

Predicting Wind Turbine Life with

Computational Testing

Predicting Wind Turbine Life with Computational Testing

Engineering

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