Improving Gear Life and Performance with Computational Testing

15
Improving Gear Life and Performance with Computational Testing Science

Transcript of Improving Gear Life and Performance with Computational Testing

Improving Gear Life and Performance

with Computational Testing

Science

Agenda

• What is Computational Testing? – Rotorcraft Case Study

• What is the Technology Breakthrough?– Wind Turbine OEM Case Study

• How does it connect to the Industrial

Internet?– Wind Turbine Operator Case Study

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Improving Gear Life and Performance with Computational Testing

Computational Testing Applications

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Improving Gear Life and Performance with Computational Testing

Design Performance

ComparisonsPerformance-Driven

Product Development

Fast Field Failure

Analysis Computational Testing

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Improving Gear Life and Performance with Computational Testing

4 Main

Features

Vertical

ApplicationsOnline

Help

Private

Customer

Libraries

Online

Support

What Failure Modes do we Solve Today?

• Micropitting Fatigue

• Bending Fatigue

• Spalling Fatigue

• Fretting Fatigue

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Improving Gear Life and Performance with Computational Testing

Bearing SpallingMicropitting

Bending Fatigue Spline Fretting

What Failure Modes are Coming Soon?

• White Etching

• Metal Wear

(Abrasion,

Adhesion, Scuffing)

• Corrosion Fatigue

• Composite

Delamination

• Coating

Degradation

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Improving Gear Life and Performance with Computational Testing

Corrosion Fatigue and Wear

Composite Laminate

Metal Wear

White Layer Etching

What Sensitivity Studies Can be Analyzed?

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Improving Gear Life and Performance with Computational Testing

Improving Gear Life and Performance

How has DigitalClone Been Applied for

Rotorcraft?

• Challenge:

– A rotorcraft OEM wanted to understand how life and

performance of their gearbox spiral bevel gears changed under

different horsepower, surface finishing, and residual stresses

• Sentient Objective:

– Computationally test how different duty cycles, residual stresses

and surface finishing would affect gearbox life

– Sentient results matched the OEM’s design life under loading

conditions equivalent to those experienced during the

qualification testing

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Improving Gear Life and Performance with Computational Testing

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Improving Gear Life and Performance with Computational Testing

• Sentient results

matched the rotorcraft

OEM design life

under loading

conditions equivalent

to those experienced

during the

qualification testing

• DigitalClone assists in

increasing the

gearbox ratings

(Maximum

Continuous Power)

Improving Gear Life and Performance

How has DigitalClone Been Applied for

Rotorcraft?

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Improving Gear Life and Performance with Computational Testing

• DigitalClone assists

in comparing

different surface

treatments

• Superfinishing

process reduces the

asperity interaction,

thereby improved

fatigue resistance

over ground finish

gears

Improving Gear Life and Performance

How has DigitalClone Been Applied for

Rotorcraft?

How has DigitalClone Been Applied for Wind

Turbines?

• Challenge:

– A leading turbine manufacturer’s gearboxes failed after a

few months of operation. The manufacturer paid millions

for repairs while the operator shut down all of their

turbines. The failure was a high-speed pinion gear

experiencing fatigue from misalignment.

• Sentient Objective:

– Predict the probability of failure with computational testing

and compare to field failure results

– Recommend gearbox configuration to improve the

performance

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Improving Gear Life and Performance with Computational Testing

What is DigitalClone’s Technical

Breakthrough?

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Improving Gear Life and Performance with Computational Testing

Predict fatigue life for gearbox critical components and make

recommendations to improve the performance

What is the technical approach

of DigitalClone?

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Improving Gear Life and Performance with Computational Testing

1

Determine

Component

Hot Spot

2

Build Material

Microstructure

Models

3

Build Surface

Traction Models

4

Material

Microstructure

Response

5

Calculate Time

to Mechanical

Failure

6

Predict

Fatigue Life

Distribution

Component Life Prediction (CLP) Technology Overview

Case Study: First Wind

“Predictive maintenance allows us to be

able to manage maintenance downtime

and costs better than reactive

maintenance programs.”

Frank Silvernail,

Vice President of Engineering

Wind Turbine Make/Model:

150 Clipper Liberty 2.5MW

Number of Wind Power Plants:

Six, across four states

Business Challenge:

Liberty 2.5MW machines fail

at much higher rates than

predicted by manufacturer

during end of warranty

discussions

Solution:

Sentient Science to provide

DigitalClone Live services for

predicting and extending

RUL

Improving Gear Life and Performance with Computational Testing

October 22, 2014

Improving Gear Life and Performance

with Computational Testing

Science