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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
October 22, 2014
Improving Gear Life and Performance with Computational Testing
Computational Testing Applications
October 22, 2014
Improving Gear Life and Performance with Computational Testing
Design Performance
ComparisonsPerformance-Driven
Product Development
Fast Field Failure
Analysis Computational Testing
October 22, 2014
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
October 22, 2014
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
October 22, 2014
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?
October 22, 2014
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
October 22, 2014
Improving Gear Life and Performance with Computational Testing
October 22, 2014
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?
October 22, 2014
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
October 22, 2014
Improving Gear Life and Performance with Computational Testing
What is DigitalClone’s Technical
Breakthrough?
October 22, 2014
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?
October 22, 2014
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
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