cutting aerospace validation costs in half using computational testing

Direct Benefits of Computational Testing

Sentient Science Funded $25M by World’s Largest Operator

Uniquely built with operators – Trusted 3rd Party

Direct Benefits of Computational Testing 1/26/16

Trucking/Boats

Direct Benefits of Computational Testing 1/26/16

Rail

Mining/Construction

Aerospace

Trucking

Transportation Systems

Discuss how computational testing advances in the aerospace industry lead to reduced cost and time for validations of new designs and

modifications.

Key Objective

1/26/16Direct Benefits of Computational Testing

Article in Nov/Dec 2015 Vertiflite


Sentient Science


Sentient DigitalClone®

Computational Testing Computational Asset Management

Most tested products Assets with the lowest cost of operation

Sentient Science helps extend the remaining useful life (RUL) of new and existing mechanical systems.

DigitalClone® is the single, registered brand name


Benefits of Computational Testing

1/26/16

• Physics-based models for computational testing can accurately replicate the performance of the physical components under conditions specified by the customer.

• Cost-effectively test designs under the widest possible range of conditions, to include seeded-fault tests, with minimal impact to budget and schedule.

• Rapidly optimize product design based on proven computational test results, rather than having to wait weeks or months to schedule expensive physical testing for initial design evaluation.

• Access hundreds of data points within days, rather than relying on one data point per physical test.

• By validating product design using Sentient Science, suppliers also benefit by being able to provide objective design validation results to their customers

Direct Benefits of Computational Testing

What Can Computational Testing Do for You?Use physics-based modeling and computational testing to:

• Reduce qualification costs associated with physical testing of design prototypes

• Accelerate product development cycle by virtual evaluations of design alternatives

• Enable life predictions for key components that are based on first principles and material science

• Expand the ability to validate design effectiveness under a wider variety of environmental and loading conditions

Computational testing does not eliminate the requirement for physical testing required to establish airworthiness, but provides a powerful tool that can reduce the cycle time and costs of evaluating design efforts.


Computational TestingTo Be (Goal)As Is (Today)

Cost

Design Process (Years)

100X

10X

1X

Requirements Definition EIS

Specimen

Testing Manufacturing Trials

Subco-mponent

Level Testing

ComponentLevel

Testing

Sub-SystemLevel

Testing

SystemLevel

Testing

100X

Cost

Design Process (Years)

10X

1X

Requirements Definition

EIS

Manufacturing

Trials

Specimen

Testing

Subco-mponent

Level Testing

Physical-based Modeling

Sub-SystemLevel

Testing

SystemLevel

Testing

ComponentLevel

Testing

Sentient Science is supporting Customer initiatives to: 1. Reduce Product Time-to-Market2. Reduce Validation Expense3. Tailor Product Reliability and Cost

Reference of Graphics: Integrated computational materials engineering from a gas turbine engine perspective – John Matlik, Rolls-Royce - http://www.immijournal.com/content/3/1/13


Example: DigitalClone Computational Test Project

Customer Profile:• Component Supplier for Aerospace Industry• Sophisticated Engineering and Physical Testing Resources

Program Objectives:• Customer could not predict life of bearing with advanced material.

The life of the physical test was too high and led to suspensions. • Physical testing would require months and $100K+ to test one

sample. The test rigs had a 6-15 month queue wait time.• The customer wanted to quantify the life of the bearing to help sell

it to field applications without high cost and time of testing.


Example: DigitalClone Computational Test ProjectSolution: Sentient evaluated 3 bearings with different material microstructures, residual stresses, and microgeometries. Sentient ran a total of 163 computational tests.

Value Assessment: • Replaced $650K+ Expense of

Equivalent Bearing Fatigue Tests• Replaced 534 Days of Equivalent

Bearing Fatigue Tests• Quantified Life Extension of the

Bearing

Opportunities:• Partnership opportunities to leverage

joint Computational Testing and Hardware Testing to perform more analysis for end-user customers within bearing programs

Material Configuration

L10 Hours

L50 Hours

L90 Hours

Bearing A 1x 1x 1x

Bearing B 1x 1.05x 1.1x

Bearing C 17.9x 25x 38x

Note: Example images provided for illustrative purposes only.Specific Operating Hours Removed for this Presentation


Bearing A

Bearing B

Bearing C


Customer Profile:• Rotorcraft OEM with in-house gear design• Sophisticated Engineering and Physical Testing Resources

Program Objectives:• There are too many gear design parameters that affect life to test

them all. (Microgeometry, material microstructure, heat treatment processes, surface finish, etc.)

• It is expensive to test multiple configurations before gearbox design is finalized and enters into service.

• Customer was seeking a way to optimize the life of their spiral bevel gear design using computational tools before testing.



Solution: DigitalClone can run sensitivity studies to isolate different parameters and their effect on life. Customers can find the optimal life configuration based on their financial or manufacturing criteria.

Value Assessment: • Optimize gearbox over-

engineering and cost per unit• Optimize gearbox life

requirements and cost per flight hour for Total Cost of Ownership

• Avoid qualification testing failure and re-design loops

Opportunities:• New opportunities to compete for

new programs by quantifying TCO



Customer Profile:• Engine OEM with in-house gear design• Sophisticated Engineering and Physical Testing Resources

Program Objectives:• Customer was considering different potential suppliers to reduce

overall manufacturing costs. • All the suppliers met internal design specifications, and

qualification testing required $250K-350K investment.• If test failed, this was a risk of $300K+ for root cause analysis and

subsequent testing to validate the problem was fixed.


Example: DigitalClone Computational Test ProjectSolution: DigitalClone can run supplier comparisons before investing in qualification testing. Even if components meet traditional engineering specifications, problems in microstructure and surface roughness can lead to unexpected failure

Value Assessment: • Compare more suppliers for strategic

sourcing programs without increasing validation cost

• Run more tests of each supplier to quantify life impact of variability in supplier quality standards

• Avoid qualification testing failure and re-design loops ($250K-$350K each)

Opportunities:Engineering assessments to lead procurement strategic sourcing programs to optimize per unit AND cost per flight hour

Note: Specific Operating Hours Removed for this Presentation

Low Quality

Medium Quality

High Quality
