becoming a predictive digitally enabled company€¦ · predictive analytics 3.0 is an enabler for...

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Juan F. Betts, PhD, MBA – Managing Director

FROM SIMULATION POWERED DESIGN TO PREDICTIVE DIGITAL TWINS

BECOMING A PREDICTIVE DIGITALLY ENABLED COMPANY

June 26, 2019

THE VISION

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Design Exploration & Optimization

• Reduced Design Cycles• Upfront Product Optimization• Robust design

Virtual Validation & Test

• Automated & Consistent Virtual Validation

• Statistically Sound Simulation Validation with Test

• Cost Effective and Efficient, Meaningful Tests

Optimized Manufacturing

• Bridge the Gap between “As Designed” vs. “As Manufactured”

• Optimized Manufacturing Process

• Fast disposition of quality nonconformances

Optimized Operations

• Predictive Maintenance• Optimized Product

Performance & Tuned for Operating Conditions

• New Service and Revenue Models (e.g. PaaS)

Voice of the Customer

Conceptual Preliminary Detailed V&V Manufacturing Sales Customer Use

SIMULATION DEMOCRATIZATION POWERED BY PREDICTIVE ANALYTICS 3.0

Simulation Powered Design Predictive Digital Twins

OUR CAPABILITIES

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• Development of Simulation Apps & Guided Workflows

• Automating Simulation Processes• Development of Fast Running

Physics Informed Machine Learning Surrogates

• Performing Design Optimization Studies & Creating Robust Designs

• Providing the EASA Software Platform for Rapid & Codeless App Development

• Providing the 3DEXPERIENCE & CATIA Design Software


• Performing Complex Engineering Simulations

• Employing Advanced Uncertainty Quantification (UQ) & Model Validation Techniques

• Performing Virtual Test & Designing Effective Test Frameworks

• Development of Fast Running Physics Informed Machine Learning Surrogates

• Providing Engineering Simulation Software from SIMULIA



• Simulation & Optimization of Manufacturing Process

• App Creation and Deployment• Hybrid Modelling: Integration of

Data Driven and Physics Informed Methods

• Information Fusion: Integration of data with differing levels of uncertainty



• Simulation & Optimization of Manufacturing Process

• App Creation and Deployment• Hybrid Modelling: Integration of

Data Driven and Physics Informed Methods

• Information Fusion: Integration of data with differing levels of uncertainty


A NEW PARADIGM: -PREDICTIVE ANALYTICS 3.0

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Design Exploration & Optimization Virtual Validation & Test Optimized Manufacturing Optimized Operations


Increased Product Development Speed & Quality Fundamentally Transforms & Improves the Business Model

Predictive Analytics 3.0 enables the creation of ultrafast highly predictive solvers with a small fraction of the data that traditional data driven methods require.

PREDICTIVE ANALYTICS – THE EVOLUTION

Cloud Computing

Limitless Computing Power While Using Traditional Computational Methods

A Hardware Revolution

1.0

Big Data Analytics

Using Data Science to Understand Data

Statistics, Machine Learning & AI Will Save Us

2.0

Information Intelligence

Combining Physics Driven and Data Driven Methods to Get Actionable Insight & Reduce the Amount of Data Required

3.0

WHAT DOES PREDICTIVE ANALYTICS 3.0 ENABLE?

Information Intelligence

Combining Physics Driven and Data Driven Methods to Get Actionable Insight & Reduce the Amount of Data Required

Faster than Realtime Solving of Complex Engineering Systems

Predictive Digital Twin for Product Performance

Substantially Less Data Required to Create and to Obtain Insight

Seamless Design & Simulation Integration & Democratization

“Allows for Extrapolation” 6

PREDICTIVE ANALYTICS 3.0 – HYBRID MODELING: PHYSICS INFORMED MACHINE LEARNING

Data Driven MethodsLike Machine Learning

Hybrid Modeling:Physics Informed Machine Learning

Patent Pending

Finish with Machine Learning

Perform Critical Path Analysis

Develop Physics Multistage ROM

Determine Governing Physics

Start with Physics Simulation

Engineering Simulationand/or

Data Driven Methods

Complexity of Causal RelationshipBetween Inputs & Outputs

Dat

a Ex

pens

e

PREDICTIVE ANALYTICS 3.0– CRITICAL PATH ANALYSIS EXAMPLE

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SIMULATION POWERED DESIGN

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ENGINEERING TOOLBOX– FOR SIMULATION POWERED DESIGN

• Automate Your Simulation Processes

• Create Fast Running Surrogate Models

• Create Apps that run the Automated Workflow

• Implement User Controls on Apps

• Test App by Pilot Release to a Small User Community

• Deploy App to the Wider User Community

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Centralized Repository & Customizable Excel Wizard to Rapidly Create Apps

Revision Control & User Feedback User Specific Access Controls

Excel Functional Models Linear Models Non-linear Models

ENGINEERING TOOLBOX– CAN BE EMPLOYED FOR VARIETY OF PROBLEMS

in-house codes

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PREDICTIVE DIGITAL TWINS

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WHAT IS A DIGITAL TWIN?

Impl

emen

tatio

n

Eval

uatio

n &

Insi

ght

Field Data

Operational Improvements & Recommendations

A virtual replica of a physical asset(s) that uses field data as model inputs to predict the asset(s)’ performance

WHAT ARE THE BENEFITS OF DIGITAL TWINS?

Gives new revenue stream opportunities in Product as a Service (PaaS) business models

Enables companies to offer its customers expanded services and product options

Improves customers’ experiences by enabling products to tune their behavior to field use conditions

Allows to sell differentiated solutions rather than commoditized products

Creates valuable, long-term and sticky customer relationships

GE is transforming field services into an outcomes-based growth business through their PREDIX Digital Twin platform.

Alstom has launched HealthHub a Digital Twin platform for predictive maintenance of train systems

Komatsu and Cloudera team up to create IT platform for monitoring mining equipment

OVER 70% OF INDUSTRIAL COMPANIES ARE EITHER CURRENTLY USING OR ARE PLANNING TO USE PREDICTIVE DIGITAL TWIN TECHNOLOGIES

WHAT IS THE MARKET DOING ON DIGITAL TWINS?

The electric car manufacturer has a digital twin for every car it builds, tied to the car’s vehicle identification number (VIN). Data is constantly transmitted back and forth from the car to the factory

2017 SURVEY ON DIGITAL TWINS

• 24% Already using digital twins • 24% Not using, but planning to use in the

next year • 19% Not using, but planning to use in the

next three years • 7% Not using, but planning to use in four or

more years • 20% Not planning to use digital twins • 8% Not familiar with this technology

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System PerformanceOptimization

Drive Train EngineIndustrial Robotics

Gear BoxPredictive Maintenance

EXAMPLE APPLICATIONS

Exhaust ManifoldRemaining Useful Life

System PerformanceOptimization

Drive Train EngineIndustrial Robotics

Gear BoxPredictive Maintenance

EXAMPLE APPLICATIONS – AN ENGINE EXAMPLE


PURELY DATA DRIVEN VS. PHYSICS INFORMED

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Converted complex computational process that takes weeks to run, into an automatically updating adaptive fast

surrogate solver that runs in seconds. This surrogate required only 21 data points to create.

This fast running surrogate solver can be integrated into an Edge Computing Digital Twin Architecture as will be

shown on the demo

To get an equivalent fast running surrogate solver using Cloud Computing and Big Data Analytics methods would have required 20,000 data points* that would have taken

1,500 years and at least $100M in computational resources.

Predictive Analytics 1.0Cloud Computing

Predictive Analytics 2.0Big Data Analytics

Predictive Analytics 3.0 Solver for Exhaust Manifold Thermo-Mechanical Fatigue VS.

+

*Utilizing an Optimum Monte Carlo Sampling Method for 37 Variables

DIGITAL TWIN EXAMPLE - ENGINE EXHAUST MANIFOLDDevelopment Process

Engineering Simulation

Surrogate with Predictive Analytics

3.0App Development

DIGITAL TWIN EXAMPLE - ENGINE EXHAUST MANIFOLDEngineering Simulation



3.0App Development

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Parametric Modeling

•Feature-based and parametric models allow us to perform a design of experiment analysis with the least amount of human interaction.

Computational Fluid Dynamics

•Convective heat transfer coefficient calculation to map the gas temperature cycle to the solid part.

Structural Analysis

•Coupled Temperature-displacement analysis to perform a structural analysis and capture stress/strain cycles.

Cloud Computations

•High performance computations allow us to run multi simulations faster in parallel on the cloud while the local machine’s hardware remains available for model development.

Fatigue Life Assessment

•The theory of critical distances has been utilized to predict the durability of the product.

Process Automation

•Process automation with iSight allow us to perform a DOE analysis and explore the design space. The results are used to pick a more reliable design and create surrogate models for further investigations.

ENGINEERING SIMULATION

DIGITAL TWIN EXAMPLE - ENGINE EXHAUST MANIFOLDPredictive Analytics 3.0



3.0App Development

SURROGATE WITH PREDICTIVE ANALYTICS 3.0

Input Variables Fluid Thermal Fluid-Thermal Structural Coupling Stress-Strain Fatigue Life


3.0

Predicts simulation results in <1s

Simulation process takes weeks

Patent Pending

DOES PREDICTIVE ANALYTICS 3.0 WORK?-BLIND DATA VALIDATION

All data points are shown, for the case in which they were treated as blind

(Therefore this plot implicitly includes five different sets of coefficients)

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

1.E+08

TMF p

redi

cted

TMF Actual

All Data when Blind

Test

Blind Data Average Error (Linear):38.13%Blind Data R2 (Log): 0.89

Location - Overall Minimum

(10% Log TMF Error)

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Location - Overall Minimum

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

1.E+06

1.E+07

1.E+08

TMF p

redi

cted

TMF Actual

Refit with all Training

Train

Blind Data Average Error (Linear):19.72%Blind Data R2 (Log): 0.97

(10% Log TMF Error)

DOES PREDICTIVE ANALYTICS 3.0 WORK?-BLIND DATA VALIDATION

DIGITAL TWIN EXAMPLE - ENGINE EXHAUST MANIFOLDPredictive Analytics 3.0



3.0App Development

APP DEVELOPMENT OF DIGITAL TWIN


User inputs design details of exhaust manifold being evaluated.

Remaining Useful Life, %

APP DEVELOPMENT WITH THE EASA PLATFORM

SUMMARY Digital Twin will have transformative value for companies and at its crux is the

intersection between engineering and data science

Predictive Analytics 3.0 is an enabler for Digital Twin due to: Cost of Data Generation, Cost of Data Transmission, Cost for Sensors, Speed to Insight,…

Predictive Analytics 3.0 works and has been validated

Machine Learning approaches might not be physically viable

Predictive Analytics 3.0 “Allows us to extrapolate rather than just interpolate”

Can leverage your existing know-how and software tool set

We engage customers on a Consultative One-on-One pilots basis. This is not a cookie cutter software play.

Thank You

800 Boylston Street, Suite 1600,Boston, MA 02199

11555 Heron Bay Blvd, Suite 200,Coral Springs, FL 33321

Front End Analytics

Phone: (617) 517-9743Fax: (617) 608-5036Sales: [email protected]: [email protected]: [email protected]

mailto:[email protected]



becoming a predictive digitally enabled company€¦ · predictive analytics 3.0 is an enabler for...

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