The use of Physics Based Predictive Analytics in Digital Twins

Frode Halvorsen, PhD

Vice President Technology

www.edrmedeso.com

Outline

• Business motivation

• Enabling technology

• Customer cases

IoT Value Chain

CustomerApplicationsPlatformConnectivitySensorsSmart

Product

+ +

Industrial IoT by EDRMedeso

Understand the health of asset

Analyze Real Time Data

Collect Data

Connect Asset

Tim

e to

dev

elo

p

Market Value

Condition Based Analytics Condition & Prediction Based Analytics

Use Historical Data to Predict Asset Behavior in the future

Understand the health of asset

Analyze the Real Time Data

Collect the Data

Connect the Asset

Market Value

Tim

e to

dev

elo

p

Analytics Solutions

Copyright EDR & Medeso AS 2016

Use-Cases Data DefinitionAlgorithm Definitions

ImplementationFill the Data

LakeModelling Product

3-10

Years

Analytics Solutions

Copyright EDR & Medeso AS 2016

Use-Cases Data DefinitionAlgorithm Definitions

Generate Data via Simulation

Algorithmic Modelling

Implementation Product

Weeks

Accelerated Physics Based Solutions

Unpredictive

Physical unit

Predictive

Digital twin

Realtime Future

Analysis

Sensor on physical unit

Trigger ROM

Full 3D

Predicting maintenance

and failure

Sensor on physical unit

Real time analyticsCondition based

“Traffic light indicators”

The Digital Twin

Copyright EDR & Medeso AS 2016

Ethernet

SmartTags Local Gateway(s) Cloud connect API

WiFi

Mobile

Mesh

BLE

Implementation

Copyright EDR & Medeso AS 2016

0

50

100

150

200

250

300

350

400

450

0 500 1000 1500 2000 2500 3000 3500

Vibration rms [mg]

Copyright EDR & Medeso AS 2016

Operational Output

WindVelocity

WindAngle

Rotational speed

Mechanical loads

Currentsweep

Displacements Damage?

Power

Positionsweep

Q

U

A

L

I

T

YVoltage/Current

M

A

I

N

T

E

N

A

N

C

E

Digital Prototypes

PMSM

free

a

b

c

mech_rv

Reduced-Order

Modeling

A simplification of a high-fidelity dynamical model that

preserves essential behavior and dominant effects,

for the purpose of reducing solution time or storage

capacity required for the more complex model.

How to get

from here…

…to here?

High-Fidelity 3D Physics

System-Level Behavior

Equation / Data-Based

Traversing Physical Model Fidelity

• 3 SVD modes only need to reconstruct the unsteady temperature field

• A temperature signal excitation is provided to cover the full temperature spectrum

• Verification of the Dynarom model created

310

320

330

340

350

360

370

0 2 4 6 8 10

Fluent

Dynarom

Time (s)

Seco

nd

ary

Inle

tTe

mp

erat

ure

(K)

Tem

per

atu

reFi

eld

(°C

)

ANSYS DYNAROM – Step 1

310

320

330

340

350

360

370

0 2 4 6 8 10

Seco

nd

ary

Inle

tTe

mp

erat

ure

(K)

Time (s)

Fluent

DynaromTe

mp

erat

ure

Fiel

d (

°C)

Step 2 – Arbitrary temperature field

ANSYS Twin Builder:

Controling TSR –Tip Speed Ratio

(Ratio between blade tip speed and wind speed)

Wind direction

Pitch angle – Control Signal

Rotor speed – Control feedback

Wind speed

Setpoint TSR

Digital Prototype

Digital Twins

ANSYS Digital Twins in the Cloud

Grundfos SYSMON digital twins - harness the power of IoT to optimize your life.Grundfos, a global leader in design and manufacturing of pumps and water systems, brings advanced simulation and prediction capabilities to real-time in collaboration with simulation-powerhouse ANSYS and elite channel partner EDRMedeso. Grundfos will use digital twins to better serve its customers through improved product quality and performance, enhanced development productivity, optimized maintenance and reduced overall costs and risks associated with unplanned downtime.

Main Takeaways

• The key technologies are ready; implementation can start tomorrow

• Commercial solutions available on the market

• Physics based analytics offers an accelerated process compared to most predictive analytics

• Established and proven methods can be applied in new areas

• Numerous use cases already implemented; first runners are running