Development of Smart Digital Twin for Maritime Industry...

Development of Smart Digital Twin for Maritime Industry via O2DES Framework

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Dr. LI HaobinNational University of SingaporeDepartment of Industrial Systems Engineering and ManagementCentre of Excellence in Modelling and Simulation for Next Generation Ports

Outline

Smart Digital TwinsBackground – Industry 4.0Complexity of SystemsSimulation as an Exploration ToolThe Four Dimensions

Proposed MethodologiesClassification of SimulationFormalisms for Discrete-Event Systems (DES)Object-Oriented DES (O2DES) Framework

Future TrendsHigh-Level ArchitectureContainerization with Microservice

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3

Smart Digital Twins

Background – Industry 4.0

Explore(Mining)

Summarize(Refining)

Propagate(Transporting)

3 Scenario Variables

Responding D

ecision Variables

Unknown

Unknown

Reinforcement Learning + Deep Learning

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Smart Digital Twins

Complexity of Systems

The unknown is not about individual components,

but how they are combined and interact.Same principle applies for a complex industrial system, e.g., a mega container port

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Smart Digital Twins

Simulation as an Exploration Tool

Simulation

Bridging Physical & Digital World

Commercial off-the-shelf simulation software

- For evaluating specific design / configuration

- Manually configured

Simulation as an Exploration / Experiment Tool

An object -oriented framework / paradigm to- Facilitate DES model building- Automate model configuration- Integrate DES with optimization- Bridge DES to other AI components, e.g.,

deep learning & data analytics

Interface with Human Users

Interface with Machine / Algorithms

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Smart Digital Twins

The Four Dimensions

v0v1

v2v3

To draw the “game- board” for industrial systems to explore unknowns

To train a “grandmaster” for complex industrial system

Connectivity

Visibility

SMARTDIGITAL TWIN

Analyzability

Granularity

Conceptual models

Planning models

Operational models

Exact protocols

mor

e ev

ents

/ m

odul

ar d

etai

ls

closer to physical systems

Hard-coded / console input

parameters

Simple GUI configuration

Port XMLGraphical

configuration tools

Interface to database

Sync with IoT sensors

Simulation analytics with learning

Optimization via large scale search

Optimization embedded simulation

Simulation evaluation

tow

ards

real

-tim

e de

cisio

ns

closer to human perception

Console displayfor analytical measures

Dashboard statisticalcharts

2D/3D animation

Multi-screendisplayVR/AR/

Mix-Reality

Ranking and SelectionSimulation Analytics

Simulation Modelling

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Proposed Methodologies

Classification of Simulation

Electro-mechanical

Systems

Enterprise Systems

Socio-Economic, Ecological Systems

Subatomic World

Cosmological Systems

Discrete-Event SystemContinuous System Continuous System

- Finite element simulation- Physical / mechanical /

chemical behavior- Fluid dynamics- Material science- Equipment design

- System Dynamics- Cause loop analysis- Large scale system

engineering

- Simulation of man-made process / protocols

- Discretized decision makers- Event-based decision

making procedures

Scale of System

Quantum System

Quantum System

(by State Variables)

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Classification of Simulation (by Time Representation)

Computer Simulation

Analytic Simulation(Time-stamp simulation)

VE Simulation(Time-delay simulation)

Autonomous Constructive (HIL) Virtual Constructive (HIL) Live (HIL+MIL)

Might also include MIL (either hardware and/or software), it is also referred as an emulation

Most common Animation / movie

Game Monitoring / surveillance system / control tower

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Formalisms for Discrete-Event Systems (DES)

In the classical activity-cycle diagram an activity typically represents the interaction between an entity* and active resources. An entity or an active resource is either in a passive state called a queue or in an active state called an activity. Queue nodes and activity nodes are connected by arcs.

Passive State

Passive State

Passive State

Active State Active State

Arc (for resource)

Arc (for entities)

Passive State

Arc (for resource)

Duration

1 Token

Infinite Tokens

Zero Token

Multiple Tokens

Marking

Intuitive,

but less flexible

Activity-based Formalism

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Event-based Formalism

An event graph is a graphical formal model consisting of a set of event nodes and a set of directed edges. It provides a complete description of a discrete event system (DES) in a concise and clear manner, and its execution rules have to be described unambiguously.

A graphical model can be specified in algebraic form (to be analyzed by a human logically) as well as in computer-readable form (to be executed on a computer).

Originating Event Destination EventDestination Event

Scheduling Edge

Canceling Edge

Time Delay ConditionCondition

State Change State Change State Change

Precise,

but Sophisticated

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State-based Formalism

Generator Queue Server

State:How many arrivals are generated?

State:How many are in the queue?- When the queue has finite capacity, how many are requested but pending to enqueuer?

Arrive

Request toEnqueue

Dequeue

EnqueueRequest toStart

Start

Ready toDepart

Depart

State:How many being served?- When the queue has finite capacity, how many are requested but pending to start?- How many are “stuck” due to subsequent procedures?

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Object-Oriented DES (O2DES) Framework

Object-Oriented Formalism

Input 1(parameters)

Output 1(parameters)

Input 2(parameters)

Output 2(parameters)

Input M(parameters)

Output N(resource, quantity)

……

……

Parent Module

Sub Module1

In-1

In-2

Out-1

Sub Module2

In-1

In-2

Out-1

Evt1

Evt2

Evt3

Evt5

Evt4

Evt6

Evt7 Evt8

Out-2

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Big Data Analytics / Deep Learning

Optimal Configuration

SIMULATION ANALYTICS

Random Seed

SIMULATION IN OPTIMIZATION

Sim.-based Optimization (Search Algorithm / Ranking & Selection)

Event 1

Event 2

Event 3

Event 1

Executor

Entity 1

Entity 3

Entity 2

Load 1

Load 2

STATE

EVENTS

ConfigurationEn

viro

nmen

t /

Initi

al S

tate

Obj

ectiv

es

Event 1

Event 2

Event 4

Event 5

Event 3

Event 3(OPT.)

Event 1

Event 2

Event 4

Event 5

OPTIMIZATION

OPTIMIZATION IN SIMULATION

Conditions

ASSETS（ Static（

SANDBOX（ Dynamic（

Seamless Integration betweenSimulation and Optimization

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Hierarchical Modelling with Modularization

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Future Trends

High-Level Architecture (HLA)

Run-Time Infrastructure (RTI)


Federate 4 Federate 5 Federate 6

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Sub-Federation

Run-Time Infrastructure (RTI)High-Level Architecture (HLA) conforms to IEEE 1516-2010

Federate 1 Federate 2 Federate 3

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Federation

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Future Trends


Multi-Module Distributed Run

Multi-Formalism Synchronized Run

Integrated Simulation + Visualization + Optimization + Learning

Simulation – Emulation Seamless Transition

Digital Twins Development and Experiment

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Future Trends

Containerization with Microservice

Monolithic Application Scaling Approach

without

Functional layers such as

front endbusiness logic

and data storage

A simple update can have unforeseen effects on the rest of the tier, and so a change to an application component requires its entire tier to be retested and redeployed.

(Simulation Modules)

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Future Trends

Containerization with Microservice

Microservices Application Scaling Approach

Microservices are independent components that work together to deliver the application's overall functionality. The term microservice emphasizes that applications should be composed of services small enough to reflect independent concerns.

with

scale + agility + reliability

(Simulation Modules)

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Development of Smart Digital Twin for Maritime Industry via O2DES Framework

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