2017.Nov.2

Korea Japan Smart ship Joint Session\

Optimal Routing Development

Based on Real Voyage Data

DSME/KMA/HMM

S.W.KIM, J.W.CHOI, H.R.PARK, D.J.JUNG, S.S.Byeon, H.M.EOM

SNAK Smart-Ship Joint Session

NOV.2017.2ND

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Takeaway

▪ The state of art: Autonomous Vessel

▪ A Key Challenges : Routing Optimization

▪ Our Approach

▪ Realistic Environment Modeling

▪ Precise Performance Estimation

▪ Power Comparison based on Real Voyage Data

▪ Case Study: East Bound Container

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Recent News

• Amazon, Alibaba-Mearsk

• Rolls-Royce and Google / ABB and IBM

• Kongsberg and YARA International

• Autonomous Ship on IMO 2017 Agenda

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Bloomberg: May 17th 2017

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2017.Nov.2

Korea Japan Smart ship Joint Session

Future is already here- Global movements for developing autonomous ship-

ABS ROYAL NAVY

MUNIN,AAWAI,YARA:2019

MUNIN : Maritime Unmanned Navigation Intelligence in Networks

AAWAI : Advanced Autonomous Water-bone Application Initiative

JAPAN : NYK plant to launch Autonomous Container Carrier to 2019

CHINA : Shipyard – Shipping Industry – Research Institute Alliance

US : Remote OSV operation completed in 2017

Korea

NYK 2019

CHINESE 2020NAVY Korea

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Why Autonomous?

25% of CAPEX

20% of OPEX

Training Cost

96 % Ship Collision1)

Piracy Victim

1) Dr. Rothblum, Human Error and Marine Safety, USCG, 2012

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Challenges Towards

Autonomous Vessel

Trim

Optimization

Fouling Smart

Monitoring

Remote

Control

Cost -Efficient

Voyage

IAS

Platform

Port

Automation Logistics

Optimization

Risk

Analysis7

Cost Efficient Voyage - Technology Roadmap-

Bad Weather Avoidance

FOC(Fuel Oil Consumption)

Response Based/Multi Objects

Route Optimization

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Information Optimization Decision

Optimization Frame

(Input)

Weather, Ship..(Evaluation) (Output)

Optimal

Route

Performance

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Performance Estimation

Resistance, Motion

Weather, Trim, Fouling

Propeller Emerging

Engine Dynamics

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Object (ex: FOC)

Constraints

Piracy

Cargo Safety

Land Avoidance

Arrival Time

Ice

Fuel Factors

Calm Resis.

Wave Added

Wind Load

Generic Input

Sea Chart

Weather Forecast

Case Input

Target

Fuel Consumption

Ship Response

Main Parameters

Routing

Optimization

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Weather

R&D Collaboration

Ship Voyage

“Goal : Realistic

Performance Estimation”

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•Resistance

•Wind

•Wave

Increasing

Power

Consumption

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Major Factors of

Power Consumption

Increased Power

_

_

, ,

, ,

: , ,

:

:

calm wave added wind

calm wave added wind

Power R U R U R U UT

R R R

Calm Water Wave Added and Wind R esistance

U Vessel Speed

Vessel Heading Angle

T VoyageTime

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Estimated

Power

(Analysis

+ Model Test)

Measured

Power

(Voyage

Data)

VS

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Power Comparison based

on Real Voyage Data

Case Study : HMM HopeLPP: 349.5m

Breadth:48.4m

Draft:14m

Volume : 162517 m3

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Target : from Yokohama to Sanfrancisco

Period: June 1ST 2016 TO JUNE 10TH 2016

Great Circle Distance : 8,206 km

Yokohama San Francisco

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Routing Optimization Problem

_

_

,

, ,

:

, , : , ,

:

:

calm wave added wind

calm wave added wind

Object to minimize FOC

where

FOC R U R U R U UT SFOC

FOC Fuel Oil Consumption

R R R Calm Water Wave Added and Wind R esistance

U Vessel Speed

Vessel Heading Angle

T VoyageTime

SF

: ( / )

: , , ,

OC Specific Fuel Oil Consumption ton kwh

Suject to VoyageTime Ta rget Position Speed Range Heading Angle Range

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Optimization Algorithm: Iterative DP

A method for solving complex optimization problem by breaking it down

into a collection of simpler sub-problems using the iterative calculation

based the back propagation optimum theory.

Concept Diagram 19

Routing Optimization Procedure

1) Divide Whole Voyage Routes into Unit Step

2) Load Ship/Weather/Voyage Data

3) Create Speed and Heading Command Seeds

4) Find Optimum which has Minimal Fuel Oil Consumption

• Calculate FOC based on Estimated Power

(Total Resistance)

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Calm Water Resistance

Calm Water Resistance due to Speed

21 22 23 24 [Knot]

:calmR Calm Water Resistance

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_ , ,wave addca inl e w dm dPower R U R UR UTU

Wind Load Estimation

2 20.5 0.5 0wind A AA WR T WR A AA L GR C A V C A V

ρA : Air Density

VWR /ψWR : Relative Wind Speed and Direction

AT / VG : The Projected Area and Advancing Speed of the Ship

CAA : Wind Load Coefficients

Wind Load Coefficients : CAA 22

_ ,,calm wave adde windd RPower R U UR U UT

Wave Added Resistance

via Wish SNU

Mean Drift Force Estimation based on Potential Theory due to various Speeds and Drafts

Strip Model RANKINE Panel Model23

_ ,,wave addcal inem w ddPower R U R U UR TU

_ 2

0

,,wave added

A

QTFR E d

ω, α, and ζA : Frequency, Direction, and Amplitude of the Wave

E(ω,α): The wave spectrum.

Wave Added Resistance

Model Test under Irregular Waves

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Wave Added Resistance

:Design Draft

Mean Drift Force due to Wave Heading Angle

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Realistic Weather Forecast Data

by KMA

3 Hour Based

Ensemble Forecast Data

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Time

Position dataLatitude (radians & degrees)Longitude (radians & degrees)

Environmental data

Wave height (m), wave period (seconds)Wave direction (degrees)Wind speed (knots)Wind direction (degrees)

Ship data

Speed over ground (knots)Longitudinal water speed (knots)Draft Aft, Fwd (m)Trim Dynamic (m)Propulsion RPM (rpm)Propulsion Power (kW)Propulsion Torque (Nm)Rudder Angle (degrees)

Real Voyage Data from HMM

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Power Comparison

Power History

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Routing Optimization Result

Calculated Route

Calculated Route

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Conclusion

▪ The optimal FOC routing was conducted by DSME, HMM, and KMA.

▪ The model test results (calm water, wave added, and wind resistance) were

considered to estimate the power increase.

▪ The measured power and the estimated power were compared based on

real voyage data.

▪ The 4% FOC reduction was achieved compared to great circle on eastbound

route for 13.1K container carrier on June 2016.

▪ Lesson Learned :

: A precise performance estimation is a crucial factor for the optimal routing

▪ Next Goal

: Hydrodynamic performance analysis integration

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Autonomous Vessels is not

far away but is closer than

you might think.

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Thank You So Much!

SEWON KIMsewonkim@dsme.co.kr

+82-2-2129-3707

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