tomoya yamauchi munehiko minoura

2013 International Research Exchange Meeting of Ship and Ocean Engineering (SOE) in Osaka

December, 20th, 2013

Osaka University Division of Global Architecture Dept. of Naval Architecture & Ocean Engineering Kashiwagi Laboratory Second year master's student

Tomoya Yamauchi Munehiko Minoura

Background (1)

In the recent international marine traffic... the concerns to the environmental issues and the economic efficiency have been raised.

To acquire the sea conditions ships encounter is important.

・ Sailing safety and effectively

・ Estimating and assessing performances of the ship

■ Using onboard monitoring data 【Stochastic and statistical method】

Estimating and Assessing methods of the sea condition in the real sea

Kinematical inverse analysis method

Radar image analysis method

Wave prediction and hindcasting

Each method has each defect,

hence these methods are combined

to use practically as possible.

Try to estimate the sea conditions using onboard monitoring data measured on the ship. ship speed, motion, structure response...

■ Previous research （Minoura, Koike）

■ The proposal of the stochastic and statistical model for onboard

monitoring analysis and practical use for the performance estimation.

■ The correlation between sea conditions and ship responses are indicated

using a conditional probability density function（CPDF）, and the density

function is identified utilizing the independent component analysis(ICA).

■ The estimations of the power curve and sensitivity of performances in

the real sea using onboard monitoring data (sea conditions and ship

responses).

■ The new proposal ■ The method of estimating sea conditions from onboard monitoring data measured on a ship. (only ship response data)

Background (2)

（１） The correlation between ship responses and sea conditions are acquired statistically from onboard monitoring data.

（２） Utilizing the acquired correlation, the sea conditions are estimated from only ship response onboard monitoring data.

（３） We need some data sets of ship responses and sea conditions to identify the correlation. These data sets are given through some different ways.

【 Analysis outline 】

Sea conditions are estimated statistically from monitoring data. We do not use kinematical calculations.

All measured data are utilized except outliers due to troubles of monitoring systems. We do not choose specific conditions.

Condition 1 U = 25kts, 𝑁𝑁 = 98rpm, 𝑑𝑑 = 11𝑚𝑚,𝑉𝑉𝑎𝑎 = 0,𝑉𝑉𝑐𝑐 = 0

Wave height = 𝐸𝐸 𝐻𝐻1/3|𝑈𝑈,𝑁𝑁, 𝑑𝑑,𝑉𝑉𝑎𝑎 ,𝑉𝑉𝑐𝑐 = 1𝑚𝑚

Identification of CPDF

CPDF of a wave height：

Ship speed, Engine motion, Sea condition...

Onboard monitoring

data

【Analysis flow】

Statistical model for analysis

Analysis flow

),,,,( 3/1 cVVdNUHp a

Condition 2 U = 15kts, 𝑁𝑁 = 60rpm, 𝑑𝑑 = 11𝑚𝑚,𝑉𝑉𝑎𝑎 = 10𝑚𝑚/𝑠𝑠,𝑉𝑉𝑐𝑐 = 5𝑚𝑚/𝑠𝑠


Condition 3 U = 5kts, 𝑁𝑁 = 40rpm, 𝑑𝑑 = 11𝑚𝑚,𝑉𝑉𝑎𝑎 = 15𝑚𝑚/𝑠𝑠,𝑉𝑉𝑐𝑐 = 10𝑚𝑚/𝑠𝑠



Short term ship response data ■ Measured onboard monitoring data

【Wave height (m)】

【Ship speed (ｋｎｏｔ)】

【Sig. of pitch motion (degree)】

【Sig. of heave motion (m)】

Identify a conditional probability density function of the sea condition under some ship response conditions.

Data have correlations each other, hence we can’t identify it directly.

06

12

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Wave height(m)

01530

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Ship speed(knot)

024

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Pitch motion(degree)

0

3

6

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Heave motion (m)

Onboard monitoring data z Wave height ( ) ・Ship speed( ) Engine speed ( ) ・Pitch ( ) Roll ( ) ・Yaw ( )

【Correlation】

ICA

z = Ws

【No correlation】

Independent component s

s1

s2

s3

s4 s5

s6

r1(s1)

r2(s2)

【 CPDF of the sea condition under the ship response conditions 】

∫∞

∞−

− =nnn

nnnnz

dzsrsrsrsrsrsrzzzzp

)()()()()()(),,,(

2211

2211121

1z 2z

2z

1z3z

3z

4z

4z

5z

5z

6z

6z


Independent component analysis (ICA)

Joint probability density function Conditional probability density function

𝑓𝑓𝑌𝑌|𝑋𝑋 𝑦𝑦 𝑎𝑎 =𝑓𝑓𝑌𝑌𝑋𝑋 𝑦𝑦,𝑎𝑎𝑓𝑓𝑋𝑋 𝑎𝑎

𝑓𝑓𝑋𝑋 𝑥𝑥

𝑓𝑓𝑋𝑋 𝑎𝑎

𝑥𝑥 = 𝑎𝑎

𝑓𝑓𝑌𝑌𝑋𝑋 𝑦𝑦, 𝑎𝑎

𝑓𝑓𝑌𝑌𝑋𝑋 𝑦𝑦, 𝑥𝑥

Area= 𝑓𝑓𝑋𝑋 𝑎𝑎

𝑥𝑥

𝑦𝑦

𝑓𝑓

𝑦𝑦

𝑓𝑓

Normalization of the area = 1


Conditional probability density function (CPDF)

1x2x3x4x5x

➣ Wave height ➣ Ship speed ➣ Pitch motion ➣ Roll motion ➣ Yaw motion...

z 【Variable transformations】

Onboard monitoring data

Z is decomposed into independent components s

z = Ws ICA

)()()(

221

nz

znz zzp

pzzzp

z

=

∫∞

∞−

=1)(

)(

dzp

p

z

z

z

z

∫∞

∞−

=1)(

)(

dzr

r

s

s

【 CPDF of the sea condition 】

⇒ ⇒ ⇒ ⇒ ⇒


Statistical model of independent components

-20 0 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

s1

Prob

. den

sity

-20 0 200

0.2

0.4

0.6

0.8

1

1.2

1.4

s2

Prob

. den

sity

-20 0 200

0.5

1

1.5

2

2.5

3

s3

Prob

. den

sity

【Joint probability density function

)()()()( 2211 nn srsrsrr =sof independent components】

Statistical estimations using onboard analysis

Estimation of sea conditions

■ Onboard monitoring data in the real sea (In this estimation, simulation data are substituted)

➣ Calculated by RIOS （The Research Initiative on Oceangoing Ships） ➣ The container ship sailing between Japan and North America （LPP×B×D＝230.0m×32.2m×19.0m） ➣ Time series data measured every one hour for two years ➣ Consideration of conscious ship speed decreasing

120oE 160oE 160oW 120oW

0o

15oN

30oN

45oN

60oN

0

1

2

3

4

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000

pitch (deg)

Time (h)

Using these data group for conditions

CPDF is identified by these data group

・ Wave height (m) ・ Wave period (s) ・ Wave direction (deg)

・ Ship speed (knot) ・ Surge motion (m) ・ Sway motion (m)

Subjects of estimation Condition data

・ Heave motion (m) ・ Pitch motion (deg) ・ Roll motion (deg) ・ Yaw motion (deg)

Wave height (m)

Ship speed (knot)

Surge (m)

Sway (m)

Heave (m)

Pitch (degree)

Roll (degree)

Yaw (degree)

Conditional components

■ The estimation of the wave height

1x

4x

5x

3x

6x

2x

8x7x

Comparison with simulation data

Simulation value (m)

Estimation value (m)

0

10

20

30

40

50

60

0 0.5 1 1.5 2 2.5 3 3.5

Error

(%)

Error distribution

00.5

11.5

22.5

33.5

0 0.5 1 1.5 2 2.5 3 3.5


Estimation of wave height (1)

0 100 200 300 400 500 600 700 800 9000

1

2

3

4

Time (h)

Wave h

eig

ht

(m)


Red dot : estimated data

Black line : simulation data

100×−

SimulationEstimationSimulation

■ The estimation of the wave height (add Current speed and direction)




Error

(%)

Error distribution




00.5

11.5

22.5

33.5

0 0.5 1 1.5 2 2.5 3 3.50

10

20

30

40

50

60

0 0.5 1 1.5 2 2.5 3 3.5

Wane height (m)

Ship speed (knot)

Current speed (m/s)

Current direction (sin)

Current direction (cos)

Surge (m)

Sway (m)

Heave (m)

Pitch (degree)

Roll (degree)

Yaw (degree)

1x

4x5x

3x

6x

2x

8x7x

9x10x11x

0 100 200 300 400 500 600 700 800 9000

1

2

3

4

Time (h)

Wave h

eig

ht

(m)

In this situation, changing conditions do not influence the estimation

➣ Ship speed and ship motions are enough for the estimation of wave height ?

➣ Wave height and current speed and direction have less correlation hence current data are not needed for the estimation ?



Time (h)

■ Comparison of estimation results Wave height (m)

0

0.5

1

1.5

2

2.5

3

3.5

0 100 200 300 400 500 600 700 800 900

SimulationEstimation(add Current)Estimation

Wave height (m) Wave period (s) Wave direction (degree)

Ship speed (knot) -0.40 -0.33 -0.16

Rudder angle (degree) 0.22 0.10 0.02

Current speed (m/s) -0.19 -0.33 -0.25

surge motion (m) 0.38 0.25 0.12

sway motion (m) 0.65 0.64 0.15

heave motion (m) 0.93 0.67 0.12

roll motion (degree) 0.70 0.67 0.15

pitch motion (degree) 0.94 0.64 0.18

yaw motion (degree) 0.58 0.54 0.15

■ Correlation coefficients

Wave period (s)

Ship speed (knot)

Surge (m)

Sway (m)

Heave (m)

Pitch (degree)

Roll (degree)

Yaw (degree)


■ The estimation of the wave period

1x

4x

5x

3x

6x

2x

8x7x




Error

(%)

Error distribution


Estimation of wave period


5

6

7

8

9

10

11

12

5 6 7 8 9 10 11 120

10

20

30

40

50

60

5 6 7 8 9 10 11 12

0 100 200 300 400 500 600 700 800 9005

10

15

Time (h)

Wav

e pe

riod

(s)

Wave direction

(degree)

Ship speed (knot)

Surge (m)

Sway (m)

Heave (m)

Pitch (degree)

Roll (degree)

Yaw (degree)


■ The estimation of the wave direction

1x

4x

5x

3x

6x

2x

8x7x




Error

(%)

Error distribution


Estimation of wave direction


0 100 200 300 400 500 600 700 800 9000

100

200

300

400

500

600

Time (h)

Wav

e di

rect

ion　

（deg

ree)

0

100

200

300

400

500

600

0 100 200 300 400 500 6000

20

40

60

80

100

0 100 200 300 400 500

Conclusions

■ The statistical model for onboard monitoring analysis ➣ The statistical and analytical estimation method is proposed.

➣ The correlations of sea conditions and ship responses were indicated as CPDF and those functions were derived by ICA.

■ Estimations of sea conditions using ship response data ➣ The CPDF of sea conditions were identified by ship response time

series data and wave height, wave period, and wave direction were estimated by ship speed and ship response data.

As results, wave height and wave period were estimated quantitatively. However the estimation of wave direction should be improved. For future works, we will try to estimate sea conditions using real onboard monitoring data measured on a ship.

tomoya yamauchi munehiko minoura

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