Διαφάνεια 1 - On The MoS Way

Safety and Energy Efficient Marine Operations

University of Strathclyde

17th – 19th November, 2015

Prof. Apostolos Papanikolaou, NTUA-SDL

Email: [email protected]

URL: http://www.naval.ntua.gr/sdl

Contents:

2 Nov-15 Assessment of Operational Risks and Guidance to the Master, Prof. Apostolos Papanikolaou, NTUA-SDL

Background

Definition of DSS (Decision Support System for Ship Masters)

DSS Structure

Risk evaluation

Hazards formulation

Data uncertainties

Probabilistic analysis

DSS implementation and case studies

The way ahead

Resume

Acknowledgements

Guidance to the Master – Background/References

• Takaishi, Y. : Dangerous Encounter Wave Conditions for Ship Navigating in Following and Quartering Seas, Proceedings of the 5th International Conference on Stability of Ships and Ocean Vehicles, Florida, Vol. 1, 1994.

• IMO (1995) MSC Circ. 707: Guidance to the Master for Avoiding Dangerous Situations in Following and Quartering Seas.

• Papanikolaou, A., Spyrou, K., Boulougouris, E., Improved Guidance to the Master for Avoiding Dangerous Situations in Extreme Weather Conditions, 1st joint Prize Award at the Annual Safer Ship Competition organized by the Royal Institution of Naval Architects (RINA), Dec. 1999, publ. Summary at the Journal Naval Architect, London (United Kingdom), July 2000.

• Papanikolaou et al (2000): Operational Measures for Avoiding Dangerous Situations in Extreme Weather Conditions, Proceedings of the 7th International Conference on Stability of Ships and Ocean Vehicles, Tasmania, STAB2000 .

• IMO (2007) MSC Circ. 1228: Revised Guidance to the Master for Avoiding Dangerous Situations in Adverse Weather and Sea Conditions.

• Spanos, A., Papanikolaou, A., Papatzanakis, G., Risk-based Onboard Guidance to the Master for Avoiding Dangerous Seaways, Proceedings of the 6th Int. Osaka Colloquium on Seakeeping and Stability of Ships, Osaka, March 26-28, 2008.

• Papanikolaou, A., Jensen, J.J., Dealing with Uncertainties in Risk Based Design – Risk Analysis Tools, Proc. SAFEDOR Year 3 Public Seminar, Glasgow, May 5-6, 2008.

• EU project ADOPT (2005-2008): Advanced Decision Support System for Ship Design, Operation and Training.

• Papatzanakis, G., Papanikolaou, A., Liu, S., Optimization of Routing considering Uncertainties, Journal of Marine Science Applications, Springer Publ., Harbin, 2012.


The Risk-based Onboard DSS A risk-based Decision Support System (DSS) has been developed for the mitigation of risks arising from the intact ship motion in waves.

With the present implementation of DSS the next two properties are achieved, which enable the practical onboard application:

1. The number of individual risks can be variable and is determined by the onboard available data and processing capabilities for evaluation

2. Risk evaluation is independent to any existing (or to be specified) criteria and is based on the differential risk concept

The introduced DSS exhaustively explores alternative sailing conditions for lower risk.


DSS MODULAR STRUCTURE


Data modules (left)

Processing of data modules (right)

Probabilistic assessment

Risk assessment

DSS exhaustively explores alternative sailing conditions for lower risk

RISK MODULE

PROBABILITY MODULE

OPERATOR/ SOCIETY

HAZARDS

SHIP

SEA

Ship Data

WAVE ENVIRONMENT

Wave Data

SHIP MOTION (NEWDRIFT)

Sea Sensor

PROBABILITY ANALYSIS (PROBAN)

Conse- quences

Probabilities

Threshold Values

RISK CALC.

Risk Criteria

Risk

RISK EVALUATION

Risk level & recommend.

Risk Controls

Risk Evaluation and Options

Total risk

R: risk, P: probability, C: consequences, N: number of risks that can be quantified with the current data

Risk mitigation options are identified on the basis of

the Differential Risk

ΔR: potential loss (risk difference between current and alternative sailing)

ΔC: related cost (additional cost of the considered alternative)

M: Critical risk value (subject to decision maker)

ΔR>0 (reduction of risk),

ΔC: may be < 0 (very favorable scenario) or >0 (still acceptable if risk reduced)

N

i

ii

N

i

i CPRR11

Mc

RR

c

R altercurrent


Hazards Hazardous events related to seakeeping behavior are identified,

formulated and assessed...

The probability of these events is herein numerically estimated online,

exploiting information available onboard.

Given these probabilities and assuming corresponding consequences,

the risk evaluation follows. Consequences may be

economic or

safety critical. Hazard is considered any event to which quantifiable consequences can

be assigned. For the ship operation such events are defined over the ship responses

(RAOs, continuous variables) by use of appropriate threshold value(s). Example of Hazard:

Large accelerations >>>>Threshold: 2.0 m/s2 Consequences>>>>Structural failures, Cargo damage, Injuries (might be translated in

monetary units), …


Hazard Formulation (1)

Each hazard is formulated with a characteristic Limit State Function, which is a function of the related basic variables (design/operational parameters) and is positively valued when the ship remains safe and negatively when unsafe, namely when the hazard threshold value is violated.

The related probabilistic problem is the evaluation of the probability that the limit state function

0,...,, 21 NXXXg


Hazards Formulation (2)

Formulation of limit states results to the evaluation of the mean up-crossing (or out-crossing) rate of a variable

For Gaussian, zero-mean, narrow-band processes, the mean up-crossing rate v+ of a level α can be approached by

where m0, m2 spectral moments of the response variable SR in consideration.

For linear ship responses SR is calculated from

where the response operator H

0

2

0

2

2exp

2

1

mm

mv

)()()(2

SHSR

9 Nov-15 Assessment of Operational Risks and Guidance to the Master , Prof. Apostolos Papanikolaou, NTUA-SDL

DSS Guidance to the Master:

Environmental Conditions

• The onboard guidance-DSS aims to exploit the

available onboard information of the specific sailing

conditions during the assessment period.

• It incorporates provisions for quantifying-measuring

the prevailing wave conditions and includes anyway

information about weather forecast.

• The onboard measured sea state is represented by

the wave energy spectrum, which contains enough

data for the seakeeping analysis.


DSS Guidance to the Master:

Uncertainties

The parameters of the seakeeping problem the loading condition,

the ship’s forward speed and

the wave height and heading

are assumed to be correlated to some degree of uncertainty, namely they are assumed to follow a probability model instead of having some fixed values.


Uncertainties


A parameter is random (uncertain) when the related uncertainty affects the formulated hazard, while when the related hazard probabilities are tolerable to such variations then this parameter is still considered as deterministic

Distribution of the out-crossing rate of the bow vertical

acceleration due to uncertainty on Tp

RoRo, 15 kn, head waves, Hs = 4.0 m and Tp~N(10.0,

0.2) sec

Probabilistic Analysis - Uncertainties • Two set of Uncertainties:

– aleatory and epistemic

• Aleatory: intrinsic randomness of phenomenon (seaway, wind, physical phenomena..); uncertainty cannot be reduced!

• Epistemic (from Greek: episteme): lack of knwoledge (data); hydrodynamic model; unceratinty can be reduced

• Uncertainties in the parameters describing the stationary conditions, e.g. Hs, Tz, Speed, Heading, Hydrodynamic model (i.e. through a model correction factor)

• Total set of random parameters: X


Stationary Gaussian Processes: X=0


0 20 40 60 80 100 120 140 160 180 200

toft Independen

)( 00

stP

0

21

2( ) (0)r e

Mean out-crossing rate:

Stationary Non-linear Processes: X=u


0 20 40 60 80 100 120 140 160 180 200

1 1 2 2 0 0 1 2 21( , , , ,..., , ) ( , , , ,..., , ) 0

n n nnG u u u u u u t u u u u u u

1

( , ) ( , ) ( , )n

i i i i

i

H x t u c x t u c x t

2

( , ) cos( )

( , ) sin( )

( )

i i i i

i i i i

i i i

c x t t k x

c x t t k x

S d

( ) : any non-linear wave-induced response

Example: Parametric rolling of ships

t

Stochastic Wave

n = 15-50 depending on t0

Parametric Rolling of Ships

Threshold wave needed


Probabilistic Analysis (example)


If perfect information available (no uncertainty) then in average 1.5 slammings per hour are assumed

With a low uncertainty on the wave spectrum parameters the average slamming rate is probabilistically distributed with up to 5.0 slammings per hour

RoRo, 15 kn, 160 deg wave heading

Hs~N(5.0,0.2) m, Tp~N(10.0, 0.2) sec

Probability Simulation


When assessing low probability events the employed probability simulation methods suffer by the huge number of simulations, as necessary to achieve a certain level of accuracy-confidence.

The relative convergence is important in the risk framework and for low probabilities

Taking into account current computational performance, the number of simulations should be in the order of 100.

Hence 2000 simulations proved quite excessive. Thus, alternative probability evaluation methods are required.

Bow Vertical Acceleration

Probability of out-crossing (> 0.1g) rate > limit rate

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 500 1000 1500 2000 2500

No. of Simulations

rel. d

evia

tion =

(std

dev)

/ (m

ean)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

2.60%

0.43%

0.13%

Probability

MC simulation convergence

Reliability Methods


First Order Reliability Method – FORM

Linear approximation of the limit surface g-function, which separates the parametric space into safe and failure sub-sets

FORM

SORM

U1

U2

Failure set g(u)<0

Safe set g(u)>0

g-function g(u)=0

Design point u*

2D u-space

Reliability Methods


Reliability assessment methods (like FORM and SORM) were successful in the range of low probabilities and for low number of parameters.

Where the performance was not satisfactory, then Monte Carlo simulation should be additionally applied (however problem occurs for larger probabilities where MC converges faster).

Related studies were until now not conclusive when the parametric space increases.

0.0

0.2

0.4

0.6

0.8

1.0

0.4 0.6 0.8 1.0 1.2 1.4

RMS (m/sec2), in head waves

Pro

babili

ty o

f exceedance

0.0

0.2

0.4

0.6

0.8

1.0

0.010 0.015 0.020 0.025 0.030 0.035 0.040

RMS (m/sec2), in following waves

MC

FORM

SORM

following head

Probability of the bow vertical acceleration

Reliability Methods


DSS development studies have indicated that g-function is not strongly non-

linear

while it defines convex failure sets

So, the estimated probabilities with FORM were overestimated

This bias is a valuable property for the reliability of the DSS, which is defined by the differential risk consideration

Slamming Rate (u-space)Heading = 160 deg, Speed = 15 Kn, GM = 1.615 m,

Hs (mean = 5.0 m, σ = 0.2 m), Tp (mean = 10.0 sec, σ = 0.2 sec)

-4

-3

-2

-1

0

1

2

3

4

-5 -4 -3 -2 -1 0 1 2 3 4 5U1(Hs)

U2(T

p)

Design Point

failure set

safe set

Slamming Rate > 4/hr

MC 2.2 %

FORM 2.9 %

Implementation


Implementation of the Probabilistic Module of DSS

NEWDRIFT(NTUA) Seakeeping code

PROBAN(DNV) Probabilistic code

Five limit state are implemented as a separate set of sub-libraries

The program SPECTRA (NTUA) performs the spectral analysis

Hydrodynamics are updated when changes in the ship loading condition.

The developed implementation is modular enabling the addition/replacement of any limit state and the customization of DSS to a ship

DSS

run_newdrift.exe (initialization of seakeeping)

rnewdrift.bat

Funclib.dll

(interface externals)

FUNCLB: NEWDRIFT (Definition Library)

SUBLIB: BowVertAcc (limit state 1)

SUBLIB: BridgeAcc (limit state 2)

SUBLIB: Propracing (limit state 4)

SUBLIB: GreenWater (limit state 3)

SUBLIB: Slamming (limit state 5)

Scels.bat (spectral analysis)

Spectra_v5_1.exe (spectral calcs.)

newdrift_to_spectra_ Operational2.exe

(initialization)

Upcrossing_Operation.exe

(event rate)

PROBAN.exe (probabilistic analysis)

NEWDRIFT.exe (seakeeping code)

newdrift_to_spectra_Oper.exe (RAOs manager)

feed_DSS.exe (return results to DSS)

Computational Performance A fast computational performance is a basic requirement for a computer-

based onboard/online DSS

While the computational time depends on the employed computer(hardware), besides on processing software tools, the currently achieved times at SDL laboratory prove the system’s feasibility for onboard application

With reference to a single PC computer, Intel Core2 CPU 6600 @ 2.40 GHz, 2 GB Ram and for a dense hull representation (2x500 panels) the next computational times have been recorded.

5 sec per Limit State evaluation, when using FORM

35 sec per Limit State evaluation, when using Monte Carlo


Computational Performance


For an evaluation of the elaborated 5 limit states it takes 12.5 min to evaluate the total risk for 30 alternative sailing conditions.

The results cover a range of speed-heading combinations.

Probability for propeller

emergence rate > (1 / min)

The Way Ahead


Optimization of routing with uncertainties Minimization of fuel

consumption Added resistance/powering in

waves

Minimization of wave-induced risks Stability

Motions

Loads

Papatzanakis, G., Papanikolaou, A.,

Liu, S.,

Optimization of Routing considering Uncertainties,

Journal of Marine Science Applications, Springer Publ., Harbin, 2012.

../../BERLIN OCT 2013/FRIENDSHIP ANNIVERSARY BERLIN 2013/Added Resistance_BEST_June meeting.ppt

../../BERLIN OCT 2013/FRIENDSHIP ANNIVERSARY BERLIN 2013/Added Resistance_BEST_June meeting.ppt

Resume

A ship specific, risk-based decision support system DSS for onboard guidance with respect to the seakeeping of the ship has been introduced.

An efficient probabilistic method in the core of the system is necessary for the feasibility of the onboard application

Navigational options with respect to the risk mitigation could be developed on the basis of the differential risk (alternative sailing conditions with lower risk compared to the current)

Validation studies have proved the efficiency of the reliability method(s) FORM (SORM) for most cases of the operational mode of DSS. Where these methods are less reliable, Monte Carlo simulation is applied.


Acknowledgements The European research project ADOPT (2005-2008), Advanced

Decision Support System for Ship Design, Operation and Training, within which the presented work has been conducted

The collaboration with Prof. P.F. Hansen and Prof. J.J. Jensen from the Technical University of Denmark, with respect to the introduction and implementation of reliability methods

The PROBAN license disposed by DNV to NTUA for use in ADOPT project


Prof. Apostolos Papanikolaou National Technical University of Athens

Ship Design Laboratory (NTUA-SDL) GREECE

Email: [email protected] , URL: http://www.naval.ntua.gr/sdl

Documents

Διαφάνεια 1 - On The MoS Way