Safety and Energy Efficient Marine Operations

Transcription

1 University of Strathclyde 17 th 19 th November, 2015 Safety and Energy Efficient Marine Operations Prof. Apostolos Papanikolaou, NTUA-SDL URL:

2 Background Contents: 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 2

3 Guidance to the Master Background/References Takaishi, Y. : Dangerous Encounter Wave Conditions for Ship Navigating in Following and Quartering Seas, Proceedings of the 5 th International Conference on Stability of Ships and Ocean Vehicles, Florida, Vol. 1, 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, 1 st 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 Papanikolaou et al (2000): Operational Measures for Avoiding Dangerous Situations in Extreme Weather Conditions, Proceedings of the 7 th 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 6 th Int. Osaka Colloquium on Seakeeping and Stability of Ships, Osaka, March 26-28, 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, EU project ADOPT ( ): 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,

4 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. 4

5 DSS MODULAR STRUCTURE SEA Sea Sensor WAVE ENVIRONMENT Data modules (left) Processing of data modules (right) Probabilistic assessment Risk assessment SHIP HAZARDS Ship Data Threshold Values Wave Data PROBABILITY MODULE SHIP MOTION (NEWDRIFT) PROBABILITY ANALYSIS (PROBAN) Probabilities DSS exhaustively explores alternative sailing conditions for lower risk OPERATOR/ SOCIETY Consequences Risk Criteria RISK MODULE RISK CALC. Risk RISK EVALUATION Risk level & recommend. Risk Controls 5

6 Risk Evaluation and Options Total risk R N i1 R i N i1 PC i i 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 R Rcurrent c R c alter M 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) 6

7 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), 7

8 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 g X, X 0 2,..., X 1 N 8

9 Hazards Formulation (2) Formulation of limit states results to the evaluation of the mean upcrossing (or out-crossing) rate of a variable For Gaussian, zero-mean, narrow-band processes, the mean upcrossing rate v+ of a level α can be approached by v 1 2 m m 2 0 exp 2 2m 0 where m0, m2 spectral moments of the response variable S R in consideration. For linear ship responses SR is calculated from S R ( ) H( ) 2 S( ) where the response operator H Nov-15 Assessment of Operational Risks and Guidance to the Master, Prof. Apostolos Papanikolaou, NTUA-SDL 9

10 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. 10

11 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. 11

12 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 12

13 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 13

14 Stationary Gaussian Processes: X=0 P s Independent of 0 ( t) 0 0 t Mean out-crossing rate: 1 2 ( r) (0) e 2 14

15 Stationary Non-linear Processes: X=u G( u, u, u, u,..., u, u ) ( t u, u, u, u,..., u, u ) 0 n n n n n i1 Stochastic Wave H( x, t) u c ( x, t) u c ( x, t) i i i i c ( x, t) cos( t k x) i i i i c ( x, t) sin( t k x) i i i i S( ) d 2 i i i n = depending on t 0 ( t) : any non-linear wave-induced response Example: Parametric rolling of ships 15

16 Parametric Rolling of Ships Threshold wave needed 16

17 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 17

18 rel. deviation = (std dev) / (mean) 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 accuracyconfidence. 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 No. of Simulations MC simulation convergence Probability 2.60% 0.43% 0.13%

19 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 U1 FORM SORM Safe set g(u)>0 2D u-space g-function g(u)=0 Failure set g(u)<0 Design point u* U2 19

20 Probability of exceedance 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. RMS (m/sec2), in following waves MC 0.8 FORM 0.6 SORM following head RMS (m/sec2), in head waves Probability of the bow vertical acceleration 20

21 U2(Tp) Reliability Methods DSS development studies have indicated that g-function is not strongly nonlinear 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 = m, Hs (mean = 5.0 m, σ = 0.2 m), Tp (mean = 10.0 sec, σ = 0.2 sec) Slamming Rate > 4/hr MC 2.2 % FORM 2.9 % safe set failure set Design Point U1(Hs) 5 21

22 Implementation DSS 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 feed_dss.exe (return results to DSS) PROBAN.exe (probabilistic analysis) Funclib.dll (interface externals) FUNCLB: NEWDRIFT (Definition Library) SUBLIB: BowVertAcc (limit state 1) SUBLIB: BridgeAcc (limit state 2) SUBLIB: GreenWater (limit state 3) SUBLIB: Propracing (limit state 4) SUBLIB: Slamming (limit state 5) rnewdrift.bat run_newdrift.exe (initialization of seakeeping) NEWDRIFT.exe (seakeeping code) newdrift_to_spectra_oper.exe (RAOs manager) Scels.bat (spectral analysis) newdrift_to_spectra_ Operational2.exe (initialization) Spectra_v5_1.exe (spectral calcs.) Upcrossing_Operation.exe (event rate) 22

23 Computational Performance A fast computational performance is a basic requirement for a computerbased 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 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 23

24 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) 24

25 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,

26 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. 26

27 Acknowledgements The European research project ADOPT ( ), 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 27

28 Prof. Apostolos Papanikolaou National Technical University of Athens Ship Design Laboratory (NTUA-SDL) GREECE URL: