DNV Managing Risk DNV corporate presentation Elzbieta Bitner-Gregersen 25 February 2010
DNV an independent foundation Our Purpose To safeguard life, property and the environment Our Vision Global impact for a safe and sustainable future Slide 2
More than 140 years of managing risk Det Norske Veritas (DNV) was established in 1864 in Norway The main scope of work was to identify, assess and manage risk initially for maritime insurance companies Slide 3
New risk reality Companies today are operating in an increasingly more global, complex and demanding risk environment with zero tolerance for failure Climate change Increased demands for transparency and business sustainability Stricter regulatory requirements Increasing IT vulnerability Slide 4
300 offices in 100 countries Head office Local offices Slide 5
Maritime DNV is a world leading classification society 15.4% of the world fleet to DNV class Over 20% of ships ordered in 2008 70% of maritime fuel testing market Authorised by 130 national maritime authorities Continuous high performance in Port State Control worldwide Slide 6
Class societies market share IACS Fleet Development 1965-2007 Total IACS Fleet by the end of 2007 (including RINA, CCS, KRS and RS) was 732.9 million GT Million GT Million GT 150 140 130 120 110 100 90 ABS 16,9% 80 70 LR 18,4% NK 20% 60 DNV 15.4% 50 40 30 20 BV 8% GL 9,8% 10 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Vessels > 100 gt. 50% dual class included, MOU excluded. Year-end figures. Slide 7
Energy Safeguarding and improving business performance Cross-disciplinary competence within risk, management, technology and operational expertise Our services and solutions are built on leading edge technology Offshore pipeline technology leader - DNV Offshore Rules for pipelines recognised as world class Deep water technology - Providing reliable verification and qualification of unproven technology Broad experience with LNG / Natural Gas Slide 8
Research and innovation Competitive advantage from continuously updated knowledge and expertise DNV invests some 5% of revenue on Research and Innovation Enhance and develop services, rules, and industry standards Ensures DNV's position at the forefront of technological development Key research areas: - Maritime Transport Systems - Marine Structures - Future energy solutions - Information processes and technology - Biorisk - Multifunctional materials and surfaces - Arctic Operations Slide 9
Organisation CEO CEO & President President Henrik Henrik O. O. Madsen Madsen CEO s CEO s Office Office Communication Tore Høifødt Relations Communication Sven Tore Mollekleiv Høifødt Relations Sven Mollekleiv Corporate Corporate units units Finance, IT & Legal Jostein Furnes HR Finance, & Org. IT & Legal Cecilie Jostein B. Heuch Furnes HR & Org. Cecilie B. Heuch Maritime Maritime Tor Tor E. E. Svensen Svensen Energy Energy Remi Remi Eriksen Eriksen Business Business Assurance Assurance Bjørn Bjørn K. K. Haugland Haugland IT IT Global Global Services Services Annie Annie Combelles Combelles Independent Independent business business units units DNV DNV Climate Climate Change Change Stein Stein B B Jensen Jensen DNV DNV Research Research and and Innovation Innovation Elisabeth Elisabeth Harstad Harstad DNV DNV Software Software Elling Rishoff Elling Rishoff Slide 10
Metocean research activities in DNV R&I Climate change Probabilistic and spectral wave, wind, current and ice modelling Extreme and rogue waves 20 15 height [s] 10 5 0-5 -10 0 200 400 600 800 1000 1200 time [s] Slide 11
Torsethaugen Spectrum Uncertainties of Wind Sea and Swell Prediction Elzbieta Maria Bitner-Gregersen and Alessandro Toffoli
Uncertainties of Wind Sea and Swell Prediction from the Torsethaugen Spectrum EC Marie Curie Network Applied stochastic models for ocean engineering, climate and safe transportation SEAMOCS Slide 13
Uncertainties of Wind Sea and Swell Prediction from the Torsethaugen Spectrum Safe Offloading from Floating LNG Platforms (Safe Offload) partially funded by the European Union through the Sustainable Surface Transport Programme - contract TST-CT-2005-012560 Shell International Exploration and Production B.V. Shell provided the data for the study Instituto Superior Tecnico DHI Water & Environment Det Norske Veritas Imperial College Noble Denton Oxford University LISNAVE Ocean Wave Engineering Limited Slide 14
Double-peaked Spectra Wave spectra including wind sea and swell compnents Strekalov and Massel (1971) - high frequency spectrum for a wind sea component and a Gaussian shaped model for a swell component. Ochi and Hubble (1976) - a JONSWAP and a Pierson-Moskowitz spectrum describing the two individual wave components. Guedes Soares (1984, 1992, 2001) - represents both sea components by JONSWAP spectra of different peak frequencies Torsethaugen (1989, 1993, 1996) - also two JONSWAP models to describe the bimodal spectra. The model was later simplified by Torsethaugen Torsethaugen and Haver (2004). Frequency Spectrum S(f) 14 12 10 8 6 4 S(f)_p S(f)_s S(f)_tot S(f)_meas Hs= 2.6505 Tp= 15.0588 19801221.11 2 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Frequency f Slide 15
Two-peak Torsethaugen Spectrum S( f ) S ( f ) S ( f sw Spectrum defined from H s and T p for sea state (2 input parameters) - trade-off between simplicity and accuracy Parametric model for the two peaks were established from data from Norwegian Continental Shelf Each sea state is classified as swell dominated sea or wind dominated sea swell windsea if if T T p p T T a f =6.6 adopted from the JONSWAP exp. f f w ) T a H f f 1/3 mo Ewans, Bitner-Gregersen & Guedes Soares(2006) Slide 16
Locations considered in the study Locations of the grids used for data generation Slide 17
Data specification Data received from Shell Hindcast data generated by the Oceanweather wave model. The wave data have been post-processed by the program APL Waves, developed by the Applied Physics Department of Johns Hopkins University. The program divides 3D spectra (i.e., directional spectra) into separate peaks. Parameters significant wave height (total sea, wind sea and swell), and spectral wave period (total sea, wind sea and swell) Three locations: NW Australia - water depth 250m (1994-2005) Nigeria - water depth 1000m (1985-1999) West Shetland - water depth 500m (1988-1998) Slide 18
Wind Sea and Swell Prediction West Shetland Hs and Tp predicted by the Torsethaugen spectrum and the wave spectral model data wind sea component swell component ok ok incorrectly classified as windsea Slide 19
Wind Sea and Swell Prediction NW Australia Hs and Tp predicted by the Torsethaugen spectrum and the wave spectral model data wind sea component swell component good correspondence Slide 20
Wind Sea and Swell Prediction Nigeria Swell dominated region The Torsethaugen spectrum predicts wind sea and swell swell windsea total Slide 21
West Shetlands Extreme sea states Paramet Sea Total sea Wind sea Hs (m) 16.91 3.08 Tp (s) 100-year return period 17.44 7.42 Dominated sea acc. to the Torsethaugen spectrum swell Swell 16.63 17.44 Total sea Wind sea 10-year return period 14.55 16.36 1.20 4.83 swell Swell 14.50 16.36 Total sea Wind sea 1-year return period 12.07 15.16 0.075 2.5 swell Swell 12.07 15.16 Slide 22
West Shetlands - Extreme sea states Design values Total H s increased by 1m ( 1σ) Paramet Sea Hs (m) Tp (s) 100-year return period Dominated sea acc. to the Torsethaugen spectrum Parameter Sea (m) (s) 100-year return period Dominated sea acc. to the Torsethaugen spectrum Total sea 16.91 17.44 swell Total sea 17.91 17.44 Swell Wind sea 3.08 7.42 Wind sea 1.19 4.73 Swell 16.63 17.44 Swell 17.87 17.44 10-year return period 10-year return period Total sea 14.55 16.36 swell Total sea 15.55 16.36 windsea Wind sea 1.20 4.83 Wind sea 15.55 16.36 Swell 14.50 16.36 Swell 0.32 18.47 1-year return period 1-year return period Total sea 12.07 15.16 swell Total sea 13.07 15.16 windsea Wind sea 0.075 2.5 Wind sea 13.04 15.16 Swell 12.07 15.16 Swell 0.939 17.55 Slide 23
West Shetlands - Extreme sea states Design values total T p reduced by 1s (1σ) Paramet Sea Hs (m) Tp (s) 100-year return period Dominated sea acc. to the Torsethaugen spectrum Parameter Sea (m) (s) 100-year return period Dominated sea acc. to the Torsethaugen spectrum Total sea 16.91 17.44 swell Total sea 16.91 16.44 windsea Wind sea 3.08 7.42 Wind sea 16.84 16.44 Swell 16.63 17.44 Swell 1.50 18.94 10-year return period 10-year return period Total sea 14.55 16.36 swell Total sea 14.55 15.36 windsea Wind sea 1.20 4.83 Wind sea 14.41 15.36 Swell 14.50 16.36 Swell 1.97 18.11 1-year return period 1-year return period Total sea 12.07 15.16 swell Total sea 12.07 15.16 windsea Wind sea 0.075 2.5 Wind sea 11.87 14.16 Swell 12.07 15.16 Swell 2.20 17.14 Slide 24
Consequences for Estimation of Skewness of the Sea Surface Skewness as a function of design sea states (Hs and Tp) at different return periods. The two spectral peaks of Torsethaugen spectrum overlap skewness as for JONSWAP Skewness as a function of design sea states (Hs and Tp+σ) at different return periods. The two spectral peaks of Torsethaugen spectrum separated skewness different Slide 25
Conclusions The study shows that the Torsethaugen spectrum should be used with caution for sites outside the Norwegian waters (for which it was established in the first place). Further validation of the Torsthaugen spectrum for locations outside the Norwegian waters is called for. The validation should include directional wave measurements as the hindcast data are affected by the model uncertainty. The Torsethaugen partitioning procedure is sensitive to accuracy of H s and T p estimates for the total sea. Uncertainties related to these estimates may result in predicting a wrong sea state type (e.g. a wind dominated sea instead of a swell dominated sea) when the Torsethaugen model is applied. This inaccuracy will affect simulated short-term sea surface characteristics. Slide 26
EXTREME WAVES IN DIRECTIONAL WAVE FIELDS TRAVERSING UNIFORM CURRENTS A. Toffoli (1)(6), F. Ardhuin (2), A. V. Babanin (1), M. Benoit(3), E. M. Bitner-Gregersen (4), L. Cavaleri (5), J. Monbaliu (6), M. Onorato (7), A. R. Osborne (7) (1) Swinburne University of Technology (2) French Naval Oceanographic Centre (3) Saint-Venant Laboratory, Univ. Paris-Est (EDF R\&D-CETMEF-Ecole des Ponts) (4) Det Norske Veritas (5) Institute of Marine Sciences (6) K. U. Leuven (7) Universita' di Torino Supported by EU European Community's Sixth Framework Programme through the grant to the budget of the Integrated Infrastructure Initiative HYDROLAB III, Contract no. 022441
Local increase of steepness: wave-current interaction Ambient Current Waves If waves propagate (partially) against an ambient current, the wavecurrent interaction results in a local increase of wave steepness, which may induce modulational instability. Can the wave-current interaction enhance the probability of occurrence of extreme waves? 28
Directional wave tank (Marintek, Norway) 29
Laboratory experiments β = 110 and 120 deg 30
The instability of a wave train 31
Maximum kurtosis 32
Conclusions If waves are sufficiently steep, narrow banded and long-crested, modulational instability leads to strong non-gaussian properties If waves are more short-crested, the percentage of extreme waves is decreased (weakly non-gaussian properties) The presence of a (partial) opposing current increases the wave steepness and hence triggers the instability of wave trains. In a random wave system, the increase of steeppness compensates (partially) the effect of directionality 33
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