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DNV – Managing Risk

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. More than 140 years of managing risk.

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DNV – Managing Risk

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  1. DNV – Managing Risk DNV corporate presentation Elzbieta Bitner-Gregersen 25 February 2010

  2. DNV – an independent foundation Our PurposeTo safeguard life, property and the environmentOur Vision Global impact for a safe and sustainable future ISSC 2012 I.1, Paris

  3. 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 ISSC 2012 I.1, Paris

  4. 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 ISSC 2012 I.1, Paris

  5. Local offices Head office 300 offices in 100 countries ISSC 2012 I.1, Paris

  6. Maritime • 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 DNV is a world leading classification society ISSC 2012 I.1, Paris

  7. DNV 15.4% 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 ABS 16,9% LR 18,4% NK 20% GL 9,8% BV 8% Vessels > 100 gt. 50% dual class included, MOU excluded. Year-end figures. ISSC 2012 I.1, Paris

  8. Energy • 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 Safeguarding and improving business performance ISSC 2012 I.1, Paris

  9. 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 ISSC 2012 I.1, Paris

  10. Organisation CEO & President Henrik O. Madsen Corporate units Finance, IT & Legal Jostein Furnes HR & Org. Cecilie B. Heuch CEO’s Office Communication Tore Høifødt Relations Sven Mollekleiv IT Global Services Annie Combelles Maritime Tor E. Svensen Energy Remi Eriksen Business Assurance Bjørn K. Haugland Independent business units DNV Climate Change Stein B Jensen DNV Research and Innovation Elisabeth Harstad DNV Software Elling Rishoff ISSC 2012 I.1, Paris

  11. Metocean research activities in DNV R&I • Climate change • Probabilistic and spectral wave, wind, current and ice modelling • Extreme and rogue waves ISSC 2012 I.1, Paris

  12. Torsethaugen Spectrum Uncertainties of Wind Sea and Swell Prediction Elzbieta Maria Bitner-Gregersen and Alessandro Toffoli

  13. 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 ISSC 2012 I.1, Paris

  14. 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. Instituto Superior Tecnico DHI Water & Environment Det Norske Veritas Imperial College Noble Denton Oxford University LISNAVE Ocean Wave Engineering Limited Shell provided the data for the study ISSC 2012 I.1, Paris

  15. 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 and Haver (2004). ISSC 2012 I.1, Paris

  16. Two-peak Torsethaugen Spectrum • Spectrum defined from Hs and Tp 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 af =6.6 adopted from the JONSWAP exp. Ewans, Bitner-Gregersen & Guedes Soares(2006) ISSC 2012 I.1, Paris

  17. Locations considered in the study • Locations of the grids used for data generation ISSC 2012 I.1, Paris

  18. Data specification • Data received from Shell • Hindcast data generated by the Oceanweather wave model. • The wave data have been post-processed by the programAPL 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) ISSC 2012 I.1, Paris

  19. Wind Sea and Swell PredictionWest 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 ISSC 2012 I.1, Paris

  20. Wind Sea and Swell PredictionNW Australia Hsand Tp predicted by the Torsethaugen spectrum and the wave spectral model data wind sea component swell component good correspondence ISSC 2012 I.1, Paris

  21. swell windsea total Wind Sea and Swell PredictionNigeria • Swell dominated region • The Torsethaugen spectrum predicts wind sea and swell ISSC 2012 I.1, Paris

  22. West Shetlands – Extreme sea states ISSC 2012 I.1, Paris

  23. West Shetlands - Extreme sea states Total Hsincreased by 1m(≈1σ) Design values ISSC 2012 I.1, Paris

  24. West Shetlands - Extreme sea states Design values total Tp reduced by 1s (1σ) ISSC 2012 I.1, Paris

  25. Consequences for Estimation of Skewness of the Sea Surface Skewness as a function of design sea states (Hs and Tp) at different return periods. Skewness as a function of design sea states (Hs and Tp+σ) at different return periods. The two spectral peaks of Torsethaugen spectrum overlap→ skewness as for JONSWAP The two spectral peaks of Torsethaugen spectrum separated→ skewness different ISSC 2012 I.1, Paris

  26. 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 ofHs and Tp 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. ISSC 2012 I.1, Paris

  27. 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

  28. Local increase of steepness:wave-current interaction Ambient Current Waves If waves propagate (partially) against an ambient current, the wave-current 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

  29. Directional wave tank (Marintek, Norway) 29

  30. Laboratory experiments 30 β = 110 and 120 deg

  31. The instability of a wave train 31

  32. Maximum kurtosis 32

  33. Conclusions • If waves are sufficiently steep, narrow banded and long-crested, modulational instability leads tostrong 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

  34. ISSC 2012 I.1, Paris

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