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DNV – Managing Risk

DNV corporate presentation

Elzbieta Bitner-Gregersen25 February 2010

ISSC 2012 I.1, Paris Slide 2February 25, 2010

DNV – an independent foundation

Our PurposeTo safeguard life, property and the environment

Our VisionGlobal impact for a safe and sustainable future


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


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

New risk reality


300 offices in 100 countries

Head office Local offices


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


Class societies’ market share

Million GT

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60

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90

100

110

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130

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150

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

IACS Fleet Development 1965 - 2007 Total IACS Fleet by the end of 2007 (including RINA, CCS, KRS and RS) was 732.9 million GT

Vessels > 100 gt. 50% dual class included, MOU excluded. Year-end figures.

Million GT

LR 18,4%

ABS 16,9%

NK 20%DNV 15.4%

BV 8%GL 9,8%


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


Research and innovation

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

Competitive advantage from continuously updated knowledge and expertise


OrganisationCEO &

PresidentHenrik O. Madsen

CEO & President

Henrik O. Madsen

Corporate unitsFinance, IT & Legal Jostein FurnesHR & Org. Cecilie B. Heuch

Corporate unitsFinance, IT & Legal Jostein FurnesHR & Org. Cecilie B. Heuch

Maritime

Tor E. Svensen

Maritime

Tor E. Svensen

Independent business units

Independent business unitsBusiness

AssuranceBjørn K. Haugland

Business AssuranceBjørn K. Haugland

Energy

Remi Eriksen

Energy

Remi Eriksen

DNV SoftwareElling Rishoff

DNV SoftwareElling Rishoff

IT Global ServicesAnnie Combelles

IT Global ServicesAnnie Combelles

CEO’s OfficeCommunication Tore Høifødt Relations Sven Mollekleiv

CEO’s OfficeCommunication Tore Høifødt Relations Sven Mollekleiv

DNV Research and InnovationElisabeth Harstad

DNV Research and InnovationElisabeth Harstad

DNV Climate ChangeStein B Jensen

DNV Climate ChangeStein B Jensen


Metocean research activities in DNV R&I

Climate change

Probabilistic and spectral wave, wind, current and ice modelling

Extreme and rogue waves

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time [s]

heig

ht [s

]

0 200 400 600 800 1000 1200-10

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time [s]

heig

ht [s

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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


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 TecnicoDHI Water & Environment

Det Norske Veritas Imperial College

Noble Denton Oxford University

LISNAVE Ocean Wave Engineering Limited

Shell provided the data for the study


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-Moskowitzspectrum 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). Torsethaugen

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0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Frequency f

Freq

uenc

y Sp

ectr

um S

(f)

S(f)_pS(f)_sS(f)_totS(f)_measHs= 2.6505 Tp= 15.058819801221.11


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)

)()()( fSfSfS wsw

fp

fp

TTifwindseaTTifswell

3/1moff HaT


Locations considered in the study

Locations of the grids used for data generation


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)


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

okok

incorrectly classified as windsea


Wind Sea and Swell PredictionNW Australia

Hs and Tp predicted by the Torsethaugen spectrum and the wave spectral model data

wind sea component swell component

good correspondence


Wind Sea and Swell PredictionNigeria

Swell dominated region

The Torsethaugen spectrum predicts wind sea and swell

swellwindseatotal


West Shetlands – Extreme sea states

15.1612.07Swell

2.50.075Wind sea

swell15.1612.07Total sea

1-year return period

16.3614.50Swell

4.831.20Wind sea



17.4416.63Swell

7.423.08Wind sea



Dominated sea acc. to the Torsethaugen

spectrum

Tp(s)

Hs(m)

Paramet

Sea


West Shetlands - Extreme sea statesDesign values

17.550.939Swell

15.1613.04Wind sea

windsea15.1613.07Total sea


18.470.32Swell

16.3615.55Wind sea



17.4417.87Swell

4.731.19Wind sea

Swell17.4417.91Total sea



spectrum

(s)(m)ParameterSea

15.1612.07Swell

2.50.075Wind sea



16.3614.50Swell

4.831.20Wind sea



17.4416.63Swell

7.423.08Wind sea




spectrum

Tp(s)

Hs(m)

Paramet

Sea

Total Hs increased by 1m (≈1σ)


West Shetlands - Extreme sea statestotal Tp reduced by 1s (1σ)

17.142.20Swell

14.1611.87Wind sea



18.111.97Swell

15.3614.41Wind sea



18.941.50Swell

16.4416.84Wind sea




spectrum

(s)(m)ParameterSea

Design values

15.1612.07Swell

2.50.075Wind sea



16.3614.50Swell

4.831.20Wind sea



17.4416.63Swell

7.423.08Wind sea




spectrum

Tp(s)

Hs(m)

Paramet

Sea


Consequences for Estimation of Skewness of the Sea SurfaceSkewness 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


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 Hsand 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.

EXTREME WAVES IN DIRECTIONAL WAVE FIELDS TRAVERSING UNIFORM CURRENTS

Supported by EUEuropean Community's Sixth Framework Programme through the grant to the budget of the Integrated Infrastructure Initiative HYDROLAB III, Contract no. 022441

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

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?

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Directional wave tank (Marintek, Norway)

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Laboratory experiments

β = 110 and 120 deg

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The instability of a wave train

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Maximum kurtosis

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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

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