dnv – managing risk
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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. - PowerPoint PPT PresentationTRANSCRIPT
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 Vision Global impact for a safe and sustainable future
ISSC 2012 I.1, Paris Slide 3February 25, 2010
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 Slide 4February 25, 2010
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
ISSC 2012 I.1, Paris Slide 5February 25, 2010
300 offices in 100 countries
Head office Local offices
ISSC 2012 I.1, Paris Slide 6February 25, 2010
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 Slide 7February 25, 2010
Class societies’ market share
Million GT
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10
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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%
ISSC 2012 I.1, Paris Slide 8February 25, 2010
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 Slide 9February 25, 2010
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
ISSC 2012 I.1, Paris Slide 10February 25, 2010
Organisation
CEO & President
Henrik 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
Assurance
Bjørn K. Haugland
Business Assurance
Bjørn K. Haugland
Energy
Remi Eriksen
Energy
Remi Eriksen
DNV SoftwareElling Rishoff
DNV SoftwareElling Rishoff
IT Global Services
Annie Combelles
IT Global Services
Annie Combelles
CEO’s Office
Communication Tore Høifødt Relations Sven Mollekleiv
CEO’s Office
Communication 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
ISSC 2012 I.1, Paris Slide 11February 25, 2010
Metocean research activities in DNV R&I
Climate change
Probabilistic and spectral wave, wind, current and ice modelling
Extreme and rogue waves
0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0-1 0
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Torsethaugen Spectrum
Uncertainties of Wind Sea and Swell Prediction
Elzbieta Maria Bitner-Gregersen and Alessandro Toffoli
ISSC 2012 I.1, Paris Slide 13February 25, 2010
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 Slide 14February 25, 2010
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 Slide 15February 25, 2010
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).
Torsethaugen
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Frequency f
Freq
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pect
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S(f)
S(f)_pS(f)_sS(f)_totS(f)_measHs= 2.6505 Tp= 15.058819801221.11
ISSC 2012 I.1, Paris Slide 16February 25, 2010
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
TTifwindsea
TTifswell
3/1
moff HaT
ISSC 2012 I.1, Paris Slide 17February 25, 2010
Locations considered in the study
Locations of the grids used for data generation
ISSC 2012 I.1, Paris Slide 18February 25, 2010
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)
ISSC 2012 I.1, Paris Slide 19February 25, 2010
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
ISSC 2012 I.1, Paris Slide 20February 25, 2010
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
ISSC 2012 I.1, Paris Slide 21February 25, 2010
Wind Sea and Swell PredictionNigeria
Swell dominated region
The Torsethaugen spectrum predicts wind sea and swell
swell
windsea
total
ISSC 2012 I.1, Paris Slide 22February 25, 2010
West Shetlands – Extreme sea states
Paramet
Sea
Hs(m)
Tp(s)
Dominated sea acc. to the Torsethaugen
spectrum
100-year return period
Total sea 16.91 17.44 swell
Wind sea 3.08 7.42
Swell 16.63 17.44
10-year return period
Total sea 14.55 16.36 swell
Wind sea 1.20 4.83
Swell 14.50 16.36
1-year return period
Total sea 12.07 15.16 swell
Wind sea 0.075 2.5
Swell 12.07 15.16
ISSC 2012 I.1, Paris Slide 23February 25, 2010
West Shetlands - Extreme sea statesDesign values
Parameter Sea
(m) (s) Dominated sea acc. to the Torsethaugen
spectrum
100-year return period
Total sea 17.91 17.44 Swell
Wind sea 1.19 4.73
Swell 17.87 17.44
10-year return period
Total sea 15.55 16.36 windsea
Wind sea 15.55 16.36
Swell 0.32 18.47
1-year return period
Total sea 13.07 15.16 windsea
Wind sea 13.04 15.16
Swell 0.939 17.55
Paramet
Sea
Hs(m)
Tp(s)
Dominated sea acc. to the Torsethaugen
spectrum
100-year return period
Total sea 16.91 17.44 swell
Wind sea 3.08 7.42
Swell 16.63 17.44
10-year return period
Total sea 14.55 16.36 swell
Wind sea 1.20 4.83
Swell 14.50 16.36
1-year return period
Total sea 12.07 15.16 swell
Wind sea 0.075 2.5
Swell 12.07 15.16
Total Hs increased by 1m (≈1σ)
ISSC 2012 I.1, Paris Slide 24February 25, 2010
West Shetlands - Extreme sea states total Tp reduced by 1s (1σ)
Parameter Sea
(m) (s) Dominated sea acc. to the Torsethaugen
spectrum
100-year return period
Total sea 16.91 16.44 windsea
Wind sea 16.84 16.44
Swell 1.50 18.94
10-year return period
Total sea 14.55 15.36 windsea
Wind sea 14.41 15.36
Swell 1.97 18.11
1-year return period
Total sea 12.07 15.16 windsea
Wind sea 11.87 14.16
Swell 2.20 17.14
Design values
Paramet
Sea
Hs(m)
Tp(s)
Dominated sea acc. to the Torsethaugen
spectrum
100-year return period
Total sea 16.91 17.44 swell
Wind sea 3.08 7.42
Swell 16.63 17.44
10-year return period
Total sea 14.55 16.36 swell
Wind sea 1.20 4.83
Swell 14.50 16.36
1-year return period
Total sea 12.07 15.16 swell
Wind sea 0.075 2.5
Swell 12.07 15.16
ISSC 2012 I.1, Paris Slide 25February 25, 2010
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 Slide 26February 25, 2010
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 Hs 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.
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
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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
ISSC 2012 I.1, Paris Slide 34February 25, 2010