0
Research Papers

Statistical Assessment and Validation of Experimental and Computational Ship Response in Irregular Waves

[+] Author and Article Information
Matteo Diez

CNR-INM,
National Research Council,
Institute of Marine Engineering,
Via di Vallerano 139,
Rome 00128, Italy
e-mail: matteo.diez@cnr.it

Riccardo Broglia

CNR-INM,
National Research Council,
Institute of Marine Engineering,
Via di Vallerano 139,
Rome 00128, Italy
e-mail: riccardo.broglia@cnr.it

Danilo Durante

CNR-INM,
National Research Council,
Institute of Marine Engineering,
Via di Vallerano 139,
Rome 00128, Italy
e-mail: danilo.durante@cnr.it

Angelo Olivieri

CNR-INM,
National Research Council,
Institute of Marine Engineering,
Via di Vallerano 139,
Rome 00128, Italy
e-mail:angelo-olivieri@cnr.it

Emilio F. Campana

CNR-DIITET,
National Reserach Council,
Department of Engineering,
ICT and Technologies for Energy and
Transportation,
Piazzale Aldo Moro 7,
Roma 00185, Italy
e-mail: emiliofortunato.campana@cnr.it

Frederick Stern

IIHR—Hydroscience and Engineering,
The University of Iowa,
Iowa City, IA 52242
e-mail: frederick-stern@uiowa.edu

1Corresponding author.

Manuscript received March 6, 2017; final manuscript received August 29, 2018; published online October 10, 2018. Assoc. Editor: William Rider.

J. Verif. Valid. Uncert 3(2), 021004 (Oct 10, 2018) (18 pages) Paper No: VVUQ-17-1012; doi: 10.1115/1.4041372 History: Received March 06, 2017; Revised August 29, 2018

The objective of this work is to provide and use both experimental fluid dynamics (EFD) data and computational fluid dynamics (CFD) results to validate a regular-wave uncertainty quantification (UQ) model of ship response in irregular waves, based on a set of stochastic regular waves with variable frequency. As a secondary objective, preliminary statistical studies are required to assess EFD and CFD irregular wave errors and uncertainties versus theoretical values and evaluate EFD and CFD resistance and motions uncertainties and, in the latter case, errors versus EFD values. UQ methods include analysis of the autocovariance matrix and block-bootstrap of time series values (primary variable). Additionally, the height (secondary variable) associated with the mean-crossing period is assessed by the bootstrap method. Errors and confidence intervals of statistical estimators are used to define validation criteria. The application is a two-degrees-of-freedom (heave and pitch) towed Delft catamaran with a length between perpendiculars equal to 3 m (scale factor equal to 33), sailing at Froude number equal to 0.425 in head waves at scaled sea state 5. Validation variables are x-force, heave and pitch motions, vertical acceleration of bridge, and vertical velocity of flight deck. Autocovariance and block-bootstrap methods for primary variables provide consistent and complementary results; the autocovariance is used to assess the uncertainty associated with expected values and standard deviations and is able to identify undesired self-repetition in the irregular wave signal; block-bootstrap methods are used to assess additional statistical estimators such as mode and quantiles. Secondary variables are used for an additional assessment of the quality of experimental and simulation data as they are generally more difficult to model and predict than primary variables. Finally, the regular wave UQ model provides a good approximation of the desired irregular wave statistics, with average errors smaller than 5% and validation uncertainties close to 10%.

Copyright © 2018 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Benek, J. A. , and Luckring, J. M. , 2017, “ Overview of the AVT-191 Project to Assess Sensitivity Analysis and Uncertainty Quantification Methods for Military Vehicle Design,” AIAA Paper No. AIAA 2017-1196.
Stern, F. , Volpi, S. , Gaul, N. J. , Choi, K. K. , Diez, M. , Broglia, R. , Durante, D. , Campana, E. , and Iemma, U. , 2017, “ Development and Assessment of Uncertainty Quantification Methods for Ship Hydrodynamics,” AIAA Paper No. AIAA 2017-1654.
Diez, M. , Serani, A. , Campana, E. F. , and Stern, F. , 2017, “ CFD-Based Stochastic Optimization of a Destroyer Hull Form for Realistic Ocean Operations,” 14th International Conference on Fast Sea Transportation (FAST), Nantes, France, Sept. 27–29, pp. 1–9. https://www.researchgate.net/publication/320035523_CFD-based_Stochastic_Optimization_of_a_Destroyer_Hull_Form_for_Realistic_Ocean_Operations
Serani, A. , and Diez, M. , 2018, “ Shape Optimization Under Stochastic Conditions by Design-Space Augmented Dimensionality Reduction,” AIAA Paper No. 2018-3416.
Pisaroni, M. , Nobile, F. , and Leyland, P. , 2017, “ A Continuation Multi Level Monte Carlo (C-MLMC) Method for Uncertainty Quantification in Compressible Inviscid Aerodynamics,” Comput. Methods Appl. Mech. Eng., 326, pp. 20–50. [CrossRef]
Wunsch, D. , Hirsch, C. , Nigro, R. , and Coussement, G. , 2015, “ Quantification of Combined Operational and Geometrical Uncertainties in Turbo-Machinery Design,” ASME Paper No. GT2015-43399.
Quagliarella, D. , Petrone, G. , and Iaccarino, G. , 2014, “ Optimization Under Uncertainty Using the Generalized Inverse Distribution Function,” Modeling, Simulation and Optimization for Science and Technology, Springer, Dordrecht, The Netherlands, pp. 171–190.
Mousaviraad, S. M. , He, W. , Diez, M. , and Stern, F. , 2013, “ Framework for Convergence and Validation of Stochastic Uncertainty Quantification and Relationship to Deterministic Verification and Validation,” Int. J. Uncertainty Quantif., 3(5), pp. 371–395.
He, W. , Diez, M. , Zou, Z. , Campana, E. F. , and Stern, F. , 2013, “ URANS Study of Delft Catamaran Total/Added Resistance, Motions and Slamming Loads in Head Sea Including Irregular Wave and Uncertainty Quantification for Variable Regular Wave and Geometry,” Ocean Eng., 74, pp. 189–217. [CrossRef]
Diez, M. , He, W. , Campana, E. F. , and Stern, F. , 2014, “ Uncertainty Quantification of Delft Catamaran Resistance, Sinkage and Trim for Variable Froude Number and Geometry Using Metamodels, Quadrature and Karhunen–Loève Expansion,” J. Mar. Sci. Technol., 19(2), pp. 143–169. [CrossRef]
Volpi, S. , Diez, M. , Gaul, N. J. , Song, H. , Iemma, U. , Choi, K. K. , Gaul, N. J. , and Stern, F. , 2015, “ Development and Validation of a Dynamic Metamodel Based on Stochastic Radial Basis Functions and Uncertainty Quantification,” Struct. Multidiscip. Optim., 51(2), pp. 347–368. [CrossRef]
Diez, M. , Campana, E. F. , and Stern, F. , 2015, “ Development and Evaluation of Hull-Form Stochastic Optimization Methods for Resistance and Operability,” 13th International Conference on Fast Sea Transportation (FAST), Washington, DC, Sept. 1–4, pp. 1–18. https://www.researchgate.net/publication/281839894_Development_and_evaluation_of_hull-form_stochastic_optimization_methods_for_resistance_and_operability
Diez, M. , Campana, E. F. , and Stern, F. , 2018, “ Stochastic Optimization Methods for Ship Resistance and Operational Efficiency Via CFD,” Struct. Multidiscip. Optim., 57(2), pp. 735–758. [CrossRef]
Diez, M. , Broglia, R. , Durante, D. , Campana, E. F. , and Stern, F. , 2015, “ Validation of High-Fidelity Uncertainty Quantification of a High-Speed Catamaran in Irregular Waves,” 13th International Conference on Fast Sea Transportation (FAST), Washington, DC, Sept. 1–4, pp. 1–21. https://www.researchgate.net/publication/281840423_Validation_of_high-fidelity_uncertainty_quantification_of_a_high-speed_catamaran_in_irregular_waves
Diez, M. , Broglia, R. , Durante, D. , Olivieri, D. , Campana, E. F. , and Stern, F. , 2016, “ Statistical Validation of a High-Speed Catamaran in Irregular Waves,” 31st Symposium on Naval Hydrodynamics, Monterey, CA, Sept. 11–16, pp. 1–16. https://www.researchgate.net/publication/308889413_Statistical_Validation_of_a_High-speed_Catamaran_in_Irregular_Waves
Diez, M. , Broglia, R. , Durante, D. , Olivieri, A. , Campana, E. , and Stern, F. , 2017, “ Validation of Uncertainty Quantification Methods for High-Fidelity CFD of Ship Response in Irregular Waves,” AIAA Paper No. 2007-1655.
Van't Veer, R. , 1998, “ Experimental Results of Motions, Hydrodynamic Coefficients and Wave Loads on the 372 Catamaran Model,” TU Delft, Delft, The Netherlands, Report, No. 1129.
Van't Veer, R. , 1998, “ Experimental Results of Motions, and Structural Loads on the 372 Catamaran Model in Head and Oblique Waves,” TU Delft, Delft, The Netherlands, Report, No. 1130.
Diez, M. , Chen, X. , Campana, E. F. , and Stern, F. , 2013, “ Reliability-Based Robust Design Optimization for Ships in Real Ocean Environment,” 12th International Conference on Fast Sea Transportation (FAST2013), Amsterdam, The Netherlands, pp. 1–12.
North Atlantic Treaty Organization, Military Agency for Standardization, 2000, STANAG 4154 - Common Procedures for Seakeeping in the Ship Design Process, IHS Inc., Englewood, CO.
Ochi, M. K. , 2005, Ocean Waves: The Stochastic Approach, Cambridge University Press, Cambridge, UK.
Stern, F. , Wilson, R. V. , Coleman, H. W. , and Paterson, E. G. , 2001, “ Comprehensive Approach to Verification and Validation of CFD Simulations—Part 1: Methodology and Procedures,” ASME J. Fluids Eng., 123(4), pp. 793–802. [CrossRef]
Sadat-Hosseini, H. , Kim, D. H. , Toxopeus, S. , Diez, M. , and Stern, F. , 2015, “ CFD and Potential Flow Simulations of Fully Appended Free Running 5415M in Irregular Waves,” World Maritime Technology Conference, Providence, RI, Nov. 3–7, pp. 3–7.
Silverman, B. W. , 1986, Density Estimation for Statistics and Data Analysis, Chapman & Hall/CRC, London, p. 48.
Belenky, V. , Pipiras, V. , Kent, C. , Hughes, M. , Campbell, B. , and Smith, T. , 2013, “ On the Statistical Uncertainty of Time-Domain-Based Assessment of Stability Failure: Confidence Interval for the Mean and the Variance of a Time Series,” 13th International Ship Stability Workshop, Brest, France, Sept. 23–26, pp. 1–12.
Belenky, V. , Pipiras, V. , and Weems, K. , 2015, “ Statistical Uncertainty of Ship Motion Data,” 12th International Conference on the Stability of Ships and Ocean Vehicles, (STAB), Glasgow, UK, June 14–19, pp. 1–12. http://www.shipstab.org/files/Proceedings/STAB/STAB2015/Papers/11.3-1-Belenky.pdf
Carlstein, E. , 1986, “ The Use of Subseries Values for Estimating the Variance of a General Statistic From a Stationary Sequence,” Ann. Stat., 14(3), pp. 1171–1179. [CrossRef]
Künsch, H. R. , 1989, “ The Jackknife and the Bootstrap for General Stationary Observations,” Ann. Stat., 17(3), pp. 1217–1241. [CrossRef]
Politis, D. N. , and Romano, J. P. , 1994, “ The Stationary Bootstrap,” J. Am. Stat. Assoc., 89(428), pp. 1303–1313. [CrossRef]
Efron, B. , 1981, “ Nonparametric Estimates of Standard Error: The Jackknife, the Bootstrap and Other Methods,” Biometrika, 68(3), pp. 589–599. [CrossRef]
Stern, F. , Diez, M. , Sadat-Hosseini, H. , Yoon, H. , and Quadvlieg, F. , 2018, “ Statistical Approach for Computational Fluid Dynamics State-of-the-Art Assessment: N-Version Verification and Validation,” ASME J. Verif. Validation Uncertainty Quantif., 2(3), p. 031004. [CrossRef]
Huang, J. , Carrica, P. , and Stern, F. , 2008, “ Semi-Coupled Air/Water Immersed Boundary Approach for Curvilinear Dynamic Overset Grids With Application to Ship Hydrodynamics,” Int. J. Numer. Meth. Fluids, 58(6), pp. 591–624. [CrossRef]
Stern, F. , Wang, Z. , Yang, J. , Sadat-Hosseini, H. , Bhushan, S. , Mousaviraad, S. M. , Diez, M. , Yoon, S.-H. , Wu, P.-C. , Yeon, S. M. , Dogan, T. , Kim, D.-H. , Volpi, S. , Conger, M. , Michael, T. , Xing, T. , Thodal, R. S. , and Grenestedt, J. L. , 2015, “ Recent Progress in CFD for Naval Architecture and Ocean Engineering,” J. Hydrodyn., Ser. B, 27(1), pp. 1–23. [CrossRef]
Noack, R. , 2005, “ SUGGAR: A General Capability for Moving Body Overset Grid Assembly,” AIAA Paper No. AIAA 2005-5117.
Larsson, L., Stern, F., Visonneau, M., Hino, T., Hirata, N., and Kim, J., eds., 2015, Proceedings, Tokyo 2015 Workshop on CFD in Ship Hydrodynamics, National Maritime Research Institute, Tokyo, Japan.
Volpi, S. , Diez, M. , Sadat-Hosseini, H. , Kim, D. H. , Stern, F. , Thodal, R. S. , and Grenestedt, J. L. , 2017, “ Composite Bottom Panel Slamming of a Fast Planing Hull Via Tightly Coupled Fluid-Structure Interaction Simulations and Sea Trials,” Ocean Eng., 143, pp. 240–258. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Delft Catamaran geometry and INSEAN model 2554 in the towing tank

Grahic Jump Location
Fig. 2

Definition of height associated with mean-crossing periods from the time series

Grahic Jump Location
Fig. 3

Computational domain and grid for current URANS simulations

Grahic Jump Location
Fig. 4

Uncertainty for EV (a) and SD (b) versus run length and number of wave components by AC analysis (values are normalized with 2 SD)

Grahic Jump Location
Fig. 5

Wave elevation and autocovariance of EFD (a) and CFD (b)

Grahic Jump Location
Fig. 6

Encounter wave energy spectrum: comparison of EFD and CFD to theoretical values

Grahic Jump Location
Fig. 7

Quantile function (a) and probability density function (b) of wave elevation: comparison of EFD and CFD to theoretical values

Grahic Jump Location
Fig. 8

Quantile function (a) and probability density function (b) of wave height: comparison of EFD and CFD to theoretical values

Grahic Jump Location
Fig. 9

Fourier transform of x-force, surge and inertia force (a), heave (b), pitch (c), acceleration at bridge (d), and velocity at deck (e): comparison of CFD to EFD

Grahic Jump Location
Fig. 10

Quantile function (a) and probability density function (b) of x-force values: comparison of CFD to EFD

Grahic Jump Location
Fig. 11

Quantile function (a) and probability density function (b) of x-force height: comparison of CFD to EFD

Grahic Jump Location
Fig. 12

Quantile function (a) and probability density function (b) of heave values: comparison of CFD to EFD

Grahic Jump Location
Fig. 13

Quantile function (a) and probability density function (b) of heave height: comparison of CFD to EFD

Grahic Jump Location
Fig. 14

Quantile function (a) and probability density function (b) of pitch values: comparison of CFD to EFD

Grahic Jump Location
Fig. 15

Quantile function (a) and probability density function (b) of pitch height: comparison of CFD to EFD

Grahic Jump Location
Fig. 16

Quantile function (a) and probability density function (b) of acceleration values: comparison of CFD to EFD

Grahic Jump Location
Fig. 17

Quantile function (a) and probability density function (b) of acceleration height: comparison of CFD to EFD

Grahic Jump Location
Fig. 18

Quantile function (a) and probability density function (b) of velocity values: comparison of CFD to EFD

Grahic Jump Location
Fig. 19

Quantile function (a) and probability density function (b) of velocity height: comparison of CFD to EFD

Grahic Jump Location
Fig. 20

Regular wave UQ items for x-force (a), pitch (b), acceleration at bridge (c), and velocity at deck (d): comparison of CFD to EFD

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In