Statistical Assessment and Validation of Experimental and Computational Ship Response in Irregular Waves
-
Published:2018-06-01
Issue:2
Volume:3
Page:
-
ISSN:2377-2158
-
Container-title:Journal of Verification, Validation and Uncertainty Quantification
-
language:en
-
Short-container-title:
Author:
Diez Matteo1, Broglia Riccardo1, Durante Danilo1, Olivieri Angelo2, Campana Emilio F.3, Stern Frederick4
Affiliation:
1. CNR-INM, National Research Council, Institute of Marine Engineering, Via di Vallerano 139, Rome 00128, Italy e-mail: 2. CNR-INM, National Research Council, Institute of Marine Engineering, Via di Vallerano 139, Rome 00128, Italy 3. CNR-DIITET, National Reserach Council, Department of Engineering, ICT and Technologies for Energy and Transportation, Piazzale Aldo Moro 7, Roma 00185, Italy e-mail: 4. IIHR—Hydroscience and Engineering, The University of Iowa, Iowa City, IA 52242 e-mail:
Abstract
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%.
Funder
Office of Naval Research
Publisher
ASME International
Subject
Computational Theory and Mathematics,Computer Science Applications,Modeling and Simulation,Statistics and Probability
Reference36 articles.
1. 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. 10.2514/6.2017-1196 2. 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 2017-1654.10.2514/6.2017-1654 3. 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 4. Serani, A., and Diez, M., 2018, “Shape Optimization Under Stochastic Conditions by Design-Space Augmented Dimensionality Reduction,” AIAA Paper No. 2018-3416. 10.2514/6.2018-3416 5. A Continuation Multi Level Monte Carlo (C-MLMC) Method for Uncertainty Quantification in Compressible Inviscid Aerodynamics;Comput. Methods Appl. Mech. Eng.,2017
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|