Efficient uncertainty quantification of a fully nonlinear and dispersive water wave model with random inputs

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• 作者：Daniele Bigoni ; Allan P. Engsig-Karup…
• 关键词：Free surface water waves ; Generalized polynomial chaos ; High ; performance computing ; Sensitivity analysis ; Uncertainty quantification
• 刊名：Journal of Engineering Mathematics
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：101
• 期：1
• 页码：87-113
• 全文大小：2,724 KB
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Mechanics
  Applications of Mathematics
  Analysis
  Mathematical Modeling and IndustrialMathematics
  Numeric Computing
  
• 出版者：Springer Netherlands
• ISSN：1573-2703
• 卷排序：101

文摘

A major challenge in next-generation industrial applications is to improve numerical analysis by quantifying uncertainties in predictions. In this work we present a formulation of a fully nonlinear and dispersive potential flow water wave model with random inputs for the probabilistic description of the evolution of waves. The model is analyzed using random sampling techniques and nonintrusive methods based on generalized polynomial chaos (PC). These methods allow us to accurately and efficiently estimate the probability distribution of the solution and require only the computation of the solution at different points in the parameter space, allowing for the reuse of existing simulation software. The choice of the applied methods is driven by the number of uncertain input parameters and by the fact that finding the solution of the considered model is computationally intensive. We revisit experimental benchmarks often used for validation of deterministic water wave models. Based on numerical experiments and assumed uncertainties in boundary data, our analysis reveals that some of the known discrepancies from deterministic simulation in comparison with experimental measurements could be partially explained by the variability in the model input. Finally, we present a synthetic experiment studying the variance-based sensitivity of the wave load on an offshore structure to a number of input uncertainties. In the numerical examples presented the PC methods exhibit fast convergence, suggesting that the problem is amenable to analysis using such methods.