Uncertainty quantification of Delft catamaran resistance, sinkage and trim for variable Froude number and geometry using metamodels, quadrature and Karhunen–Loève expansion
Uncertainty quantification of Delft catamaran resistance, sinkage and trim for variable Froude number and geometry using metamodels, quadrature and Karhunen–Loève expansion
复制标题
DOI：
10.1007/s00773-013-0235-0
复制
发表时间：
2014-06
影响因子：
2.6
通讯作者：
M. Diez;Wei He;E. Campana;F. Stern
中科院分区：
文献类型：
--
作者：
M. Diez;Wei He;E. Campana;F. Stern
研究了一种非侵入式不确定性量化（UQ）方法的收敛性和有效性评估框架，并将其应用于船舶设计中的一个复杂工业问题，即高速德尔夫特双体船在静水中前进，具有可变的弗劳德数和几何形状。讨论了UQ研究与确定性验证和确认的关系。使用高（URANS）和低（势流）保真度模拟进行计算。在截断正态分布上，佛汝德数的期望值和标准差分别等于0.5和0.05。几何不确定性与基于仿真的设计优化的研究空间有关，并通过Karhunen-Loève展开（KLE）进行评估。采用拉丁超立方抽样的蒙特卡罗方法（MC-LHS）计算阻力、下沉和纵倾的期望值、标准差、分布和不确定度区间。采用CFD的MC-LHS被用作验证成本较低的UQ方法的基准，包括采用元模型和标准求积公式的MC-LHS。高斯求积被认为是最有效的方法，然而，MC-LHS与元模型是首选，因为提供了一个简单的方式和合理的小计算成本的置信区间和分布。UQ的结果相比，早期确定性的单目标和多目标优化;降维KLE的几何变异性研究表明，随机优化将不会有很大的好处，目前的问题。
A framework for assessing convergence and validation of non-intrusive uncertainty quantification (UQ) methods is studied and applied to a complex industrial problem in ship design, namely the high-speed Delft Catamaran advancing in calm water, with variable Froude number and geometry. Relationship between UQ studies and deterministic verification and validation is discussed. Computations are performed using high- (URANS) and low- (potential flow) fidelity simulations. Froude number has expected value and standard deviation equal to 0.5 and 0.05, respectively, on a truncated normal distribution. Geometric uncertainty is related to the research space of a simulation-based design optimization, and assessed through the Karhunen–Loève expansion (KLE). Monte Carlo method with Latin hypercube sampling (MC-LHS) is used to compute expected value, standard deviation, distribution and uncertainty intervals for resistance, sinkage and trim. MC-LHS with CFD is used as a benchmark for validating less costly UQ methods, including MC-LHS with metamodels and standard quadrature formulas. Gaussian quadrature is found the most efficient method; however, MC-LHS with metamodels is preferred since provides with confidence intervals and distributions in a straightforward way and at reasonably small computational cost. UQ results are compared to earlier deterministic single- and multi-objective optimization; reduced-dimensional KLE studies for geometric variability indicate that stochastic optimization would not be of great benefit for the present problem.