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VALIDATION: WHAT, WHY AND HOW

机译:验证:原因,原因和方式

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摘要

Offshore and Naval engineering have relied on physical models, i.e. experimental fluid dynamics (EFD), for several decades. Although the role of experiments in engineering is still unquestionable, some of the limitations of physical models, as for example domain size (blockage and scale effects), can be addressed using mathematical models, i.e. computational fluid dynamics (CFD). However, to gain confidence in the use of CFD it is fundamental to determine the modelling accuracy, i.e. to determine the difference between the "physical reality" and the selected mathematical model. The quantification of the modelling error is the goal of Validation. It must be emphasized that Validation applies to the mathematical model (and not the code) and is performed for selected flow quantities (the so-called quantities of interest). Ideally, Validation would be performed comparing an exact measurement of the "physical reality" with the exact solution of the selected mathematical model. However, exact measurements do not exist and mathematical models for turbulent flows do not have analytical solutions. Therefore, procedures must be developed to take into account experimental and numerical uncertainties. Furthermore, the exact values of the flow parameters as for example Reynolds number, fluid viscosity or inlet turbulence quantities are often unknown, which leads to the so-called parameter uncertainty that also has to be dealt within the assessment of the modelling error. The main goal of this paper is to demonstrate that the very popular designation of "code X is validated" is meaningless without saying what is the mathematical model embedded in the code, what are the quantities of interest for the specific applica- tion and what is the Validation uncertainty imposed by the experimental, numerical and parameter uncertainties. Furthermore, we also illustrate that Validation is not a pass or fail exercise. A modelling error of 10% may be acceptable for a given application, whereas 1% may not be enough for a different one. To this end, we present the application of the ASME V&V 20 Validation procedure for local set points and the metric for multiple set points to several practical test cases: prediction of transition from laminar to turbulent regime for the flow over aflat plate; flow around a circular cylinder; flow around the KVLCC2 tanker and current loads in shallow water for a LNG carrier. In most of these exercises, parameter uncertainty is assumed to be zero, which is an assumption often required for the so-called practical calculations due to the computational effort required to address it. Nonetheless, as an illustration of its application, the flow over the flat plate includes parameter uncertainty for the specification of the inlet turbulence quantities.
机译:近几十年来,海上和海军工程一直依靠物理模型,即实验流体动力学(EFD)。尽管实验在工程中的作用仍然是毋庸置疑的,但是物理模型的某些局限性,例如域大小(阻塞和比例效应),可以使用数学模型来解决,即计算流体动力学(CFD)。然而,为了获得对使用CFD的信心,确定建模精度,即确定“物理现实”与所选择的数学模型之间的差异是基本的。建模误差的量化是验证的目标。必须强调的是,验证适用于数学模型(而非代码),并且是针对选定的流量(所谓的目标流量)执行的。理想情况下,将对“物理现实”的精确测量结果与所选数学模型的精确解进行比较来进行验证。但是,不存在精确的测量值,湍流的数学模型也没有解析解。因此,必须制定程序以考虑到实验和数值上的不确定性。此外,流动参数的精确值例如雷诺数,流体粘度或入口湍流量通常是未知的,这导致所谓的参数不确定性,该不确定性也必须在建模误差的评估中处理。本文的主要目的是证明“代码X已验证”这一非常流行的名称是没有意义的,而无需说明代码中嵌入的数学模型是什么,特定应用感兴趣的量是多少以及什么是有效的。实验不确定性,数值不确定性和参数不确定性所带来的验证不确定性。此外,我们还说明了验证不是通过或失败的练习。对于给定的应用程序,10%的建模误差是可以接受的,而对于另一应用程序,1%的建模误差可能还不够。为此,我们介绍了针对局部设定点的ASME V&V 20验证程序的应用,以及针对多个实际测试案例的多个设定点的度量标准:预测平板上流动从层流状态到湍流状态的转换;绕圆柱体流动;液流绕过KVLCC2油轮,而目前的液化天然气载运到LNG船上。在大多数此类练习中,参数不确定性假定为零,由于解决该问题需要进行大量的计算工作,因此这通常是所谓的实际计算所需的假设。尽管如此,作为其应用的说明,在平板上的流动包括用于确定进口湍流量的参数不确定性。

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