Comprehensive Approach to Verification and Validation of CFD Simulations—Part 2: Application for Rans Simulation of a Cargo/Container Ship
Comprehensive Approach to Verification and Validation of CFD Simulations—Part 2: Application for Rans Simulation of a Cargo/Container Ship
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DOI：
10.1115/1.1412236
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发表时间：
2001-12
影响因子：
2
通讯作者：
R. Wilson;F. Stern;H. Coleman;E. Paterson
中科院分区：
文献类型：
--
作者：
R. Wilson;F. Stern;H. Coleman;E. Paterson
这篇由两部分组成的论文的第2部分提供了一个示例案例研究，该案例研究遵循了第1部分中介绍的最近开发的综合验证和确认方法。案例研究是一个RANS模拟的船舶水动力学使用船舶流体动力学CFD代码建立的基准。对阻力（积分变量）和波形（点变量）的验证表明，迭代的不确定性远小于网格的不确定性，模拟的数值不确定性约为2% S1（S1为最细网格的模拟值）。阻力和波浪剖面的验证表明，建模误差约为8%D（D是测量的阻力）和6%z max（z max是最大波浪高程），应在3%D和4%z max水平下进行验证。降低验证水平主要需要降低实验的不确定性。减少建模误差和实验不确定性将产生验证和验证的解决方案，在低水平的应用，使用目前的CFD代码。虽然有许多问题的实际应用，方法和程序被证明是成功的评估水平的验证和确认，并在某些情况下识别建模错误。对于实际应用，解决方案是远离渐近范围，因此，结果的分析和解释被证明是重要的，在评估变异的准确性，验证水平，减少数值和建模误差和不确定性的策略。@DOI：10.1115/1.1412236#
Part 2 of this two-part paper provides an example case study following the recently developed comprehensive verification and validation approach presented in Part 1. The case study is for a RANS simulation of an established benchmark for ship hydrodynamics using a ship hydrodynamics CFD code. Verification of the resistance (integral variable) and wave profile (point variable) indicates iterative uncertainties much less than grid uncertainties and simulation numerical uncertainties of about 2%S 1 (S1 is the simulation value for the finest grid). Validation of the resistance and wave profile shows modeling errors of about 8%D (D is the measured resistance) and 6% z max (z max is the maximum wave elevation), which should be addressed for possible validation at the 3%D and 4%z max levels. Reducing the level of validation primarily requires reduction in experimental uncertainties. The reduction of both modeling errors and experimental uncertainties will produce verified and validated solutions at low levels for this application using the present CFD code. Although there are many issues for practical applications, the methodology and procedures are shown to be successful for assessing levels of verification and validation and identifying modeling errors in some cases. For practical applications, solutions are far from the asymptotic range; therefore, analysis and interpretation of the results are shown to be important in assessing variability for order of accuracy, levels of verification, and strategies for reducing numerical and modeling errors and uncertainties. @DOI: 10.1115/1.1412236#