Full‐field data from digital image correlation (DIC) provide rich information for finite‐element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. In this paper, we investigate two full‐field validation approaches: (1) the direct interpolation approach, which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC‐levelling approach, which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo‐DIC simulator to ‘level' the FEA data to the DIC data in a regularisation sense. Synthetic ‘experimental' DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errors were computed, even though there was no model form error, because the filtering effect of the DIC engine was neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach, the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the ‘experimental' DIC data were purposefully misaligned slightly from the FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In summary, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data have been ‘levelled' to the DIC data can meaningful, quantitative error maps be computed.

中文翻译：

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使用全场实验数据验证有限元模型：通过数字图像相关引擎平整有限元分析数据

来自数字图像相关性（DIC）的全场数据为有限元分析（FEA）验证提供了丰富的信息。但是，在进行有意义的定量比较之前，必须纠正FEA和DIC数据之间存在的一些固有矛盾，包括应变公式，坐标系，数据位置，应变计算算法，空间分辨率和数据过滤。在本文中，我们研究了两种全场验证方法：（1）直接插值方法，该方法通过将感兴趣的数量从一个网格插入另一个网格来解决前三个不一致性，以及（2）提出的DIC调平方法，它可以通过立体声DIC模拟器处理FEA数据，同时将所有FEA数据“平整”为DIC数据，从而同时解决了这六个不一致问题。合成的“实验性” DIC数据是基于示例测试样品的参考FEA生成的。由于没有考虑DIC引擎的滤波效果，因此应用了直接插值方法，并且即使没有模型形式误差，也计算出了显着的应变误差。相反，找平方法提供了准确的验证结果，当没有模型形式误差时，没有应变误差。接下来，通过边界条件的不匹配有目的地引入了模型形式错误。使用直接插值方法时，边界条件的不匹配被完全消除了，而使用调平方法时，清楚地观察到。最后，“实验性” DIC数据有意与FEA数据略有偏离。两种验证技术都存在未对准的问题，因此激发了持续的努力来开发可靠的对准过程。总而言之，直接插值是不够的，并且需要提出的调平方法来确保FEA和DIC数据具有相同的空间分辨率和数据过滤。只有将FEA数据“平衡”到DIC数据后，才能计算出有意义的定量误差图。需要采用建议的调平方法来确保FEA和DIC数据具有相同的空间分辨率和数据过滤。只有将FEA数据“平衡”到DIC数据后，才能计算出有意义的定量误差图。需要采用建议的调平方法来确保FEA和DIC数据具有相同的空间分辨率和数据过滤。只有将FEA数据“平衡”到DIC数据后，才能计算出有意义的定量误差图。