Quantitative Full-Field Data Fusion for Evaluation of Complex Structures
Quantitative Full-Field Data Fusion for Evaluation of Complex Structures
复制标题
用于评估复杂结构的定量全场数据融合
DOI：
10.1007/s11340-023-00973-8
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发表时间：
2023
影响因子：
2.4
通讯作者：
Callaghan J
中科院分区：
文献类型：
--
作者：
Callaghan J
背景使用全场实验技术对模型进行验证传统上依赖于本地数据比较。目前，通常使用选定的数据字段，例如局部最大值或选定的线状图。这里提出了一种新的方法，称为全场数据融合(FFDF)，它利用整个图像，确保技术的保真度得到充分利用。FFDF有可能提供一种评估设计修改和材料选择的直接手段。目的定义一种FFDF方法，该方法能够结合来自各种实验和数值来源的数据，以实现定量比较和验证，并创建新的参数来评估材料和结构性能。方法利用数字图像相关(DIC)和热弹性应力分析(TSA)的全场实验技术获得实验数据，然后将它们相互融合，并用有限元分析(FEA)进行预测。此外，FFDF方法实现了一种新的高保真验证技术，基于融合的数据集和度量，利用与实验数据的精确的全场逐点相似性评估。结果表明，由于估计数据集中的可比较位置而引入的不准确被消除，FFDF还使得实验数据中的不准确能够在相同的尺度上相互评估，而不考虑摄像机传感器的不同。例如，DIC中的工艺参数，如子集尺寸和应变窗口，可以通过与TSA的相似性评估来评估。结论FFDF方法提供了一种比较复杂复合材料子结构的不同设计构型和材料选择的方法，以及对数值模型的定量验证，最终可能减少对昂贵且耗时的全尺寸试验的依赖。
BackgroundValidation of models using full-field experimental techniques traditionally rely on local data comparisons. At present, typically selected data fields are used such as local maxima or selected line plots. Here a new approach is proposed called full-field data fusion (FFDF) that utilises the entire image, ensuring the fidelity of the techniques are fully exploited. FFDF has the potential to provide a direct means of assessing design modifications and material choices.ObjectiveA FFDF methodology is defined that has the ability to combine data from a variety of experimental and numerical sources to enable quantitative comparisons and validations as well as create new parameters to assess material and structural performance. A section of a wind turbine blade (WTB) substructure of complex composite construction is used as a demonstrator for the methodology.MethodsThe experimental data are obtained using the full-field experimental techniques of Digital Image Correlation (DIC) and Thermoelastic Stress Analysis (TSA), which are then fused with each other, and with predictions made using Finite Element Analysis (FEA). In addition, the FFDF method enables a new high-fidelity validation technique for FEA utilising a precise full-field point by point similarity assessment with the experimental data, based on the fused data sets and metrics.ResultsIt is shown that inaccuracies introduced because of estimation of comparable locations in the data sets are eliminated, The FFDF also enables inaccuracies in the experimental data to be mutually assessed at the same scale regardless of differences in camera sensors. For example, the effect of processing parameters in DIC such as subset size and strain window can be assessed through similarity assessment with the TSA.ConclusionsThe FFDF methodology offers a means for comparing different design configurations and material choices for complex composite substructures, as well as quantitative validation of numerical models, which may ultimately reduce dependence on expensive and time-consuming full-scale tests.
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影响因子：
4.6
作者：
I. Jiménez;D. Bull;O. Thomsen;J. Dulieu‐Barton
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影响因子：
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作者：
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DOI：
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发表时间：
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影响因子：
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影响因子：
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作者：
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影响因子：
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作者：
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