This work deals with the identification of hyperelastic constitutive parameters using the virtual fields method. The choice of the virtual displacement fields is a crucial aspect of the method, typically for reducing the sensitivity to the measurement noise. A first and simple option is to generate the virtual displacement fields randomly. Nevertheless, in case of hyperelastic models for which the stress is not a linear function of the constitutive parameters, improving the choice of the virtual displacement fields is not trivial and an alternative strategy has to be found. In the present study, the sensitivity-based virtual fields approach is applied and compared with the randomly generated virtual displacement fields approach. Two material models were considered: the Mooney model, which describes quite well the behavior of hyperelastic materials for small and moderate strains, and the Ogden model, which accounts for the stress hardening phenomenon observed at higher strains. The full kinematic fields are measured by using the digital image correlation technique during an equibiaxial tensile test performed on a cruciform specimen. Identification results are discussed through their capability to predict the external force measured during the test. The sensitivity-based virtual fields approach is found to improve significantly the prediction compared with the randomly generated virtual displacement fields approach.

中文翻译：

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用基于灵敏度的虚拟场识别超弹性本构参数

这项工作涉及使用虚拟场方法识别超弹性本构参数。虚拟位移场的选择是该方法的一个关键方面，通常用于降低对测量噪声的敏感性。第一个简单的选择是随机生成虚拟位移场。然而，对于应力不是本构参数的线性函数的超弹性模型，改进虚拟位移场的选择并非易事，必须找到替代策略。在本研究中，应用了基于灵敏度的虚拟场方法并与随机生成的虚拟位移场方法进行了比较。考虑了两种材料模型：Mooney 模型，它很好地描述了超弹性材料在小应变和中等应变下的行为，以及 Ogden 模型，它解释了在较高应变下观察到的应力硬化现象。在十字形试样上进行等双轴拉伸试验期间，通过使用数字图像相关技术测量完整的运动场。识别结果通过其预测测试期间测量的外力的能力进行讨论。与随机生成的虚拟位移场方法相比，基于灵敏度的虚拟场方法被发现显着改善了预测。在十字形试样上进行等双轴拉伸试验期间，通过使用数字图像相关技术测量完整的运动场。识别结果通过它们预测测试期间测量的外力的能力进行讨论。与随机生成的虚拟位移场方法相比，基于灵敏度的虚拟场方法被发现显着改善了预测。在十字形试样上进行等双轴拉伸试验期间，通过使用数字图像相关技术测量完整的运动场。识别结果通过它们预测测试期间测量的外力的能力进行讨论。与随机生成的虚拟位移场方法相比，基于灵敏度的虚拟场方法被发现显着改善了预测。