On the optimisation efficiency for the inverse identification of constitutive model parameters

• Main Author: Oliveira, Miguel Guimarães
• Publication Date: 2021
• Other Authors: Martins, João Miguel Peixoto, Coelho, Bernardete, Thuillier, Sandrine, Andrade-Campos, António
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10773/31364
• topic: Optimisation algorithms; Finite element model updating; Heterogeneous thermomechanical tests; Thermoelastoviscoplasticity

The development of full-field measurement techniques paved the way for the design of new mechanical tests. However, because these mechanical tests provide heterogeneous strain fields, no closed-form solution exists between the measured deformation fields and the constitutive parameters. Therefore, inverse identification techniques should be used to calibrate constitutive models, such as the widely known finite element model updating (FEMU) and the virtual fields method (VFM). Although these inverse identification techniques follow distinct approaches to explore full-field measurements, they all require using an optimisation technique to find the optimum set of material parameters. Nonetheless, the choice of a suitable optimisation technique lacks attention and proper research. Most studies tend to use a least-squares gradient-based optimisation technique, such as the Levenberg-Marquardt algorithm. This work analyses optimisation algorithms, gradient-based and -free algorithms, for the inverse identification of constitutive model parameters. To avoid needless implementation and take advantage of highly developed programming languages, the optimisation algorithms available in optimisation libraries are used. A FEMU based approach is considered in the calibration of a thermoelastoviscoplastic model. The material parameters governing strain hardening, temperature and strain rate are identified. Results are discussed in terms of efficiency and the robustness of the optimisation processes.