A Methodology for the Statistical Calibration of Complex Constitutive Material Models: Application to Temperature-Dependent Elasto-Visco-Plastic Materials

Pablos, Juan Luis de and Menga, Edoardo and Romero Olleros, Ignacio (2020). A Methodology for the Statistical Calibration of Complex Constitutive Material Models: Application to Temperature-Dependent Elasto-Visco-Plastic Materials. "Materials", v. 13 (n. 19); pp. 4402-4423. ISSN 1996-1944. https://doi.org/10.3390/ma13194402.

Description

Title: A Methodology for the Statistical Calibration of Complex Constitutive Material Models: Application to Temperature-Dependent Elasto-Visco-Plastic Materials
Author/s:
  • Pablos, Juan Luis de
  • Menga, Edoardo
  • Romero Olleros, Ignacio
Item Type: Article
Título de Revista/Publicación: Materials
Date: 2 October 2020
ISSN: 1996-1944
Volume: 13
Subjects:
Freetext Keywords: model calibration; sensitivity analysis; elasto-visco-plasticity; Gaussian process
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería Mecánica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (3MB) | Preview

Abstract

The calibration of any sophisticated model, and in particular a constitutive relation, is a complex problem that has a direct impact in the cost of generating experimental data and the accuracy of its prediction capacity. In this work, we address this common situation using a two-stage procedure. In order to evaluate the sensitivity of the model to its parameters, the first step in our approach consists of formulating a meta-model and employing it to identify the most relevant parameters. In the second step, a Bayesian calibration is performed on the most influential parameters of the model in order to obtain an optimal mean value and its associated uncertainty. We claim that this strategy is very efficient for a wide range of applications and can guide the design of experiments, thus reducing test campaigns and computational costs. Moreover, the use of Gaussian processes together with Bayesian calibration effectively combines the information coming from experiments and numerical simulations. The framework described is applied to the calibration of three widely employed material constitutive relations for metals under high strain rates and temperatures, namely, the Johnson?Cook, Zerilli?Armstrong, and Arrhenius models.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020821044HUCASOCIACION CENTRO TECNOLOGICO CEITDevelopment and validation of a powder HIP route for high temperature Astroloy to manufacture Ultrafan® IP Turbine Casings
Government of SpainRTI2018-098245-B-C21HEXAGBUnspecifiedBORDE DE GRANOS EN MICROESTRUCTURAS HEXAGONALES: ENLACE DE PROCESAMIENTO Y PROPIEDADES EN ALEACIONES ESTRUCTURALES LIGERAS

More information

Item ID: 64448
DC Identifier: http://oa.upm.es/64448/
OAI Identifier: oai:oa.upm.es:64448
DOI: 10.3390/ma13194402
Official URL: https://www.mdpi.com/1996-1944/13/19/4402
Deposited by: Memoria Investigacion
Deposited on: 15 Oct 2020 15:02
Last Modified: 15 Oct 2020 15:02
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM