Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels. (15th February 2020)

• Record Type: Journal Article
• Authors: Wagner, P.-R.; Fahrni, R.; Klippel, M.; Frangi, A.; Sudret, B.
• Is Part Of: Engineering structures. Volume 205(2020)
• Publication Date: 2020-02-15
• Subjects: Bayesian model calibration -- Sensitivity analysis -- Surrogate modelling -- Component additive method -- Polynomial chaos expansions; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2019.110063
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: We present a method for the calibration of temperature-dependent material properties. The calibration of the parameters is carried out with a Bayesian inversion framework. The analysis is accelerated through advanced surrogate modelling techniques. A time-dependent sensitivity analysis of the model parameters is conducted. Abstract: A common approach to assess the performance of fire insulation panels is the component additive method (CAM). The parameters of the CAM are based on the temperature-dependent thermal material properties of the panels. These material properties can be derived by calibrating finite element heat transfer models using experimentally measured temperature records. In the past, the calibration of the material properties was done manually by trial and error approaches, which was inefficient and prone to error. In this contribution, the calibration problem is reformulated in a probabilistic setting and solved using the Bayesian model calibration framework. This not only gives a set of best-fit parameters but also confidence bounds on the latter. To make this framework feasible, the procedure is accelerated through the use of advanced surrogate modelling techniques: polynomial chaos expansions combined with principal component analysis. This surrogate modelling technique additionally allows one to conduct a variance-based sensitivity analysis at no additional cost by giving access to the Sobol' indices. The calibration is finally validated by using the calibrated material properties to predict the temperature development in different experimental setups.