Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems. (June 2015)

• Record Type: Journal Article
• Authors: Sankararaman, Shankar; Mahadevan, Sankaran
• Is Part Of: Reliability engineering & system safety. Volume 138(2015:Jun.)
• Page Start: 194
• Page End: 209
• Publication Date: 2015-06
• Subjects: Multi-level system -- Uncertainty quantification -- Bayesian network -- Calibration -- Validation -- Verification; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2015.01.023
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: This paper proposes a Bayesian methodology to integrate model verification, validation, and calibration activities for the purpose of overall uncertainty quantification in different types of engineering systems. The methodology is first developed for single-level models, and then extended to systems that are studied using multi-level models that interact with each other. Two types of interactions amongst multi-level models are considered: (1) Type-I, where the output of a lower-level model (component and/or subsystem) becomes an input to a higher level system model, and (2) Type-II, where parameters of the system model are inferred using lower-level models and tests (that describe simplified components and/or isolated physics). The various models, their inputs, parameters, and outputs, experimental data, and various sources of model error are connected through a Bayesian network. The results of calibration, verification, and validation with respect to each individual model are integrated using the principles of conditional probability and total probability, and propagated through the Bayesian network in order to quantify the overall system-level prediction uncertainty. The proposed methodology is illustrated with numerical examples that deal with heat conduction and structural dynamics. Abstract : Author-Highlights: A Bayesian approach is used to integrate verification, validation, and calibration. Single-level models and systems with multiple component-level models are presented. System-level configurations with two types of model-interactions are considered. A Bayesian network connects multiple models, inputs, parameters, and outputs. Total probability theorem is used to quantify system-level prediction uncertainty.