Identification and quantification of uncertainties in a rotor machine using Bayesian inference with generalized polynomial chaos expansion

• Grant number: 18/02976-9
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: October 29, 2018
• End date: October 28, 2019
• Field of knowledge: Engineering - Mechanical Engineering - Mechanics of Solids
• Principal Investigator: Helio Fiori de Castro
• Grantee: Gabriel Yuji Garoli
• Supervisor: Rainer Nordmann
• Host Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: Fraunhofer-Gesellschaft, Germany
• Associated to the scholarship: 16/13223-6 - Uncertainty estimation and quantification applied to fault models of rotating machines, BP.DD

Abstract

This Research Internship Abroad (BEPE) proposal consists mainly on the experimental aspect of the currently doctoral project (grant #2016/13223-6), which is part of the ongoing thematic project (#2015/20363-6).Rotating machines are present in different segments of the industries. Therefore, the knowledge of the phenomena and behaviors of such elements is important. Theses machineries have inherent uncertainties, which must be included on the mathematical model. The Bayesian inference can identify unknown parameters, taking into account stochastic characteristic. Markov Chain Monte Carlo method is commonly used to solve the inference, but the computational cost of this method is high due to the large number of simulations needed. It is proposed to use the generalized polynomial chaos expansion; its coefficients are evaluated with the stochastic collocation method. This approach of the stochastic solution is easy to implement, as the Monte Carlo method, and the polynomial expansion allows to evaluate the response in a fast way. This last one makes the likelihood function, needed on the Bayesian inference, simple to be constructed. In the abroad internship the quantification of some uncertainties of a rotor machine in the Fraunhofer Institut will be made, the validation of the results will be made by the comparison of simulations using the uncertainties and experimental tests. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

GAROLI, GABRIEL Y.; PILOTTO, RAFAEL; NORDMANN, RAINER; DE CASTRO, HELIO F.. Identification of active magnetic bearing parameters in a rotor machine using Bayesian inference with generalized polynomial chaos expansion. Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 43, n. 12, DEC 2021. (15/20363-6, 18/02976-9, 16/13223-6)