Bayesian inference applied to journal bearing parameter identification
ARTIGO
Inglês
Agradecimentos: The authors would like to thank CENPES-PETROBRAS, FAPESP, FAEPEX-UNICAMP, CAPES and CNPq for supporting this research
Stochastic methods application is emergent in engineering field, leading designers to better solutions during product development. The stochastic characteristic of system parameters, such as geometric dimensions, operating conditions, among others, may lead to unexpected or even undesirable...
Stochastic methods application is emergent in engineering field, leading designers to better solutions during product development. The stochastic characteristic of system parameters, such as geometric dimensions, operating conditions, among others, may lead to unexpected or even undesirable behavior, making it mandatory to take into account the parameters' uncertainties aiming a robust project. An approach considered here to the uncertainties distribution model is the Bayesian inference. This method gives the estimation of the stochastic parameter from previous information and observations of experimental response. After that, it is possible to proceed with the correspondent propagation on the system response. In the context of rotor dynamics, stochastic methods are not yet scattered and deterministic approaches still prevail. This work aims the use of Bayesian inference, particularly the Markov Chain Monte Carlo method, in a simple rotor-bearing system model to evaluate the influence of uncertainties in the journal bearings parameters on the overall behavior of these components. The critical parameters considered here are radial clearance and oil viscosity as function of temperature
FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ
COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES
Fechado
Bayesian inference applied to journal bearing parameter identification
Bayesian inference applied to journal bearing parameter identification
Fontes
Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 39, no. 8 (Aug., 2017), p. 2983-3004 |