Bayesian inference applied to journal bearing parameter identification

Bayesian inference applied to journal bearing parameter identification

Natalia C. Tyminski, Felipe W. S. Tuckmantel, Katia L. Cavalca, Helio F. de Castro

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...

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

Natalia C. Tyminski, Felipe W. S. Tuckmantel, Katia L. Cavalca, Helio F. de Castro


										

Bayesian inference applied to journal bearing parameter identification

Natalia C. Tyminski, Felipe W. S. Tuckmantel, Katia L. Cavalca, Helio F. de Castro

    Fontes

    Journal of the Brazilian Society of Mechanical Sciences and Engineering

    Vol. 39, no. 8 (Aug., 2017), p. 2983-3004