Maio 2014 vol. 1 num. 1 - 10th World Congress on Computational Mechanics
Full Article - Open Access.
ESTIMATION OF THE CONVECTIVE HEAT TRANSFER COEFFICIENT IN PIPELINES WITH THE MARKOV CHAIN MONTE CARLO METHOD
Varón, L.A.B. ; Orlande, H.R.B. ; Vianna, F.L.V. ;
Full Article:
The flow of hydrocarbons through deep sea pipelines is a challenging issue for the petroleum industry. Typical operating conditions involve high hydrostatic pressures and low sea bed temperatures, which can favor the formation of solid deposits and result in pipeline blockages and, consequently, incur in large financial losses. Heat transfer analysis plays a fundamental role in the design of deep sea pipelines. Thermal insulation is designed to avoid the formation of solid deposits during regular operating conditions. On the other hand, dur-ing shutdown periods heat losses from the produced fluid to the surrounding environment can result on fluid temperatures sufficiently low that the formation of deposits becomes inevitable, unless other techniques are used, such as injection of chemical inhibitors or active heating of the pipeline. In this work, we solve the inverse problem of estimating the transient heat trans-fer coefficient from the pipeline surface to the surrounding sea water, in a pipe-in-pipe sys-tem. The transient external heat transfer coefficient is estimated with the Markov Chain Mon-te Carlo method, implemented via the Metropolis-Hastings algorithm. Simulated temperature measurements of one single sensor, located at the external surface of the inner pipe, are used in the inverse analysis. A smooth prior is used for the transient heat transfer coefficient, while the measurement errors are assumed to be Gaussian, additive, with zero mean and known covariance matrix.
Full Article:
Palavras-chave: pipelines, Markov Chain Monte Carlo, convection coefficient, direct problem, inverse problem.,
Palavras-chave:
DOI: 10.5151/meceng-wccm2012-18647
Referências bibliográficas
- [1] Flávio L. V. Vianna, Helcio R. B. Orlande and George S. Dulikravich. “Temperature field prediction of a multilayered composite pipeline based on the particle filter meth-od.”Proceedings of the 14th International Heat Transfer Conference IHTC-14, Washing-ton D.C., USA, August 8-13, 2010.
- [2] Flávio L. V. Vianna, Helcio R. B. Orlande and George S. Dulikravich. “Optimal heating control to prevent solid deposits in pipelines.”V European Conference on Computational Fluid Dynamics ECCOMAS CFD 2010.
- [3] R. M. T. Camargo, M. A. L. Gonçalves, J. R. T. Montesanti, C. A. B. R. Cardoso, and K. Minami, A Perspective View of Flow Assurance in Deepwater Fields in Brazil, OTC 16687, Offshore Technology Conference, May 3-6, Houston, Texas, USA (2004).
- [4] Beck, J. V. and Arnold, K. J., “Parameter Estimation in Engineering and Science”, Wiley Interscience, New York. 1977.
- [5] Kaipio, J. e Somersalo, E., “Statistical and Computational Inverse Problems”, Applied Mathematical Sciences 160, Springer-Verlag, 2004.
- [6] Calvetti, D., Somersalo, E.,” Introduction to Bayesian Scientific Computing”, Springer, New York. 2007.
- [7] Tan, S., Fox, C., and Nicholls, G., “Inverse Problems”, Course Notes for Physics 707, University of Auckland. 2006.
- [8] Winkler, R., “An Introduction to Bayesian Inference and Decision”, Probabilistic Publish-ing, Gainsville. 2003.
- [9] Lee, P. M., “Bayesian Statistics”, Oxford University Press, London. 2004.
- [10] Gamerman, D. and Lopes, H.F., “Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference”, Chapman Andamp; Hall/CRC, 2nd edition, Boca Raton, 2006.
- [11] Helcio R. B. Orlande, Marcelo José Colaço, Carolina P. N. Cotta, Gilmar Guimarães, ValerioLuiz Borges. “Problemas inversos em Transferência de calor”. Sociedade Brasi-leira de Matemática Aplicada e Computacional. São Carlos - SP, Brasil 20
- [12] Helcio R. B. Orlande. “Inverse problems in heat transfer”, New trends on solution meth-odologies and applications Proceedings of the 14th International Heat Transfer Confer-ence IHTC-14 Washington D.C., USA. August 8-13, 2010.
- [13] Bolstad, William M. “Understanding Computational Bayesian Statistics”, John Wiley 2010.
- [14] Kaipio, J., Fox, C., “The Bayesian Framework for Inverse Problems in Heat Transfer”, Heat Transfer Eng., 2010.
- [15] Helcio R. B. Orlande, O. Fudym, D. Maillet e R. Cotta, “Thermal Measurements and Inverse Techniques”, CRC Press, Boca Raton, 2011.
- [16] Su, J. and Cerqueira, D.R., “Simulation of Transient Heat Transfer in Multilayered Com-posite Pipeline”, Proceeding of OMAE01, 20nd International Conference on Offshore Mechanics and Arctic Engineering, Rio de Janeiro, Brazil. June 3-8, 2001.
- [17] Incropera, F. P., Dewitt, D.P., 2006, “Fundamentals of Heat and Mass Transfer”, Jonh Wiley and Sons.
Como citar:
Varón, L.A.B.; Orlande, H.R.B.; Vianna, F.L.V.; "ESTIMATION OF THE CONVECTIVE HEAT TRANSFER COEFFICIENT IN PIPELINES WITH THE MARKOV CHAIN MONTE CARLO METHOD", p. 2014-2025 . In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1].
São Paulo: Blucher,
2014.
ISSN 2358-0828,
DOI 10.5151/meceng-wccm2012-18647
últimos 30 dias | último ano | desde a publicação
downloads
visualizações
indexações