Tuning of Thermal FEA Using Krylov Parametric MOR for Subsea Application
A dead leg is a typical subsea production system
component. CFD is required to model heat transfer within the dead
leg. Unfortunately its solution is time demanding and thus not
suitable for fast prediction or repeated simulations. Therefore there is
a need to create a thermal FEA model, mimicking the heat flows and
temperatures seen in CFD cool down simulations.
This paper describes the conventional way of tuning and a new
automated way using parametric model order reduction (PMOR)
together with an optimization algorithm. The tuned FE analyses
replicate the steady state CFD parameters within a maximum error in
heat flow of 6 % and 3 % using manual and PMOR method
respectively. During cool down, the relative error of the tuned FEA
models with respect to temperature is below 5% comparing to the
CFD. In addition, the PMOR method obtained the correct FEA setup
five times faster than the manually tuned FEA.
[1] U. Baur, C. Beattie, P. Benner and S. Gugercin, "Interpolatory
Projection Methods for Parameterized Model Reduction," SIAM J.
Sci. Comput., vol. 33, no. 5, pp. 2489-2518, oct 2011.
[2] T. Bechtold, D. Hohlfeld, E. B. Rudnyi and M. Gunther, "Efficient
extraction of thin-film thermal parameters from numerical models via
parametric model order reduction," Journal of Micromechanics and
Microengineering, vol. 20, no. 4, p. 045030, 2010.
[3] L. Daniel, C. S. Ong, S. C. Low, K. H. Lee and J. K. White, "A
multiparameter moment-matching model-reduction approach for
generating geometrically parameterized interconnect performance
models," IEEE Trans. on CAD of Integrated Circuits and Systems,
pp. 678-693, 2004.
[4] O. Farle, V. Hill, P. Ingelstram and R. Dyczij-Edlinger, "Multiparameter
polynomial order reduction of linear finite element
models," Mathematical and Computer Modelling of Dynamical
Systems, vol. 14, no. 5, pp. 421-434, 2008..
[5] A. T.-M. Leung and R. Khazaka, "Parametric model order reduction
technique for design optimization," in 2005 International Symposium
on Circuits and Systems, 2005.
[6] P. Li, F. Liu, X. Li, L. T. Pileggi and S. R. Nassif, "Modeling
Interconnect Variability Using Efficient Parametric Model Order
Reduction," in Proceedings of the conference on Design, Automation
and Test in Europe - Volume 2, Washington, DC, USA, 2005.
[7] E. B. Rudnyi, L. H. Feng, M. Salleras, S. Marco and J. G. Korvink,
"Error Indicator to Automatically Generate Dynamic Compact
Parametric Thermal Models," in THERMINIC 2005, 11th
International Workshop on Thermal Investigations of ICs and
Systems, 27 - 30 September 2005, Belgirate, Lake Maggiore, Italy.
[8] M. Avriel, Nonlinear Programming: Analysis and Methods, Dover
Publications, 2003.
[9] K. Miettinen, Nonlinear Multiobjective Optimization, Kluwer
Academic Pub, 1999.
[10] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," Evolutionary
Computation, IEEE Transactions on, vol. 6, no. 2, pp. 182-197, apr
2002.
[11] J. L. Morales and J. Nocedal, "Remark on algorithm 778: L-BFGSB:
Fortran subroutines for large-scale bound constrained
optimization," ACM Trans. Math. Softw., vol. 38, no. 1, pp. 71-74,
dec 2011.
[12] A. C. Antoulas, Approximation of Large-Scale Dynamical Systems
(Advances in Design and Control), Philadelphia, PA, USA: Society
for Industrial and Applied Mathematics, 2005.
[13] P. Sebastiani and H. P. Wynn, "Maximum entropy sampling and
optimal Bayesian experimental design," Journal of the Royal
Statistical Society: Series B (Statistical Methodology), vol. 62, no. 1,
pp. 145-157, 200.
[14] C.-W. Ko, J. Lee and M. Queyranne, "An Exact Algorithm for
Maximum Entropy Sampling," Operations Research, vol. 43, no. 4,
pp. 684-691, July/August 1995.
[1] U. Baur, C. Beattie, P. Benner and S. Gugercin, "Interpolatory
Projection Methods for Parameterized Model Reduction," SIAM J.
Sci. Comput., vol. 33, no. 5, pp. 2489-2518, oct 2011.
[2] T. Bechtold, D. Hohlfeld, E. B. Rudnyi and M. Gunther, "Efficient
extraction of thin-film thermal parameters from numerical models via
parametric model order reduction," Journal of Micromechanics and
Microengineering, vol. 20, no. 4, p. 045030, 2010.
[3] L. Daniel, C. S. Ong, S. C. Low, K. H. Lee and J. K. White, "A
multiparameter moment-matching model-reduction approach for
generating geometrically parameterized interconnect performance
models," IEEE Trans. on CAD of Integrated Circuits and Systems,
pp. 678-693, 2004.
[4] O. Farle, V. Hill, P. Ingelstram and R. Dyczij-Edlinger, "Multiparameter
polynomial order reduction of linear finite element
models," Mathematical and Computer Modelling of Dynamical
Systems, vol. 14, no. 5, pp. 421-434, 2008..
[5] A. T.-M. Leung and R. Khazaka, "Parametric model order reduction
technique for design optimization," in 2005 International Symposium
on Circuits and Systems, 2005.
[6] P. Li, F. Liu, X. Li, L. T. Pileggi and S. R. Nassif, "Modeling
Interconnect Variability Using Efficient Parametric Model Order
Reduction," in Proceedings of the conference on Design, Automation
and Test in Europe - Volume 2, Washington, DC, USA, 2005.
[7] E. B. Rudnyi, L. H. Feng, M. Salleras, S. Marco and J. G. Korvink,
"Error Indicator to Automatically Generate Dynamic Compact
Parametric Thermal Models," in THERMINIC 2005, 11th
International Workshop on Thermal Investigations of ICs and
Systems, 27 - 30 September 2005, Belgirate, Lake Maggiore, Italy.
[8] M. Avriel, Nonlinear Programming: Analysis and Methods, Dover
Publications, 2003.
[9] K. Miettinen, Nonlinear Multiobjective Optimization, Kluwer
Academic Pub, 1999.
[10] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," Evolutionary
Computation, IEEE Transactions on, vol. 6, no. 2, pp. 182-197, apr
2002.
[11] J. L. Morales and J. Nocedal, "Remark on algorithm 778: L-BFGSB:
Fortran subroutines for large-scale bound constrained
optimization," ACM Trans. Math. Softw., vol. 38, no. 1, pp. 71-74,
dec 2011.
[12] A. C. Antoulas, Approximation of Large-Scale Dynamical Systems
(Advances in Design and Control), Philadelphia, PA, USA: Society
for Industrial and Applied Mathematics, 2005.
[13] P. Sebastiani and H. P. Wynn, "Maximum entropy sampling and
optimal Bayesian experimental design," Journal of the Royal
Statistical Society: Series B (Statistical Methodology), vol. 62, no. 1,
pp. 145-157, 200.
[14] C.-W. Ko, J. Lee and M. Queyranne, "An Exact Algorithm for
Maximum Entropy Sampling," Operations Research, vol. 43, no. 4,
pp. 684-691, July/August 1995.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:62562", author = "A. Suleng and T. Jelstad Olsen and J. Šindler and P. Bárta", title = "Tuning of Thermal FEA Using Krylov Parametric MOR for Subsea Application", abstract = "A dead leg is a typical subsea production system
component. CFD is required to model heat transfer within the dead
leg. Unfortunately its solution is time demanding and thus not
suitable for fast prediction or repeated simulations. Therefore there is
a need to create a thermal FEA model, mimicking the heat flows and
temperatures seen in CFD cool down simulations.
This paper describes the conventional way of tuning and a new
automated way using parametric model order reduction (PMOR)
together with an optimization algorithm. The tuned FE analyses
replicate the steady state CFD parameters within a maximum error in
heat flow of 6 % and 3 % using manual and PMOR method
respectively. During cool down, the relative error of the tuned FEA
models with respect to temperature is below 5% comparing to the
CFD. In addition, the PMOR method obtained the correct FEA setup
five times faster than the manually tuned FEA.", keywords = "CFD, convective heat, FEA, model tuning, subseaproduction", volume = "7", number = "5", pages = "913-8", }
