Modelling the characteristics of turbocompressors for fuel cell systems using hybrid method based on moving least squares

Applied Energy

Volumn 86, Issue 7-8, 2009, Pages 1283-1289

  Tirnovan, R ^a   Giurgea, S ^a   Miraoui, A ^a   Cirrincione, M ^a  

^a UNIVERSITÉ DE TECHNOLOGIE DE BELFORT MONTBÉLIARD   (France)

Author keywords

Characteristic map; Fuel cell; Hybrid; Modelling; Moving least squares; Turbocompressor

Indexed keywords

FUEL CELLS; MODELS; NUMERICAL METHODS;

COMPARATIVE STUDIES; FUEL CELL APPLICATION; HYBRID; LINE IDENTIFICATIONS; MOVING LEAST SQUARES; SEMI-EMPIRICAL APPROACH; THEORETICAL EQUATION; TURBOCOMPRESSOR;

LEAST SQUARES APPROXIMATIONS;

ALGORITHM; EXPERIMENTAL STUDY; FUEL CELL; MODELING; NUMERICAL MODEL;

EID: 61749099981     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2008.09.023     Document Type: Article

References (15)

1. Kulp GW. A comparison of two air compressors for PEM fuel cell systems. Virginia Polytechnic Institute and State University Master of Science, Blacksburg, Virginia; December 14, 2001.
2. Gravdahl JT, Egeland O, Vatland SO. Active surge control of centrifugal compressors using drive torque. In: Proceedings of the 40th IEEE conference on decision and control, Orlando (FL) USA; December 2001. p. 1286-91.
3. Pukrushpan JT, Stefanopoulou Anna G, Peng H. Control of fuel cell power systems. Principles, modeling, analysis and feedback design, advances in industrial control, Springer-Verlag, London Limited; 2004 [Printed in United States of America].
4. Tirnovan R, Miraoui A, Giurgea S. Modeling and analysis of a high pressure operating fuel cell hydrogen/air system. In: International conference on clean electrical power ICCEP 2007. Renewable_energy_resources_impact. IEEE Catalog Number: 07EX1528, Capri (Italy); May 21-23, 2007. p. 433-8.
5. Youhong Y., Lingen C., Fengrui S., and Chih W. Neural-network based analysis and prediction of a compressor's characteristic performance map. Appl Energy 84 (2007) 48-55
6. Tirnovan R., Giurgea S., Miraoui A., and Cirrincione M. Surrogate modelling of compressor characteristics for fuel cell applications. Appl Energy 85 (2008) 394-403
7. Queipo N.V., Haftka R.T., Shyy W., Goel T., Vaidyanathan R., and Tucker P.K. Surrogate-based analysis and optimization. Prog Aerospace Sci 41 (2005) 1-28
8. Bandler J.W., Cheng Q.S., Dakroury A.S., Mohamed A.S., Bakr M.H., Madsen K., et al. Space mapping: the state of the art. IEEE Trans Microwave Theory Tech 52 (2004) 337-360
9. Lancaster P, Salkauskas K. Surfaces generated by moving least squares methods. Mathematics of computation, vol. 37(155), Academic Press Inc.; 1981. p. 141-58
10. Belytschko T., Lu Y., and Gu L. Element-free Galerkin methods. Int J Numer Methods Eng 37 (1994) 229-256
11. Duarte CA, Oden JT. A review of some meshless methods to solve partial differential equations. Technical Report 95-06, TICAM, The University of Texas at Austin; May 1995.
12. Youn B.D., and Choi K.K. A new response surface methodology for reliability-based design optimization. Comput Struct 82 (2004) 241-256
13. Ebner T., Magele C., Brandstatter B.R., and Richter K.R. Utilizing feed forward neural networks for acceleration of global optimization procedures. IEEE Trans Magn 34 September (1998) 2928-2931
14. Krishnamurthy T, Romero VJ. Construction of response surface with higher order continuity and its application to reliability engineering, 43rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference 22-25 April, 2002 Denver (CO); AIAA-2002-1466. p. 1-11.
15. Stone M. Cross-validatory choice and assessment of statistical predictions. J Roy Statist Soc B 36 (1974) 111-147