Surrogate modelling of compressor characteristics for fuel-cell applications

被引：27

作者：

Tirnovan, R. ^{[1
]}

Giurgea, S. ^{[1
]}

Miraoui, A. ^{[1
]}

Cirrincione, M. ^{[1
]}

机构：

[1] Univ Technol Belfort Montbeliard, L2ES UTBM Lab, F-90010 Belfort, France

来源：

APPLIED ENERGY | 2008年 / 85卷 / 05期

关键词：

compressor; characteristic map; moving least-squares; surrogate model; fuel-cell;

D O I：

10.1016/j.apenergy.2007.07.003

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The compressor is an important auxiliary for fuel-cell (FC) operation. Growing fuel-cell system efficiency involves an optimal fuel cell energy management and the air management is a key issue. Thus, a good modelling for static and dynamic operation of all components of the FC system, and in particular of the compressor, is required. The difficulties, due to a lack of information about the performance of compressors, demand predictive and modern approximation methods to be used for compressor modelling. To overcome these issues, the paper proposes and presents a moving least squares (MLS) algorithm for obtaining a surrogate model of the centrifugal compressor. The experimental data provided by manufacturers are used for this task. The results can be used for the development of an off-design model or the overall dynamic simulation of the behaviour of a FC system. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：394 / 403

页数：10

共 26 条

[1]

[Anonymous], T ASME J ENG GAS TUR

[2]

[Anonymous], PILES COMBUSTIBLE PR

[3] Space mapping: The state of the art [J].

Bandler, JW ;

Cheng, QSS ;

Dakroury, SA ;

Mohamed, AS ;

Bakr, MH ;

Madsen, K ;

Sondergaard, J .

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2004, 52 (01) :337-361

[4] ELEMENT-FREE GALERKIN METHODS [J].

BELYTSCHKO, T ;

LU, YY ;

GU, L .

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 1994, 37 (02) :229-256

[5]

Bishop C., 1998, NEURAL NETWORKS PATT

[6]

BLUNIER B, 2007, ACEMP 07 ELECTROMOTI

[7]

BLUNIER B, 2006, 18 INT COMPR ENG C P

[8]

BLUNIER B, 2007, IND APPL SOC IAS ANN

[9]

DUARTE CAM, 1995, 9506 TICAM U TEXAS A

[10] Utilizing Feed Forward Neural Networks for acceleration of global optimization procedures [J].

Ebner, T ;

Magele, C ;

Brandstatter, BR ;

Richter, KR .

IEEE TRANSACTIONS ON MAGNETICS, 1998, 34 (05) :2928-2931

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