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E-grāmata: Reduced Modelling of Planar Fuel Cells: Spatial Smoothing and Asymptotic Reduction

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  • Formāts: EPUB+DRM
  • Izdošanas datums: 25-Dec-2016
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319426464
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Reduced Modelling of Planar Fuel Cells: Spatial Smoothing and Asymptotic ReductionPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 25-Dec-2016
  • Izdevniecība: Springer International Publishing AG
  • Valoda: eng
  • ISBN-13: 9783319426464
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This book focuses on novel reduced cell and stack models for proton exchange membrane fuel cells (PEMFCs) and planar solid oxide fuel cells (P-SOFCs) that serve to reduce the computational cost by two orders of magnitude or more with desired numerical accuracy, while capturing both the average properties and the variability of the dependent variables in the 3D counterparts. The information provided can also be applied to other kinds of plate-type fuel cells whose flow fields consist of parallel plain channels separated by solid ribs.These fast and efficient models allow statistical sensitivity analysis for a sample size in the order of 1000 without prohibitive computational cost to be performed to investigate not only the individual, but also the simultaneous effects of a group of varying geometrical, material, and operational parameters. This provides important information for cell/stack design, and to illustrate this, Monte Carlo simulation of the reduced P-SOFC model is condu

cted at both the single-cell and stack levels.

1.- Introduction. 2.- Full 3D Modeling of Planar Fuel Cells. 3.- Development of Reduced PEMFC Models. 4.- development of Reduced P-SOFC Models. 5.- Integrated Stochastic and Deterministic Sensitivity Analysis of Cell and Stack Performances. 6.- Conclusions.
1 Introduction
1(20)
1.1 Background
1(9)
1.1.1 Proton Exchange Membrane Fuel Cell (PEMFC)
5(3)
1.1.2 Planar Solid Oxide Fuel Cell (P-SOFC)
8(2)
1.2 Motivation of Model Reduction
10(3)
1.3 Book Outline
13(8)
References
15(6)
2 Literature Review
21(34)
2.1 Introduction
21(1)
2.2 Single-Cell Modelling
21(19)
2.2.1 Polarizations
22(7)
2.2.2 Transport Phenomena in Electrodes
29(8)
2.2.3 Spatial Dimension
37(3)
2.3 Stack Modelling
40(1)
2.4 Model Simplification
41(1)
2.5 Numerical Methods
42(1)
2.6 Sensitivity Analysis
43(1)
2.7 Remarks
44(11)
References
46(9)
3 Full Three-Dimensional Modelling of PEMFC and Planar SOFC
55(34)
3.1 Introduction
55(1)
3.2 Three-Dimensional Two-Phase PEMFC Model
55(13)
3.2.1 Assumptions
56(1)
3.2.2 Mathematical Model
57(9)
3.2.3 Remarks
66(2)
3.3 Three-Dimensional P-SOFC Model
68(21)
3.3.1 Assumptions
68(1)
3.3.2 Modelling Domains
69(1)
3.3.3 Mathematical Model
69(8)
3.3.4 Numerical Implementation
77(2)
3.3.5 Model Validation
79(3)
3.3.6 Numerical Convergence Test
82(2)
3.3.7 Remarks
84(1)
References
85(4)
4 Development of Reduced PEMFC Models
89(78)
4.1 Introduction
89(1)
4.2 Spatially-Smoothed Isothermal Two-Phase PEMFC Model
90(19)
4.2.1 Spatial Smoothing
90(10)
4.2.2 Numerical Implementation
100(1)
4.2.3 Model Verification
101(5)
4.2.4 Remarks
106(3)
4.3 Asymptotic Non-isothermal Two-Phase PEMFC Model
109(18)
4.3.1 Mathematical Formulation
109(7)
4.3.2 Numerical Implementation
116(2)
4.3.3 Calibration, Verification, and Validation
118(2)
4.3.4 Thermal Decoupling
120(4)
4.3.5 Computational Cost and Efficiency
124(1)
4.3.6 Remarks
125(2)
4.4 Reduced Non-isothermal PEMFC Stack Model
127(18)
4.4.1 Mathematical Formulation
127(7)
4.4.2 Numerical Implementation
134(2)
4.4.3 Model Verification
136(6)
4.4.4 Computational Cost Analysis
142(2)
4.4.5 Remarks
144(1)
4.5 Aggregate Measure for Local Current Density Coupling in Fuel Cell Stacks
145(8)
4.5.1 Mathematical Formulation
145(2)
4.5.2 Analysis
147(3)
4.5.3 Model Verification
150(3)
4.5.4 Remarks
153(1)
4.6 Computationally-Efficient Hybrid Strategy for Mechanistic Modelling of PEMFC Stacks
153(14)
4.6.1 Mathematical Formulation
154(1)
4.6.2 Hybrid Coupling Methodology
155(1)
4.6.3 Numerical Implementation
156(1)
4.6.4 Model Verification
157(2)
4.6.5 Computational Cost and Efficiency
159(2)
4.6.6 Remarks
161(1)
References
162(5)
5 Development of Reduced P-SOFC Models
167(60)
5.1 Introduction
167(1)
5.2 Asymptotic Spatially-Smoothed Isothermal (ASSI) P-SOFC Cell Model
167(26)
5.2.1 Spatial Smoothing with Correlation Factors Derived Based on a Full Cell Model
168(7)
5.2.2 Asymptotic Reduction
175(9)
5.2.3 Numerical Implementation
184(2)
5.2.4 Model Verification
186(5)
5.2.5 Computational Cost Analysis
191(1)
5.2.6 Remarks
192(1)
5.3 Advanced Spatially-Smoothed Model
193(11)
5.3.1 Novel Variation Factor to Capture the Variability of Dependent Variables Along Cell Width
193(5)
5.3.2 Full and Reduced Cell Models
198(2)
5.3.3 Numerical Implementation
200(1)
5.3.4 Model Verification
200(3)
5.3.5 Remarks
203(1)
5.4 Asymptotic Spatially-Smoothed Non-isothermal (ASST) P-SOFC Cell and Stack Models
204(23)
5.4.1 Cell and Stack Modelling
205(1)
5.4.2 Spatially-Smoothed Energy Equation
206(5)
5.4.3 Asymptotic Reduction
211(4)
5.4.4 Numerical Implementation
215(2)
5.4.5 Model Verification
217(7)
5.4.6 Remarks
224(1)
References
225(2)
6 Integrated Stochastic and Deterministic Sensitivity Analysis: Correlating Variability of Design Parameters with Cell and Stack Performance
227(44)
6.1 Introduction
227(1)
6.2 Monte Carlo Simulation of a P-SOFC Single Cell
227(20)
6.2.1 Quasi-3D Asymptotic Spatially-Smoothed Isothermal (ASSI) Single-Cell Model
228(3)
6.2.2 Monte Carlo Simulation
231(3)
6.2.3 Numerical Implementation
234(1)
6.2.4 Statistical Results and Sensitivity Analysis
235(10)
6.2.5 Remarks
245(2)
6.3 Monte Carlo Simulation of a P-SOFC Stack
247(24)
6.3.1 Quasi-3D Spatially-Smoothed Non-Isothermal (SST) Stack Model
247(5)
6.3.2 Monte Carlo Simulation
252(1)
6.3.3 Numerical Implementation
253(1)
6.3.4 Statistical Results and Sensitivity Analysis
254(10)
6.3.5 Remarks
264(3)
References
267(4)
7 Conclusions
271(6)
7.1 Conclusions from the Present Work
271(4)
7.2 Recommendations for Future Work
275(2)
References
276(1)
Appendix A Scaling Analysis for Current Collector 277(2)
Appendix B Scaling Analysis for Flow Field 279(4)
Appendix C Scaling Analysis for Backing Layer 283(4)
Appendix D Scaling Analysis for Reaction Zone Layer 287(4)
Appendix E Scaling Analysis for Electrolyte 291
Dr. He received his B. Eng. Degree in Mechanical Engineering (major in Industrial Design) and Ph.D. degree in Engineering Mechanics from Nanyang Technological University in Singapore in 2009 and 2014, respectively. Currently, Dr. He is a research fellow in Energy Research Institute at Nanyang Technological University (ERIAN). He is strong at multiphysics modelling and simulation, especially at developing fast and efficient models for fuel cells whose performance typically involves multiphysical behaviours such as transport phenomena, thermal dynamics, and electrochemical performance. His research interests also include electrostatic precipitation, steam soaking, and acoustic agglomeration to remove particulate matters from air.





Dr. Hua Li received his B.Sc and M.Eng degrees in Engineering Mechanics from Wuhan University of Technology, P.R.C., in 1982 and 1987, respectively. He obtained his Ph.D degree in Mechanical Engineering from the National University of Singaporein 1999. From 2000 to 2001, Dr. Li was a Postdoctoral Associate at the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign. At the end of 2005, he was a Visiting Scientist (on invitation) at the Department of Chemical and Biomolecular Engineering of Johns Hopkins University. From 2001 to 2006, he was a Research Scientist in the A*STAR Institute of High Performance Computing. Dr Li joined Nanyang Technological University (NTU) as an Assistant Professor in June 2006 and he was promoted to Associate Professor in March 2013. He is currently in the School of Mechanical & Aerospace Engineering at NTU.

His research interests include the multiphysics modelling of soft matters (smart hydrogel in bioMEMS and biological cell in microscale fields); development of highly efficient numerical computational methodology (meshless & multiscale algorithms); simulation of sustainable energy (building energy efficiency and fuel cell system); and dynamics (high-speed rotating shell and composite materials structure). He has sole-authored a monograph book entitled Smart Hydrogel Modelling published by Springer, co-authored two monograph books entitled Meshless Methods and Their Numerical Properties by CRC Press and Rotating Shell Dynamics by Elsevier, and 2 book chapters, one on MEMS simulation and the other on hydrogel drug delivery system modelling, and authored/co-authored over 140 articles published in peer-reviewed international journals. He received the Silver Award in HPC Quest 2003 - The Blue Challenge presented by IBM & IHPC in 2003. He is also extensively funded by agencies and industry, for example, the principal investigator of a computational BioMEMS project awarded under A*STARs strategic research programme in MEMS.









Dr. Karl Erik BIRGERSSON is an associate professor at the Chemical and Biomolecular Department and an affiliate of the Engineering Science Programme at the National University of Singapore (NUS) and the Solar Energy Research Institute of Singapore. He was awarded his Ph.D. in Fluid Mechanics from the Royal Institute of Technology (KTH), Stockholm, in 2004. He also holds an MS degree in Chemical Engineering from KTH (1998) and a Licentiate degree (2003). He was a postdoctoral fellow (2004-2005) and research engineer (2005-2006) at the Institute of High Performance Computing, A*Star, Singapore. He specializes in mathematical modelling and transport phenomena; his current research focuses on electrochemical energy systems and organic solar cells. He has published around 150 papers (journal, conference and book chapters). Besides research, he enjoys teaching and experimenting with teaching strategies; he has been awarded twelve teaching awards since 2006 in NUS.
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