Use of a Reduced Order Model (ROM) to Simulate SOFC Performance in System Models
| Autor: | Zhijie Xu, Arun Iyengar, Gregory A. Hackett, Canhai Lai, Chao Wang, Brian J. Koeppel |
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| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | ECS Transactions. 78:2595-2605 |
| ISSN: | 1938-5862 1938-6737 |
| DOI: | 10.1149/07801.2595ecst |
| Popis: | Use of SOFC-based hybrid power generation systems has the potential to reduce emissions while producing electricity from conventional fossil energy sources at costs that are competitive with other technologies. The numerical models used to perform predictive design analysis and optimization of the power generation system require an accurate representation of the SOFC stack performance over a varying range of operating conditions. Detailed stack models may contain the required information necessary for design purposes, but are often too computationally expensive for use in the overall system model. However, other desirable information about the state of the stack, such as the internal temperature gradient, is generally not available from simple performance or thermodynamic models often used to represent the SOFC. Such additional information can be important in system optimization studies to preclude operation under off-design conditions that can adversely impact overall system reliability. Response surface techniques were used with detailed SOFC stack model results to create a computationally efficient reduced order model (ROM) of the stack. A tool was developed to randomly sample selected input parameters over the expected range of interest, perform stack solutions for these sampled points using the SOFC-MP 2D software, use regression to obtain response surfaces for the stack performance and other output metrics of interest, and export the response surfaces as a ROM. The procedure is shown in Figure 1. The ROM was then successfully implemented in an Aspen Plus® system model for a natural gas fuel cell (NGFC) power system. Additional work is in progress to improve the tool’s implementation by including the fuel/oxidant recirculation loops and to better characterize the expected approximation error of the ROM. The developed modeling approach, results of the system model integration, and ongoing efforts to improve the ROM accuracy will be presented. Figure 1. Overview of ROM Generation Procedure and Usage Figure 1 |
| Databáze: | OpenAIRE |
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