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Development and Application of a Dynamic Decomposition Strategy for the Optimal Synthesis/Design and Operational/Control of a SOFC Based APU Under Transient Conditions

[+] Author Affiliations
Diego Rancruel, Michael von Spakovsky

Virginia Polytechnic Institute and State University

Paper No. IMECE2005-82986, pp. 573-585; 13 pages
doi:10.1115/IMECE2005-82986
From:
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Advanced Energy Systems
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Advanced Energy Systems Division
  • ISBN: 0-7918-4211-8 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME

abstract

A typical approach to the synthesis/design optimization of energy systems is to only use steady state operation and high efficiency (or low total life cycle cost) at full load as the basis for the synthesis/design. Transient operation is left as a secondary task to be solved by system and control engineers once the synthesis/design is fixed. However, transient regimes may happen quite often and the system response to them is a critical factor in determining the system feasibility. Therefore, it is important to consider the system dynamics in the creative process of developing the system. A dynamic optimization approach developed by the authors and called Dynamic Iterative Local-Global Optimization (DILGO) is applied to the dynamic synthesis/design and operational/control optimization of a solid oxide fuel cell based auxiliary power unit. The approach is based on a decomposed optimization of individual units (components and sub-systems), which simultaneously takes into account the interactions between all the units which make up the overall system. The approach was developed to support and enhance current engineering synthesis/design practices, producing improvements in the initial synthesis/design state of the system and its components at all stages of the process and allowing for any degree of detail (from the simple to the complex) at the unit (component or sub-system) level. The total system is decomposed into three sub-systems: stack sub-system (SS), fuel processing sub-system (FPS), and the work and air recovery sub-system (WRAS). Mixed discrete, continuous, and dynamic operational decision variables are considered. Detailed thermodynamic, kinetic, geometric, physical, and cost models are developed for the dynamic system using advanced state-of-the-art tools. DILGO is then applied to the dynamic synthesis/design and operational/control optimization of the system using total life cycle costs as the objective function. Results for this system and component optimization are presented and discussed.

Copyright © 2005 by ASME

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