2007 Annual Meeting
(231b) Fuel Processing Models For Navy Fuel Cell Applications
Author
The U.S. Navy's Office of Naval Research (ONR) is developing and demonstrating advanced fuel cell technologies pertaining to processing logistics fuels for use in conjunction with fuel cell generators for a variety of applications. As the technology development efforts proceed, it is necessary to evaluate the capabilities and assess the system impacts of the specific components and subsystems. Prototypes of the various components are delivered to the US Navy and are undergoing performance verification and durability testing in US Navy laboratories.
In support of the ongoing testing and design of notional integrated fuel processing and fuel cell systems, iterations of advanced process models in various configurations are being produced, to compare the benefits of the technologies. The models critically examine process benefits of various improvements, both separately and in combination, on a system basis. Modeling of fuel cell technologies encompass a wide variety of components, layouts, and potential integration schemes. Consideration must be made for efficiency, as well as transient operation, ship interface requirements, water neutrality, waste product removal, and size. Modeling and simulation must be used to find the optimum balance between system efficiency, complexity, and volume to meet shipboard operational and environmental requirements.
As component testing proceeds, static and dynamic models are created and modified based upon parameters provided by OEM data and testing parameters, and the models are validated by ongoing laboratory tests. This paper shall discuss the latest in fuel processing subcomponent models, including models for desulfurization systems, reformers, reformate gas cleanup and other related technologies. Further, comparison with the latest laboratory test results will be provided. Additionally, the lessons learned from operation of this iterative process and integration of subsystem and system models into larger-scale platform models will also be discussed.