Direct methanol fuel cells (DMFCs) have emerged in recent years as potential power sources for portable electronic devices due to the high energy density of methanol and low power requirements of the portable electronic devices. Fuel cell system modeling plays an important role in the design of DMFC systems. Despite the progress in modeling of DMFCs, most of these models considered only some of the key operating parameters with overly simplified geometric shapes. In addition, since extensive simulations are usually required in design and control of DMFC systems, advanced modeling tools with high computation quality and efficiency are expected. This research focuses on study of adaptive metamodeling methods and applications of these methods in modeling and design of DMFC systems.
A semi-empirical model is developed to build the relationships between all important operating parameters and DMFC performance measures. Coefficients of this semi-empirical model are obtained through experiments and data fitting. The semi-empirical model provides a basis to identify the optimal operating parameters of the DMFC system considering different power requirements. In addition, adaptive metamodeling has been employed to describe the electrochemical relationships in a computational fluid dynamics (CFD) based DMFC model to study influences of both geometric parameters and operating parameters on DMFC performance. The CFD-based DMFC model can be used in optimal design of geometric parameters and optimal control of operating parameters.
Metamodeling methods, which were initially developed as “surrogates” of the expensive simulation process, can be used to model the relationship between input and output parameters in DMFC systems. Influences of two factors, noise level and initial sample size, on quality of adaptive metamodeling considering different metamodel schemes and test functions are studied in this work. Guidelines have been developed for selection of the proper adaptive metamodeling methods. In addition, a new sampling method namely weighted sequential sampling (WSS) method is introduced in this research to improve the accuracy of adaptive metamodeling considering influences of sample quality measures in both input and output parameter spaces. Quality of the global optimization can be improved based on the metamodel built using the WSS method
