Wenbo Du

Effective Transport Properties of LiMn2O4 Electrode via Particle-Scale Modeling

The extension of Li-ion batteries, from portable electronics to hybrid and electric vehicles, is significant. Developing a better understanding of the role of material properties and manipulating the morphology of the particle clusters comprising Li-ion electrodes could lead to potential opportunities for attaining higher performance goals, for which the effect of both material properties and morphology needs to be considered in a physics-based model. In this work, different particle packing arrangements are analyzed for the calculation of effective transport properties and reaction density that appear in the porous-electrode formulation due to the volume-averaging process. Surrogate-based analysis is used to systematically construct and validate reduced-order models for species transport at the particle-electrolyte interface. The low effective solid transport predicted through microscale modeling indicates the effect of packing arrangement and tortuosity, an aspect not captured by the Bruggemans relation. Particle cluster simulations reveal a Li-ion flux quantitatively different than that predicted by the porous-electrode model due to the variation of overpotential at the microscale. The present study offers a first-step towards integration of the effect of microstructure into a macroscale simulation.

Surrogate-Based Modeling and Dimension-Reduction Techniques for Thermo-Fluid and Energy Systems

Successful modeling and/or design of thermo-fluid and energy systems often requires one to address the impact of multiple “design variables” on the prescribed outcome. There are often multiple, competing objectives based on which we assess the outcome of optimization. Since accurate, high fidelity models are typically time consuming and computationally expensive, comprehensive evaluations can be conducted only if an efficient framework is available. Furthermore, informed decisions of model/hardware’s overall performance rely on an adequate understanding of the global, not local, sensitivity of the individual design variables on the objectives. The surrogate-based approach, which involves approximating the objectives as continuous functions of design variables from limited data, offers a rational framework to reduce the number of important input variables, i.e., the dimension of a design or modeling space. In this paper, we discuss the fundamental issues that arise in surrogate-based analysis and optimization, highlighting concepts, methods, techniques, as well as practical implications. To aid the discussions of the issues involved, we will summarize recent efforts in investigating cryogenic cavitating flows, active flow control based on dielectric barrier discharge concepts, and Li-ion batteries.© 2011 ASME

Value-driven design and sensitivity analysis of hybrid energy systems using surrogate modeling

A surrogate modeling and analysis methodology is applied to study dynamic hybrid energy systems (HES). The effect of battery size on the smoothing of variability in renewable energy generation is investigated. Global sensitivity indices calculated using surrogate models show the relative sensitivity of system variability to dynamic properties of key components. A value maximization approach is used to consider the tradeoff between system variability and required battery size. Results are found to be highly sensitive to the renewable power profile considered, demonstrating the importance of accurate renewable resource modeling and prediction. The documented computational framework and preliminary results represent an important step towards a comprehensive methodology for HES evaluation, design, and optimization.