2週內演講

Colloquium, 中正大學數學系 陳孟豁助理教授 Thursday, June 4, 16:10—17:00 數學系3174 Title: Fluid-Structure Interactions: One-Field Monolithic Fictitious Domain Method and its Parallelization Abstract: In this research we implement the parallelization of the method: one-field monolithic fictitious domain (MFD), an algorithm for simulation of general fluid-structure interactions (FSI). In this algorithm only one velocity field is solved in the whole domain (one-field) based upon the use of an appropriate L2 projection. ”Monolithic” means the fluid and solid equations are solved synchronously (rather than sequentially). For simulation of fluid-structure in-teractions on 3D domain the algorithm and the solving of the linear systems arising from the discretization need to be parallelized in order to reduce the simulation time from several months to few days. At the initial stage of the research we focus on parallelizing the algorithm on uniform meshes. The imple-mented parallel algorithm is then extended to the simulations on nonuniform meshes, where an adaptive mesh refinement scheme is used to improve the ac-curacy and robustness. Our goal is to provide an eycient, robust algorithm which can handle the diycult fluid-structure interactions such as the collision of multiple immersed solids in fluid where the high resolution mesh is necessary for resolving the phenomena near the collision and fluid-structure interfaces.

Colloquium, 中正大學數學系 王義富助理教授 Thursday, June 11, 16:10—17:00 數學系3174 Title: Lifetime Prediction for Rechargeable Lithium-ion Batteries Abstract: Rechargeable batteries are critical components for the performance of portable electronics and electric vehicles. The long term health performance of rechargeable batteries is characterized by state of health which can be quantified by end of performance (EOP) and remaining useful performance. Focusing on EOP prediction, this paper first proposes an accelerated testing version of the trend-renewal process model to address this decision problem. The proposed model is also applied to a real case study. Finally, a NASA dataset is used to address the prediction performance of the proposed model. Comparing with the existing prediction methods and time series models, our proposed procedure has better performance in the EOP prediction.