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Kamp, Carl J.

Publications and source records attributed to Kamp, Carl J..

The origin, transport, and evolution of ash in engine particulate filters

Engine particulate filters have been widely applied across the world to control engine exhaust particulate matter (or particulate number) emissions. With increasing vehicle mileage, ash accumulation deteriorates vehicle fuel economy and complicates on-board control. Extending filter service life with ash loading has significant economic and environmental impacts. Many studies have been conducted in characterizing ash accumulation and evaluating its impacts on filter performance. However, comprehensive reviews covering all the key issues in the field are rather rare. This paper reviews the extensive prior research on filter ash, and not only summarizes the experimental observations but also elucidates the fundamental mechanisms. The review covers the areas of ash origin, accumulation, transport, evolution, and artificial acceleration methods. The previously reported data of ash properties is compiled and analyzed. Furthermore, the advantages and disadvantages of ash acceleration approaches are also discussed in detail. Based on the cumulative understanding, a few potential ways to improve ash management are discussed in this paper. In summary, the present work systematically reviews the previous observations and understanding of ash aging in particulate filters and identifies areas that need further research, which can be useful guidance for future studies.

42 ENGINEERING↗

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗