Pouch cells of LiFePo4 and graphite at 2700 mAh capacity were made from commercial electrodes with four different separators: 25 micron polypropylene (PP), 25 micron ceramic coated polyethylene (PE/C), 25 micron nanofiber nonwoven (Dreamweaver Silver – DWI/S), and 25 micron nanofiber nonwoven containing para-aramid fibers (Dreamweaver Gold – DWI/G). The cells had nearly identical electrical performance. The cells underwent hard short, overcharge, hot box and nail penetration tests. All cells passed hard short and overcharge tests. In hot box tests, the PE/C and PP cells experienced internal shorts when the cell temperature reached 145 C. The DWI/S and DWI/G cells showed a steady voltage for 1 hour, and continued to function after the test was completed. In the nail penetration test, the PP and PE/C cells experienced an immediate drop to zero voltage, and the temperature rose quickly to 115 C (PE/C) and 85 C (PP). The DWI/S and DWI/G cells rose to 60 C and 40C respectively, but exhibited only a ~100 mV drop in voltage and continued to function with the nail in the cell. Autopsies of the cells revealed significant shrinkage and cracking of the polyolefin separators, but primarily mechanical damage to the DWI separators.
Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model which is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 hours prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 hours by applying a Deep Autoregressive Generative Model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a “nowcasting” convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center’s official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.