This work quantifies water contamination in jet fuel (Jet A-1), using silica-based Bragg gratings. The optical sensor geometry exposes the evanescent optical field of a guided mode to enable refractometery. Quantitative analysis is made in addition to the observation of spectral features consistent with emulsification of water droplets and Stokes’ settling. Measurements are observed for cooling and heating cycles between ranges of 22oC and -60oC. The maximum spectral sensitivity for water contamination was 2.4 pm/ppm-v with a resolution of < 5 ppm-v.
This work quantifies water contamination in jet fuel (Jet A-1), using silica-based Bragg gratings. The optical sensor geometry exposes the evanescent optical field of a guided mode to enable refractometery. Quantitative analysis is made in addition to the observation of spectral features consistent with emulsification of water droplets and Stokes’ settling. Measurements are observed for cooling and heating cycles between ranges of 22oC and -60oC. The maximum spectral sensitivity for water contamination was 2.4 pm/ppm-v with a resolution of < 5 ppm-v.
DECA is widely used as a protocol to describe the fuel economy and battery load of vehicles. Meanwhile, Carsim is universally accepted as a reliable tool to analyze vehicle dynamics. Therefore, it is of great importance to simulate the DECA with Carsim and provide a proper way to measure the virtual fuel economy of vehicles. It may also contribute to explement related experiments.
An effective use of electrochemical Energy Storage Systems (ESSs) is mandatory for improving the performances of next generation electric vehicles and hybrid electric vehicles, as well as of smart grid and microgrid systems.In particular, Battery Management Systems (BMSs) are useful devices aiming at monitoring, managing, and controlling the battery pack. One of the main task of any BMS is performing State of Charge estimation of the cells composing the ESS.
This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.
The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.
