Deep learning-driven analysis for cellular structure characteristics of spherical premixed hydrogen-air flames

The extraction of cellular structure feature on the spherical premixed flame surface faces accuracy challenges. The Schlieren technique was employed to obtain the hydrogen-air premixed spherical flames images in a constant volume vessel at room temperature and atmospheric pressure under an equivalent ratio of 0.8 in this work. A bio-inspired Cellpose 2.0, driven by deep learning, is innovatively introduced to train the cell segmentation model in the combustion field. After labeling and training cells of different shapes and sizes, an efficient and accurate model suitable for cell feature extraction was finally obtained to identify and quantify various cells characteristics, such as number, size, and distribution. Results show that the average precision (AP) during the model online pre-training process reaches 0.625. Meanwhile, the critical flame radius of transition acceleration obtained is 36 mm and the crack length tends to grow linearly after the flame radius exceeds this critical point. Additionally, the average cell area gradually converges to a stable value after the flame radius exceeds the uniform cellularity critical radius. The cell segmentation model obtained in this work can be further used to train different spherical flames under various conditions, helping to develop hydrogen combustion and explosion modelling

The effect of oxygenate fuels on PN emissions from a highly boosted GDI engine

Gasoline Direct Injection (GDI) engines are increasingly available in the market. Such engines are known to emit more Particulate Matter (PM) than their port-fuel injected predecessors. There is also a widespread use of oxygenate fuels in the market, up to blends of E85, and their impact on PN emissions is widely studied. However the impact of oxygenate fuels on PN emissions from downsized, and hence highly-boosted engines is not known. In this work, PN emissions from a highly boosted engine capable of running at up to 35 bar Brake Mean Effective Pressure (BMEP) have been measured from a baseline gasoline and three different oxygenate fuels (E20, E85, and GEM – a blend of gasoline, ethanol, and methanol) using a DMS500. The engine has been run at four different operating points, and a number of engine parameters relevant to highly-boosted engines (such as EGR, exhaust back pressure, and lambda) have been tested – the PN emissions and size distributions have been measured from all of these. The results show that the oxygenate content of the fuel has a very large impact on its PN emissions, with E85 giving low levels of PN emissions across the operating range, and GEM giving very low and extremely high levels of PN emissions depending on operating point. These results have been analysed and related back to key fuel properties

Monte Carlo and theoretical studies of aqueous lamellar systems

Imperial Users onl

A chemical kinetic interpretation of the octane appetite of modern gasoline engines

Fuel anti-knock quality is a critical property with respect to the effective design of next-generation spark-ignition engines which aim to have increased efficiency, and lower emissions. Increasing evidence in the literature supports the fact that the current regulatory measures of fuel anti-knock quality, the research octane number (RON), and motor octane number (MON), are becoming decreasingly relevant to commercial engines. Extrapolation and interpolation of the RON/MON scales to the thermodynamic conditions of modern engines is potentially valuable for the synergistic design of fuels and engines with greater efficiency. The K-value approach, which linearly weights the RON/MON scales based on the thermodynamic history of an engine, offers a convenient experimental method to do so, although complementary theoretical interpretations of K-value measurements are lacking in the literature. This work uses a phenomenological engine model with a detailed chemical kinetic model to predict and interpret known trends in the K-value with respect to engine intake temperature, pressure, and engine speed. The modelling results support experimental trends which show that the K-value increases with increasing intake temperature and engine speed, and decreases with increasing intake pressure. A chemical kinetic interpretation of trends in the K-value based on fundamental ignition behaviour is presented. The results show that combined experimental/theoretical approaches, which employ a knowledge of fundamental fuel data (gas phase kinetics, ignition delay times), can provide a reliable means to assess trends in the real-world performance of commercial fuels under the operating conditions of modern engines. (C) 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved.Funding from the European Commission Marie Curie Transfer of Knowledge Scheme (FP7) pursuant to Contract PIAP-GA-2013-610897 GENFUEL is greatly acknowledged. KPS would like to thank Dr. Ultan Burke and Dr. Colin Banyon for fruitful discussions on the topic.peer-reviewed2020-06-2