Experimental investigation on electrical characteristics and ignition performance of multichannel plasma igniter

Relighting of jet engines at high altitudes is very difficult because of the high velocity, low pressure, and low tempera- ture of the inlet airflow. Successful ignition needs sufficient ignition energy to generate a spark kernel to induce a so-called critical flame initiation radius. However, at high altitudes with high-speed inlet airflow, the critical flame initiation radius becomes larger; therefore, traditional ignition technologies such as a semiconductor igniter （SI） become infeasible for use in high-altitude relighting of jet engines. In this study, to generate a large spark kernel to achieve successful ignition with high-speed inlet airflow, a new type of multichannel plasma igniter （MCPI） is proposed. Experiments on the electrical char- acteristics of the MCPI and SI were conducted under normal and sub-atmospheric pressures （P = 10-100 kPa）. Ignition experiments for the MCPI and SI with a kerosene/air mixture in a triple-swirler combustor under different velocities of inlet airflow （60-110 m/s）, with a temperature of 473 K at standard atmospheric pressure, were investigated. Results show that the MCPI generates much more arc discharge energy than the SI under a constant pressure; for example, the MCPI generated 6.93% and 16.05 % more arc discharge energy than that of the SI at 30 kPa and 50 kPa, respectively. Compared to the SI, the MCPI generates a larger area and height of plasma heating zone, and induces a much larger initial spark kernel. Furthermore, the lean ignition limit of the MCPI and SI decreases with an increase in the velocity of the inlet airflow, and the maximum velocity of inlet airflow where the SI and MCPI can achieve successful and reliable ignition is 88.7 m/s and 102.2 m/s, respectively. Therefore, the MCPI has the advantage of achieving successful ignition with high-speed inlet airflow and extends the average ignition speed boundary of the kerosene/air mixture by 15.2%.

Development of artificial neural network to predict the performance of spark ignition engine fuelled with waste pomegranate ethanol blends

In this study,an artificial neural network(ANN)is developed to predict the performance of a spark-ignition engine using waste pomegranate ethanol blends.A series of experiments on a single-cylinder,four-stroke spark-ignition engine yielded the data needed for neural network training and validation.70 percent of the experimental data was used to train the network using the feed-forward back propagation(FFBP)algorithm.The developed network model’s performance was evaluated by contrasting its output with experimental results.Input parameters included engine speed,ethanol blends,and output parameters included indicated and brake power,thermal,volumetric,and mechanical efficiencies.Training and testing data had regression coefficients that were almost identical to one.The research revealed that the ANN model can be a better option for predicting engine performance with a higher level of accuracy.