Investigating the relative contribution of operational parameters on performance and emissions of a common-rail diesel engine using neural network

Engine performance and emissions depend on a variety of parameters affecting the engine. Thanks to utilization of modern diesel engine with mechatronic systems, the number engine actuators increase significantly. The actuators can affect the internal states (operational parameters) of diesel engine such as inlet manifold pressure, EGR rate, quantity and timing of pilot and main injection which in turn will influence the engine emissions and performance. These internal states can be considered as boundary conditions of in-cylinder combustion process. Due to large number of effective parameters, study of relative contribution of these states on engine outputs will be helpful in better controlling and calibration of diesel engines. In this paper, comparative effects of internal states on both performance and emissions are investigated using statistical method and ranked based on their importance. Ten engine operational parameters including: injected fuel mass, pilot and main injection mass, main and pilot injection timing, inlet air pressure and temperature, exhaust pressure, fuel rail pressure and exhaust gas recirculation rate (EGR) are considered and their influence on brake torque, Soot, NOx and brake specific fuel consumption (BSFC) is investigated. A thermodynamic model of engine cycle is developed in AVL Boost®; the model is tuned and validated using experimental data. In order to better and faster study the effects of operational parameters on engine performance, a neural network is employed. The required data to train the neural networks is provided by using AVL Boost Design Explorer. Due to large number of inputs and outputs, a low-discrepancy and low-dispersion sequences generator called Sobol method is used to generate quasi random sequences of input data. More than 4000 engine operation points are generated and simulated in AVL Boost. The provided data is then used to train a feed forward neural network using Bayesian training method. Comparison between experimental data and simulated results shows about 6% error in prediction of the outputs. The engine performance and emission is then analyzed using both graphical and statistical methods to study how different input parameters can influence the engine emissions and performance. Finally, the relative importance of each parameter on different engine performance and emission characteristics are investigated using perturbation method and most influential parameters on different outputs are obtained

Engine Downsizing; Global Approach to Reduce Emissions: A World-Wide Review:A World-Wide Review

Engine downsizing is a promising method to reduce emissions and fuel consumption of internal combustion engines. The main concept is to reduce engine displacement volume while keeping the needed output characteristics unchanged. The issue has become one of the most current fields of interest in recent years after the International Energy Agency set a target of a 50% reduction in global average emissions by the year 2030. In this review paper, different aspects of researchers’ efforts on engine downsizing are configured and, due to overlaps, categorized into five main areas. Each category is discussed thoroughly, and recent works are highlighted. The global attention in these categories, the countries involved and the trend change in the last four years are presented in detail. Doi: 10.28991/HIJ-2021-02-04-010 Full Text: PD

Investigating a new model-based calibration procedure for optimizing the emissions and performance of a turbocharged diesel engine

Today, diesel engines are no longer mentioned for generating huge amount of soot and high level of noise. These achievements are owing to the employment of numerous mechatronic systems implemented in the engine. Altogether, with the increase of the number of controllable parameters, the complexity of control and calibration tasks has been increased. In the conventional calibration processes, numerous tests are required for calibration of engine controllers, making it a time consuming and expensive procedure. However, in this paper a model-based calibration procedure based on evolutionary algorithms is investigated to fulfill the feed-forward controller look-up tables. The look-up tables obtain the fuel injection and air induction system parameters based on engine speed and relative load and guarantee the optimal operation of engine. The developed procedure guarantees the maximum attainable torque in full load. The proposed method decreased the time, cost and complexity of whole calibration procedure to high extent. Artificial neural network is employed for modeling the combustion process while steady-state mass and energy balance equations are used for inlet and exhaust models. The models have been validated using experimental data. The optimization is done in two phases: full load curve shaping and part load optimization. The aim of former is attaining maximum possible torque with the minimum emissions and fuel consumption in every engine speed while the aim of latter is delivering the required torque with the lowest possible emissions and fuel consumption. The results of tests show that the proposed model-based calibration method can effectively reduce the fuel consumption and emissions in whole engine operation regime and decrease the time and cost of calibration