Investigating the relative contribution of operational parameters on performance and emissions of a common-rail diesel engine using neural network
Investigating the relative contribution of operational parameters on performance and emissions of a common-rail diesel engine using neural network
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DOI：
10.1016/j.fuel.2014.02.021
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发表时间：
2014-06
期刊：
影响因子：
7.4
通讯作者：
Kamyar Nikzadfar;A. Shamekhi
中科院分区：
文献类型：
--
作者：
Kamyar Nikzadfar;A. Shamekhi
发动机性能和排放取决于影响发动机的各种参数。由于现代柴油发动机与机电一体化系统的使用，发动机执行器的数量显着增加。执行器可以影响柴油机的内部状态（工作参数），如进气歧管压力、EGR率、预喷射和主喷射的量和正时，从而影响发动机的排放和性能。这些内部状态可以作为缸内燃烧过程的边界条件。由于柴油机的有效参数较多，研究这些状态对柴油机输出功率的相对贡献有助于更好地控制和标定柴油机。在本文中，内部状态对性能和排放的比较影响进行了研究，使用统计方法和排名的基础上，他们的重要性。研究了喷油量、预喷和主喷量、主喷和预喷正时、进气压力和进气温度、排气压力、燃油轨压力和废气再循环率（EGR）等10个发动机运行参数对制动扭矩、Soot、NOx和制动燃油消耗率（BSFC）的影响。在AVL Boost®中开发了发动机循环的热力学模型;使用实验数据对模型进行了调整和验证。为了更好、更快地研究发动机工作参数对发动机性能的影响，采用了神经网络方法。训练神经网络所需的数据通过使用AVL Boost Design Explorer提供。由于输入输出数据量大，采用Sobol方法产生输入数据的准随机序列。在AVL Boost中生成和模拟了4000多个发动机工作点。所提供的数据，然后使用贝叶斯训练方法来训练前馈神经网络。实验数据与模拟结果的比较表明，预测的输出约6%的误差。然后，使用图形和统计方法来分析发动机的性能和排放，以研究不同的输入参数如何影响发动机的排放和性能。最后，采用摄动法研究了各参数对发动机性能和排放特性的影响，得到了对不同输出影响最大的参数。
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.