scholarly journals Prediction of heat energy from the naturally aspirated internal combustion engine exhaust gas using artificial neural network

2018 ◽  
Vol 135 ◽  
pp. 267-274 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Kamarulhelmy Talib ◽  
Azma Putra
Author(s):  
Qijun Tang ◽  
Jianqin Fu ◽  
Jingping Liu ◽  
Feng Zhou ◽  
Xiongbo Duan

To promote the energy utilization efficiency of internal combustion engine, the approach of electronically controlled turbocharger (ECT) for IC engine exhaust gas energy recovery was investigated by the method of test coupling with numerical simulation. First, the tests for turbocharged gasoline engine and high-speed motor were conducted so as to provide experimental data for numerical simulation. Then, the simulation model of ECT engine was built and calibrated, and the working processes of ECT engine were simulated. The results show that the recovered exhaust gas energy by ECT increases with the decrease of by-pass valve opening due to the rising of exhaust gas mass flow rate, but the pumping loss also ascends; limited by the original engine turbocharger map, the engine working points are beyond turbine map when the by-pass valve opening increases to a certain degree. To further improve the energy recovery potential of ECT, a larger turbine was rematched, and the working processes of ECT engine under the whole operating conditions were resimulated. The results indicate that engine exhaust gas energy cannot be recovered by ECT in low-load and low-speed area due to the low exhaust gas pressure. In the effective working area, as the load and speed ascend, both the recovery efficiency of ECT and the utilization efficiency of exhaust gas energy increase, and their maximum values reach 8.4% and 18.4%, respectively. All those demonstrate that ECT can effectively recover engine exhaust gas energy.


2013 ◽  
Vol 805-806 ◽  
pp. 1861-1864
Author(s):  
Jian Kun Xiao ◽  
Dai Fen Chen ◽  
Zhao Wang Xia

On type 380 diesel engine, orthogonal experimental studying on exhaust noise was carried out. This is to study the influence factors and degree on exhaust noise of internal combustion engine, and determine the main and secondary influence factors on diesel exhaust noise and their interaction. A noise prediction equations was established by studying the test samples based on genetic algorithm neural network. The results is very precise by test.


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