Cylinder Pressure Prediction of An HCCI Engine Using Deep Learning

Engine tests are both costly and time consuming in developing a new internal combustion engine.Therefore,it is of great importance to predict engine characteristics with high accuracy using artificial intelligence.Thus,it is possible to reduce engine testing costs and speed up the engine development process.Deep Learning is an effective artificial intelligence method that shows high performance in many research areas through its ability to learn high-level hidden features in data samples.The present paper describes a method to predict the cylinder pressure of a Homogeneous Charge Compression Ignition(HCCI)engine for various excess air coefficients by using Deep Neural Network,which is one of the Deep Learning methods and is based on the Artificial Neural Network(ANN).The Deep Learning results were compared with the ANN and experimental results.The results show that the difference between experimental and the Deep Neural Network(DNN)results were less than 1%.The best results were obtained by Deep Learning method.The cylinder pressure was predicted with a maximum accuracy of 97.83%of the experimental value by using ANN.On the other hand,the accuracy value was increased up to 99.84%using DNN.These results show that the DNN method can be used effectively to predict cylinder pressures of internal combustion engines.

Experimental Analysis on Influencing Factors of NO_(x)emission in Gas-Fired Heating and Hot Water Combi-Boilers

To study the influencing factors of NO_(x)emission in gas-fired heating and hot water combi-boilers,a boiler with the maximum heat input of 26.0 k W was selected,and influencing factors including flue restrictor diameter,fan power,nozzle aperture,nozzle ejection distance and air relative humidity on NO_(x)formation were determined.The NO_(x)test rig has been built and the concentration of NO_(x)at the rated heat input and the NO_(x)weight value(NO_(x))_(pond)with different heat input in the dry flue gas have been tested respectively according to the test methods in Chinese national standard GB 25034-2010.The results show that with the increase of the diameter of flue restrictor at exhaust outlet,the NO_(x)concentration at the rated heat input and the NO_(x)weight value(NO_(x))_(pond)with different heat input in the dry flue gas decreased by 26.9%and 5.9%;with the increase of the diameter of flue restrictor at air intake inlet,the NO_(x)and(NO_(x))_(pond)decreased by 36.5%and 16.0%;with the increase of fan power,the NO_(x)and(NO_(x))_(pond)can be decreased by 48.4%and 16.1%;with the increase of ejection distance of nozzle,the NO_(x)and(NO_(x))_(pond)decreased by 7.7%and 6.8%;with the increase of aperture of nozzle,the NO_(x)and(NO_(x))_(pond)increased by 5.2%and 2.3%;with the increase of air relative humidity,the NO_(x)decreased by 16.4%and the(NO_(x))_(pond)basically remains unchanged.The analysis of the influence factors of NO_(x)emission can be provided as reference for the optimization design of combi-boilers with low NO_(x)emission.

Effects of Optimized Operating Parameters on Combustion Characteristics and NO_(x)Emissions of a Burner based on Orthogonal Analysis

To optimize the structure of the burner,improve the combustion performance,and reduce the emission of NO_(x),a self-circulating low NO_(x)combustion technology was used to design a new type of flue gas self-circulating low NO_(x)burner.Based on previous research on the numerical model of combustion and the composition of mixed gas on combustion and NO_(x)emissions,the effect of various factors on the ejection coefficient of the flue gas self-circulating structure was analyzed using the orthogonal test method,and the burner operating parameters,such as preheating temperature and excess air coefficient,were deeply studied through the three-dimensional finite element numerical model in this paper.The results show that the diameter ratio of the nozzle and the length of the cylindrical section of the flue gas self-circulating structure have great influence on its ejection and mixing ability.The optimal ejection coefficient was 0.4829.Overall,the amount of NO_(x)emissions greatly increased from 6.23×10^(-6)(volume fraction)at the preheating temperature 973 K to 3.5×10^(-3)at preheating temperature 1573 K.When the excess air coefficient decreased from 1.2 to 1,the maximum combustion temperature decreased from 2036.3 K to 1954.22 K,and the NO_(x)emissions decreased from 352.29×10^(-6)to 159.73×10^(-6).