A New Design to Achieve Variable Compression Ratio in a Spark Ignition Engine

2018 ◽  
pp. 389-396
Author(s):  
Aditya Roy ◽  
Chetan Mishra ◽  
Sarthak Jain ◽  
Naveen Solanki
Keyword(s):  
Compression Ratio ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Variable Compression Ratio ◽  
Variable Compression

Exergy evaluation of equivalence ratio, compression ratio and residual gas effects in variable compression ratio spark-ignition engine using quasi-dimensional combustion modeling

Energy ◽  
2022 ◽  
pp. 123080
Author(s):  
Constantine D. Rakopoulos ◽  
Dimitrios C. Rakopoulos ◽  
Dimitrios C. Kyritsis ◽  
Eleftherios C. Andritsakis ◽  
George C. Mavropoulos
Keyword(s):  
Compression Ratio ◽  
Equivalence Ratio ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Variable Compression Ratio ◽  
Combustion Modeling ◽  
Residual Gas ◽  
Variable Compression

Development of an Artificial Neural Network Model for the Performance Prediction of a Variable Compression Ratio Gasoline Spark Ignition Engine

2019 ◽  
Author(s):  
Srinibas Tripathy ◽  
Sridhar Sahoo ◽  
Dhananjay Kumar Srivastava
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compression Ratio ◽  
Performance Prediction ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Variable Compression Ratio ◽  
Ann Modeling ◽  
Output Layer ◽  
Variable Compression

Abstract The purpose of this study is to develop an artificial neural network (ANN) model for performance prediction of a variable compression ratio gasoline port fuel injection spark ignition engine. For ANN modeling, a large experimental data set was generated in which at random 85% was assigned for training the network, and 15% that are not included during the training process was used for testing the network. A multilayer perception feed forward neural network was used to predict the correlation between input and output layer. The input layer consists of engine speed, throttle position, spark timing, and compression ratio. Whereas, the output layer consists of torque, brake power and indicated mean effective pressure (IMEP). Neurons in the hidden layer were varied and optimized based on a specified goal error. A standard supervised back propagation learning algorithm was used in which the error between the target and network output was calculated and minimized. In the hidden and output layers, a non-linear tan-sigmoid and a linear transfer function were used, respectively, for input-output mapping. The performance of the network was evaluated by statistical parameters like correlation coefficient (R), mean relative error (MRE) and root mean square error (RMSE). It was found from the test data that the R and MRE values are lies in between 0.99853 to 0.99875 and 0.42% to 0.58%, respectively. Whereas, RMSE value for all performance parameters was found to be very low. Hence, this study reveals that the application of ANN modeling has the ability to predict the performance of a variable compression ratio gasoline engine and is the best alternative tool over all classical modeling techniques.

Analysis of the Combustion Process of a Spark ignition Engine with a Variable Compression Ratio

1987 ◽  
Author(s):  
W. H. Adams ◽  
H. G. Hinrichs ◽  
F. F. Pischinger ◽  
P. Adamis ◽  
V. Schumacher ◽  
...  
Keyword(s):  
Compression Ratio ◽  
Combustion Process ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Variable Compression Ratio ◽  
Variable Compression
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