Some aspects concerning the combination of downsizing with turbocharging, variable compression ratio, and variable intake valve lift

Author(s):  
A C Clenci ◽  
G Descombes ◽  
P Podevin ◽  
V Hara

The inefficient running of the spark ignition engine at part loads due to the load control method but, mostly, their major weighting in the vehicle's operation time justifies the interest in the technical solutions, which act in this particular operating range. These drawbacks encountered at low part loads are even more amplified when considering larger engines. For instance, it is well known that, at the same engine load, a larger engine is more throttled than a smaller engine; therefore the concerns are the higher pumping work, the lower real compression ratio, and the overall mechanical efficiency, which is also lower. One solution is a reduction in the displacement without affecting the power output. This is what is now commonly known as the downsizing technique. The combination of downsizing and uploading an engine has been known for a long time. However, the conversion, in an acceptable way, of this potential to actual practice is very challenging. On the one hand, the degree of the downsizing is related to the boost pressure. In order to cope with the knocking phenomenon, the downsized high-pressure turbocharged gasoline engine requires a lower volumetric compression ratio that limits the efficiency on part loads. Therefore, the degree of the downsizing has been limited and, thus, the possible fuel consumption reduction has not yet been fully achieved. On the other hand, other problems are encountered when considering a downsized turbocharged gasoline engine: insufficient low-end torque, poor starting performance, and turbo lag. In order to solve these problems an effective combination of the downsized turbocharged gasoline engine with additional technologies is needed. Thus, the paper will present a so-called adaptive thermal engine, which has at the same time a variable compression ratio and a variable intake valve lift. It will then be demonstrated that it is highly suitable for turbocharging, thus resulting in a high downsizing factor.

Author(s):  
Srinibas Tripathy ◽  
Sridhar Sahoo ◽  
Dhananjay Kumar Srivastava

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.


Sign in / Sign up

Export Citation Format

Share Document