Engine knock evaluation using a machine learning approach Universita degli Studi di Perugia

Format:

Book

Conference/Event

Author/Creator:

petrucci, Luca, author.

Contributor:

Cruccolini, Valentino

Mariani, Francesco

Ricci, Federico

Violi, Mattia

Conference Name:

Conference on Sustainable Mobility (2020-10-04 : Catania, Italy)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2020

Summary:

Artificial Intelligence is becoming very important and useful in several scientific fields. Machine learning methods, such as neural networks and decision trees, are often proposed in applications for internal combustion engines as virtual sensors, faults diagnosis systems and engine performance optimization. The high pressure of the intake air coupled with the demand of lean conditions, in order to reduce emissions, have often close relationship with the knock events. Fuels autoignition characteristics and flame front speed have a significant impact on knock phenomenon and producing high internal cylinder pressures and engine faults. The limitations in using pressure sensors in the racing field and the challenge to reduce the costs of commercial cars, push the replacement of a sensor redundancy with a software redundancy. Therefore, it becomes strategically important to develop a robust predictive model that, using the physical properties such as air temperature and pressure, fuel consumption and engine speed, could increase the engine performance under a large range of operating conditions, without computational efforts.In this paper, machine learning methods were implemented to predict the knock onset and knock intensity of a SI engine. The tool is fed by several input variables that are generated by a CFD model calibrated with real data. Input parameters influencing the knock phenomenon, such as engine speed, air-fuel ratio, max internal cylinder pressure, combustion timing, and physical air conditions in the plenum, have been used as dataset for training and test phases. Once trained, the machine learning models were tested on their ability to predict outputs based on the engine conditions not used in the training set. The outputs predicted were compared with the target ones and the accuracy of the model was tested in terms of RMS and R2

Notes:

Vendor supplied data

Publisher Number:

2020-24-0005

Access Restriction:

Restricted for use by site license