Tank cover for a petrol engine

2013 ◽  
Vol 2013 (4) ◽  
pp. 13
Keyword(s):  
Petrol Engine

Scavenging of Two Stroke Petrol Engine by Using Secondary Air Injection System

2019 ◽  
pp. 913-922
Author(s):  
Sagar Namdev Khurd ◽  
U. B. Andh ◽  
S. V. Kulkarni ◽  
Sandeep S. Wangikar ◽  
P. P. Kulkarni
Keyword(s):  
Air Injection ◽  
Petrol Engine ◽  
Injection System ◽  
Secondary Air Injection ◽  
Secondary Air

scholarly journals MATHEMATICAL MODEL OF PETROL ENGINE WORKING ON ATKINSON CYCLE

2019 ◽  
Vol 0 (1) ◽  
pp. 8-14
Author(s):  
Т. М. Колеснікова ◽  
В. Г. Заренбін ◽  
О. П. Сакно ◽  
В. П. Олло
Keyword(s):  
Mathematical Model ◽  
Petrol Engine ◽  
Atkinson Cycle

scholarly journals Synthesis Gas from Pyrolysed Plastics for Combustion Engine

2015 ◽  
Vol 23 (1) ◽  
pp. 18-24
Author(s):  
Andrej Chríbik ◽  
Marián Polóni ◽  
Ján Lach ◽  
Ľubomír Jančošek ◽  
Peter Kunc ◽  
...  
Keyword(s):  
Natural Gas ◽  
Synthesis Gas ◽  
Combustion Engine ◽  
Petrol Engine ◽  
Experimental Measurements ◽  
Internal Parameters

Abstract The article discusses the application of synthesis gas from pyrolysis of plastics in petrol engine. The appropriate experimental measurements were performed on a combustion engine LGW 702 designated for micro-cogeneration unit. The power parameters, economic and internal parameters of the engine were compared to the engine running on the reference fuel - natural gas and synthesis gas. Burning synthesis gas leads to decreased performance by about 5% and to increased mass hourly consumption by 120%. In terms of burning, synthesis gas has similar properties as natural gas. More significant changes are observed in even burning of fuel in consecutive cycles.

scholarly journals Automatic Petrol and Diesel Engine Sound Identification Based on Machine Learning Approaches

2019 ◽  
Vol 130 ◽  
pp. 01011
Author(s):  
Halim Frederick ◽  
Astuti Winda ◽  
Mahmud Iwan Solihin
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Diesel Engine ◽  
Fast Fourier Transform ◽  
Intelligent System ◽  
Mechanical Energy ◽  
Petrol Engine ◽  
Support Vector ◽  
Learning Approaches ◽  
Feature Extraction Method

Petrol and diesel engine have a significantly different way to convert chemical energy into mechanical energy. In this work, the intelligent system approach is used to automatically identify the type of engine based on the sound of the engine. The combination of signal processing and machine learning technique for automatic petrol and diesel engine sound identification is presented in this work. After a signal preprocessing step of the engine sound, a Fast Fourier Transform (FFT)-based frequency characteristic modelling technique is applied as the feature extraction method. The resulting features extracted from the sound signal, in the form of frequency in the FFT matrix, are used as the inputs for the machine learning, the Support Vector Machine (SVM), step of the proposed approach. The experiment of FFT with SVM-based diesel and petrol engine sound identification has been carried out. The results show that the proposed approach produces a good accuracy in the relatively short training time. Experimental results show the training and testing accuracy of 100 % and 100 % respectively. They confirm the effectiveness of the proposed intelligent automatic diesel and petrol engine sound identification based on Fast Fourier Transform (FFT) and Support Vector Machines (SVMs).

Sign in / Sign up

Export Citation Format

Share Document