Engine downsizing is a promising method to reduce emissions and fuel consumption of internal combustion engines. The main concept is reducing engine displacement volume keeping needed output characteristics unchanged. The issue became one of the most filed of interest in recent years after the International Energy Agency defined a target of 50 % reduction in global average emissions by the year 2030. In this review paper, different aspects of researchers’ efforts on engine downsizing are configured and due to overlaps, categorized into five main areas. Each category is discussed thoroughly and recent works are pointed. The global attention into these categories, countries involved and the trend changed in recent four years are presented in details. Doi: 10.28991/HIJ-2021-02-04-010 Full Text: PDF

In this work, 11 regression models based on machine learning techniques were employed to provide a fast-response and accurate model for the prediction of the start of combustion in homogeneous charge compression ignition engines fueled with methane. These regression models are categorized into linear and nonlinear types. Although the robust random sample consensus (RANSAC) model is a nonlinear type as well as SAM (simple algebraic model), the prediction accuracy is enhanced from 89.3% to 98.4%. Such accuracy is also achieved for the linear models, namely, ordinary least squares, ridge, and Bayesian ridge models. Indeed, due to the linear hypothesis (the correlation for the start of combustion prediction), the presented models have an acceptable response time to be used in real-time control applications like the electronic control units of the engines.