Conceptual Model for Developing a Digital Twin of an Automotive Engine

In line with global objectives to reduce pollution and optimize energy consumption, improving performance and reducing fuel consumption in automotive engines holds significant importance. This study presents a conceptual Digital Twin model for automotive engines, aiming to bridge the gap between the physical engine and its virtual representations. By utilizing data collected from advanced sensors, this approach enables accurate simulation of engine behavior. These data, combined with three-dimensional modeling, provide a comprehensive view of engine performance and facilitate the analysis and identification of complex patterns. The integration of machine learning algorithms within this model allows for prediction, optimization, and the discovery of intricate relationships among various engine performance variables. Ultimately, this lays the groundwork for enhancing efficiency and reducing fuel consumption. Overall, the proposed conceptual model offers a comprehensive analytical framework that supports fuel reduction and performance enhancement in internal combustion engines.

کلیدواژه ها:

Optimization ، digital twin ، fuel consumption ، artificial intelligence