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In an engine system, the piston pin is subjected to high loading and severe lubrication conditions, and pin seizures still occur during new engine development. A better understanding of the lubricating oil behavior and the dynamics of the piston pin could lead to cost- effective solutions to mitigate these problems. However, research in this area is still limited due to the complexity of the lubrication and the pin dynamics. In this work, a numerical model that considers structure deformation and oil cavitation was developed to investigate the lubrication and dynamics of the piston pin. The model combines multi-body dynamics and elasto-hydrodynamic lubrication. A routine was established for generating and processing compliance matrices and further optimized to reduce computation time and improve the convergence of the equations. A simple built-in wear model was used to modify the pin bore and small end profiles based on the asperity contact pressures.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

Recent studies have shown that for a given RON, fuels with a higher sensitivity (RON-MON) tend to have better antiknock performance at most knock-limited conditions in modern engines. The underlying chemistry behind fuel sensitivity was therefore investigated to understand why this trend occurs. Chemical kinetic models were used to study fuels of varying sensitivities; in particular their autoignition delay times and chemical intermediates were compared. As is well known, non-sensitive fuels tend to be paraffins, while the higher sensitivity fuels tend to be olefins, aromatics, diolefins, napthenes, and alcohols. A more exact relationship between sensitivity and the fuel's chemical structure was not found to be apparent. High sensitivity fuels can have vastly different chemical structures. The results showed that the autoignition delay time (τ) behaved differently at different temperatures. At temperatures below 775 K and above 900 K, τ has a strong temperature dependence.