| dc.description.abstract | Assessing the quality of an engine lubricant through real-time sensing could pave the way for remote monitoring of engine health and the development of machine learning algorithms for engine health management. However, implementing sensors in an engine for real-time interrogation of oil quality requires a comprehensive understanding of the characteristics of the oil flow at the sensor’s location in the engine. In this work, extensive flow visualization studies were first conducted to examine the pre-filter oil flow characteristics in a Volkswagen 4-cylinder 1.9L Turbocharged Diesel Injection engine operating with fresh lubricating oil. Pre-filter engine oil flow was routed to an external flow visualization cell and imaged using a high-speed, high-resolution digital camera. The still images of the flow were analyzed to determine flow speed, bubble size, number density, and volumetric aeration percentage. Image processing analysis showed the peak bubble size to be around 60 µm and volumetric aeration to be around 0.1%. In general, the aeration percentage increases with increasing engine speed and decreases with increasing temperature.
Following the flow visualization studies, a permittivity-based oil quality sensor was fully characterized on a benchtop facility and then tested in situ on the diesel engine. Both fresh and used samples of the engine’s recommended oil were tested, and the sensor’s loss factor measurements showed noticeable differences between the engine and benchtop, a feature attributable to the presence of the aeration observed in the flow visualization studies. These results show that implementation of permittivity-based sensors on engines will require comparative assessment of oil quality measurements in aerated and non-aerated flow fields for their eventual adaptation for effective engine health monitoring. | |
