In conventional internal combustion engines, due to the unstable and “unpredictable” turbulent combustion phenomena, heavy crank shafts are used to ensure stable and robust operations by their large inertia. Such heavy crank shafts limit the flexibility of the piston motion such as compression ratio, which is a bottleneck for the improvement in fuel economy. In addition, the piston trajectory is fixed for all operation conditions (i.e., speed and load). Based on the recent advance in control system capacity and the better understanding of combustion and turbulence, we can potentially remove the heavy crank shafts to enhance engine efficiency and can use variable piston trajectories and compression ratios to match with the unstable combustion processes (similar to a free piston engine) and to attain robust operations. The key to enable such engines is an accurate prediction of the combustion responses to provide effective feedback signals for the advanced control system. As an early stage investigation of such operations, in this study, several piston trajectories and compression ratios are simulated for a controlled trajectory rapid compression and expansion machine (CT-RCEM). First, pure diluent gases are simulated to illustrate the turbulent flow evolution and vortex generation under different piston trajectories. Second, lean fuel-air mixtures are simulated to capture the auto-ignition and combustion under the same trajectories. Local pressure prediction is compared with the experimental data for model validation.
|Original language||English (US)|
|Title of host publication||AIAA Scitech 2020 Forum|
|Publisher||American Institute of Aeronautics and Astronautics Inc, AIAA|
|State||Published - 2020|
|Event||AIAA Scitech Forum, 2020 - Orlando, United States|
Duration: Jan 6 2020 → Jan 10 2020
|Name||AIAA Scitech 2020 Forum|
|Conference||AIAA Scitech Forum, 2020|
|Period||1/6/20 → 1/10/20|
Bibliographical noteFunding Information:
S. Yang gratefully acknowledges the faculty start-up funding from the Department of Mechanical Engineering at the University of Minnesota – Twin Cities. Z. Sun and A. Tripathi gratefully acknowledge the funding from NSF through grant CMMI-1428318. The simulations were conducted at the Minnesota Supercomputing Institute (MSI).