Prediction of airfuel ratio control of a large-bore natural gas engine using computational fluid dynamic modeling of reed valve dynamics
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abstract
Airfuel ratio control of large-bore, two-stroke, natural gas engines, typically used in the oil and gas field, is critically important to maintain stable operation and emission compliance. Many two-stroke applications rely on reed valves to control air and gas induction, which can involve complicated gas flow behavior; standard gas dynamic relationships are typically insufficient to characterize such behavior. Computational fluid dynamic simulations offer the needed complexity, but even so the computational fluid dynamic models, as shown in this work, must also capture the dynamic behavior of the valves themselves. The current work reports on a computational fluid dynamicsbased model representing this type of large-bore, two-stroke, natural gas engine using commercially available computational fluid dynamic software. The engine under study is an AJAX E-565 with rated power of 30kW (40HP), a bore of 216mm (8), and a stroke of 254mm (10). The large engine geometry makes a relatively large solution domain, hence requiring an intense, time-consuming numerical investigation. This large-bore engine works at a rated speed of 525RPM with a compression ratio of 6 to 1. Two approaches to modeling the reed valve are investigated: (1) a pressure differencebased user-defined function and (2) a fluidstructure interaction user-defined function. The pressure differencebased user-defined function captures reed valve behavior in a simple, binary fashion (i.e. valves are either open or closed based on the pressure difference between the intake pipe and the engines stuffing box). The fluidstructure interaction user-defined function, however, predicts the motion of the reed valve strips based on fluid and body motions; although a more complex solution, the fluidstructure interaction user-defined function accurately predicts the engines gas exchange process. In this article, the results of each method are presented and validated to show that the added complexity is necessary to properly predict (and thus eventually improve) the engines airfuel ratio control.
