Abstract
Cycle-to-cycle variation (CCV) in internal combustion engines is expected due to variabilities in operating conditions such as in-cylinder fuel stratification, mixing behavior, manifold air pressure, injection timing etc. Generally, engine experimental studies provide averaged quantities such as cylinder pressure, emissions, indicated mean effective pressure (IMEP) from multiple cycles and their CCV of IMEP, and CCV of cylinder pressure from consecutive cycles for a given test condition. Capturing 3D spatial distributions of relevant measures such as fuel-air mixing, temperature, turbulence levels and emissions from such experiments is challenging. Computational Fluid Dynamics (CFD) is an alternative to experiments, that can be used effectively to understand the spatial and temporal distributions of parameters of interest.
Quantitative understanding of CCV in dual-fuel (DF) engines will accelerate the development and deployment of engines that provide fuel flexibility, economic advantage and emission compliance. Earlier work [1] performed to develop an understanding of the physics of CCV in large-bore, medium-speed, dual-fuel engines was partially successful. That effort entailed a sparse design of experiment (DOE) of full-cylinder geometry, closed-cycle, dual-fuel CFD simulations in which variabilities within some of the identified global parameters were exercised. The Coefficient of Variation (COV) of cylinder pressure was promising compared with experimental COV. This reinforced the understanding of which global parameters are significant contributors to CCV in DF combustion.
The authors have used several high-performance supercomputing facilities in the process of developing predicting capability of CCV in large-bore engines, while operating in dual-fuel, diesel and natural gas (NG), combustion mode [1, 2]. In the current study CCV influencing parameters in a diesel-natural gas dual-fuel engine was studied using a 4th order DOE consisting of 137 concurrent cases. These full-geometry, closed-cycle, CFD simulations were run on the Mira supercomputing facility at the Argonne National Laboratory (ANL). This paper describes key learnings of that study along with a validation of the DOE. Further, the CCV prediction methodology, which is applicable to simulation-based design and testing, is described.
