A Comprehensive Evaluation of Diesel Engine CFD Modeling Predictions Using a Semi-Empirical Soot Model over a Broad Range of Combustion Systems

Single-cylinder engine experiments and computational fluid dynamics (CFD) modeling were used in this study to conduct a comprehensive evaluation of the accuracy of the modeling approach, with a focus on soot emissions. A semi-empirical soot model, the classic two-step Hiroyasu model with Nagle and Strickland-Constable oxidation, was used. A broad range of direct-injected (DI) combustion systems were investigated to assess the predictive accuracy of the soot model as a design tool for modern DI diesel engines. Experiments were conducted on a 2.5 liter single-cylinder engine. Combustion system combinations included three unique piston bowl shapes and seven variants of a common rail fuel injector. The pistons included a baseline “Mexican hat” piston, a reentrant piston, and a non-axisymmetric piston similar to the Volvo WAVE design. The injectors featured six or seven holes and systematically varied included angles from 120 to 150 degrees and hole sizes from 170 to 273 μm. A single nominal operating condition was studied: 100% load at 1800 rpm. Variations in the start of injection (SOI), injection pressure, intake pressure, and exhaust gas recirculation (EGR) level were also studied. These broad hardware and operational variations were modeled using Reynolds-averaged Navier-Stokes (RANS) CFD simulations with direct combustion chemistry integration. The focus of the work was to assess the ability of the model with Chalmers n-heptane combustion chemistry to predict the soot emissions from the various combustion systems. The results show that while the model predicts some general trends regarding SOI and injection pressure, it tends to fail as a comprehensive predictive simulation tool for designing DI diesel combustion systems regarding soot emissions. This suggests that further improvements in diesel engine CFD modeling for predicting soot emissions are needed. © 2018 SAE International. All Rights Reserved.