CFD-Guided Combustion System Optimization of a Gasoline Range Fuel in a Heavy-Duty Compression Ignition Engine Using Automatic Piston Geometry Generation and a Supercomputer

Abstract

<div class="section abstract"><div class="htmlview paragraph">A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NO<sub>x</sub> within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions. The second DoE campaign was conducted to optimize injector spray patterns, fuel injection strategies and in-cylinder swirl motion for the best performing piston bowl designs from the first DoE campaign. This comprehensive optimization study was performed on a supercomputer, Mira, to accelerate the development of an optimized fuel-efficiency focused design. Compared to the production combustion system in the baseline engine, the new combustion recipe from this study showed significantly improved closed-cycle fuel efficiency across key engine operating points while meeting the engine-out NO<sub>x</sub> targets. Optimized piston bowl designs and injector spray patterns were predicted to provide enhanced in-cylinder air utilization and more rapid mixing-controlled combustion, thereby leading to a fuel efficiency improvement. In addition, shifting the engine thermal boundary conditions toward leaner operation was also key to the improved fuel efficiency.</div></div>

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

“…The optimization of piston bowl geometry of a heavy-duty gasoline compression ignition (HD-GCI) engine is performed here. Pei et al [39] conducted a CFD guided combustion system optimization study of a heavy-duty diesel engine running on a gasoline fuel with research octane number (RON) of 80. A two-stage design of experiments (DoE) approach was adopted in their work.…”

“…Hence, the geometry can be generated simply as a surface of revolution from a parameterized profile. More details about the design generation Optimum design approach, the constraints and the design variable limits may be found in [39]. A set of 256 piston bowl designs were generated by CAESES using a Sobol sequence and were evaluated at four engine operating conditions using multidimensional CFD simulations.…”

“…Different optimum designs were identified based on the CFD simulations and these are shown in Figure 5 for the four studied engine operating points. Further details about the engine, its operating points, optimization techniques and results may be found in [39]; only a brief description is provided here. It may be seen that the overall shape of the top five designs at each operating point share some key features (bowl depth, bowl width, center height, etc.)…”

“…The purpose of the current work is to extend the CFD study conducted by Pei et al [39] using the ML-GGA methodology developed here to obtain a better design compared to their best CFD design.…”

“…Better mixing of fuel and air prior to combustion (due to the higher volatility of gasoline) reduces soot production relative to the conventional mixing-controlled (diesel) combustion. Our research group in Saudi Aramco extensively investigated the combustion of various fuels in GCI engines [27][28][29][30][31][32][33][34][35][36][37][38][39].…”