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A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

A wide variety of spray models and their associated sub-models exist to assist with numerical spray development studies in the many applicable areas viz., turbines, internal combustion engines etc. The accuracy of a simulation when compared to the experiments varies, as these models chosen are varied. Also, the computational grid plays a crucial role in model correctness; a grid-converged CFD study is more valuable and assists in proper validation at later stages. Of primary relevance to this paper are the combustion models for a grid-converged Lagrangian spray modeling scenario. CONVERGE CFD code is used for simulation of split injection diesel (n-heptane) sprays and a structured methodology, using RNG k-ε turbulence model, is followed to obtain a grid-converged solution for the key Computational Fluid Dynamics (CFD) parameters viz., grid size, injected parcels and spray break-up time constant.

High-speed spray-to-spray liquid impingement could be an effective phenomenon for the spray propagation and droplet vaporization. To achieve higher vaporization efficiency, impingement from two-hole nozzles is analyzed in this paper. This paper focuses on investigating vaporization mechanism as a function of the impingement location and the collision breakup process provided by two-hole impinging jet nozzles. CFD (Computational Fluid Dynamics) is adopted to do simulation. Lagrangian model is used to predict jet-to-jet impingement and droplet breakup conditions while KH-RT breakup and O'Rourke collision models are implemented for the simulation. The paper includes three parts: First, a single spray injected into an initially quiescent constant volume chamber using the Lagrangian approach is simulated to identify the breakup region, which will be considered as a reference to study two-hole impinging jet nozzles. Lagrangian simulation results would be validated via experimental results.