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A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

Combustion simulations utilizing the characteristic time combustion model have been performed for four DI diesel engines ranging in size from heavy-duty to large-bore designs. It has been found that the pre-factor to the turbulent characteristic time acts as a scaling parameter between the engines. This phenomenon is explained in terms of the non-equilibrium behavior of the turbulent time and length scales, as is encountered in the rapidly distorting, spray-induced flows of DI diesel engines. In fact, the equilibrium assumption between turbulence production and dissipation, which forms the basis for the employed k-ε-type turbulence models, does not hold in these situations. For such flows, the real turbulent dissipation time scale is locally proportional to the turbulent characteristic time scale which is determined by a typical eddy turnover time.

A correction for the turbulence dissipation, based on non-equilibrium turbulence considerations from rapid distortion theory, has been derived and implemented in combination with the RNG k - ε model in a KIVA-based code. This model correction has been tested and compared with the standard RNG k - ε model for the compression and the combustion phase of two heavy duty DI diesel engines. The turbulence behavior in the compression phase shows clear improvements over the standard RNG k - ε model computations. In particular, the macro length scale is consistent with the corresponding time scale and with the turbulent kinetic energy over the entire compression phase. The combustion computations have been performed with the characteristic time combustion model. With this dissipation correction no additional adjustments of the turbulent characteristic time model constant were necessary in order to match experimental cylinder pressures and heat release rates of the two engines.