Computational optimization of the dual-mode dual-fuel concept through genetic algorithm at different engine loads

Abstract

[EN] The diesel/gasoline dual-mode dual-fuel (DMDF) combustion concept was optimized in a compression-ignition engine by combining the computational fluid dynamics (CFD) simulations with the genetic algorithm. Seven operating parameters with remarkable influences on the engine performance were chosen as the variables to be optimized for simultaneously minimizing the fuel efficiency, nitrogen oxides (NOx), and soot emissions. Moreover, the potential of the further improvement of the DMDF combustion concept was discussed, and the rationality of this strategy was demonstrated. The results indicate that, at low load, simultaneous improvement of the fuel economy and emissions can be realized by strengthening the homogeneous combustion. At mid load, the fuel economy can be improved by reducing the heat transfer losses, while the NOx emissions are sacrificed to some extent. At high load, improved fuel economy can be realized by transferring a part of diffusion combustion to premixed reactivity-controlled compression ignition (RCCI) combustion. Concerning the operating parameters, lower intake temperature is beneficial to decrease the transfer losses, and the control of intake temperature is crucial for the stable operation of DMDF combustion under wide load conditions. Overall, gross indicated thermal efficiency above 45% is achieved, and the NOx and soot emission can be maintained under the Euro 6 standard for the test load range.