Performance analysis and multi-objective optimization of irreversible Diesel cycle with non-ideal gas working fluid

In the early research process, the ideal gas was taken as the research object, but in practice, the working fluid was all non-ideal gas, so it is of great significance to study performance of actual internal combustion engine with non-ideal gas. This study utilizes an irreversible Diesel cycle model, which has been established in the previous literature, and considers various irreversible loss terms and specific heat model of non-ideal gas working fluid, to perform cycle performance analysis and multi-objective optimization. Compression ratio ( \(\gamma\) $\gamma$ ) is taken as optimization variable to optimize efficiency ( \(\eta\) $\eta$ ), dimensionless power ( \(\overline{P}\) $\overline{P}$ ), dimensionless power density ( \(\overline{{P_{{\text{d}}} }}\) $\overline{P_{\text{d}}}$ ) and dimensionless ecological function ( \(\overline{E}\) $\overline{E}$ ). The results show that there are optimal \(\gamma\) $\gamma$ s to maximize the four-objective functions ( \(\eta_{\max }\) ${\eta }_{max}$ , \(\overline{P}_{\max }\) ${\overline{P}}_{max}$ , \(\overline{{P_{{\text{d}}} }}_{\max }\) ${\overline{P_{\text{d}}}}_{max}$ and \(\overline{E}_{\max }\) ${\overline{E}}_{max}$ ); with the rises of irreversible loss terms, the \(\eta_{\max }\) ${\eta }_{max}$ , \(\overline{P}_{\max }\) ${\overline{P}}_{max}$ , \(\overline{{P_{{\text{d}}} }}_{\max }\) ${\overline{P_{\text{d}}}}_{max}$ and \(\overline{E}_{\max }\) ${\overline{E}}_{max}$ all drop. As freedom degree of monatomic gas changes from 1 to 3, only \(\eta_{\max }\) ${\eta }_{max}$ drops and the other three-objective functions rise. When \(\overline{P} - \eta - \overline{E} - \overline{P}_{{\text{d}}}\) $\overline{P}-\eta -\overline{E}-{\overline{P}}_{\text{d}}$ is optimized and \(\gamma_{{{\text{opt}}}}\) ${\gamma }_{\text{opt}}$ is mainly concentrated between 3.6 and 5.3, the calculation results of \(\overline{P}_{{}}\) $\overline{P}_{}^{}$ are distributed between 0.85 and 1. The calculation results of \(\eta\) $\eta$ are distributed between 0.46 and 0.52. The calculation results of \(\overline{E}\) $\overline{E}$ are distributed between 0.6 and 1. The calculation results of \(\overline{{P_{{\text{d}}} }}\) $\overline{P_{\text{d}}}$ are distributed between 0.9 and 1. When \(\overline{P} - \eta - \overline{E} - \overline{P}_{{\text{d}}}\) $\overline{P}-\eta -\overline{E}-{\overline{P}}_{\text{d}}$ and \(\overline{P} - \overline{E} - \overline{P}_{{\text{d}}}\) $\overline{P}-\overline{E}-{\overline{P}}_{\text{d}}$ are optimized, deviation indexes obtained by using LINMAP decision-making are the smallest and the best among all optimization results. Multi-objective optimization algorithm is an optimization method to solve multiple conflicting objectives by simulating the competition mechanism in nature. It can find a balance point among multiple objective extremes and thus improve comprehensive performance of Diesel cycle.