Performance Optimization and Knock Investigation of Marine Two-Stroke Pre-Mixed Dual-Fuel Engine Based on RSM and MOPSO

Abstract

The two-stroke pre-mixed dual-fuel marine engine is prone to knocking at full load in gas mode, which affects the overall dynamic and economic performance of the engine. In this paper, the 7X82DF engine produced by Winterthur Gas & Diesel Ltd. (WinGD) was selected as the research object, aiming to investigate the effect of different parameters on engine power and knocking. Multi-objective optimizations were carried out. First, we used the one-dimensional simulation software AVL-BOOST to build the gas mode model of 7X82DF. Second, the pilot fuel start of combustion timing (SOC), the gas injection pressure, and the mass of diesel were taken as independent variables. The response surface methodology analysis of the independent variables was completed using the Design-Expert software and corresponding prediction model equations were generated. Finally, we took ringing intensity (RI) as the knock intensity evaluation index, combined with multi-objective particle swarm optimization (MOPSO) to optimize multiple-parameters to improve the overall performance and reduce combustion roughness of the engine. The optimization results showed that when the SOC was −8.36 °CA ATDC, the gas injection pressure was 20.00 bar, the mass of diesel was 14.96 g, the corresponding power was 22,668 kW, which increased by 0.68%, the brake-specific fuel consumption was 156.256 g/kWh, which was reduced by 3.58%, the RI was 4.4326 MW/m2, and the knock intensity decreased by 6.49%.

Study on the Effect of Parameter Sensitivity on Engine Optimization Results

“…By simulating the social cooperation and individual competition behaviors of bird flocks and fish schools and guiding the particle swarm to evolve to the global optimum based on the currently searched optimal solution, it has a very fast convergence speed. Jin et al [26] and Cong et al [27] both optimized the engine using MOPSO and obtained good optimization results. Figure 5 shows the computational flow of the MOPSO algorithm.…”