摘要：An intelligent cooling system directly influences the thermal load of high‐temperature components, heat distribution, and fuel economy of a diesel engine. An optimal coolant pump rotational speed map is a key factor in intelligent cooling control strategies. In this study, we designed an experimental variable coolant flow system for a maritime diesel engine. Experiment design and D‐optimal designs were used to optimize the parameters of the diesel engine cooling system. The diesel engine speed, load, and freshwater rotational pump speed were selected as variables. The temperature of the high‐thermal‐load zone of the combustion chamber components, fuel consumption rate, effective power, and peak cylinder pressure were selected as response variables, and the D‐optimal method was used to sample the experimental points. Polynomial response surface models were obtained using a stepwise algorithm. A multiobjective optimization problem was converted into a simple‐objective optimization problem using the ideal point method. A genetic algorithm was used to optimize the single‐objective function globally to obtain the optimal freshwater pump speed map for a diesel engine under all conditions. On average, the optimized cooling system decreased the fuel consumption by 1.901%. Six typical propulsive conditions were selected to confirm the validity of the optimization results. The experimental results indicate that the fuel consumption decreased by 2.35%, the effective power increased by 2.26%, and the power consumption of the water pump decreased by 17.83%. The combination of experiment design and D‐optimal designs offers the advantages of low cost, high efficiency, and high precision in solving multiobjective optimization problems involving strong coupling and nonlinear systems. The results of this research provide data support and a theoretical basis for intelligent cooling control strategies.
关键词：design of experiments;diesel engine cooling system;D-optimal designs;fuel consumption rate;multiobjective optimization
