A Mathematical Model for Predicting the Sauter Mean Diameter of Liquid-Medium Ultrasonic Atomizing Nozzle Based on Orthogonal Design

As a new type of atomizing nozzle with superior atomizing performance, the liquid-medium ultrasonic atomization nozzle has been widely applied in the field of spray dust reduction. In this study, in order to establish a mathematical model for predicting the Sauter mean diameter (SMD) of such nozzles, the interaction between the SMD of the nozzle and the three influencing factors, i.e., air pressure, water pressure, and outlet diameter were investigated based on the custom-designed spraying experiment platform and orthogonal design methods. Through range analysis, it was obtained that the three parameters affecting the SMD of the nozzle are in the order of air pressure > water pressure > outlet diameter. On this basis, using the multivariate nonlinear regression method, the mathematical model for predicting the SMD of the nozzle was constructed. Comparison of the experimental results with the predicted values of the SMD of the nozzle by the multivariate nonlinear regression mathematical model, showed strong similarity with an average relative error of only about 5%. Therefore, the established mathematical model in this paper can be used to predict and calculate the droplet size for liquid-medium ultrasonic atomizing nozzles.

Multiobjective Optimization of the Performance and Emissions of a Large Low-Speed Dual-Fuel Marine Engine Based on MNLR-MOPSO

With increasingly strict emission regulations and growing environmental concerns, it is urgent to improve engine performance and reduce emissions. In this paper, multivariate nonlinear regression (MNLR) combined with multiobjective particle swarm optimization (MOPSO) was implemented to optimize the performance and emissions of a large low-speed two-stroke dual-fuel marine engine. First, a simulation model of a dual-fuel engine was established using AVL-BOOST software. Next, a single-factor scanning value method was applied to control a range of variables, including intake pressure, intake temperature, and natural gas mass fraction. Then, a nonlinear regression model was established using the statistical multivariate nonlinear regression equation. Finally, the multiobjective optimization algorithm implementing MOPSO was used to solve the trade-off between performance and emissions. It was found that when the intake pressure was 3.607 bar, the intake temperature was 297.15 K and the natural gas mass fraction was 0.962. The engine power increased by 0.34%, the brake specific fuel consumption (BSFC) reduced by 0.21%, and the NOx emissions reduced by 39.56%. The results show that the combination of multiple nonlinear regression and intelligent optimization algorithm is an effective method to optimize engine parameter settings.

Mathematical Model of SMD of Hydrodynamic Ultrasonic Atomizing Nozzle Based on Orthogonal Design

The hydrodynamic ultrasonic atomization nozzle has excellent atomization performance and has a wide range of applications in the field of spray dust reduction. A mathematical model the SMD of the nozzle was established to evaluate the SMD of such nozzles using the custom-designed spraying experiment platform and orthogonal design methods. The interaction between the SMD of the nozzle and the three influencing factors, i.e., air pressure, water pressure and outlet diameter were obtained. Through range analysis, the primary and secondary order of the three parameters affecting SMD of the nozzle is: air pressure > water pressure > outlet diameter. On this basis, a mathematical model was constructed using a multivariate nonlinear regression method to estimate the SMD of the nozzle. The predicted values of the SMD of the nozzle by the multivariate nonlinear regression mathematical model were basically consistent with the experimental results, with an average relative error of only about 5%. Thus the established mathematical model in this paper can be used to predict and calculate the droplet size for hydrodynamic ultrasonic atomizing nozzles.