Reverse analysis of a spiral injector to find geometrical parameters and flow conditions us-ing a GA-based program

TL;DR

This study uses a genetic algorithm-based reverse analysis to optimize the geometrical parameters and flow conditions of a spiral injector, enhancing spray quality through combined experimental and computational methods.

Contribution

Introduces a GA-based reverse analysis method for optimizing spiral injector design parameters considering flow and geometric effects.

Findings

Orifice diameter, spiral cone angle, and Reynolds number significantly influence spray quality.

The GA-based approach effectively identifies optimal design configurations.

Human inspection further refines the best design solutions.

Abstract

In this article, the effect of geometrical parameters and flow conditions on the performance of a swirl atomizer is studied. Dimensional analysis and experimental investigations are utilized to define significant terms. The PDA system used for the measurements was able to supply information about the size, concentration, and particle velocity at each measurement location. The orifice diameter, the spiral cone angle, and also the flow Reynolds number, which is defined based on the injector orifice diameter, play an important role in spray quality, and their significance is summarized in a correlation. In order to achieve the appropriate combination of design variables that satisfy the design constraints, a GA-based program was used in a reverse analysis process. Finally, the advantages of human inspection were employed to provide true best performers from a small group of final answers.