A Study of a Genetic Algorithm for Polydisperse Spray Flames

TL;DR

This paper explores using a genetic algorithm to optimize initial droplet size distribution for polydisperse spray flames, aiming to improve combustion efficiency through a novel application of evolutionary algorithms.

Contribution

It introduces a new approach applying genetic algorithms to optimize droplet size distribution in combustion problems, which was not previously done.

Findings

GA effectively identifies optimal droplet distributions

Improved understanding of combustion dynamics

Potential for enhanced flame stability

Abstract

Modern technological advancements constantly push forward the human-machine interaction. Evolutionary Algorithms (EA) are an machine learning (ML) subclass inspired by the process of natural selection - Survival of the Fittest, as stated by the Darwinian Theory of Evolution. The most notable algorithm in that class is the Genetic Algorithm (GA) - a powerful heuristic tool which enables the generation of a high-quality solutions to optimization problems. In recent decades the algorithm underwent remarkable improvement, which adapted it into a wide range of engineering problems, by heuristically searching for the optimal solution. Despite being well-defined, many engineering problems may suffer from heavy analytical entanglement when approaching the derivation process, as required in classic optimization methods. Therefore, the main motivation here, is to work around that obstacle. In this piece of work, I would like to harness the GA capabilities to examine optimality with respect to a unique combustion problem, in a way that was never performed before. To be more precise, I would like to utilize it to answer the question : What form of an initial droplet size distribution (iDSD) will guarantee an optimal flame ? To answer this question, I will first provide a general introduction to the GA method, then develop the combustion model, and eventually merge both into an optimization problem.