Chemical laser modeling with genetic algorithms

Abstract

A genetic algorithm technique was implemented to determine a set of unknown parameters that best matched the Blaze II chemical laser model predictions with experimental data. This is the first known application of the genetic algorithm technique for modeling lasers, chemically reacting flows, and chemical lasers. Overall, the genetic algorithm technique worked exceptionally well for this chemical laser modeling problem in a cost effective and time efficient manner. Blaze II was baselined to existing chemical oxygen-iodine laser data taken with the research assessment and device improvement chemical laser device with very good agreement. Mixing calculations for the research assessment and device improvement chemical laser nozzle indicate that higher iodine flow rates are necessary to maintain a significant fraction of the nominal performance as the total pressure is increased by the addition of helium ; this agrees with research assessment and device improvement chemical laser experimental data. It may be possible to implement this genetic algorithm technique to optimize the performance of any chemical laser as a function of any of the flow rates, mirror location, mirror size, nozzle configuration, injector sizes, and other factors. This modeling procedure can be used as a method to guide experiments to improve chemical laser performance.