数字化赋能自燃推进剂点火延迟时间的实验研究OA

Hypergolic propellants represent a predominant development direction in propulsion systems due to their ignition device-free characteristic,with ignition delay time serving as their primary performance metric.This study employed a custom-designed drop-test apparatus to examine the ignition characteristics between propellant fuel and high-concentration hydrogen peroxide oxidizer,while optimizing the experimental procedure through digital design.To overcome the challenge of processing extensive image data in ignition delay time measurement,we developed Python-based software incorporating the YOLOv8 deep learning algorithm.This implementation of image recognition technology enables precise analysis of propellant self-ignition delay time,facilitating efficient and accurate experimental data processing.Furthermore,integrating this innovative experimental approach into the"University Chemistry"curriculum has demonstrated significant enhancement in student engagement and learning motivation.