Predictive Models for Single-Droplet Ignition in Static High-Temperature Air in Different Gravity Environments

To address the design and optimization of the ignition system for the microgravity single-droplet combustion experiment module within the Combustion Science Experimental System (CSES) aboard the Chinese Space Station (CSS), it is essential to first determine the ignition temperatures required for typical liquid fuel droplets. In this study, ignition experiments were conducted on droplets of three representative hydrocarbon fuels—ethanol, n-heptane, and n-dodecane—in static air at high temperatures ranging from 760 K to 1100 K. The experimental results show that the initial droplet diameter is inversely correlated with the ambient temperature at which ignition occurs. Subsequently, based on Frank-Kamenetskii’s analytical method and combined with experimental data, a semi-empirical predictive model for droplet ignition temperatures in a normal-gravity environment was derived. Building upon this, and considering the characteristics of the microgravity environment, an appropriate empirical formula was applied to refine the model, resulting in a predictive model for droplet ignition temperatures in the microgravity environment. Furthermore, by comparing the experimental data and the observed phenomena from existing microgravity experiments, this semi-empirical predictive model used in the microgravity environment effectively reflects the trend of droplet ignition temperature variations.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-06-11
DOI
https://doi.org/10.3390/app15126558
Primary Topic
Advanced Combustion Engine Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictive Models for Single-Droplet Ignition in Static High-Temperature Air in Different Gravity Environments

Xiaowu Zhang, Xiaoyang Lan, Xianzhang Peng, Yu Fang et al.
4 links
Applied Sciences
Advanced Combustion Engine Technologies
article

Predictive Models for Single-Droplet Ignition in Static High-Temperature Air in Different Gravity Environments

Xiaowu Zhang, Xiaoyang Lan, Xianzhang Peng, Yu Fang, Huilong Zheng, Xiaofang Yang
article en

Abstract

To address the design and optimization of the ignition system for the microgravity single-droplet combustion experiment module within the Combustion Science Experimental System (CSES) aboard the Chinese Space Station (CSS), it is essential to first determine the ignition temperatures required for typical liquid fuel droplets. In this study, ignition experiments were conducted on droplets of three representative hydrocarbon fuels—ethanol, n-heptane, and n-dodecane—in static air at high temperatures ranging from 760 K to 1100 K. The experimental results show that the initial droplet diameter is inversely correlated with the ambient temperature at which ignition occurs. Subsequently, based on Frank-Kamenetskii’s analytical method and combined with experimental data, a semi-empirical predictive model for droplet ignition temperatures in a normal-gravity environment was derived. Building upon this, and considering the characteristics of the microgravity environment, an appropriate empirical formula was applied to refine the model, resulting in a predictive model for droplet ignition temperatures in the microgravity environment. Furthermore, by comparing the experimental data and the observed phenomena from existing microgravity experiments, this semi-empirical predictive model used in the microgravity environment effectively reflects the trend of droplet ignition temperature variations.

Applied SciencesVol. 15(12)
Jiangsu University (CN), Chinese Academy of Sciences (CN), Institute of Engineering Thermophysics (CN)
Openalex Percentile: Top 89%
Advanced Combustion Engine Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.