Estimating temperature and gas concentrations in propellant flames using metamodeling and global optimization
Abstract
We propose a methodology combining polynomial metamodeling and global optimization for estimating temperature and gas concentrations in propellant flames from moderate-resolution spectroradiometer measurements. We retrieve seven parameters: temperature, soot absorbance, concentrations of CO2 , H2O, CO, and HCl and the projected source area, and we estimate a Cramér-Rao Lower Bound, which provides a reliable estimate of the minimal achievable error. We propose two sampling-based methods for optimization that give parameter estimation close to this bound. Our methodology is validated on representative synthetic cases and one experimental measurement, showing promising results for most parameters. A more complex retrieval method is discussed in the outlook section.
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BibTeX
@article{lavigne2026estimating,
title = {{Estimating temperature and gas concentrations in propellant flames using metamodeling and global optimization}},
author = {Claire Lavigne and Sidonie Lefebvre and Christian Musso and Sylvain Rommeluère},
journal = {Applied Mathematics in Science and Engineering},
year = {2026},
month = jun,
doi = {10.1080/27690911.2026.2680753},
url = {https://hal.science/hal-05856743v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Estimating temperature and gas concentrations in propellant flames using metamodeling and global optimization
AU - Claire Lavigne
AU - Sidonie Lefebvre
AU - Christian Musso
AU - Sylvain Rommeluère
PY - 2026
DA - 2026/06/09
JO - Applied Mathematics in Science and Engineering
DO - 10.1080/27690911.2026.2680753
UR - https://hal.science/hal-05856743v1
AB - We propose a methodology combining polynomial metamodeling and global optimization for estimating temperature and gas concentrations in propellant flames from moderate-resolution spectroradiometer measurements. We retrieve seven parameters: temperature, soot absorbance, concentrations of CO2 , H2O, CO, and HCl and the projected source area, and we estimate a Cramér-Rao Lower Bound, which provides a reliable estimate of the minimal achievable error. We propose two sampling-based methods for optimization that give parameter estimation close to this bound. Our methodology is validated on representative synthetic cases and one experimental measurement, showing promising results for most parameters. A more complex retrieval method is discussed in the outlook section.
SN - 2769-0911
ER -