Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models
Hydroxyl-terminated-polybutadiene (HTPB)-based composite solid propellants are extensively used in aerospace and defense applications due to their high energy density, thermal stability, and processability. However, the presence of highly sensitive energetic components in their formulations leads to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/7/622 |
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| author | Fei Wang Kai Cui Jinxiang Liu Wenhai He Qiuyu Zhang Weihai Zhang Tianshuai Wang |
| author_facet | Fei Wang Kai Cui Jinxiang Liu Wenhai He Qiuyu Zhang Weihai Zhang Tianshuai Wang |
| author_sort | Fei Wang |
| collection | DOAJ |
| description | Hydroxyl-terminated-polybutadiene (HTPB)-based composite solid propellants are extensively used in aerospace and defense applications due to their high energy density, thermal stability, and processability. However, the presence of highly sensitive energetic components in their formulations leads to a significant risk of accidental ignition under electrostatic discharge, posing serious safety concerns during storage, transportation, and handling. To address this issue, this study explores the prediction of electrostatic sensitivity in HTPB propellants using machine learning techniques. A dataset comprising 18 experimental formulations was employed to train and evaluate six machine learning models. Among them, the Random Forest (RF) model achieved the highest predictive accuracy (R<sup>2</sup> = 0.9681), demonstrating a strong generalization capability through leave-one-out cross-validation. Feature importance analysis using SHAP and Gini index methods revealed that aluminum, catalyst, and ammonium perchlorate were the most influential factors. These findings provide a data-driven approach for accurately predicting electrostatic sensitivity and offer valuable guidance for the rational design and safety optimization of HTPB-based propellant formulations. |
| format | Article |
| id | doaj-art-3cacd7664669439d87e4e87cc08090dc |
| institution | DOAJ |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-3cacd7664669439d87e4e87cc08090dc2025-08-20T02:48:19ZengMDPI AGAerospace2226-43102025-07-0112762210.3390/aerospace12070622Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning ModelsFei Wang0Kai Cui1Jinxiang Liu2Wenhai He3Qiuyu Zhang4Weihai Zhang5Tianshuai Wang6Xi’an Key Laboratory of Functional Organic Porous Materials, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi’an 710000, ChinaXi’an Key Laboratory of Functional Organic Porous Materials, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi’an 710000, ChinaXi’an North Huian Chemical Industry Co., Ltd., Xi’an 710302, ChinaXi’an North Huian Chemical Industry Co., Ltd., Xi’an 710302, ChinaXi’an Key Laboratory of Functional Organic Porous Materials, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi’an 710000, ChinaXi’an North Huian Chemical Industry Co., Ltd., Xi’an 710302, ChinaXi’an Key Laboratory of Functional Organic Porous Materials, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi’an 710000, ChinaHydroxyl-terminated-polybutadiene (HTPB)-based composite solid propellants are extensively used in aerospace and defense applications due to their high energy density, thermal stability, and processability. However, the presence of highly sensitive energetic components in their formulations leads to a significant risk of accidental ignition under electrostatic discharge, posing serious safety concerns during storage, transportation, and handling. To address this issue, this study explores the prediction of electrostatic sensitivity in HTPB propellants using machine learning techniques. A dataset comprising 18 experimental formulations was employed to train and evaluate six machine learning models. Among them, the Random Forest (RF) model achieved the highest predictive accuracy (R<sup>2</sup> = 0.9681), demonstrating a strong generalization capability through leave-one-out cross-validation. Feature importance analysis using SHAP and Gini index methods revealed that aluminum, catalyst, and ammonium perchlorate were the most influential factors. These findings provide a data-driven approach for accurately predicting electrostatic sensitivity and offer valuable guidance for the rational design and safety optimization of HTPB-based propellant formulations.https://www.mdpi.com/2226-4310/12/7/622hydroxyl-terminated polybutadiene solid propellantsstatic sensitivitymachine learning |
| spellingShingle | Fei Wang Kai Cui Jinxiang Liu Wenhai He Qiuyu Zhang Weihai Zhang Tianshuai Wang Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models Aerospace hydroxyl-terminated polybutadiene solid propellants static sensitivity machine learning |
| title | Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models |
| title_full | Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models |
| title_fullStr | Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models |
| title_full_unstemmed | Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models |
| title_short | Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models |
| title_sort | prediction of propellant electrostatic sensitivity based on small sample machine learning models |
| topic | hydroxyl-terminated polybutadiene solid propellants static sensitivity machine learning |
| url | https://www.mdpi.com/2226-4310/12/7/622 |
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