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 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 (R² = 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.