Artificial Intelligence in the Design of Solid Propellants for Aerospace Applications

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Astronautics & Space Science".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 499

Special Issue Editor


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Guest Editor
School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi’an 710129, China
Interests: advanced composite materials; structural integrity and safety; computational modeling; energetic materials; solid propellants; safety simulations; machine learning for solid propellants applications in aerospace

Special Issue Information

Dear Colleagues,

Solid propellants are critical power sources for solid rocket engines used in missiles and space vehicles, playing an essential role in aerospace propulsion. With increasing demands for high burning rates, enhanced safety, and cost-effective formulations, there is a pressing need for more efficient design and optimization strategies. Traditional trial-and-error methods are often costly, time-consuming, and potentially hazardous. In this context, artificial intelligence (AI) and data-driven approaches are rapidly emerging as transformative tools for predicting and optimizing the key performance metrics of solid propellants, such as safety, burning rate, mechanical performance, and specific impulse.

The aim of this Special Issue is to contribute to the development of solid propellant technologies by exploring the integration of artificial intelligence (AI) and data-driven approaches, with a particular emphasis on aerospace applications. This Special Issue invites original research articles, reviews, and communications focusing on AI-assisted formulation design, the predictive modeling of key performance indicators (such as burning rate, specific impulse, and sensitivity), and experimental validation guided by machine learning. Contributions addressing hybrid modeling strategies that combine physics-based simulations with data-driven techniques are particularly welcome. Additional areas of interest include the physical and chemical properties of solid propellant components, interpretable and trustworthy AI models for material design, and intelligent design frameworks tailored for aerospace propulsion systems.

All submitted papers will undergo a rigorous peer review process to ensure they make a novel and meaningful contribution. A high academic standard will be upheld throughout the editorial process. The Editors are committed to conducting an efficient review and publication process to ensure the timely dissemination of high-impact research in this rapidly evolving field.

Dr. Tianshuai Wang
Guest Editor

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Keywords

  • artificial intelligence
  • aerospace applications
  • solid propellant
  • modeling
  • energetic materials

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Published Papers (1 paper)

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Research

10 pages, 1102 KiB  
Article
Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models
by Fei Wang, Kai Cui, Jinxiang Liu, Wenhai He, Qiuyu Zhang, Weihai Zhang and Tianshuai Wang
Aerospace 2025, 12(7), 622; https://doi.org/10.3390/aerospace12070622 - 11 Jul 2025
Viewed by 328
Abstract
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 [...] Read more.
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. Full article
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