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Related Experiment Video

Updated: Sep 11, 2025

Conducting Elevated Temperature Normal and Combined Pressure-Shear Plate Impact Experiments Via a Breech-end Sabot Heater System

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Published on: August 7, 2018

Mesoscale Damage Evolution, Localization, and Failure in Solid Propellants Under Strain Rate and Temperature Effects.

Bo Gao^1,2, Youcai Xiao¹, Wanqian Yu³

¹College of Mechatronic Engineering, North University of China, Taiyuan 030051, China.

|August 14, 2025

Summary

This summary is machine-generated.

This study quantifies the thermomechanical response of high-energy solid propellants using a cohesive finite element method (CFEM). The findings reveal a strong dependence of mechanical behavior on strain rate and temperature, crucial for predicting propellant performance.

Keywords:

cohesive finite element model crack extension mode high-energy solid propellants neural network temperature effect

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Published on: February 6, 2021

Area of Science:

Materials Science
Mechanical Engineering
Computational Mechanics

Background:

High-energy solid propellants are complex multiphase materials.
Mechanical behavior is primarily dictated by embedded crystalline particles.
Quantitative links between microstructure and macroscopic properties are underexplored.

Purpose of the Study:

Develop a cohesive finite element method (CFEM) framework.
Quantify the thermomechanical response at the microstructural scale.
Analyze impact loading at high strain rates (10³–10⁴ s⁻¹).

Main Methods:

Implemented a 3D cohesive finite element model.
Incorporated large deformation and thermomechanical coupling.
Utilized a neural network-based inverse method for damage evaluation and parameter identification.

Main Results:

Demonstrated strong dependence of mechanical behavior on strain rate and temperature.
Identified temperature-dependent cohesive parameters.
Accurately predicted damage progression and macroscopic responses.

Conclusions:

The temperature-sensitive CFEM framework accurately models propellant behavior.
Understanding microstructural influences is key for predicting performance.
Validated model against experimental data for reliability.