Numerical simulation of thermal sensitivity of combustible cartridge based on two-step thermochemical decomposition kinetic model

Zhengjie Wang; Meng Wang; Chao Wang

doi:10.1117/12.2675187

25 May 2023 Numerical simulation of thermal sensitivity of combustible cartridge based on two-step thermochemical decomposition kinetic model

Zhengjie Wang, Meng Wang, Chao Wang

Author Affiliations +

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360T (2023) https://doi.org/10.1117/12.2675187
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In order to analyze the thermal safety of combustible cartridge case under direct thermal stimulation, a simplified three-dimensional finite element model of contact heat transfer of combustible cartridge case was established. It is assumed that the thermochemical decomposition process of combustible cartridges follows a two-step reaction kinetic model. The reliability of the thermochemical decomposition kinetic model is verified by cigarette sensitivity test. At the same time, the combustible cartridges under different thermal contact temperatures and coating thermal conductivity are numerically simulated. The results show that the simulation results of cigarette sensitivity are in good agreement with the test results, which proves the reliability of the thermochemical decomposition kinetic model of combustible cartridge. In the contact temperature range of 524 ~ 774 K, with the increase of temperature, the temperature resistance time of the cartridge decreases exponentially. In the range of 0.01 ~ 0.1W / m · K, with the decrease of the thermal conductivity of the coating, the temperature resistance time of the cartridge increases exponentially.

Citation Download Citation

Zhengjie Wang, Meng Wang, and Chao Wang "Numerical simulation of thermal sensitivity of combustible cartridge based on two-step thermochemical decomposition kinetic model", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360T (25 May 2023); https://doi.org/10.1117/12.2675187

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available