Numerically Predicting High Explosive Violent Response (HEVR) in Air Force Explosives

Abstract

The extreme conditions experienced by Air Force energetics in operational settings can lead to failed initiation, sub-detonative reactions, and unintended violent response. This effort investigated methods for simulating and predicting this behavior using continuum and mesoscale models and methods for calibrating and validating these models. Artificial intelligence/machine learning methods were leveraged for calibrating highly non-linear models for various aspects of the problem and these were, in turn, generalized into frameworks to be used with future energetic materials. The main result of this research is an understanding of the data and models needed to simulate mechanical initiation in high explosives and a process, or workflow, for calibrating these models.

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Document Details

Document Type
Technical Report
Publication Date
Apr 04, 2023
Accession Number
AD1230630

Entities

People

  • Joseph Maestas

Organizations

  • Applied Research Associates (United States)

Tags

Readers

  • Computational Fluid Dynamics (CFD)
  • Rocket Propulsion.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks