Series: energetic-materials-research
Starting from quantum mechanics calculations of individual bond-breaking events and ending with a model that correctly predicts burn rates across pressures — this is the story of assembling a first-principles combustion model for HMX, and what it took to make the pieces fit together.
Series: energetic-materials-research
Before you can model or predict the combustion of an energetic material, you have to know what actually happens at the molecular level when it starts to break down. This is the story of using quantum mechanics calculations to answer that question for RDX and HMX — two of the most important explosives and propellant ingredients in use today.
Detailed reaction mechanisms are physically accurate but computationally intractable in engineering simulation. This is the story of two methods — tabulated chemistry and machine learning tabulation — that recover that accuracy at engineering speeds, and what they enable for engine design and emissions prediction.
Series: combustion-foundations
A fully self-contained first-principles walkthrough of stiffness, implicit integration, Newton solves, GMRES, and structure-aware preconditioning for chemistry ODE systems — with an interactive lab embedded to explore the solver data directly.
An interactive visual lab embedded in the Newton–GMRES article — explore solver traces from a 60-species stiff chemistry system and see why preconditioning collapses GMRES iteration counts from 172 to 4.
Problem
Solver internals are opaque from text alone. The interaction between nonlinear residual reduction and inner linear convergence — and the dramatic impact of preconditioning — is best understood by seeing real solver traces directly.
Outcome
An interactive lab embedded in the full article, giving readers a hands-on way to explore Newton outer iterations, GMRES convergence curves, and the sparsity structure that explains why banded preconditioning wins.
From the desk of research engineer
Field notes on why combustion ODE systems are stiff, what stiffness actually means numerically, and how implicit integrators handle it in practice.