A Zero-Tuning DEM-to-Continuum Framework for Thermite Front Propagation: Contact-Resistance Conductivity and Diffusion-Limited Kinetics, Implemented Through an AI-Agent Workflow

Thermite mixtures are attractive for downhole well plug-and-abandonment (P&A) sealing, where a consistent, controllable burn matters more than peak energy. Because propagation is governed by particle size and packing, predictive blend design needs a model that resolves microstructure. I present a zero-tuning multiscale framework that links a discrete element method (DEM) packing directly to combustion-front behaviour. The DEM contact graph sets a contact-resistance effective conductivity (a thermal-network solve on the contacts, cross-checked against a packed-bed model); the particle sizes set a diffusion-limited, product-layer (shrinking-core) reaction rate with the measured activation energy of the Al-Fe2 O3 reaction (Eₐ = 145 kJ mol−1); and these feed an analytical condensed-phase travelling-wave speed that is mesh-free by construction and confirmed against a converged numerical eigenvalue solve. Here “zero-tuning” means no coefficient is fitted to the blend dataset: every transport and kinetic parameter is DEM-derived or taken from the literature, and a single diffusion pre-factor is anchored to an independent fine-powder benchmark. Applied to a generic Fe2 O3/Al+ sand system across eight coarse (∼256–462 µm) +40/+70 blends, the framework predicts front speeds of ∼2–4 mm/s—about an order of magnitude below fine powders (27–47 mm/s)—i.e., finer-is-faster, as expected for diffusion-controlled aluminothermic reactions; the residual size dependence is the net of competing diffusion-kinetic and radiative effects rather than a clean monotonic lever. A DEM-derived Kozeny–Carman permeability shows gas convection contributes ≲15% of the front enthalpy, justifying the conduction–radiation formulation. The DEM-microstructure-to-rate mapping is validated externally on Ni–Al self-propagating high-temperature synthesis (SHS), whose measured particle-size ordering it reproduces. The contribution is the framework itself—a predictive, microstructure-resolved route requiring no blend-specific fitting. The computational implementation used a supervised AI-agent workflow (implementation, execution, verification); all scientific content was conceived and verified by the author.