Predicting random packing and thermophysical properties of powders using Monte Carlo simulation
Document Type
Article
Date of Original Version
7-1-2025
Abstract
Reaction-Bonded Silicon Nitride (RBSN) is an important technical ceramic due to its unique high temperature resistance, wear resistance, mechanical strength, and electrical properties. RBSN fabrication is a three-step process: (1) compaction of Si powder, (2) pre-sintering up to ∼ 1100 °C, and (3) reaction with nitrogen at 1100 to 1300 °C. While pre-sintering is used to increase compact bulk density, it must also preserve porosity for gas diffusion and reaction from Si to Si3N4. Virtually all studies of RBSN do not consider the effect of pre-sintering on void volume (or packing fraction). Monte Carlo simulations are used to quantify changes in packing and thermophysical properties of mono-dispersed, spherical, Si powder particles from 25 to 1100 °C. The novel simulation results included (1) an initial swelling region to 100 °C, (2) quantified average changes in packing fraction and mean coordination number of 2.46 % and 1.51 %, respectively (3) the identification of a transition region [600,900°C] containing a transition temperature of 680 °C, and a softening temperature of 800 °C from a plot of heat capacity (Cp) vs temperature, and (4) a prediction of Cp at high temperature that was consistent with the Petit-Dulong law. Simulations showed that powder bulk density increased while volumetric coefficient of thermal expansion decreased with increasing temperature. All modeling predictions agreed well with data available in the open literature and show that Monte Carlo simulation is a robust tool for quantifying properties of powders and a competitive alternative to Discrete Element Methods (DEMs).
Publication Title, e.g., Journal
Computational Materials Science
Volume
257
Citation/Publisher Attribution
Lucia, Angelo, Christopher Miller, and Otto J. Gregory. "Predicting random packing and thermophysical properties of powders using Monte Carlo simulation." Computational Materials Science 257, (2025). doi: 10.1016/j.commatsci.2025.113984.