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This presentation summarizes an integrated workflow to quantify and model how real char particle morphology influences gas–solid multiphase transport, with a focus on improving the fidelity of reactor-scale simulations for biomass–coal conversion systems. Char particles produced from bituminous coal and biomass (pine sawdust) are imaged using high-resolution micro-CT, and their 3D geometries are reconstructed in ScanIP for particle-scale analysis. Using these realistic morphologies, particle-scale CFD simulations resolve coupled conservation equations (mass, momentum, species, and energy) under combustion-relevant boundary conditions, enabling direct evaluation of morphology-driven flow and thermal fields and their impact on aerodynamic drag. The study further assesses the accuracy of classical drag correlations developed for idealized shapes by comparing them against 3D simulation results across multiple particle orientations and Reynolds numbers (Re = 20–200), highlighting substantial model discrepancies when irregularity and orientation effects are present. Unlike the conventional models, to consider the full picture of the particle morphology and reduce the computational burden of high-fidelity CFD, a deep-learning algorithm has been developed in which a CNN ingests voxelized 3D particle images along with Reynolds number/orientation encoding to predict drag coefficients, achieving high accuracy and enabling rapid parameter sweeps that are impractical with CFD alone.