This work presents a novel approach for calibrating the surface and energy properties of powder through rotating drum simulations using Discrete Element Method (DEM). In this study, we focus on determining rolling (μr) and sliding (μs) friction coefficients and cohesive energy density (CED) of wood powder. In our previous research, we identified relevant flow descriptors to compare unconfined powder flowability using the rotating drum setup. The drum cylinder, partially filled with powder, rotated at 1 rpm while images were captured to determine flow descriptors, including the powder bed area fluctuations and surface linearity. Two types of wood, spruce and poplar, were used due to their different flow behaviors. The physical properties of the wood powder samples, such as size, shape, and density, were measured experimentally, and it's important to note their irregular morphology and cohesive behavior. A digital twin of the drum was created using DEM, precisely replicating its physical dimensions. The wood powders were represented by multi-sphere particles that are scaled by 2 with respect to their volume, and the simulation of particle-particle interactions was achieved by employing the Hertz-Mindlin contact model with Type C Rolling Friction and a Linear Cohesion V2 model. As a validation of the DEM simulations, the results were compared with experimental outcomes from rotating drum test. Using HyperStudy, we identified the optimal combination of (μr, μs, CED) parameters for both samples. The study has also emphasized how variations in these input parameters have significant impact on the powder bed behavior, transforming it from a cohesive, poorly flowing state into one characterized by free-flowing dynamics [1]. Our work highlights the significance of DEM as a useful framework for the calibration of mechanical properties of granular material, opening doors to optimizing wood powder handling as well as their processing in actual applications.