Speaker
Description
Particle transport and radiation physics simulations rely on accurate representations of detector systems, experimental environments and material compositions. In many established Monte Carlo frameworks, the creation of detailed geometries remains a labor-intensive process that requires manual coding of hierarchical volumes, surfaces, and materials. As simulation studies increasingly involve complex experimental configurations, this geometry definition process becomes a significant bottleneck. Moreover, lacking interoperability of geometry workflows often requires the same scene to be recreated independently for multiple simulation packages, introducing duplication of effort and potential inconsistencies.
To address some of these challenges, Blender-to-Geant4 (B2G4) was developed and published. It defined a modular workflow that uses the open-source 3D modeling software Blender to design simulation geometries. While B2G4 proved valuable for large-scale 3D datasets creation in the context of muon tomography, it is only available for Geant4. This work introduces Blender-to-X (B2X), generalizing B2G4 across multiple physics simulation frameworks. This is done through a custom Blender addon that allows users to easily parameterize and configure 3D scenes and models for export. These scenes are then automatically parsed into input configurations for several particle transport codes, including Geant4, MCNP, and CORSIKA 8. To support potential extensions to other codes, an open-source interface is defined.
B2X focuses on simplifying simulation workflows by enabling researchers to design, modify, and reuse complex geometries within a unified visual environment. In contrast to structured geometry formats such as Geometry Description Markup Language or mesh-based CAD import approaches such as DAGMC, B2X emphasizes interactive scene construction, support for randomization and large-scale datasets. Example workflows are demonstrated using different test setups in Geant4, MCNP and CORSIKA 8 with a focus on muon-based imaging.