This dataset highlights a specialized application of automation and optimization of infill structures using the Synera platform. By developing a Synera code to automatically apply infill structures to any model, the design process becomes significantly more efficient, allowing engineers to expedite the transition from concept to production. The practical relevance of this technology is demonstrated in its application to injection molds for bipolar plates within the Metal Injection Molding (MIM) process. Bipolar plates are critical components in fuel cells that require precisely manufactured parts to ensure structural integrity and optimal material usage. Furthermore, the use of Material Extrusion of Metals (MEX/M) processes emphasizes the interdisciplinary integration of additive and traditional manufacturing technologies. Insights from automated infill generation can thus be directly applied to the physical production of prototypes or end-use parts, showcasing the potential of Synera in design automation and the synergy between digital design and conventional manufacturing methods.

In this upload, all required information for the theoretical and experimental analysis in the related paper (DOI: 10.1016/j.fluid.2024.114182) is included. The accompanying README file provides detailed descriptions of all included files and explains the research context, methodological information, and the measured variables.

In this upload, all required information for the experimental analysis in the related paper (DOI: 10.1016/j.heliyon.2024.e39138) is included.

This dataset contains experimental data of NMR relaxometry probes solvent-polarity-dependent molecular interactions in stimuli-responsive lyogels. The accompanying README file provides detailed descriptions of all included files and explains the research context, methodological information, and the measured variables.

This dataset contains experimental data on the polymeric structure and swelling kinetics related to solvent-induced swelling of pNIPAM lyogels. The accompanying README file provides detailed descriptions of all included files and explains the research context, methodological information, and the measured variables.

This dataset includes 3D tomographic reconstruction image files, volume mesh files for finite element simulations, and data on the structural and mechanical properties of nanoporous gold (NPG) structures. It serves as a supplement to a dataset paper, with the corresponding DOI provided in the “Related Identifiers” section. Detailed descriptions of the data, as well as the procedures for their preparation and curation, are presented in that paper. The base material, nanoporous gold, was fabricated via a dealloying process and has a solid fraction of approximately 0.30. NPG samples with ligament sizes ranging from 20 nm to 400 nm were prepared through dealloying and subsequent thermal annealing. Tomographic TIFF files were obtained via Focused Ion Beam/Scanning Electron Microscopy (FIB/SEM) 3D reconstruction, with the procedure detailed in Philosophical Magazine (2016, 96(32–34), 3322–3335). Based on the 3D image data, new simulations and analyses were performed. The resulting structural and mechanical property data of nanoporous gold are reported for the first time in the dataset paper and are archived here. This dataset provides a valuable database for the study of nanoporous network materials and can be reused for numerical simulations, additive manufacturing, and machine learning applications within the materials science community.

The supplementary data includes detailed information on machine learning (ML) models, specifically MLP and Bagging, and the datasets used to predict surface roughness and density in metal extrusion additive manufacturing (MEX/M) components. Leveraging experimental data, these models incorporate input parameters like layer thickness, print velocity, infill, overhang angle, and sinter profile. Demonstrating a prediction accuracy ranging from 39% to 97%, the data underscores the models' effectiveness in optimizing MEX/M processes, enhancing quality control, and improving design efficiency, particularly for complex geometrical structures.

MRI-Validated CFD-DEM and rCFD Dataset for Gas-Solid Fluidized Beds This comprehensive research dataset provides complete computational and experimental data for validating coupled CFD-DEM (Computational Fluid Dynamics - Discrete Element Method) simulations and recurrence-based CFD (rCFD) predictions of gas-solid fluidized beds. The repository contains CFD-DEM simulation cases for four drag models (Beetstra, DiFelice, Gidaspow, Koch-Hill), rCFD time-extrapolation implementations achieving 100-1000× computational speedup, MRI experimental measurements of bubble dynamics in poppy seed fluidization, automated post-processing workflows, and quantitative validation results. This dataset enables researchers to reproduce published results, validate their own CFD-DEM models against MRI experimental data, develop and benchmark rCFD methodologies for real-time multiphase flow predictions, optimize drag model selection for fluidized bed applications, and apply advanced image processing and bubble detection algorithms. The data follows FAIR principles with open formats (CSV, OpenFOAM, Python scripts), comprehensive documentation in eight README files, complete provenance from CAD geometry to validation results, and example workflows for researchers, students, and engineers working with fluidized bed systems, particle technology, and multiphase flow simulations.

A) Three maps that were published in the welfare ticket atlas, as PNG (with legends in German) // B) Geometries for all welfare tickets in Germany at a district/municipality level, as GPKG and CSV. For details, see README.html // C) README.html.