Simulation Dataset for Monomer Infiltration Saturation Time in Transparent Wood Fabrication

This dataset contains simulation results for monomer infiltration into delignified wood templates, relevant to the fabrication of transparent wood composites. Full saturation of the porous wood by the monomer precursor is a critical step that governs both the optical transparency and mechanical integrity of the final material. Simulations were carried out using a Darcy-flow-based infiltration model implemented in FEniCSx 0.9. The simulation code is openly available at: https://github.com/AI-TranspWood/AITW-Darcy-Infiltration-Model Experimental designThe dataset follows a full factorial design across six physical parameters, resulting in 31,104 simulation cases (4 × 6⁵): Parameter Symbol Levels Values Sample length (m) L 4 0.01, 0.04, 0.07, 0.10 Fluid dynamic viscosity (Pa·s) μ 6 2.00×10⁻⁴, 6.93×10⁻⁴, 2.40×10⁻³, 8.32×10⁻³, 2.88×10⁻², 1.00×10⁻¹ Longitudinal permeability (m²) k_long 6 10⁻¹³, 10⁻¹², 10⁻¹¹, 10⁻¹⁰, 10⁻⁹, 10⁻⁸ Mean pore radius (m) r_μ 6 1.0×10⁻⁵, 1.5×10⁻⁵, 2.0×10⁻⁵, 2.5×10⁻⁵, 3.0×10⁻⁵, 3.5×10⁻⁵ Surface tension (N/m) γ 6 0.020, 0.025, 0.030, 0.035, 0.040, 0.045 Porosity φ 6 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 File: surrogate_data.csv Each row is one simulation case. Columns: L_value — sample length (m) mu_value — fluid dynamic viscosity (Pa·s) k_long — longitudinal permeability of the wood (m²) r_mu_value — mean pore radius (m) gamma_value — surface tension (N/m) phi_value — porosity (–) sat_time — time to full saturation of the wood sample (s) This dataset is intended for training surrogate and machine learning models to rapidly predict infiltration saturation time across the parametric space.