KinetiForge uses physics-informed neural networks to generatively design stackable, 3D-printable kinetic energy converters. Each design is validated against real physics before it ever touches a printer.
Select the governing equations for your converter type: aerodynamics, electromagnetics, structural mechanics, thermodynamics.
Multi-modal PINNs explore the design space, generating converter geometries that satisfy all physics constraints simultaneously.
A self-correcting loop validates each design against simulation truth. Failed designs feedback into the network for iterative refinement.
Output STL/STEP files with embedded printability guarantees: no unsupported overhangs, optimized wall thickness, stackable interfaces.
Blade profiles, airfoil optimization, and flow field prediction using Navier-Stokes informed networks. Integrated with QBlade simulation data.
Generator topology, magnet placement, and flux density optimization. Built on NREL MADE3D-AML methodologies for rare-earth-free designs.
Load-bearing analysis, fatigue life prediction, and material stress distribution. Ensures printed parts withstand operational forces.
Heat dissipation, thermal expansion modeling, and operating temperature bounds. Critical for generator coil and bearing assemblies.
The future of energy hardware is generative, physics-validated, and manufactured anywhere there's a 3D printer.
KinetiForge is building the bridge between simulation and fabrication for kinetic energy systems.