Overview
What this module delivers
CoreMagna AI Design helps teams move from electrical targets to manufacturable core candidates by evaluating topology, geometry, winding window utilization, and production constraints early in the design path.
Capability focus
Inputs
Target inductance, current envelope, winding window goals, dimensional constraints, and preferred topology assumptions.
Outputs
Ranked geometry candidates, design parameter bundles, and manufacturability-aware handoff data for quotation, simulation, and validation.
Guardrails
Strip-width limits, annealing-sensitive dimensions, winding feasibility, and mechanical stress boundaries.
Deep dive
Functionality
The module generates geometry candidates from electrical targets, then ranks toroidal, EQ, and custom forms against inductance fit, current handling, and usable winding window.
Manufacturing-aware constraints are applied early, including strip-width limits, annealing-sensitive dimensions, winding feasibility, and stress-prone form factors that are difficult to build consistently.
A constraint-aware ranking score balances magnetic performance, thermal behavior, and mechanical robustness so teams can shortlist candidates with stronger fit and buildability before detailed simulation and prototype release.
Evaluation dimensions
- Inductance fit
- Usable winding window
- Strip-width feasibility
- Mechanical robustness
- Simulation readiness
Example workflow
An EV onboard charger team starts with inductance, current, footprint, and winding constraints. The Design module evaluates toroidal, EQ, and custom forms, filters out options that violate strip-width or stress-related limits, and ranks feasible geometries for quotation, simulation, and prototype review.
Use the links below for the CoreMagna AI hub, adjacent modules, and CenturaCores tools and product context that fit your program.
Advantages
Faster screening of feasible core geometries before detailed CAD work
Better alignment between electrical targets and winding feasibility
Reduced iteration caused by non-manufacturable shapes
Cleaner handoff from concept design to quotation, simulation, and validation
Target users
- Magnetics design engineers
- Power electronics architects in EV, solar, and industrial programs
- R&D teams developing nanocrystalline prototypes for scale-up
Industry use cases
- EV onboard chargers and high-frequency charging infrastructure
- Solar inverter and energy-storage magnetics
- Medical and industrial power programs requiring traceable design rationale
Ready to explore CoreMagna AI?
Join the beta or speak with a nanocrystalline specialist about your program requirements and validation path.
Frequently asked questions
How does CoreMagna AI Design differ from classical core sizing spreadsheets?
It generates and ranks geometry candidates under manufacturing-aware constraints instead of relying only on static factors and manual spreadsheet iteration.
What inputs are required to use the Design module?
Teams provide target inductance, current envelope, winding window goals, dimensional constraints, and preferred topology assumptions.
Can the Design module support custom core shapes beyond standard toroids?
Yes. It can evaluate custom forms alongside standard families when they satisfy strip-width, stress, and winding-feasibility guardrails.
Can outputs from the Design module support CenturaCores quotation and validation workflows?
Yes. Outputs include ranked geometry candidates and handoff parameters aligned with quotation review, simulation, and validation workflows.
How does the Design module relate to Simulation and Loss workflows?
Shortlisted geometries are intended for follow-on simulation, loss estimation, and validation. The Design module focuses on feasible geometry ranking rather than field solves or detailed loss prediction.