MAGNEETO: Democratizing Deep-Tech Engineering for Underrepresented Builders

⚡ TL;DR

• The Barrier: High-end physical engineering is traditionally gatekept by expensive industrial labs, excluding independent creators and underrepresented builders.
• The Solution: MAGNEETO is an open-source hardware-in-the-loop physics sandbox powered by an affordable 600MHz ARM Cortex-M7 microcontroller.
• The Proof: By utilizing the Homotopy Perturbation Method (HPM) in C++ firmware, it manages 500V surge states with 72x greater grounding efficiency than baseline models.

💡 Inspiration

MAGNEETO was created to remove financial and institutional barriers to advanced hardware validation. By proving that hyper-optimized code can run on accessible hardware, it gives independent researchers and students the tools to build cutting-edge tech from home.

🔬 What It Does

The system mitigates high-voltage surges in under 1 millisecond using solid-state magnetic force fields instead of slow mechanical relays. It runs fluid and electrodynamic equations in real time on low-power hardware by applying the Homotopy Perturbation Method (HPM):

[H(p, v) = (1 - p)L(v) + p[N(v) - f(r)] = 0]

This closed-form expansion eliminates runtime overhead and allows safe grounding of 500V transients with significantly higher efficiency.

🛠 How It Was Built

• The Bare-Metal Core: Programmed in ISO C++ and ARM assembly targeting the ARM Cortex-M7 microchip.
• Low-Level Precision: Uses hardware memory barriers and custom SPI drivers to capture transient data without instruction lag.
• The Digital Twin Sandbox: Includes an open-source Python simulation modeling atmospheric boundary physics and Joule heating ((I^2R)) as a free testing environment.

🛑 Challenges & Accomplishments

• Challenges: Running non-linear floating-point physics within a tight 1000Hz fixed-interval loop without a high-performance computing cluster risked loop overruns. This was solved by scaling the HPM series into an ultra-efficient quadratic form.
• Accomplishments: Demonstrated that complex computational physics can execute deterministically on affordable edge hardware, democratizing deep-tech development.

🧠 What We Learned & AI Disclosure

• What We Learned: Optimized, physics-informed code lowers hardware constraints, proving high-end research requires disciplined logic rather than multi-million dollar facilities.
• AI Use Disclosure: AI assisted with the GitHub Actions directory layout and Markdown/LaTeX formatting. Human oversight was responsible for the core physics models, HPM derivations, and bare-metal assembly.

Built With

• assembly
• c++
• dynamicsplasma
• embedded
• engineeringfluid
• latex
• modelingaerospace
• physicselectromagnetismsignal
• physicsmathematical
• plasma
• platformiogitgithub
• python
• systemsbare-metalfirmwarehardware-in-the-loopdigital
• teensy
• twincomputational