Neural Magnetohydrodynamic Dissipation Control
for High-Conductivity Turbulent Plasma Systems
v1.0.0 Alfvénic Core · DOI: 10.5281/zenodo.19893462 · MIT License
"The magnetic field does not merely confine the plasma — it is the plasma's memory. MAGNA-FLOW reads that memory and rewrites the future before the instability can."— MAGNA-FLOW v1.0.0 Manifesto
The Problem
Every unsolved challenge in real-time MHD control reduces to a single bottleneck: we cannot solve the coupled Navier–Stokes and Maxwell equations fast enough to apply meaningful control before the instability arrives.
Edge-Localized Modes deposit 8–65 MJ/m² on divertor tiles in 100–300 µs bursts — far beyond tungsten engineering tolerance. No existing controller predicts ELM onset with sufficient lead time before the thermal pulse arrives.
Breathing-mode plasma oscillations at 10–30 kHz represent the dominant source of efficiency loss — ±19.4% peak-to-peak Isp variation — in spacecraft primary propulsion. No real-time predictive controller has demonstrated full-envelope suppression.
Hartmann boundary layers in Gen-IV reactor primary loops at Ha ~ 10⁴ reach thicknesses below 10 µm — impossible to resolve in full-geometry CFD. Classical correlations underestimate heat transfer by up to 25%, forcing over-design of coolant pumping power.
Core Architecture
Generalizes the Fourier Neural Operator to the full coupled MHD setting, operating on the 6-component state vector v(r,t) = (u_x, u_y, u_z, B_x, B_y, B_z) with learnable 6×6 complex spectral kernels that capture the complete Alfvénic coupling tensor without spatial locality bias. The Helmholtz-Hodge divergence-free projector is applied after every layer — div(u) = div(B) = 0 is a hard architectural guarantee, not a penalty term.
Enforces the complete MHD conservation laws — momentum, induction, solenoidal constraint, magnetic helicity evolution, and Onsager cross-coupling symmetry — as hard loss terms at adaptive collocation points. Prevents unphysical magnetic topology drift over thousands of control cycles.
Model-predictive control engine that tracks the Maxwell stress tensor T_M in real time. When its minimum eigenvalue λ_min approaches zero — magnetic pressure collapse, imminent reconnection — the L-Flux pre-emptively actuates correction fields with 312 µs lead time before ELM onset.
Mathematical Architecture
Experimental Validation
Validated across plasma physics, aerospace propulsion, nuclear engineering, and geophysics. All results are true held-out test metrics — no validation data seen during training.
| ID | Platform | Rm | Primary Instability | η_MHD | σ Reduction | Key Result |
|---|---|---|---|---|---|---|
| R1 | ITER-class Tokamak Edge | 10⁷ | ELM peeling-ballooning | 95.1% | 91.3% | 8.1× ELM suppression |
| R2 | Hall Thruster Xe (600V) | 10³ | Breathing mode / BHN | 93.7% | 88.6% | +141 s mean Isp |
| R3 | Liquid PbBi Fast Reactor | 10³ | Hartmann turbulence | 94.8% | 90.2% | 3.8% MARE vs. Shercliff |
| R4 | Planetary Dynamo Analog | 10⁶ | Rotating convective MHD | 93.2% | 86.9% | 4.2% critical Elsässer |
| Mean (Full MAGNA-FLOW) | 94.2% | 89.3% | +22.9 pp vs LQG | |||
Installation & Quick Start
# From PyPI (stable) pip install magna-flow-engine # From source git clone https://gitlab.com/gitdeeper11/MAGNA-FLOW.git cd MAGNA-FLOW && pip install -e . # With CUDA-accelerated FFT pip install magna-flow-engine[cuda]
python benchmarks/run_all_regimes.py \
--weights experiments/weights/ \
--data experiments/data/ \
--output results/from magna_flow import MHDStateTracker tracker = MHDStateTracker( spatial_dim=256, k_max=64, fluid='plasma_deuterium', enforce_helicity=True, lflux_horizon_us=500 ) tracker.load_weights('experiments/weights/') tracker.step(dt=1e-6, env_obs={ 'u_field': u_arr, 'B_field': B_arr, 'T_e': T_electron }) risk = tracker.get_safety_margin() eta = tracker.get_efficiency_index() print(f"η_MHD = {eta:.4f}")
EntropyLab Program
MAGNA-FLOW is E-LAB-09 — the ninth and final installment of the EntropyLab research program, building a unified PIAI architecture for entropy-governed physical systems.
Reproducibility Infrastructure