MAGNA-FLOW: Neural Magnetohydrodynamic Dissipation Control for High-Conductivity Turbulent Plasma Systems

We introduce MAGNA-FLOW, a Physics-Informed Artificial Intelligence (PIAI) framework engineered to model, predict, and actively suppress magnetohydrodynamic (MHD) turbulence and irreversible dissipation in high-conductivity fluid media including plasma confinement systems, liquid-metal cooling channels, and astrophysical dynamo analogs. Classical MHD solvers become intractable at real-time control timescales when Reynolds numbers exceed 10⁶ and magnetic Prandtl numbers deviate far from unity — precisely the regime governing tokamak edge instabilities and ionic thruster plumes.MAGNA-FLOW addresses these limitations through three mathematically rigorous constructs: (1) the Magnetic Fourier Neural Operator (M-FNO), which solves the coupled Navier–Stokes/Maxwell system in spectral space with learnable 6×6 complex frequency-domain kernels; (2) the Hydromagnetic Physics-Informed Network (H-PINN), which enforces divergence-free magnetic field constraints, magnetic helicity conservation, and Onsager reciprocal symmetry as hard architectural loss terms; and (3) the Lorentz Flux Resolver (L-Flux), a spatiotemporal model-predictive control engine that tracks the Maxwell stress tensor minimum eigenvalue to pre-empt current-sheet reconnection events and Edge-Localized Modes (ELMs).Validation across four canonical MHD regimes — ITER-class tokamak plasma edge, ionic Hall thruster, liquid-metal lead-bismuth fast-reactor coolant loop, and planetary core dynamo analog — demonstrates a 94.2% mean MHD Efficiency Index, an 89.3% mean entropy production reduction relative to uncontrolled baselines, an 8.1× ELM energy suppression factor, and a magnetic confinement efficiency approaching within 4.7% of the ideal Alfvénic limit. Transfer learning experiments demonstrate 92–98% reduction in fine-tuning compute across five new MHD substrates. Statistical significance is confirmed via Wilcoxon signed-rank tests (p < 10⁻⁹, effect size r > 0.86 in all regimes).MAGNA-FLOW is the ninth and final installment of the EntropyLab research program (E-LAB-09), connecting directly to the Unified Dissipation State Function introduced in ENTROPIA (E-LAB-01). The framework is released as the open-source Python library magna-flow-engine (PyPI) under the MIT License. All reproducibility assets — pre-trained model weights, experimental datasets, training scripts, and validation benchmarks — are archived in this record.