Data Physics & Cyber-Physical Systems - Part VI: Thermo-Algorithmic Determinism in Deep-Space AI Architectures: Microgravity Fluid Dynamics, Radiative Entropy Dissipation, and ECLSS Co-Integration

Abstract Part VI extends the Data Physics paradigm from terrestrial hyperscale data centers to deep-space autonomous AI architectures operating in zero-gravity (g → 0) and vacuum environments. In microgravity, classical convective cooling collapses due to zero buoyancy, inducing severe gas bubble pinning (α_void > 0) within 100–200 μm microchannels and triggering Marangoni surface-tension instabilities. This work demonstrates that microscale silicon thermal spikes directly modulate PMIC DVFS clock frequencies f(t), inserting hydraulic variables into the cycle-domain execution integral: t_exec = ∫ [ CPI(f(t)) / f(T_j(t)) ] dC and causing severe multi-node synchronization deadlocks (Orbital Straggler Effect) across deep-space light-lag communication bounds. To resolve this, we implement Anticipatory Entropic Coupling driven by 100 Hz piezoelectric telemetry: d/dt (σ²_ΔP) > θ_crit and the TACE-Alg control framework. By detecting the onset of gas desorption 300–800 ms prior to thermal junction spikes, the system proactively modulates fluid momentum (ṁ_fluid) to forcibly detach bubbles from cold-plate microchannels. Furthermore, compute entropy rejection (60–70°C) is co-integrated with spacecraft Environmental Control and Life Support Systems (ECLSS) and Stefan-Boltzmann vacuum radiation: Q_rad = ε · σ_SB · A · (T_radiator⁴ - T_space⁴) optimizing the orbital Joules-per-Token ratio. This establishes liquid cooling not as passive plumbing, but as a deterministic, cyber-physical governor of deep-space AI runtime execution.