Phase-Locked Control Architecture for Eliminating Long-Tail Failure in Stochastic Cleanup Systems: A Monte Carlo Validation Study

This dataset presents a complete Monte Carlo validation study of control architectures for stochastic contaminant cleanup systems. Multiple controller designs (v12–v17) were evaluated under identical stochastic conditions to analyze convergence behavior, robustness, and resource efficiency. The study includes both successful and negative-result models, providing a full experimental and developmental trajectory. Key findings: Baseline and stress-test models (v12) exhibit long-tail failure behavior characterized by slow or incomplete convergence. Intermediate adaptive and selective control strategies (v13–v16) improve average performance but fail to eliminate tail-risk outcomes. A phase-locked control architecture (v17) achieves: 100% success rate for cleanup times under 6 hours Mean cleanup time of approximately 2.68 hours Low variance (σ ≈ 0.25 hours) Stable and efficient resource utilization Primary conclusion: Long-tail failure in stochastic cleanup systems is eliminated when early-phase system dynamics are constrained through a phase-locking mechanism that guarantees sufficient initial kinetic activity. This prevents divergence into low-efficiency regimes and stabilizes overall system trajectories. The dataset includes: Comparative performance metrics across all model versions Correlation analysis of system parameters Monte Carlo simulation outputs Visualization figures (performance, cost, and correlation structure) Structured data files (CSV and JSON formats) Summary report and documentation This work provides a reproducible computational framework for analyzing and stabilizing stochastic nonlinear systems using early-phase constrained control strategies.