We present a comprehensive numerical study of irrotational (curl-free) shift-vector warp metrics in the ADM formalism, focusing on energy-condition diagnostics and parameter optimization. Using Rodal-style dipole potentials with smoothed wall functions, we compute both fast Eulerian energy densities and invariant diagnostics via Einstein tensor eigenvalues. We demonstrate systematic convergence of 3D volume integrals with tail corrections, validate against literature results (Celmaster & Rubin 2025), and explore superluminal parameter regimes (v > 1). Bayesian optimization with Gaussian Process regression efficiently identifies minimal negative-energy configurations, requiring 5 fewer evaluations than grid search methods. Our results establish quantitative bounds on negative energy requirements for various parameter combinations, providing defensible inputs for theoretical assessments of irrotational warp drive feasibility.
Ryan Sherrington (Fri,) studied this question.