A real 5000-run sweep through Docker found the evolutionary optimizer's
best genome reporting efficiency=387%. Root cause: efficiency was
computed as exit_kinetic_energy / capacitor_energy_in, but exit kinetic
energy includes the fixed initial_v_mps launch push (a modeling
assumption, not something paid for by the capacitors) -- for a light
enough projectile, that free energy dominates and the ratio blows past 1.
Fixed by using kinetic_energy_delta (the energy the coils actually added)
instead of absolute exit KE. The per-stage energy identity (energy_in =
remaining_cap + dissipated + kinetic_delta) guarantees kinetic_delta <=
energy_in, so this metric can never exceed 1.0 -- provably, not by luck.
Added a regression test with a light projectile + large initial velocity
reproducing the exact failure mode.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- sim/coilgun.py: chains StageConfig results through a global coordinate,
stops honestly on the first infeasible stage (with which stage and why),
and computes overall efficiency = exit KE / total capacitor energy in
- Discovered while wiring up the chain: a high-capacitance single-stage test
configuration let current stay high past the coil center, decelerating and
reversing the slug (exit velocity negative). This is a real coilgun tuning
failure mode (not a bug) since KE ~ v^2 doesn't care about direction and
the energy balance still holds to ~1%. Kept it as an explicit regression
test (test_overpowered_discharge_can_fling_slug_backward) instead of
quietly picking parameters that hide it, and tightened the "kinetic energy
increases" test to also require forward (exit_v > 0) motion.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
