1. Iron saturation now in the dynamics (was the "13 Tesla" bug). Flux
linkage lambda(x,I) = L_air*I + L_iron*overlap(x)*g(I) with
g(I)=I_sat*tanh(I/I_sat) saturating at the current where iron reaches
B_sat. Both the circuit back-EMF (dlambda/dx) and the force (coenergy,
dW'/dx) derive from the SAME lambda, so energy stays conserved (0.025%
error) AND the field caps at B_sat instead of running to 13 T. Reduces
to the old 0.5*I^2*dL/dx in the low-current limit. On the config that
reported 60% efficiency / 11.6 T, it now gives 40% / 1.90 T.
2. Switch surge-current limit is now a hard feasibility constraint: a
config whose peak discharge current exceeds the switch's pulse rating
(continuous * surge factor per device kind) is infeasible -- otherwise
the optimizer "wins" with configs that vaporize their own thyristor
(e.g. 119 A through a 12 A BT151). Capacitor current stays a warning
(electrolytics tolerate pulses; DB has continuous not pulse ratings).
Regression tests added for both (field cap near saturation, energy
conservation under strong discharge). 87 tests pass.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- report/plots.py: current/field/velocity plots per stage, Agg backend so
it works headless in Docker/on a server
- report/bom.py: itemized bill of materials with real component prices
- report/summary.py: honest text/JSON summary including a
"model limitations" section and a saturation warning flag per stage
- report/animate.py: the visualization the user explicitly asked for --
a GIF of the slug flying through the tube with each coil glowing by its
instantaneous current, reconstructing the pre-trigger ballistic flight
segment (not just the stored discharge phase) for a continuous timeline
- cli.py: sweep/evolve/simulate/report subcommands now actually call the
underlying modules instead of being stubs
- Extended StageResult/StageOutcome with the coil/entry-state fields the
report layer needed (mu_eff, turns, coil_length, entry_x/v) rather than
recomputing them by other means
Verified end-to-end through `docker compose run`, not just pytest: a real
sweep (30 runs, 11 feasible) followed by `report --animate` produced a
correct GIF/plots/BOM/summary for the actual best result found (32.8%
efficiency, 982 RUB) -- not a synthetic fixture.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
