Three fixes from user feedback:
1. Single-pulse discharge (user spotted 3 current humps for one stage):
a thyristor fires ONCE per shot -- you can't recharge the cap in
microseconds. The discharge now terminates at the first current zero
OR first local minimum (where the slug starts pumping current back),
whichever comes first. Residual coil energy at cutoff is accounted as
freewheel-diode dissipation using the exact saturating magnetic energy
integral, so energy still balances. Verified: 3 humps -> 1.
2. Real tube geometry: the genome now carries tube INNER diameter (bore,
the projectile flies through) and wall thickness; outer diameter =
inner + 2*wall = the coil's inner diameter (which drives the field).
The projectile must fit the bore (diameter < inner - clearance).
3. Projectiles up to 200 g: diameter to 28mm, length to 150mm, with mass
capped at 200g (length clamped by density).
Detail/BOM now report inner/outer/wall tube diameters, projectile mass in
grams, and whether it fits the bore. Same single-pulse + eddy physics
mirrored into the GPU batch integrator. Evolutionary polish updated for
the new tube params. All test groups pass.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- optim/worker_context.py: shared per-process DB/bounds init, extracted
from sweep.py so evolutionary.py doesn't reach into another module's
private state
- optim/objective.py: build_run_record() shared between sweep and
evolutionary so both write identically-shaped rows
- optim/evolutionary.py: tournament selection, whole-stage-swap crossover,
elitism, structural mutation (stage count itself evolves), then a serial
Nelder-Mead polish of the best genome's continuous parameters with
discrete component choices frozen
- Found and fixed a real crash: crossover() indexed into an empty
inter_stage_gaps_m list when both parents had only 1 stage (0 gaps),
raising IndexError. Fixed the fallback to only choose from gaps that
actually exist, defaulting to a neutral value (clipped later by
repair()) when neither parent has one. Added a regression test.
- Verified 59/59 tests pass both locally and inside the Docker image
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- optim/search_space.py: Genome encodes only indices into the real
component database plus continuous/discrete geometry parameters; number
of stages is itself mutable via add/remove/duplicate-stage operators.
repair() clips everything back into bounds after mutation/crossover so
decode() never sees an invalid genome.
- optim/objective.py: evaluate() decodes a genome, computes real BOM cost
from wire length/price and component prices, runs the full chain, and
scores fitness = efficiency for feasible runs or a soft penalty scaled
by how many stages it got through for infeasible ones (so the GA gets
gradient instead of a wall).
Tested against the real component database (not synthetic fixtures) —
including an explicit infeasible case using the real inductive sensor's
actual sensitivity/threshold values.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
