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Add scheduler to tidalfarm.py #455

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Add scheduler to tidalfarm.py #455

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f340cbb
Add scheduler to tidalfarm.py
cpjordan May 6, 2026
a838367
Merge remote-tracking branch 'origin/main' into tidalfarm-scheduler
cpjordan May 14, 2026
8508fb0
Remove logging workaround
cpjordan May 14, 2026
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30 changes: 27 additions & 3 deletions examples/tidalfarm/tidalfarm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,11 +21,19 @@

from thetis import *
from firedrake.adjoint import *
from checkpoint_schedules import SingleMemoryStorageSchedule
op2.init(log_level=INFO)

# start annotating all Firedrake opterations in the forward model
continue_annotation()

tape = get_working_tape()
tape.enable_checkpointing(
SingleMemoryStorageSchedule(),
gc_timestep_frequency=56, # every 56 time steps we will do garbage collection
gc_generation=2
)

# setup the Thetis solver obj as usual:
mesh2d = Mesh('headland.msh')

Expand Down Expand Up @@ -131,7 +139,9 @@ def update_forcings(t):

# run as normal (this run will be annotated by firedrake adjoint)
solver_obj.assign_initial_conditions(uv=as_vector((1e-7, 0.0)), elev=tidal_elev)
solver_obj.iterate(update_forcings=update_forcings)
num_steps = int(numpy.ceil(solver_obj.options.simulation_end_time / solver_obj.dt))
adj_timestepper = tape.timestepper(iter(range(num_steps)))
solver_obj.iterate(update_forcings=update_forcings, adj_timestepper=adj_timestepper)


# compute maximum turbine density (based on a minimum of 1.5D between
Expand Down Expand Up @@ -164,19 +174,33 @@ def update_forcings(t):
# farm if multiple are defined)
callback_list = optimisation.OptimisationCallbackList([
optimisation.ControlsExportOptimisationCallback(solver_obj),
optimisation.DerivativesExportOptimisationCallback(solver_obj),
optimisation.UserExportOptimisationCallback(solver_obj, (farm_density,)),
optimisation.FunctionalOptimisationCallback(solver_obj),
turbines.TurbineOptimisationCallback(solver_obj, cb),
])
derivative_callback_list = optimisation.OptimisationCallbackList([
optimisation.DerivativesExportOptimisationCallback(solver_obj),
])


def update_control(value):
turbine_density.assign(value)
projector.project()

@cpjordan cpjordan May 13, 2026

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Should be able to avoid this when dolfin-adjoint/pyadjoint#257 is merged



# anything that follows, is no longer annotated:
pause_annotation()

# this reduces the functional J(u, td) to a function purely of the control td:
# rf(td) = J(u(td), td) where the velocities u(td) of the entire simulation
# are computed by replaying the forward model for any provided turbine density td
rf = ReducedFunctional(scaled_functional, c, derivative_cb_post=callback_list)
rf = ReducedFunctional(
scaled_functional,
c,
eval_cb_pre=update_control,
eval_cb_post=callback_list,
derivative_cb_post=derivative_callback_list,
)


if test_gradient:
Expand Down