6. Dynamic optimization of friction coefficient

6.1. Introduction

This example optimizes the friction coefficient of 32 turbines in a channel with sinusoidal inflow velocity the west boundary, fixed surface height on the east boundary, and no-slip flow on the north and south boundaries.

It shows how to:
  • specify velocity and surface elevation boundary conditions;

  • set up the shallow water solver;

  • set up the redused functional for the energy output;

  • optimize the friction coefficient dynamically for the energy output;

The shallow water equations to be solved are

\[\begin{split}\frac{\partial u}{\partial t} + u \cdot \nabla u - \nu \nabla^2 u + g \nabla \eta + \frac{c_b}{H} \| u \| u = 0, \\ \frac{\partial \eta}{\partial t} + \nabla \cdot \left(H u \right) = 0, \\\end{split}\]


  • \(u\) is the velocity,

  • \(\eta\) is the free-surface displacement,

  • \(H=\eta + h\) is the total water depth where \(h\) is the water depth at rest,

  • \(c_b\) is the (quadratic) natural bottom friction coefficient,

  • \(\nu\) is the viscosity coefficient,

  • \(g\) is the gravitational constant.

The boundary conditions are:

\[\begin{split}u = \begin{pmatrix}3*\sin(\frac{\pi t}{600}) \frac{y (320-y)}{12800}\\0\end{pmatrix} & \quad \textrm{on } \Gamma_1, \\ \eta = 0 & \quad \textrm{on } \Gamma_2, \\ u \cdot n = 0 & \quad \textrm{on } \Gamma_3, \\\end{split}\]

where \(n\) is the normal vector pointing outside the domain, \(\Gamma_1\) is the west boundary of the channel, \(\Gamma_2\) is the east boundary of the channel, and \(\Gamma_3\) is the north and south boundaries of the channel.

6.2. Implementation

We begin with importing the OpenTidalFarm module.

from opentidalfarm import *
import os

# Create a rectangular domain.
domain = FileDomain("mesh/mesh.xml")

Specify boundary conditions. For time-dependent boundary condition use a parameter named t in the dolfin.Expression and it will be automatically be updated to the current timelevel during the solve.

bcs = BoundaryConditionSet()
u_expr = Expression(("3*sin(pi*t/600.)*x[1]*(320-x[1])*2/(160.*160.)", "0"), t=Constant(0), degree=3)
bcs.add_bc("u", u_expr, facet_id=1)
bcs.add_bc("eta", Constant(0), facet_id=2)

# Apply a strong no-slip boundary condition. This can be changed to
# free slip (weakly enforced), by leaving out the Constant((0, 0))
# argument and changing bctype to "free_slip"
bcs.add_bc("u", Constant((0, 0)), facet_id=3, bctype="strong_dirichlet")

Next we get the default parameters of a shallow water problem and configure it to our needs.

# Set the problem_params for the Shallow water problem
problem_params = SWProblem.default_parameters()
problem_params.bcs = bcs
problem_params.domain = domain

# Activate the relevant terms
problem_params.include_advection = True
problem_params.include_viscosity = True
problem_params.linear_divergence = False

# Physical settings
problem_params.friction = Constant(0.0025)
problem_params.viscosity = Constant(300)
problem_params.depth = Constant(50)
problem_params.g = Constant(9.81)

# Set time parameters
problem_params.start_time = Constant(0)
problem_params.finish_time = Constant(1200)
problem_params.dt = Constant(30)

problem_params.functional_final_time_only = False

# The initial condition consists of three components: u_x, u_y and eta
# Note that we do not set all components to zero, as some components of the
# Jacobian of the quadratic friction term is non-differentiable.
problem_params.initial_condition = Constant((DOLFIN_EPS, 0, 0))

The next step is to create the turbine farm. In this case, the farm consists of only 1 turbine placed in the midle of the channel.

# Before adding the turbine we must specify the type of turbine used in the
# array and what to optimize for.
# Here we used the default BumpTurbine and set the controls to optimize for
# dynamic friction. The diameter and friction are set. The minimum distance
# between turbines if not specified is set to 1.5*diameter.
turbine = BumpTurbine(diameter=20.0, friction=10.0,

# A rectangular farm is defined using the domain and the site dimensions.
# The number of time steps must be specifed when optimizing dynamically, but
# the problem_params have a property which calculates it for you.
farm = RectangularFarm(domain, site_x_start=160, site_x_end=480,
                       site_y_start=80, site_y_end=240, turbine=turbine,
                       n_time_steps = problem_params.n_time_steps)

# Turbines are then added to the site in a regular grid layout.
farm.add_regular_turbine_layout(num_x=8, num_y=4)

problem_params.tidal_farm = farm

Once the parameter have been set, we create the shallow water problem:

problem = SWProblem(problem_params)

Next we create a shallow water solver. Here we choose to solve the shallow water equations in its fully coupled form. Again, we first ask for the default parameters, adjust them to our needs and then create the solver object. Here we set the solver to output lots of information.

solver_params = CoupledSWSolver.default_parameters()
solver_params.dump_period = 1
solver_params.output_abs_u_at_turbine_positions = True
solver_params.output_j = True
solver_params.output_temporal_breakdown_of_j = True
solver_params.output_control_array = True
solver_params.cache_forward_state = False
solver = CoupledSWSolver(problem, solver_params)

Next we create a reduced functional, that is the functional considered as a pure function of the control by implicitly solving the shallow water equations. For that we need to specify the objective functional (the value that we want to optimize), the control (the variables that we want to change), and our shallow water solver.

functional = PowerFunctional(problem)
control = TurbineFarmControl(farm)
rf_params = ReducedFunctionalParameters()
rf_params.automatic_scaling = False
rf = ReducedFunctional(functional, control, solver, rf_params)

Now we can define the constraints for the controls and start the optimisation. (The callback parameter must be set to solver.update_optimisation_iteration to get the correct iteration.)

lb, ub = farm.constraints(lower_friction_bounds=0, upper_friction_bounds=1000)
f_opt = maximize(rf, bounds=[lb, ub], method="L-BFGS-B", options={'maxiter':
10, 'ftol':1e-2}, callback=solver.update_optimisation_iteration)

Reset the scale of the reduced functional, which maximize have changed, before calculating the energy output.

rf.scale = 1.0
solver_params.dump_period = -1
energy = rf(f_opt)

Finally we print out the result.

print "The opimized friction coefficient for each timestep is: "
print f_opt
print "This gives a energy output of {}.".format(energy)

6.3. How to run the example

The example code can be found in examples/channel-dynamic-optimization/ in the OpenTidalFarm source tree, and executed as follows:

$ python channel-dynamic-optimization.py