Determing real simulation time

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1

I was wondering if there is a method to determine the run simulation time for different stages of depletion simulations. I want to compare the use of different depletion predictor integrators for simulating the depletion of a full-core CANDU reactor. I use Jupyter notebooks to run my code. I read from a paper by Yu and Forget and they mentioned that for most simulations the transport portion of the depletion simulation is what is time dominant, except for big models such as a full-core every rod depletion, where the depletion step can become dominant. Since the different integrators can have a greater impact on one or the other stage, I wanted to complete my own analysis on my reactor design by gathering the simulation time for each transport and depletion step. From what I can see I will have to monitor the simulation and time the transport section myself with a stopwatch, and then time the depletion section, which I assume is being completed when the simulation is creating the statepoint though I would like confirmation about this as well. Lastly, if you have a tip on which integrator is preffered that would also be appreciated.

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2

Hello @Jarret,

One way to do it is by simply adding lines in Integrator.integrate() function in deplete/abc.py. Probably will be something like this:

for i, (dt, source_rate) in enumerate(self):
    if output and comm.rank == 0:
        print(f"[openmc.deplete] t={t} s, dt={dt} s, source={source_rate}")

    tickTransport = perf_counter()

    # Solve transport equation (or obtain result from restart)
    if i > 0 or self.operator.prev_res is None:
        n, res = self._get_bos_data_from_operator(i, source_rate, n)
    else:
        n, res = self._get_bos_data_from_restart(source_rate, n)

    tockTransport = perf_counter()

    # Solve Bateman equations over time interval
    proc_time, n_list, res_list = self(n, res.rates, dt, source_rate, i)

    # Insert BOS concentration, transport results
    n_list.insert(0, n)
    res_list.insert(0, res)

    # Remove actual EOS concentration for next step
    n = n_list.pop()

    tockDepletion = perf_counter()

    print(f"Transport time = {tockTransport-tickTransport}s\nDepletion time = {tockDepletion - tockTransport}s")

    StepResult.save(self.operator, n_list, res_list, [t, t + dt],
                    source_rate, self._i_res + i, proc_time, path)

    t += dt
3

For the recommendation of integrator algo, Dr. Josey’s thesis is very insightful: