bluemira.fuel_cycle.analysis

Fuel cycle analysis class for Monte Carlo statistics

Classes

FuelCycleAnalysis

Analysis class for compiling and analysing fuel cycle statistics.

Module Contents

class bluemira.fuel_cycle.analysis.FuelCycleAnalysis(fuel_cycle_model: bluemira.fuel_cycle.cycle.EUDEMOFuelCycleModel, **kwargs)

Analysis class for compiling and analysing fuel cycle statistics.

Parameters:

fuel_cycle_model (bluemira.fuel_cycle.cycle.EUDEMOFuelCycleModel) – The model for the fuel cycle on a single timeline

build_tweaks: ClassVar

model

m_T_req = []

t_d = []

m_dot_release = []

run_model(timelines: collections.abc.Iterable[dict[str, numpy.ndarray | int]])

Run the tritium fuel cycle model for each timeline.

Parameters:

timelines (collections.abc.Iterable[dict[str, numpy.ndarray | int]]) –

Timeline dict from LifeCycle object:

DEMO_tnp.array

Real time signal in seconds

DEMO_rtnp.array

Fusion time signal in years

DEMO_DT_ratenp.array

D-T fusion reaction rate signal (NOTE: P_fus is wrapped in here, along with maintenance outages, etc.)

DEMO_DD_ratenp.array

D-D fusion reaction rate signal (NOTE: P_fus is wrapped in here, along with maintenance outages, etc.)

bciint

Blanket change index

get_startup_inventory(query: str = 'max') → float

Get the tritium start-up inventory.

Parameters:

query (str) – The type of statistical value to return - [min, max, mean, median, 95th]

Return type:

The tritium start-up inventory [kg]

get_doubling_time(query: str = 'max') → float

Get the reactor doubling time.

Parameters:

query (str) – The type of statistical value to return - [min, max, mean, median, 95th]

Return type:

The reactor doubling time [years]

_query(p: str, s: str) → float

Parameters:

• p (str)

• s (str)

Return type:

float

plot(figsize=(12, 6), bins=20, **kwargs)

Plot the distributions of m_T_start and t_d.