scale.olm.generate

Code supporting the generation of SCALE input.

scale.olm.generate.static

Generate functions for static data.

pass_through(_type: Literal['scale.olm.generate.static:pass_through'] = None, **x)[source]

Simple pass through of static data.

Examples

>>> data={'x': 'sally', 'y': 9.0}
>>> pass_through(**data)
{'x': 'sally', 'y': 9.0}

scale.olm.generate.dynamic

Module for dynamic data generators, dependent on state.

scipy_interp(state_var: str, data_pairs: List[Tuple[float, float]], state: Dict[str, float], method: ScipyInterpMethod = ScipyInterpMethod.LINEAR, _type: Literal['scale.olm.generate.dynamic:scipy_interp'] = None)[source]

Interpolate data pairs to the value of a state variable.

Originally used to interpolate Dancoff factors as a function of moderator/coolant density to actual state conditions.

Parameters:

state_var – State variable (x) to access in the ‘state’ dictionary.
data_pairs – List of tuples, each an x,y data point.
state – State dictionary.
method – Interpolation method.

Returns:

Interpolated value.

Return type:

float

scale.olm.generate.states

full_hypercube(_type: Literal['scale.olm.generate.states:full_hypercube'] = None, **states)[source]

Generate all the permutations assuming a dense N-dimensional space.

Parameters:: ] (states list[ list[float]) –

scale.olm.generate.comp

This module contains composition creation functions.

They should return a standard format for output so they can easily be used interchangeably (somewhat).

mox_multizone_2023(state: StateWithPuFracs, zone_names: str | List[str], zone_pins: List[int], density: float, uo2: IsotopicWtDict | None = None, zone_pu_fracs: List[float] | None = None, am241: float = 1e-20, gd2o3_pins: int = 0, gd2o3_wtpct: float = 0, _type: Literal['scale.olm.generate.comp:mox_multizone_2023'] = None)[source]

Create compositions for a zoned MOX assembly with a desired average plutonium fraction.

This function has handling for additional non-plutonium bearing pins with UO2+Gd2O3.

Built-in zone plutonium fractions for BWR2016 and PWR2016 are from this publication.

Ugur Mertyurek, Ian C. Gauld, Development of ORIGEN libraries for mixed oxide (MOX) fuel assembly designs, Nuclear Engineering and Design, Volume 297,2016,Pages 220-230,ISSN 0029-5493,https://doi.org/10.1016/j.nucengdes.2015.11.027. (https://www.sciencedirect.com/science/article/pii/S0029549315005592)

Parameters:

state –
Data about the state for which this composition should be created, only uses the following fields.
- ’pu_frac’ fraction of plutonium to heavy metal (as a percentage)
- ’pu239_frac’ fraction of pu239 to plutonium (as a percentage)
zone_names –
Names of zonewise compositions to output e.g. [‘inner’,’edge’] OR a built-in composition name. Built-ins are:
- ’PWR2016’ a four-zone distribution for a generic PWR with an ‘inner’, ‘iedge’ (inner edge), ‘edge’, and ‘corner’ zone with the zone plutonium fractions varying according the 2016 Mertyurek and Gauld paper.
- ’BWR2016’ a four-zone distribution for a generic BWR an ‘inner’, ‘iedge’ (inner edge), ‘edge’, and ‘corner’ zone with the zone plutonium fractions varying according the 2016 Mertyurek and Gauld paper.
zone_pins – Number of pins in each zone (assumed to have same heavy metal fraction). E.g. if zone_names=[‘inner’,’edge’], zone_pins=[100,44] would indicate there are 100 inner pins and 44 edge pins so that the relevant heavy metal ratios are preserved to renormalize to the assembly-average target of state[‘pu_frac’].
density – Density of the fuel. This is basically a pass-through option and not used in the calculation itself.
uo2 – A uranium oxide composition to use for the uo2 part of the MOX.
zone_pu_fracs – The zone plutonium fractions to use, associated with each of the zone_names. For example, zone_names=[‘inner’,’edge’] could have zone_pu_fracs=[1.0,0.8] to indicate the the ‘edge’ zone should have 80% of the inner zone plutonium fraction, renormalized so that the assembly average is what was supplied in state[‘pu_frac’].
am241 – The weight percent in Am241 content. Typically this is not necessary because there will be a decay calculation performed to account for the buildup of Am241.
gd2o3_pins – Number of pins of Gd2O3 and UO2 which are not contributing to the assembly-average plutonium fraction. Therefore, if the assembly had 144 MOX pins and 25 non-MOX pins then gd2o3_pins=25 would make sure that heavy metal content is taken into account when renormalizing to the target state[‘pu_frac’].
gd2o3_wtpct – If the gd2o3_wtpct>0.0, then the heavy metal is reduced accordingly.

Returns:

Dictionary of compositions by name. Internal to each composition is a: ’uo2’ and ‘puo2’ dictionary which includes details to be used in creating a mixed-oxide composition with SCALE.

Return type:

dict

Examples:

Here is a basic example with two zones.

>>> x=mox_multizone_2023(state={"pu239_frac": 70.0, "pu_frac": 5.0},
... zone_names=['inner','edge'],
... density=10.38,
... zone_pins=[100,44],
... zone_pu_fracs=[1.0,0.8])

Let’s define a simple printing function so that we don’t see so many digits in the output and it is formatted consistently.

>>> def prn(x):
...     import json
...     print( json.dumps(json.loads(json.dumps(x),
...         parse_float=lambda x: round(float(x), 4)),
...         indent=4, sort_keys=True) )

For the inner zone we will have slightly higher density fraction that 5.0% because of the zone weighting of 1.0 versus 0.8 for the edge.

>>> prn(x['inner']['puo2'])
{
    "dens_frac": 0.0533,
    "iso": {
        "pu238": 0.8642,
        "pu239": 70.0,
        "pu240": 21.1768,
        "pu241": 5.5325,
        "pu242": 2.4264
    }
}

Note for the edge the isotopic distribution is identical, but the density fraction is different.

>>> prn(x['edge']['puo2'])
{
    "dens_frac": 0.0426,
    "iso": {
        "pu238": 0.8642,
        "pu239": 70.0,
        "pu240": 21.1768,
        "pu241": 5.5325,
        "pu242": 2.4264
    }
}

We should be very close to 5% with this simple reconstruction.

>>> 100*(x['inner']['puo2']['dens_frac']*100 + x['edge']['puo2']['dens_frac']*44 ) / 144
5.0

mox_ornltm2003_2(state: StateWithPuFracs, density: float, uo2: IsotopicWtDict | None = None, am241: float = 1e-20, _type: Literal['scale.olm.generate.comp:mox_ornltm2003_2'] = None)[source]: MOX isotopic vector calculation from ORNL/TM-2003/2, Sect. 3.2.2.1

uo2_nuregcr5625(state: StateWithEnrichment, density: float, _type: Literal['scale.olm.generate.comp:uo2_nuregcr5625'] = None)[source]: Enrichment formula from NUREG/CR-5625.

uo2_simple(state: StateWithEnrichment, density: float, _type: Literal['scale.olm.generate.comp:uo2_simple'] = None)[source]: Return a UO2 composition using the state enrichment as the U-235 wt%.

uo2_vera(state: StateWithEnrichment, density: float, _type: Literal['scale.olm.generate.comp:uo2_vera'] = None)[source]: Enrichment formula from: Andrew T. Godfrey. VERA core physics benchmark progression problem specifications. Consortium for Advanced Simulation of LWRs, 2014.

scale.olm.generate.time

Generate functions for time-dependent power histories. The preferred output is a simple list of power levels and time at that power level using the conventional power in MW/MTIHM and time in days.

A time generation function shall always receive the state along with any other parameters inside the input time section.

constpower_burndata(state: StateWithSpecificPower, gwd_burnups: List[float], _type: Literal['scale.olm.generate.time:constpower_burndata'] = None)[source]

Return a list of powers and times assuming constant burnup.

TODO: Instead of returning burndata dictionary, just return the list. Make this look less like a TRITON burndata specification.

Parameters:

state – state point data–only the “specific_power” key in MW/MTIHM units is used
gwd_burnups – list of cumulative burnups in GWd/MTIHM

Returns:

dictionary with single “burndata” key with values a list of power/burn pairs as TRITON would expect for its burndata block

Examples

>>> import scale.olm as olm
>>> olm.generate.time.constpower_burndata(
...     state={"specific_power": 40},
...     gwd_burnups=[0,10,20]
... )
{'burndata': [{'power': 40.0, 'burn': 250.0}, {'power': 40.0, 'burn': 250.0}, {'power': 40.0, 'burn': 250.0}]}