rompy.swan.components.physics.SURFBEAT#

pydantic model rompy.swan.components.physics.SURFBEAT[source]#

Surfbeat.

SURFBEAT [df] [nmax] [emin] UNIFORM/LOGARITHMIC

Using this optional command, the user activates the Infragravity Energy Module (IEM) of Reniers and Zijlema (2022). Besides the energy balance equation for a sea-swell wave field, another energy balance is included to account for the transfer of sea-swell energy to the bound infragravity (BIG) wave. This infragravity energy balance also involves a nonlinear transfer, expressed by the biphase, through the phase coupling between the radiation stress forcing and the BIG wave. For the prediction of the biphase for obliquely incident waves, an evolution equation is provided under the assumption that the bottom slopes are mild and alongshore uniform.

References

Reniers, A. and Zijlema, M., 2022. Swan surfbeat-1d. Coastal Engineering, 172, p.104068.

Examples:#

In [170]: from rompy.swan.components.physics import SURFBEAT

In [171]: surfbeat = SURFBEAT()

In [172]: print(surfbeat.render())
SURFBEAT

In [173]: surfbeat = SURFBEAT(df=0.01, nmax=50000, emin=0.05, spacing="logarithmic")

In [174]: print(surfbeat.render())
SURFBEAT df=0.01 nmax=50000 emin=0.05 LOGARITHMIC

Show JSON schema
{
   "title": "SURFBEAT",
   "description": "Surfbeat.\n\n.. code-block:: text\n\n    SURFBEAT [df] [nmax] [emin] UNIFORM/LOGARITHMIC\n\nUsing this optional command, the user activates the Infragravity Energy Module\n(IEM) of Reniers and Zijlema (2022). Besides the energy balance equation for a\nsea-swell wave field, another energy balance is included to account for the\ntransfer of sea-swell energy to the bound infragravity (BIG) wave. This\ninfragravity energy balance also involves a nonlinear transfer, expressed by the\nbiphase, through the phase coupling between the radiation stress forcing and the\nBIG wave. For the prediction of the biphase for obliquely incident waves, an\nevolution equation is provided under the assumption that the bottom slopes are mild\nand alongshore uniform.\n\nReferences\n----------\nReniers, A. and Zijlema, M., 2022. Swan surfbeat-1d. Coastal Engineering, 172,\np.104068.\n\nExamples:\n---------\n\n.. ipython:: python\n\n    from rompy.swan.components.physics import SURFBEAT\n    surfbeat = SURFBEAT()\n    print(surfbeat.render())\n    surfbeat = SURFBEAT(df=0.01, nmax=50000, emin=0.05, spacing=\"logarithmic\")\n    print(surfbeat.render())",
   "type": "object",
   "properties": {
      "model_type": {
         "default": "surfbeat",
         "description": "Model type discriminator",
         "enum": [
            "surfbeat",
            "SURFBEAT"
         ],
         "title": "Model Type",
         "type": "string"
      },
      "df": {
         "anyOf": [
            {
               "minimum": 0.0,
               "type": "number"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "The constant size of BIG frequency bin (in Hz) (SWAN default: 0.01)",
         "title": "Df"
      },
      "nmax": {
         "anyOf": [
            {
               "minimum": 0,
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "The maximum number of short-wave pairs for creating bichromatic wave groups (SWAN default: 50000)",
         "title": "Nmax"
      },
      "emin": {
         "anyOf": [
            {
               "type": "number"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "The energy threshold in fraction of energy spectrum peak. With this threshold one takes into account those short wave components to create bichromatic wave groups while their energy levels are larger than `emin x E_max` with `E_max` the peak of the spectrum (SWAN default: 0.05)",
         "title": "Emin"
      },
      "spacing": {
         "anyOf": [
            {
               "enum": [
                  "uniform",
                  "logarithmic"
               ],
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Define if frequencies for reflected ig waves are uniformly or logarithmically distributed",
         "title": "Spacing"
      }
   },
   "additionalProperties": false
}

Fields:
field df: float | None = None#

The constant size of BIG frequency bin (in Hz) (SWAN default: 0.01)

Constraints:

  • ge = 0.0

field emin: float | None = None#

The energy threshold in fraction of energy spectrum peak. With this threshold one takes into account those short wave components to create bichromatic wave groups while their energy levels are larger than emin x E_max with E_max the peak of the spectrum (SWAN default: 0.05)

field model_type: Literal['surfbeat', 'SURFBEAT'] = 'surfbeat'#

Model type discriminator

field nmax: int | None = None#

The maximum number of short-wave pairs for creating bichromatic wave groups (SWAN default: 50000)

Constraints:

  • ge = 0

field spacing: Literal['uniform', 'logarithmic'] | None = None#

Define if frequencies for reflected ig waves are uniformly or logarithmically distributed

cmd() → str[source]#

Command file string for this component.