environment.assessment.wind_turbine_receptor

Wind turbine sound at a receptor (IEC TS 61400-11-2:2024).

IEC 61400-11 measures what a turbine emits; IEC TS 61400-11-2:2024 measures what arrives at a dwelling, where the wind that drives the turbine also stirs the trees, the background and the propagation. This module holds its closed forms and printed tables; the amplitude modulation of clause 13 is in wind_turbine_modulation.

Wind speed at another height (Annex K, IEC 61400-11:2012 Annex D). The power law of Equation (K.1), power_law_wind_speed, and its inverse for the shear exponent, Equation (K.2), wind_shear_exponent, which Tables K.1 and K.2 tabulate for 10 m and 120 m; the logarithmic profile with the reference roughness length $z_{0\mathrm{ref}}=0.05$ m (3.31) that 9.3.2.1 uses to bring a hub-height wind speed to 10 m, logarithmic_wind_speed; and wind_shear_profile, which plots both through two measured heights. Table K.3’s roughness lengths are ROUGHNESS_LENGTHS_M.

Bins and their averages (10.1, 10.3). bin_sound_levels sorts interval levels into 1 m/s wind speed bins and 30 degree sectors and forms, per bin, the energy average of Equation (1) (or the arithmetic one of Equation (8) for statistical levels), the type A uncertainty of Equation (2) or (9), the type B one of Equations (3) and (4) and their combination, Equation (5). turbine_sound_levels subtracts the background per bin with Equations (6) and (7), under the 3 dB rule of 11.7. predicted_receptor_level sums a prediction over the turbines with the uncertainty of Equations (10) and (11), and sound_relevant_turbines picks the turbines whose wind speeds make the binning wind speed (9.3.2.3).

Low frequency sound (Annex C). wind_turbine_low_frequency_level evaluates Equation (C.1) band by band with the ground correction and air attenuation of Table C.2 and a facade insulation such as those of Table C.3. Table C.2’s air attenuation is the Danish statutory order’s, not the ISO 9613-1 value at the 70 % humidity its caption names: from 25 Hz to 100 Hz its cells are those of the order (BEK nr. 135 of 7 February 2019, Table 1.4), set for 10 degrees C and 80 % relative humidity with the Nord2000 band attenuation rates and nothing below 25 Hz, which puts them 0.01 dB/km to 0.02 dB/km under ISO 9613-1 at 70 % between 50 Hz and 100 Hz; from 125 Hz to 200 Hz the cells are ISO 9613-1 at 10 degrees C and 70 % evaluated at the nominal band centres. ISO 9613-1’s own Table 1 is computed at the exact mid-band frequencies and prints 0.584 dB/km at 160 Hz (158.5 Hz exactly), where Table C.2 prints the nominal 160 Hz value 0.59; Table 7 of the TS, in contrast, follows the exact mid-bands.

Emergence and the rating level (Annexes J and A). sound_emergence is Equation (J.1); wind_turbine_rating_level adds the most severe of the tonal, amplitude modulation and impulsive adjustments (A.1), with the amplitude modulation adjustment of Figure A.1 in amplitude_modulation_adjustment.

The upper tone search frequency (12.5.2.4, Table 7). upper_tone_search_frequency finds the lowest one-third-octave band that ISO 9613-1 attenuates by at least 20 dB over the distance to the nearest turbine.

Auto-generated from the source docstrings by scripts/generate_api_docs.py (make api-docs). Do not edit by hand.

amplitude_modulation_adjustment(
    modulation_depth_db: ArrayLike,
) -> NDArray[np.float64] | float

The amplitude modulation adjustment of Figure A.1, in dB.

The example adjustment of A.3: none below a modulation depth of 3 dB, 3 dB at 3 dB, rising linearly to 5 dB at 10 dB and 5 dB above it. The figure is drawn to 12 dB; the 5 dB is kept beyond. The depth is the reconstructed time-series modulation depth of clause 13 (the AM rating of amplitude_modulation_period).

Parameters

Returns: The adjustment $K_{\mathrm{am}}$, in dB; a float for a scalar depth.

Raises

BackgroundCorrectionRegime(*values)

Which rule of 11.7 a bin’s background correction followed.

LOGARITHMIC: the total is at least 3 dB above the background, and the background is subtracted (Equation (6)). THREE_DB: the total is at least 0 dB but less than 3 dB above it, and the suggested 3 dB correction is applied. UNDETERMINED: the background is above the total, and the turbine level cannot be determined (11.6.4).

bin_sound_levels(
    levels_db: ArrayLike,
    wind_speeds_m_s: ArrayLike,
    wind_directions_deg: ArrayLike | None = None,
    *,
    averaging: str = 'energy',
    type_b_uncertainty_db: ArrayLike = 0.0,
    bin_width_m_s: float = 1.0,
    sector_width_deg: float = 30.0,
) -> BinnedSoundLevels

Average interval levels per wind speed bin and sector (10.1, 10.3).

Each interval (typically 10 s to 10 min) goes into the wind speed bin of its binning wind speed, bin_width_m_s wide and centred on its multiples, and, with directions, into the sector_width_deg sector whose first member is centred on north. Per bin:

• the average, Equation (1) ${\overline{L}}_k=10\lg (\frac{1}{N}\sum 10^{L_{j,k}/10})$ for equivalent levels, or Equation (8), the arithmetic mean, for statistical levels such as $L_{90}$ (10.3.2 NOTE);
• the type A uncertainty, Equation (2) (or (9)), $s=\sqrt{\sum (L_{j,k}-{\overline{L}}_k{)}^2/(N(N-1))}$;
• the type B uncertainty, Equation (4), the root mean square of the per-interval type B uncertainties of Equation (3);
• their combination, Equation (5), $\sqrt{s^2+u^2}$.

Parameters

Returns: A BinnedSoundLevels.

Raises

BinnedSoundLevels(
    wind_speeds_m_s: NDArray[np.float64],
    wind_directions_deg: NDArray[np.float64] | None,
    counts: NDArray[np.int64],
    mean_levels_db: NDArray[np.float64],
    type_a_uncertainty_db: NDArray[np.float64],
    type_b_uncertainty_db: NDArray[np.float64],
    combined_uncertainty_db: NDArray[np.float64],
    *,
    averaging: str,
    interval_levels_db: NDArray[np.float64],
    interval_wind_speeds_m_s: NDArray[np.float64],
    bin_width_m_s: float,
    sector_width_deg: float,
)

Interval levels averaged per wind speed bin and sector (10.1, 10.3).

One row per occupied bin, sorted by sector and then wind speed.

Attributes

BinnedSoundLevels.background_corrected(
    background: BinnedSoundLevels,
) -> TurbineSoundLevels

Correct these total levels for a background binned the same way (11.7).

The bins the two share are corrected by turbine_sound_levels with their combined uncertainties; a bin of either without a partner in the other is left out.

Parameters

Returns: A TurbineSoundLevels over the shared bins, with their sector centres when binned by direction.

Raises

BinnedSoundLevels.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot the intervals against wind speed and the bin averages over them.

The scatter of interval levels against the binning wind speed is what 11.9 asks to be reported; the bin averages carry their combined uncertainty as error bars.

Parameters

Returns: The axes.

GroundImpedanceClass(
    flow_resistivity_kpa_s_m2: float,
    nordtest_classes_kpa_s_m2: tuple[float, ...],
    description: str,
)

One impedance class of Table C.1.

Attributes

logarithmic_wind_speed(
    reference_speed_m_s: ArrayLike,
    *,
    height_m: float,
    reference_height_m: float,
    roughness_length_m: float = 0.05,
) -> NDArray[np.float64] | float

Wind speed at another height by the logarithmic profile.

$V_z=V_{z,\mathrm{ref}}\ln (z/z_0)/\ln (z_{\mathrm{ref}}/z_0)$ (IEC 61400-11:2012 Equation (D.1)). With the default reference roughness length $z_{0\mathrm{ref}}=0.05$ m (3.31), a hub-height wind speed brought to 10 m is the binning wind speed $V_{\mathrm{bin}}$ of 9.3.2.1 (IEC 61400-11:2012 Equation (29) solved for $V_{10}$).

Parameters

Returns: The wind speed at height_m, in m/s; a float for scalar inputs.

Raises

Constant (numpy.ndarray, shape (14,)).

Constant (numpy.ndarray, shape (14,)).

Constant (mapping).

LOW_FREQUENCY_FACADE_INSULATION_DB = {'Denmark brick or similar': array([ 4.9,  5.9,  4.6,  6.6,  8.4, 10.8, 11.4, 13. , 16.6, 19.7, 21.2,
       20.2, 21.2, 21. ]), 'Denmark lightweight': array([ 6.8,  3.9,  0.4, -0.2,  4.8,  6.2,  8.4, 10.5, 11.9, 11.9, 16. ,
       17.5, 17.9, 17. ])}

Constant (numpy.ndarray, shape (14,)).

Constant (mapping).

LOW_FREQUENCY_GROUND_IMPEDANCE_CLASSES = {'A': GroundImpedanceClass(flow_resistivity_kpa_s_m2=12.5, nordtest_classes_kpa_s_m2=(10.0, 15.0), description='Very soft (snow, moss like)'), 'B': GroundImpedanceClass(flow_resistivity_kpa_s_m2=32.0, nordtest_classes_kpa_s_m2=(25.0, 40.0), description='Soft forest floor (short dense heatherlike or thick moss)'), 'C': GroundImpedanceClass(flow_resistivity_kpa_s_m2=80.0, nordtest_classes_kpa_s_m2=(63.0, 100.0), description='Uncompacted loose ground (turf, grass, loose soil)'), 'D': GroundImpedanceClass(flow_resistivity_kpa_s_m2=200.0, nordtest_classes_kpa_s_m2=(160.0, 250.0), description='Normal uncompacted ground (forest floor, pasture field)'), 'E': GroundImpedanceClass(flow_resistivity_kpa_s_m2=500.0, nordtest_classes_kpa_s_m2=(400.0, 630.0), description='Compacted field and gravel (compacted lawns, park area)'), 'F': GroundImpedanceClass(flow_resistivity_kpa_s_m2=2000.0, nordtest_classes_kpa_s_m2=(2000.0,), description='Compacted dense ground (gravel road, parking lot, ISO 10844)'), 'G': GroundImpedanceClass(flow_resistivity_kpa_s_m2=20000.0, nordtest_classes_kpa_s_m2=(), description='Hard surface (most normal asphalt, concrete)'), 'H': GroundImpedanceClass(flow_resistivity_kpa_s_m2=200000.0, nordtest_classes_kpa_s_m2=(), description='Very hard and dense surfaces (dense asphalt, concrete, water)')}

LowFrequencyLevel(
    frequencies_hz: NDArray[np.float64],
    sound_power_levels_db: NDArray[np.float64],
    a_weighting_db: NDArray[np.float64],
    distance_terms_db: NDArray[np.float64],
    ground_correction_db: NDArray[np.float64],
    air_attenuation_db: NDArray[np.float64],
    facade_insulation_db: NDArray[np.float64] | None,
    outdoor_levels_db: NDArray[np.float64],
    indoor_levels_db: NDArray[np.float64] | None,
    outdoor_level_db: float,
    indoor_level_db: float | None,
    *,
    a_weighted: bool,
)

Low frequency sound at a receptor by Equation (C.1) (Annex C).

Arrays with a turbine axis have one row per turbine and one column per band; the band arrays are summed over the turbines.

Attributes

LowFrequencyLevel.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot the outdoor and indoor band levels.

Parameters

Returns: The axes.

Constant (float).

MODELLING_UNCERTAINTY_DB = 0.5

power_law_wind_speed(
    reference_speed_m_s: ArrayLike,
    *,
    height_m: float,
    reference_height_m: float,
    shear_exponent: ArrayLike,
) -> NDArray[np.float64] | float

Wind speed at another height by the power law (Equation (K.1)).

$V_z=V_{z,\mathrm{ref}}(z/z_{\mathrm{ref}}{)}^{\alpha }$, the profile of IEC 61400-11:2012 D.3 that Table K.1 evaluates for 120 m and 10 m.

Parameters

Returns: The wind speed at height_m, in m/s; a float for scalar inputs.

Raises

predicted_receptor_level(
    turbine_levels_db: ArrayLike,
    sound_power_uncertainty_db: ArrayLike,
    *,
    prediction_model_uncertainty_db: float = 2.0,
    modelling_uncertainty_db: float = 0.5,
) -> PredictedReceptorLevel

Sum a prediction over the turbines, with its uncertainty (10.3.4).

The receptor level is the energy sum of the turbines’ predicted levels. Equation (10) carries each turbine’s sound power level uncertainty to it weighted by the turbine’s share of the energy,

(a linear, fully correlated combination), and Equation (11) adds the prediction model and the modelling in quadrature.

Parameters

Returns: A PredictedReceptorLevel.

Raises

PredictedReceptorLevel(
    turbine_levels_db: NDArray[np.float64],
    sound_power_uncertainty_db: NDArray[np.float64],
    level_db: float,
    propagated_uncertainty_db: float,
    combined_uncertainty_db: float,
    *,
    prediction_model_uncertainty_db: float,
    modelling_uncertainty_db: float,
)

A predicted receptor level and its uncertainty (10.3.4).

Attributes

PredictedReceptorLevel.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot each turbine’s contribution and the total with its uncertainty.

Parameters

Returns: The axes.

Constant (float).

PREDICTION_MODEL_UNCERTAINTY_DB = 2.0

Constant (float).

REFERENCE_ROUGHNESS_LENGTH_M = 0.05

Constant (mapping).

ROUGHNESS_LENGTHS_M = {'Water, snow or sand surfaces': 0.0001, 'Open, flat land, mown grass, bare soil': 0.01, 'Farmland with some vegetation': 0.05, 'Suburbs, towns, forests, many trees and bushes': 0.3}

sound_emergence(
    ambient_levels_db: ArrayLike,
    background_levels_db: ArrayLike,
    *,
    wind_speeds_m_s: ArrayLike | None = None,
) -> SoundEmergence

The emergence criterion of each wind speed class (Equation (J.1)).

$E(j)=L_{\mathrm{Amb}}(j)-L_{\mathrm{R}\acute{\mathrm{e}}\mathrm{s}}(j)$, the difference of the ambient sound criterion (turbines operating) and the background sound criterion (turbines stopped) for each complete wind speed class, the criterion of French regulation. The TS warns it is not to be confused with limits on the difference between the turbine sound and the background.

Parameters

Returns: A SoundEmergence.

Raises

sound_relevant_turbines(
    predicted_levels_db: ArrayLike,
) -> SoundRelevantTurbines

Select the sound relevant turbines of a wind farm (9.3.2.3).

The turbines are sorted by their predicted contribution at the receptor. The quietest is left out of the prediction, and the next quietest after it, for as long as the total of those that remain has dropped by no more than 1.0 dB from the total of all; the turbine whose exclusion would drop it by more is kept, with every louder one. Typically two or three turbines remain. A drop within a nanodecibel of 1.0 dB is a drop of 1.0 dB, not more, so the rounding of the energy sums cannot keep a turbine the TS leaves out.

Parameters

Returns: A SoundRelevantTurbines.

Raises

SoundEmergence(
    ambient_levels_db: NDArray[np.float64],
    background_levels_db: NDArray[np.float64],
    emergence_db: NDArray[np.float64],
    *,
    wind_speeds_m_s: NDArray[np.float64] | None,
)

The emergence criterion per wind speed class (Annex J, Equation (J.1)).

Attributes

SoundEmergence.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot the emergence of each wind speed class.

Parameters

Returns: The axes.

SoundRelevantTurbines(predicted_levels_db: NDArray[np.float64])

The turbines that set the binning wind speed at a receptor (9.3.2.3).

Which turbines are relevant is read from the predicted levels and the 1.0 dB of 9.3.2.3, so it is not a field.

Attributes

SoundRelevantTurbines.binning_wind_speed_m_s(
    turbine_wind_speeds_m_s: ArrayLike,
) -> float

The binning wind speed: the arithmetic mean over the relevant turbines.

Parameters

Returns: The mean of the relevant turbines’ wind speeds, in m/s.

Raises

property

Positions of the sound relevant turbines, loudest first.

SoundRelevantTurbines.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot the turbines’ levels, loudest first, the relevant ones marked.

Parameters

Returns: The axes.

property

Whether each turbine is sound relevant.

The quietest is left out, and the next quietest after it, for as long as the total of those that remain has dropped by no more than 1.0 dB from the total of all (“reduced by more than 1,0 dB”, 9.3.2.3: 1.0 dB itself is not more, and a drop within a nanodecibel of it is 1.0 dB).

property

The predicted level of the relevant ones, in dB.

property

The predicted level of all turbines, in dB.

Constant (mapping).

SWEDISH_LOW_FREQUENCY_LIMITS_DB = {31.5: 56.0, 40.0: 49.0, 50.0: 43.0, 63.0: 42.0, 80.0: 40.0, 100.0: 38.0, 125.0: 36.0, 160.0: 34.0, 200.0: 32.0}

Constant (numpy.ndarray, shape (24,)).

ToneSearchLimit(
    distance_m: float,
    frequencies_hz: NDArray[np.float64],
    attenuation_db: NDArray[np.float64],
    upper_frequency_hz: float,
    *,
    temperature_c: float,
    relative_humidity_percent: float,
    atmospheric_pressure_kpa: float,
)

The upper frequency of the tonal search range (12.5.2.4).

Attributes

ToneSearchLimit.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot the band attenuation over the distance and the 20 dB line.

Parameters

Returns: The axes.

turbine_sound_levels(
    total_levels_db: ArrayLike,
    background_levels_db: ArrayLike,
    *,
    total_uncertainty_db: ArrayLike = 0.0,
    background_uncertainty_db: ArrayLike = 0.0,
    wind_speeds_m_s: ArrayLike | None = None,
    wind_directions_deg: ArrayLike | None = None,
) -> TurbineSoundLevels

Subtract the background from the total, bin by bin (10.3.2, 11.7).

Where the bin-averaged total is at least 3 dB above the background, the turbine level is the logarithmic subtraction of Equation (6), $L_{c,k}=10\lg (10^{L_{T,k}/10}-10^{L_{B,k}/10})$, with the uncertainty of Equation (7). Where it is at least 0 dB but less than 3 dB above, 11.7 suggests a 3 dB correction instead, and a regulatory excess cannot then be found (11.7 NOTE 1). Where the background is louder than the total, no turbine level can be determined (11.6.4) and the bin is NaN. A difference within a nanodecibel of 3 dB or of 0 dB is on that limit, so the rounding of two energy means cannot move a bin from one rule to the other.

Parameters

Returns: A TurbineSoundLevels.

Raises

TurbineSoundLevels(
    total_levels_db: NDArray[np.float64],
    background_levels_db: NDArray[np.float64],
    level_differences_db: NDArray[np.float64],
    turbine_levels_db: NDArray[np.float64],
    turbine_uncertainty_db: NDArray[np.float64],
    *,
    wind_speeds_m_s: NDArray[np.float64] | None,
    wind_directions_deg: NDArray[np.float64] | None,
)

Background-corrected wind turbine levels per bin (10.3.2, 11.7).

Attributes

Which rule of 11.7 each bin followed (regimes) is read from the total and the background levels, so it is not a field.

TurbineSoundLevels.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot total, background and turbine levels per bin.

Binned by direction, each sector is drawn apart, in its own colour: the turbine level solid, the total dashed and the background dotted. The twelve 30° sectors of 10.1 take twelve colours; narrower sectors past the twelfth take them again, in the same order.

Parameters

Returns: The axes.

property

The BackgroundCorrectionRegime of each bin (11.7).

Read from total_levels_db and background_levels_db: the logarithmic subtraction where the total is at least 3 dB above the background, the 3 dB correction where it is at least 0 dB but less than 3 dB above, and undetermined where the background is louder. A difference within a nanodecibel of 3 dB or of 0 dB is on that limit.

Constant (mapping).

TYPE_B_UNCERTAINTY_EXAMPLES_DB = {'calibration': (0.3, 0.2), 'instrument': (0.5, 0.3), 'measurement position': (0.5, 0.3), 'wind screen insertion loss': (0.3, 0.2)}

Constant (tuple).

TYPICAL_WIND_SHEAR_EXPONENT_RANGE = (-0.1, 0.5)

upper_tone_search_frequency(
    distance_m: float,
    *,
    temperature_c: float,
    relative_humidity_percent: float,
    atmospheric_pressure_kpa: float = 101.325,
) -> ToneSearchLimit

Upper frequency of the search for tones at a receptor (12.5.2.4).

Tones in a one-third-octave band that ISO 9613-1 attenuates by at least 20 dB over the distance to the nearest turbine are typically of no concern to its neighbours, and every higher band is attenuated more; the lowest such band sets the top of the search range. The attenuation is evaluated at the exact mid-band frequency of each band (ISO 9613-1), which reproduces the printed rows of Table 7; the search stops at 10 kHz, the top of ISO 9613-1, when no band reaches 20 dB.

Parameters

Returns: A ToneSearchLimit.

Raises

wind_shear_exponent(
    speed_m_s: ArrayLike,
    reference_speed_m_s: ArrayLike,
    *,
    height_m: float,
    reference_height_m: float,
) -> NDArray[np.float64] | float

Wind shear exponent from two wind speeds (Equation (K.2)).

$\alpha =\ln (V_z/V_{z,\mathrm{ref}})/\ln (z/z_{\mathrm{ref}})$; with $z=120$ m and $z_{\mathrm{ref}}=10$ m this is Table K.2. Values outside TYPICAL_WIND_SHEAR_EXPONENT_RANGE are returned as computed; Table K.2 leaves them blank.

Parameters

Returns: The shear exponent; a float for scalar inputs.

Raises

wind_shear_profile(
    lower_speed_m_s: float,
    upper_speed_m_s: float,
    *,
    lower_height_m: float = 10.0,
    upper_height_m: float,
) -> WindShearProfile

The power-law wind profile through two measured wind speeds (K.4.3).

The wind shear is typically found from a wind speed at 10 m and one at hub height (K.4.3); WindShearProfile.speed_at then gives the wind speed at any height by Equation (K.1).

Parameters

Returns: A WindShearProfile.

Raises

wind_turbine_low_frequency_level(
    sound_power_levels_db: ArrayLike,
    *,
    distance_m: ArrayLike,
    hub_height_m: ArrayLike,
    frequencies_hz: ArrayLike = ...,
    ground_correction_db: ArrayLike | None = None,
    air_attenuation_db_per_km: ArrayLike | None = None,
    facade_insulation_db: ArrayLike | None = None,
    a_weighted: bool = True,
) -> LowFrequencyLevel

Low frequency sound level at a receptor (Annex C, Equation (C.1)).

band by band, with $\Delta L_{\mathrm{a}}=\alpha \sqrt{l^2+h^2}$ (the distance in km for $\alpha$ in dB/km) and 11 dB the $10\lg (4\pi )$ of the text, taken as printed. The contributions of the relevant turbines are summed energetically per band, and the equivalent level is the energy sum of the bands (C.3, C.5); the Danish criterion of C.6.1 sums 10 Hz to 160 Hz, so pass those bands for it.

Parameters

Returns: A LowFrequencyLevel.

Raises

wind_turbine_rating_level(
    equivalent_level_db: float,
    *,
    tonal_adjustment_db: float = 0.0,
    amplitude_modulation_adjustment_db: float = 0.0,
    impulsive_adjustment_db: float = 0.0,
) -> WindTurbineRatingLevel

Rating level of wind turbine sound (Annex A, A.1).

$L_{\mathrm{r}}=L_{\mathrm{eq}}+K$ with $K$ the most severe of the tonal adjustment $K_{\mathrm{T}}$, the amplitude modulation adjustment $K_{\mathrm{am}}$ and the impulsive adjustment $K_{\mathrm{I}}$: only one type is applied at a time. Local regulation usually defines the adjustments; the TS’s examples are Table A.1 for $K_{\mathrm{T}}$ (tonal_adjustment_from_mean_audibility, ISO 1996-2:2017 Table J.1, with its 3 dB-step variant), Figure A.1 for $K_{\mathrm{am}}$ (amplitude_modulation_adjustment) and ISO/PAS 1996-3 for $K_{\mathrm{I}}$ (impulse_adjustment).

Parameters

Returns: A WindTurbineRatingLevel.

Raises

WindShearProfile(
    heights_m: tuple[float, float],
    speeds_m_s: tuple[float, float],
    shear_exponent: float,
)

A wind profile through two measured heights (Annex K).

Attributes

WindShearProfile.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot the power-law profile through the two measured points.

The logarithmic profile with the reference roughness length through the lower point is drawn for comparison, as the binning wind speed of 9.3.2.1 assumes it.

Parameters

Returns: The axes.

WindShearProfile.speed_at(
    height_m: ArrayLike,
) -> NDArray[np.float64] | float

The power-law wind speed at height_m (Equation (K.1)), in m/s.

property

Whether $\alpha$ lies in the typical range (K.4.3).

The range is TYPICAL_WIND_SHEAR_EXPONENT_RANGE, bounds included.

WindTurbineRatingLevel(
    equivalent_level_db: float,
    adjustments_db: Mapping[str, float],
    governing: str | None,
    rating_level_db: float,
)

The rating level of wind turbine sound (Annex A, A.1).

Attributes

WindTurbineRatingLevel.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot the three candidate adjustments with the governing one marked.

Parameters

Returns: The axes.