Source code for ladybug_comfort.local

# coding=utf-8
"""Utility functions for computing local thermal discomfort according to ASHRAE-55."""
from __future__ import division

import math




def radiant_asymmetry_ppd(radiant_temperature_difference, asymmetry_type):
    """Calculate the percentage of people dissatisfied from radiant asymmetry.

    The comfort functions used here come from Figure 5.2.4.1 of ASHRAE 55 2010.
    Note that, if the resulting input results in a PPD beyond what is included
    in this Figure, the maximum PPD will simply be returned.

    Note:
        [1] ASHRAE Standard 55 (2010). "Thermal Environmental Conditions
        for Human Occupancy." American Society of Heating,
        Refrigerating and Air Conditioning Engineers.

    Args:
        radiant_temperature_difference: The radiant temperature difference between
            two sides of the same plane where an occupant is located [C].
        asymmetry_type: An integer that representing the type of radiant
            asymmetry being evaluated. Occupants are more sensitive to warm
            ceilings and cool walls than cool ceilings and warm walls.
            Choose from the following options.

            * 0 = WarmCeiling
            * 1 = CoolWall
            * 2 = CoolCeiling
            * 3 = WarmWall

    Returns:
        ppd -- The percentage of people dissatisfied (PPD) for the input
        radiant asymmetry.
    """
    td = radiant_temperature_difference
    if asymmetry_type == 0:
        if td > 23:
            ppd = 100 / (1 + math.exp(2.84 - 0.174 * 23)) - 5.5
        else:
            ppd = 100 / (1 + math.exp(2.84 - 0.174 * td)) - 5.5
    elif asymmetry_type == 1:
        if td > 15:
            ppd = 100 / (1 + math.exp(6.61 - 0.345 * 15))
        else:
            ppd = 100 / (1 + math.exp(6.61 - 0.345 * td))
    elif asymmetry_type == 2:
        if td > 15:
            ppd = 100 / (1 + math.exp(9.93 - 0.50 * 15))
        else:
            ppd = 100 / (1 + math.exp(9.93 - 0.50 * td))
    elif asymmetry_type == 3:
        if td > 35:
            ppd = 100 / (1 + math.exp(3.72 - 0.052 * 35)) - 3.5
        else:
            ppd = 100 / (1 + math.exp(3.72 - 0.052 * td)) - 3.5
    else:
        raise ValueError(
            'Radiant asymmetry_type "{}" was not recognized.'.format(asymmetry_type))
    return ppd





def ankle_draft_ppd(predicted_mean_vote, air_speed_at_ankle):
    """Calculate the percentage of people dissatisfied from cold drafts at ankle-level.

    The original tests used to create the model involved blowing cold air on
    subject's ankles at a height of 10 cm off of the ground.
    The formula was officially incorporated in the ASHRAE 55 standard in 2020
    with a recommendation that PPD from ankle draft not exceed 20%.

    Note:
        [1] ASHRAE Standard 55 (2020). "Thermal Environmental Conditions
        for Human Occupancy." American Society of Heating,
        Refrigerating and Air Conditioning Engineers.

        [2] Schiavon, S., D. Rim, W. Pasut, W. Nazaroff. 2016. "Sensation of
        draft at uncovered ankles for women exposed to displacement ventilation
        and underfloor air distribution systems." Building and Environment, 96, 228-236.

        [3] Liu, S., S. Schiavon, A. Kabanshi, W. Nazaroff. 2016. "Predicted
        percentage of dissatisfied with ankle draft." Accepted Author Manuscript.
        Indoor Environmental Quality. http://escholarship.org/uc/item/9076254n

    Args:
        predicted_mean_vote: The full-body predicted mean vote (PMV) of the subject.
            Ankle draft depends on full-body PMV because subjects are more likely
            to feel uncomfortably cold at their extremities when their whole body
            is already feeling colder than neutral.
            The functions in the pmv module can be used to calculate this input.
        air_speed_at_ankle: The velocity of the draft in m/s at ankle level
            (10cm above the floor).

    Returns:
        ppd -- The percentage of people dissatisfied (PPD) from cold downdraft
        at ankle-level.
    """
    c_term = math.exp(-2.58 + (3.05 * air_speed_at_ankle) - (1.06 * predicted_mean_vote))
    return 100 * (c_term / (1 + c_term))