Source code for bladedesigner.camberlines.n3dcamberline

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# ***************************************************************************
# *   Copyright (C) 2011-2013 by Andreas Kührmann [kuean@users.sf.net] and  *
# *   Fabian Schäffer                                                       *
# *                                                                         *
# *   This program is free software; you can redistribute it and/or modify  *
# *   it under the terms of the GNU General Public License as published by  *
# *   the Free Software Foundation; either version 3 of the License, or     *
# *   (at your option) any later version.                                   *
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# *   This program is distributed in the hope that it will be useful,       *
# *   but WITHOUT ANY WARRANTY; without even the implied warranty of        *
# *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the         *
# *   GNU General Public License for more details.                          *
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# *   You should have received a copy of the GNU General Public License     *
# *   along with this program; if not, write to the                         *
# *   Free Software Foundation, Inc.,                                       *
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# ***************************************************************************


import numpy as np

import bladedesigner.baseclasses as bcls
import bladedesigner.foundation as fdn


__all__ = ['N3DCamberLine']


coefficients_map = {0.05: (0.058, 60.233333333),
                    0.1: (0.126, 8.606666667),
                    0.15: (0.2025, 2.6595),
                    0.2: (0.29, 1.107166667),
                    0.25: (0.391, 0.538333333)}


class N3DCamberLine(bcls.AnalyticalCamberLine):
    """
    The NACA 3-digit camber line is formed by two parabolic segments that
    match in value and slope at variable m, which is characterized by the
    max_camber_position.
    """

    def __init__(self):
        super(N3DCamberLine, self).__init__()
        # properties (initialized by user)
        self.__max_camber_position = fdn.Uninit('max_camber_position')
        # add user properties to initialization summary
        self._properties.append('max_camber_position')

    @property
    def max_camber_position(self):
        """
        Type: ``float`` - only 0.05, 0.1, 0.15, 0.2 and 0.25 are allowed
        """
        return self.__max_camber_position

    @max_camber_position.setter
    @fdn.restrict(new_max_camber_position=[.05, .1, .15, .2, .25])
    def max_camber_position(self, new_max_camber_position):
        if self.__max_camber_position != new_max_camber_position:
            self.__max_camber_position = new_max_camber_position
            self.update()

    @fdn.memoize
    def get_derivations(self):
        """
        get_derivations()

        Returns: ``ndarray``

        Calculates camber line derivations and returns them in an array.

        .. note:: **Note**

           The return value will be cached. Recalling this method returns the
           cached value, if the attribues are unchanged.
        """
        self._check_initialization()
        self._cached = True
        m, k3 = coefficients_map[self.max_camber_position]
        x = self.distribution(self.sample_rate)
        index = np.where(x <= m)[0]
        if index.size:
            x1 = x[index]
            dydx_1 = k3 * (x1 * (3 * x1 - 6 * m) + m ** 2 * (3 - m))
        index = np.where(x > m)[0]
        if index.size:
            dydx_2 = -k3 * np.power(m, 3) / 6 * np.ones(x[index].shape)
        return np.append(dydx_1, dydx_2)

    @fdn.memoize
    def as_array(self):
        """
        as_array()

        Returns: ``ndarray``

        Calculates camber line coordinates and returns them in an array.

        .. note:: **Note**

           The return value will be cached. Recalling this method returns the
           cached value, if the attribues are unchanged.
        """
        self._check_initialization()
        self._cached = True
        m, A = coefficients_map[self.max_camber_position]
        x = self.distribution(self.sample_rate)
        index = np.where(x <= m)[0]
        if index.size:
            z = x[index]
            y1 = A * z * (np.power(z, 2) + m * (m * (3 - m) - 3 * z))
        index = np.where(x > m)[0]
        if index.size:
            y2 = A * np.power(m, 3) * (1 - x[index])
        y = np.append(y1, y2)
        return np.reshape(np.append(x, y), (-1, 2), "F")