1 | !> @file lpm_droplet_collision.f90 |
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2 | !--------------------------------------------------------------------------------! |
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3 | ! This file is part of PALM. |
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4 | ! |
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5 | ! PALM is free software: you can redistribute it and/or modify it under the terms |
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6 | ! of the GNU General Public License as published by the Free Software Foundation, |
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7 | ! either version 3 of the License, or (at your option) any later version. |
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8 | ! |
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9 | ! PALM is distributed in the hope that it will be useful, but WITHOUT ANY |
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10 | ! WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR |
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11 | ! A PARTICULAR PURPOSE. See the GNU General Public License for more details. |
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12 | ! |
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13 | ! You should have received a copy of the GNU General Public License along with |
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14 | ! PALM. If not, see <http://www.gnu.org/licenses/>. |
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15 | ! |
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16 | ! Copyright 1997-2016 Leibniz Universitaet Hannover |
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17 | !--------------------------------------------------------------------------------! |
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18 | ! |
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19 | ! Current revisions: |
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20 | ! ------------------ |
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21 | ! |
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22 | ! |
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23 | ! Former revisions: |
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24 | ! ----------------- |
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25 | ! $Id: lpm_droplet_collision.f90 1885 2016-04-21 11:12:26Z raasch $ |
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26 | ! |
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27 | ! 1884 2016-04-21 11:11:40Z hoffmann |
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28 | ! Conservation of mass should only be checked if collisions took place. |
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29 | ! |
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30 | ! 1860 2016-04-13 13:21:28Z hoffmann |
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31 | ! Interpolation of dissipation rate adjusted to more reasonable values. |
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32 | ! |
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33 | ! 1822 2016-04-07 07:49:42Z hoffmann |
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34 | ! Integration of a new collision algortithm based on Shima et al. (2009) and |
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35 | ! Soelch and Kaercher (2010) called all_or_nothing. The previous implemented |
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36 | ! collision algorithm is called average_impact. Moreover, both algorithms are |
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37 | ! now positive definit due to their construction, i.e., no negative weighting |
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38 | ! factors should occur. |
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39 | ! |
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40 | ! 1682 2015-10-07 23:56:08Z knoop |
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41 | ! Code annotations made doxygen readable |
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42 | ! |
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43 | ! 1359 2014-04-11 17:15:14Z hoffmann |
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44 | ! New particle structure integrated. |
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45 | ! Kind definition added to all floating point numbers. |
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46 | ! |
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47 | ! 1322 2014-03-20 16:38:49Z raasch |
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48 | ! REAL constants defined as wp_kind |
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49 | ! |
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50 | ! 1320 2014-03-20 08:40:49Z raasch |
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51 | ! ONLY-attribute added to USE-statements, |
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52 | ! kind-parameters added to all INTEGER and REAL declaration statements, |
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53 | ! kinds are defined in new module kinds, |
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54 | ! revision history before 2012 removed, |
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55 | ! comment fields (!:) to be used for variable explanations added to |
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56 | ! all variable declaration statements |
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57 | ! |
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58 | ! 1092 2013-02-02 11:24:22Z raasch |
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59 | ! unused variables removed |
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60 | ! |
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61 | ! 1071 2012-11-29 16:54:55Z franke |
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62 | ! Calculation of Hall and Wang kernel now uses collision-coalescence formulation |
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63 | ! proposed by Wang instead of the continuous collection equation (for more |
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64 | ! information about new method see PALM documentation) |
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65 | ! Bugfix: message identifiers added |
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66 | ! |
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67 | ! 1036 2012-10-22 13:43:42Z raasch |
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68 | ! code put under GPL (PALM 3.9) |
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69 | ! |
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70 | ! 849 2012-03-15 10:35:09Z raasch |
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71 | ! initial revision (former part of advec_particles) |
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72 | ! |
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73 | ! |
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74 | ! Description: |
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75 | ! ------------ |
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76 | !> Calculates change in droplet radius by collision. Droplet collision is |
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77 | !> calculated for each grid box seperately. Collision is parameterized by |
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78 | !> using collision kernels. Two different kernels are available: |
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79 | !> Hall kernel: Kernel from Hall (1980, J. Atmos. Sci., 2486-2507), which |
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80 | !> considers collision due to pure gravitational effects. |
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81 | !> Wang kernel: Beside gravitational effects (treated with the Hall-kernel) also |
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82 | !> the effects of turbulence on the collision are considered using |
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83 | !> parameterizations of Ayala et al. (2008, New J. Phys., 10, |
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84 | !> 075015) and Wang and Grabowski (2009, Atmos. Sci. Lett., 10, |
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85 | !> 1-8). This kernel includes three possible effects of turbulence: |
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86 | !> the modification of the relative velocity between the droplets, |
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87 | !> the effect of preferential concentration, and the enhancement of |
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88 | !> collision efficiencies. |
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89 | !------------------------------------------------------------------------------! |
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90 | SUBROUTINE lpm_droplet_collision (i,j,k) |
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91 | |
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92 | |
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93 | |
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94 | USE arrays_3d, & |
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95 | ONLY: diss, ql_v, ql_vp |
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96 | |
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97 | USE cloud_parameters, & |
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98 | ONLY: rho_l |
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99 | |
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100 | USE constants, & |
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101 | ONLY: pi |
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102 | |
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103 | USE control_parameters, & |
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104 | ONLY: dt_3d, message_string, dz |
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105 | |
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106 | USE cpulog, & |
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107 | ONLY: cpu_log, log_point_s |
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108 | |
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109 | USE grid_variables, & |
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110 | ONLY: dx, dy |
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111 | |
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112 | USE kinds |
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113 | |
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114 | USE lpm_collision_kernels_mod, & |
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115 | ONLY: ckernel, recalculate_kernel |
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116 | |
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117 | USE particle_attributes, & |
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118 | ONLY: all_or_nothing, average_impact, dissipation_classes, & |
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119 | hall_kernel, iran_part, number_of_particles, particles, & |
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120 | particle_type, prt_count, use_kernel_tables, wang_kernel |
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121 | |
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122 | USE random_function_mod, & |
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123 | ONLY: random_function |
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124 | |
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125 | USE pegrid |
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126 | |
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127 | IMPLICIT NONE |
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128 | |
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129 | INTEGER(iwp) :: eclass !< |
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130 | INTEGER(iwp) :: i !< |
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131 | INTEGER(iwp) :: j !< |
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132 | INTEGER(iwp) :: k !< |
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133 | INTEGER(iwp) :: n !< |
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134 | INTEGER(iwp) :: m !< |
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135 | INTEGER(iwp) :: rclass_l !< |
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136 | INTEGER(iwp) :: rclass_s !< |
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137 | |
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138 | REAL(wp) :: collection_probability !< probability for collection |
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139 | REAL(wp) :: ddV !< inverse grid box volume |
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140 | REAL(wp) :: epsilon !< dissipation rate |
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141 | REAL(wp) :: factor_volume_to_mass !< 4.0 / 3.0 * pi * rho_l |
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142 | REAL(wp) :: xm !< mean mass of droplet m |
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143 | REAL(wp) :: xn !< mean mass of droplet n |
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144 | |
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145 | REAL(wp), DIMENSION(:), ALLOCATABLE :: weight !< weighting factor |
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146 | REAL(wp), DIMENSION(:), ALLOCATABLE :: mass !< total mass of super droplet |
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147 | |
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148 | CALL cpu_log( log_point_s(43), 'lpm_droplet_coll', 'start' ) |
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149 | |
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150 | number_of_particles = prt_count(k,j,i) |
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151 | factor_volume_to_mass = 4.0_wp / 3.0_wp * pi * rho_l |
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152 | ddV = 1 / ( dx * dy * dz ) |
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153 | ! |
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154 | !-- Collision requires at least one super droplet inside the box |
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155 | IF ( number_of_particles > 0 ) THEN |
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156 | |
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157 | ! |
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158 | !-- Now apply the different kernels |
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159 | IF ( use_kernel_tables ) THEN |
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160 | ! |
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161 | !-- Fast method with pre-calculated collection kernels for |
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162 | !-- discrete radius- and dissipation-classes. |
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163 | !-- |
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164 | !-- Determine dissipation class index of this gridbox |
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165 | IF ( wang_kernel ) THEN |
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166 | eclass = INT( diss(k,j,i) * 1.0E4_wp / 600.0_wp * & |
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167 | dissipation_classes ) + 1 |
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168 | epsilon = diss(k,j,i) |
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169 | ELSE |
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170 | epsilon = 0.0_wp |
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171 | ENDIF |
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172 | IF ( hall_kernel .OR. epsilon * 1.0E4_wp < 0.001_wp ) THEN |
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173 | eclass = 0 ! Hall kernel is used |
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174 | ELSE |
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175 | eclass = MIN( dissipation_classes, eclass ) |
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176 | ENDIF |
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177 | |
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178 | ! |
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179 | !-- Droplet collision are calculated using collision-coalescence |
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180 | !-- formulation proposed by Wang (see PALM documentation) |
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181 | !-- Temporary fields for total mass of super-droplet and weighting factors |
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182 | !-- are allocated. |
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183 | ALLOCATE(mass(1:number_of_particles), weight(1:number_of_particles)) |
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184 | |
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185 | mass(1:number_of_particles) = particles(1:number_of_particles)%weight_factor * & |
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186 | particles(1:number_of_particles)%radius**3 * & |
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187 | factor_volume_to_mass |
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188 | weight(1:number_of_particles) = particles(1:number_of_particles)%weight_factor |
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189 | |
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190 | IF ( average_impact ) THEN ! select collision algorithm |
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191 | |
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192 | DO n = 1, number_of_particles |
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193 | |
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194 | rclass_l = particles(n)%class |
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195 | xn = mass(n) / weight(n) |
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196 | |
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197 | DO m = n, number_of_particles |
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198 | |
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199 | rclass_s = particles(m)%class |
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200 | xm = mass(m) / weight(m) |
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201 | |
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202 | IF ( xm .LT. xn ) THEN |
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203 | |
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204 | ! |
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205 | !-- Particle n collects smaller particle m |
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206 | collection_probability = ckernel(rclass_l,rclass_s,eclass) * & |
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207 | weight(n) * ddV * dt_3d |
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208 | |
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209 | mass(n) = mass(n) + mass(m) * collection_probability |
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210 | weight(m) = weight(m) - weight(m) * collection_probability |
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211 | mass(m) = mass(m) - mass(m) * collection_probability |
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212 | ELSEIF ( xm .GT. xn ) THEN |
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213 | ! |
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214 | !-- Particle m collects smaller particle n |
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215 | collection_probability = ckernel(rclass_l,rclass_s,eclass) * & |
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216 | weight(m) * ddV * dt_3d |
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217 | |
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218 | mass(m) = mass(m) + mass(n) * collection_probability |
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219 | weight(n) = weight(n) - weight(n) * collection_probability |
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220 | mass(n) = mass(n) - mass(n) * collection_probability |
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221 | ELSE |
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222 | ! |
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223 | !-- Same-size collections. If n = m, weight is reduced by the |
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224 | !-- number of possible same-size collections; the total mass |
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225 | !-- is not changed during same-size collection. |
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226 | !-- Same-size collections of different |
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227 | !-- particles ( n /= m ) are treated as same-size collections |
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228 | !-- of ONE partilce with weight = weight(n) + weight(m) and |
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229 | !-- mass = mass(n) + mass(m). |
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230 | !-- Accordingly, each particle loses the same number of |
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231 | !-- droplets to the other particle, but this has no effect on |
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232 | !-- total mass mass, since the exchanged droplets have the |
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233 | !-- same radius. |
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234 | |
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235 | !-- Note: For m = n this equation is an approximation only |
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236 | !-- valid for weight >> 1 (which is usually the case). The |
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237 | !-- approximation is weight(n)-1 = weight(n). |
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238 | weight(n) = weight(n) - 0.5_wp * weight(n) * & |
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239 | ckernel(rclass_l,rclass_s,eclass) * & |
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240 | weight(m) * ddV * dt_3d |
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241 | IF ( n .NE. m ) THEN |
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242 | weight(m) = weight(m) - 0.5_wp * weight(m) * & |
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243 | ckernel(rclass_l,rclass_s,eclass) * & |
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244 | weight(n) * ddV * dt_3d |
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245 | ENDIF |
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246 | ENDIF |
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247 | |
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248 | ENDDO |
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249 | |
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250 | ql_vp(k,j,i) = ql_vp(k,j,i) + mass(n) / factor_volume_to_mass |
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251 | |
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252 | ENDDO |
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253 | |
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254 | ELSEIF ( all_or_nothing ) THEN ! select collision algorithm |
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255 | |
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256 | DO n = 1, number_of_particles |
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257 | |
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258 | rclass_l = particles(n)%class |
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259 | xn = mass(n) / weight(n) ! mean mass of droplet n |
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260 | |
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261 | DO m = n, number_of_particles |
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262 | |
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263 | rclass_s = particles(m)%class |
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264 | xm = mass(m) / weight(m) ! mean mass of droplet m |
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265 | |
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266 | IF ( weight(n) .LT. weight(m) ) THEN |
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267 | ! |
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268 | !-- Particle n collects weight(n) droplets of particle m |
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269 | collection_probability = ckernel(rclass_l,rclass_s,eclass) * & |
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270 | weight(m) * ddV * dt_3d |
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271 | |
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272 | IF ( collection_probability .GT. random_function( iran_part ) ) THEN |
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273 | mass(n) = mass(n) + weight(n) * xm |
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274 | weight(m) = weight(m) - weight(n) |
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275 | mass(m) = mass(m) - weight(n) * xm |
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276 | ENDIF |
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277 | |
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278 | ELSEIF ( weight(m) .LT. weight(n) ) THEN |
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279 | ! |
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280 | !-- Particle m collects weight(m) droplets of particle n |
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281 | collection_probability = ckernel(rclass_l,rclass_s,eclass) * & |
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282 | weight(n) * ddV * dt_3d |
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283 | |
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284 | IF ( collection_probability .GT. random_function( iran_part ) ) THEN |
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285 | mass(m) = mass(m) + weight(m) * xn |
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286 | weight(n) = weight(n) - weight(m) |
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287 | mass(n) = mass(n) - weight(m) * xn |
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288 | ENDIF |
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289 | ELSE |
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290 | ! |
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291 | !-- Collisions of particles of the same weighting factor. |
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292 | !-- Particle n collects 1/2 weight(n) droplets of particle m, |
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293 | !-- particle m collects 1/2 weight(m) droplets of particle n. |
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294 | !-- The total mass mass changes accordingly. |
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295 | !-- If n = m, the first half of the droplets coalesces with the |
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296 | !-- second half of the droplets; mass is unchanged because |
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297 | !-- xm = xn for n = m. |
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298 | |
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299 | !-- Note: For m = n this equation is an approximation only |
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300 | !-- valid for weight >> 1 (which is usually the case). The |
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301 | !-- approximation is weight(n)-1 = weight(n). |
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302 | collection_probability = ckernel(rclass_l,rclass_s,eclass) * & |
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303 | weight(n) * ddV * dt_3d |
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304 | |
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305 | IF ( collection_probability .GT. random_function( iran_part ) ) THEN |
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306 | mass(n) = mass(n) + 0.5_wp * weight(n) * ( xm - xn ) |
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307 | mass(m) = mass(m) + 0.5_wp * weight(m) * ( xn - xm ) |
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308 | weight(n) = weight(n) - 0.5_wp * weight(m) |
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309 | weight(m) = weight(n) |
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310 | ENDIF |
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311 | ENDIF |
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312 | |
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313 | ENDDO |
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314 | |
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315 | ql_vp(k,j,i) = ql_vp(k,j,i) + mass(n) / factor_volume_to_mass |
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316 | |
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317 | ENDDO |
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318 | |
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319 | ENDIF |
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320 | |
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321 | |
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322 | |
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323 | |
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324 | IF ( ANY(weight < 0.0_wp) ) THEN |
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325 | WRITE( message_string, * ) 'negative weighting' |
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326 | CALL message( 'lpm_droplet_collision', 'PA0028', & |
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327 | 2, 2, -1, 6, 1 ) |
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328 | ENDIF |
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329 | |
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330 | particles(1:number_of_particles)%radius = ( mass(1:number_of_particles) / & |
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331 | ( weight(1:number_of_particles) & |
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332 | * factor_volume_to_mass & |
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333 | ) & |
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334 | )**0.33333333333333_wp |
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335 | |
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336 | particles(1:number_of_particles)%weight_factor = weight(1:number_of_particles) |
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337 | |
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338 | DEALLOCATE(weight, mass) |
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339 | |
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340 | ELSEIF ( .NOT. use_kernel_tables ) THEN |
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341 | ! |
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342 | !-- Collection kernels are calculated for every new |
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343 | !-- grid box. First, allocate memory for kernel table. |
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344 | !-- Third dimension is 1, because table is re-calculated for |
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345 | !-- every new dissipation value. |
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346 | ALLOCATE( ckernel(1:number_of_particles,1:number_of_particles,1:1) ) |
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347 | ! |
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348 | !-- Now calculate collection kernel for this box. Note that |
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349 | !-- the kernel is based on the previous time step |
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350 | CALL recalculate_kernel( i, j, k ) |
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351 | ! |
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352 | !-- Droplet collision are calculated using collision-coalescence |
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353 | !-- formulation proposed by Wang (see PALM documentation) |
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354 | !-- Temporary fields for total mass of super-droplet and weighting factors |
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355 | !-- are allocated. |
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356 | ALLOCATE(mass(1:number_of_particles), weight(1:number_of_particles)) |
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357 | |
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358 | mass(1:number_of_particles) = particles(1:number_of_particles)%weight_factor * & |
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359 | particles(1:number_of_particles)%radius**3 * & |
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360 | factor_volume_to_mass |
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361 | |
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362 | weight(1:number_of_particles) = particles(1:number_of_particles)%weight_factor |
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363 | |
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364 | IF ( average_impact ) THEN ! select collision algorithm |
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365 | |
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366 | DO n = 1, number_of_particles |
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367 | |
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368 | xn = mass(n) / weight(n) ! mean mass of droplet n |
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369 | |
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370 | DO m = n, number_of_particles |
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371 | |
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372 | xm = mass(m) / weight(m) !mean mass of droplet m |
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373 | |
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374 | IF ( xm .LT. xn ) THEN |
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375 | ! |
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376 | !-- Particle n collects smaller particle m |
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377 | collection_probability = ckernel(n,m,1) * weight(n) * & |
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378 | ddV * dt_3d |
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379 | |
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380 | mass(n) = mass(n) + mass(m) * collection_probability |
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381 | weight(m) = weight(m) - weight(m) * collection_probability |
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382 | mass(m) = mass(m) - mass(m) * collection_probability |
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383 | ELSEIF ( xm .GT. xn ) THEN |
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384 | ! |
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385 | !-- Particle m collects smaller particle n |
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386 | collection_probability = ckernel(n,m,1) * weight(m) * & |
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387 | ddV * dt_3d |
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388 | |
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389 | mass(m) = mass(m) + mass(n) * collection_probability |
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390 | weight(n) = weight(n) - weight(n) * collection_probability |
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391 | mass(n) = mass(n) - mass(n) * collection_probability |
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392 | ELSE |
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393 | ! |
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394 | !-- Same-size collections. If n = m, weight is reduced by the |
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395 | !-- number of possible same-size collections; the total mass |
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396 | !-- mass is not changed during same-size collection. |
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397 | !-- Same-size collections of different |
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398 | !-- particles ( n /= m ) are treated as same-size collections |
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399 | !-- of ONE partilce with weight = weight(n) + weight(m) and |
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400 | !-- mass = mass(n) + mass(m). |
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401 | !-- Accordingly, each particle loses the same number of |
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402 | !-- droplets to the other particle, but this has no effect on |
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403 | !-- total mass mass, since the exchanged droplets have the |
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404 | !-- same radius. |
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405 | !-- |
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406 | !-- Note: For m = n this equation is an approximation only |
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407 | !-- valid for weight >> 1 (which is usually the case). The |
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408 | !-- approximation is weight(n)-1 = weight(n). |
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409 | weight(n) = weight(n) - 0.5_wp * weight(n) * & |
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410 | ckernel(n,m,1) * weight(m) * & |
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411 | ddV * dt_3d |
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412 | IF ( n .NE. m ) THEN |
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413 | weight(m) = weight(m) - 0.5_wp * weight(m) * & |
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414 | ckernel(n,m,1) * weight(n) * & |
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415 | ddV * dt_3d |
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416 | ENDIF |
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417 | ENDIF |
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418 | |
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419 | |
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420 | ENDDO |
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421 | |
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422 | ql_vp(k,j,i) = ql_vp(k,j,i) + mass(n) / factor_volume_to_mass |
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423 | |
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424 | ENDDO |
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425 | |
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426 | ELSEIF ( all_or_nothing ) THEN ! select collision algorithm |
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427 | |
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428 | DO n = 1, number_of_particles |
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429 | |
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430 | xn = mass(n) / weight(n) ! mean mass of droplet n |
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431 | |
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432 | DO m = n, number_of_particles |
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433 | |
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434 | xm = mass(m) / weight(m) !mean mass of droplet m |
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435 | |
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436 | IF ( weight(n) .LT. weight(m) ) THEN |
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437 | ! |
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438 | !-- Particle n collects smaller particle m |
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439 | collection_probability = ckernel(n,m,1) * weight(m) * & |
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440 | ddV * dt_3d |
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441 | |
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442 | IF ( collection_probability .GT. random_function( iran_part ) ) THEN |
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443 | mass(n) = mass(n) + weight(n) * xm |
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444 | weight(m) = weight(m) - weight(n) |
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445 | mass(m) = mass(m) - weight(n) * xm |
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446 | ENDIF |
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447 | |
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448 | ELSEIF ( weight(m) .LT. weight(n) ) THEN |
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449 | ! |
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450 | !-- Particle m collects smaller particle n |
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451 | collection_probability = ckernel(n,m,1) * weight(n) * & |
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452 | ddV * dt_3d |
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453 | |
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454 | IF ( collection_probability .GT. random_function( iran_part ) ) THEN |
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455 | mass(m) = mass(m) + weight(m) * xn |
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456 | weight(n) = weight(n) - weight(m) |
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457 | mass(n) = mass(n) - weight(m) * xn |
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458 | ENDIF |
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459 | ELSE |
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460 | ! |
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461 | !-- Collisions of particles of the same weighting factor. |
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462 | !-- Particle n collects 1/2 weight(n) droplets of particle m, |
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463 | !-- particle m collects 1/2 weight(m) droplets of particle n. |
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464 | !-- The total mass mass changes accordingly. |
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465 | !-- If n = m, the first half of the droplets coalesces with the |
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466 | !-- second half of the droplets; mass is unchanged because |
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467 | !-- xm = xn for n = m. |
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468 | !-- |
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469 | !-- Note: For m = n this equation is an approximation only |
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470 | !-- valid for weight >> 1 (which is usually the case). The |
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471 | !-- approximation is weight(n)-1 = weight(n). |
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472 | collection_probability = ckernel(n,m,1) * weight(n) * & |
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473 | ddV * dt_3d |
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474 | |
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475 | IF ( collection_probability .GT. random_function( iran_part ) ) THEN |
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476 | mass(n) = mass(n) + 0.5_wp * weight(n) * ( xm - xn ) |
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477 | mass(m) = mass(m) + 0.5_wp * weight(m) * ( xn - xm ) |
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478 | weight(n) = weight(n) - 0.5_wp * weight(m) |
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479 | weight(m) = weight(n) |
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480 | ENDIF |
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481 | ENDIF |
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482 | |
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483 | |
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484 | ENDDO |
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485 | |
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486 | ql_vp(k,j,i) = ql_vp(k,j,i) + mass(n) / factor_volume_to_mass |
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487 | |
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488 | ENDDO |
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489 | |
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490 | ENDIF |
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491 | |
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492 | IF ( ANY(weight < 0.0_wp) ) THEN |
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493 | WRITE( message_string, * ) 'negative weighting' |
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494 | CALL message( 'lpm_droplet_collision', 'PA0028', & |
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495 | 2, 2, -1, 6, 1 ) |
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496 | ENDIF |
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497 | |
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498 | particles(1:number_of_particles)%radius = ( mass(1:number_of_particles) / & |
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499 | ( weight(1:number_of_particles) & |
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500 | * factor_volume_to_mass & |
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501 | ) & |
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502 | )**0.33333333333333_wp |
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503 | |
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504 | particles(1:number_of_particles)%weight_factor = weight(1:number_of_particles) |
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505 | |
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506 | DEALLOCATE( weight, mass, ckernel ) |
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507 | |
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508 | ENDIF |
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509 | |
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510 | ! |
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511 | !-- Check if LWC is conserved during collision process |
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512 | IF ( ql_v(k,j,i) /= 0.0_wp ) THEN |
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513 | IF ( ql_vp(k,j,i) / ql_v(k,j,i) >= 1.0001_wp .OR. & |
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514 | ql_vp(k,j,i) / ql_v(k,j,i) <= 0.9999_wp ) THEN |
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515 | WRITE( message_string, * ) ' LWC is not conserved during', & |
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516 | ' collision! ', & |
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517 | ' LWC after condensation: ', ql_v(k,j,i), & |
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518 | ' LWC after collision: ', ql_vp(k,j,i) |
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519 | CALL message( 'lpm_droplet_collision', 'PA0040', 2, 2, -1, 6, 1 ) |
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520 | ENDIF |
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521 | ENDIF |
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522 | |
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523 | ENDIF |
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524 | |
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525 | CALL cpu_log( log_point_s(43), 'lpm_droplet_coll', 'stop' ) |
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526 | |
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527 | END SUBROUTINE lpm_droplet_collision |
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