deeplearning/modulus/modulus-sym-v100/_modules/modulus/sym/eq/pdes/advection_diffusion.html

Source code for modulus.sym.eq.pdes.advection_diffusion

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"""Advection diffusion equation
Reference:
https://en.wikipedia.org/wiki/Convection%E2%80%93diffusion_equation
"""
from sympy import Symbol, Function, Number

from modulus.sym.eq.pde import PDE
from modulus.sym.node import Node


[docs]class AdvectionDiffusion(PDE): """ Advection diffusion equation Parameters ========== T : str The dependent variable. D : float, Sympy Symbol/Expr, str Diffusivity. If `D` is a str then it is converted to Sympy Function of form 'D(x,y,z,t)'. If 'D' is a Sympy Symbol or Expression then this is substituted into the equation. Q : float, Sympy Symbol/Expr, str The source term. If `Q` is a str then it is converted to Sympy Function of form 'Q(x,y,z,t)'. If 'Q' is a Sympy Symbol or Expression then this is substituted into the equation. Default is 0. rho : float, Sympy Symbol/Expr, str The density. If `rho` is a str then it is converted to Sympy Function of form 'rho(x,y,z,t)'. If 'rho' is a Sympy Symbol or Expression then this is substituted into the equation to allow for compressible Navier Stokes. dim : int Dimension of the diffusion equation (1, 2, or 3). Default is 3. time : bool If time-dependent equations or not. Default is False. mixed_form: bool If True, use the mixed formulation of the wave equation. Examples ======== >>> ad = AdvectionDiffusion(D=0.1, rho=1.) >>> ad.pprint() advection_diffusion: u*T__x + v*T__y + w*T__z - 0.1*T__x__x - 0.1*T__y__y - 0.1*T__z__z >>> ad = AdvectionDiffusion(D='D', rho=1, dim=2, time=True) >>> ad.pprint() advection_diffusion: -D*T__x__x - D*T__y__y + u*T__x + v*T__y - D__x*T__x - D__y*T__y + T__t """ name = "AdvectionDiffusion" def __init__( self, T="T", D="D", Q=0, rho="rho", dim=3, time=False, mixed_form=False ): # set params self.T = T self.dim = dim self.time = time self.mixed_form = mixed_form # coordinates x, y, z = Symbol("x"), Symbol("y"), Symbol("z") # time t = Symbol("t") # make input variables input_variables = {"x": x, "y": y, "z": z, "t": t} if self.dim == 1: input_variables.pop("y") input_variables.pop("z") elif self.dim == 2: input_variables.pop("z") if not self.time: input_variables.pop("t") # velocity componets u = Function("u")(*input_variables) v = Function("v")(*input_variables) w = Function("w")(*input_variables) # Temperature assert type(T) == str, "T needs to be string" T = Function(T)(*input_variables) # Diffusivity if type(D) is str: D = Function(D)(*input_variables) elif type(D) in [float, int]: D = Number(D) # Source if type(Q) is str: Q = Function(Q)(*input_variables) elif type(Q) in [float, int]: Q = Number(Q) # Density if type(rho) is str: rho = Function(rho)(*input_variables) elif type(rho) in [float, int]: rho = Number(rho) # set equations self.equations = {} advection = ( rho * u * (T.diff(x)) + rho * v * (T.diff(y)) + rho * w * (T.diff(z)) ) if not self.mixed_form: diffusion = ( (rho * D * T.diff(x)).diff(x) + (rho * D * T.diff(y)).diff(y) + (rho * D * T.diff(z)).diff(z) ) self.equations["advection_diffusion_" + self.T] = ( T.diff(t) + advection - diffusion - Q ) elif self.mixed_form: T_x = Function(self.T + "_x")(*input_variables) T_y = Function(self.T + "_y")(*input_variables) if self.dim == 3: T_z = Function(self.T + "_z")(*input_variables) else: T_z = Number(0) diffusion = ( (rho * D * T_x).diff(x) + (rho * D * T_y).diff(y) + (rho * D * T_z).diff(z) ) self.equations["compatibility_" + self.T + "_x"] = T.diff(x) - T_x self.equations["compatibility_" + self.T + "_y"] = T.diff(y) - T_y self.equations["compatibility_" + self.T + "_z"] = T.diff(z) - T_z self.equations["compatibility_" + self.T + "_xy"] = T_x.diff(y) - T_y.diff( x ) self.equations["compatibility_" + self.T + "_xz"] = T_x.diff(z) - T_z.diff( x ) self.equations["compatibility_" + self.T + "_yz"] = T_y.diff(z) - T_z.diff( y ) if self.dim == 2: self.equations.pop("compatibility_" + self.T + "_z") self.equations.pop("compatibility_" + self.T + "_xz") self.equations.pop("compatibility_" + self.T + "_yz") self.equations["advection_diffusion_" + self.T] = ( T.diff(t) + advection - diffusion - Q )
© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023.