ExplicitEuler

Time integration using the explicit Euler method.

The explicit Euler method is only first-order accurate in time.

Description

With $var element = document.getElementById("moose-equation-4d727662-7799-437e-afe5-3e9046848a78");katex.render("U", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ , the vector of nonlinear variables, and $var element = document.getElementById("moose-equation-7e6c1543-9dc2-481f-9d5d-93ab0e515301");katex.render("A", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ , a nonlinear operator, we write the PDE of interest as:

var element = document.getElementById("moose-equation-3363e322-558a-438f-b5df-505621730137");katex.render("\\dfrac{\\partial U}{\\partial t} = A(t, U(t))", element, {displayMode:true,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});

Using $var element = document.getElementById("moose-equation-9517e141-3dc9-4bc5-baf0-af7b3e4b6630");katex.render("t+\\Delta t", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ for the current time step, and $var element = document.getElementById("moose-equation-84450147-9297-48ec-801c-0c01f9bd7397");katex.render("t", element, {displayMode:false,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});$ for the previous step, the explicit Euler method can be written:

var element = document.getElementById("moose-equation-eb303cc6-a878-4591-b1d3-58c42494ee69");katex.render("U(t+\\Delta t) = U(t) + \\Delta t A(t, U(t))", element, {displayMode:true,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});

This method can be expressed as a Runge-Kutta method with the following Butcher Tableau:

var element = document.getElementById("moose-equation-161da8af-cae0-4888-a8c7-9ad77ccbf398");katex.render("\\begin{array}{c|c} 0 & 0 \\\\ \\hline & 1 \\end{array}", element, {displayMode:true,throwOnError:false,macros:{"\\eqc":"\\,,","\\eqp":"\\,.","\\pd":"\\frac{\\partial #1}{\\partial #2}","\\pr":"\\left(#1\\right)","\\ddt":"\\frac{d #1}{d t}"}});

note

The ActuallyExplicitEuler implements the same algorithm but without forming the non linear system, making it faster and use less memory.

warning

All kernels except time-(derivative)-kernels should have the parameter implicit=false to use this time integrator.

Input Parameters

variablesA subset of the variables that this time integrator should be applied to
C++ Type:std::vector<VariableName>
Unit:(no unit assumed)
Controllable:No
Description:A subset of the variables that this time integrator should be applied to

Optional Parameters

control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Controllable:No
Description:Adds user-defined labels for accessing object parameters via control logic.
enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Controllable:No
Description:Set the enabled status of the MooseObject.

Advanced Parameters

Input Files

(test/tests/time_integrators/scalar/scalar.i)

(test/tests/time_integrators/scalar/scalar.i)

[Mesh]
  type = GeneratedMesh
  dim = 2
  xmin = 0
  xmax = 1
  ymin = 0
  ymax = 1
  nx = 1
  ny = 1
  elem_type = QUAD4
[]

[Variables]
  [./n]
    family = SCALAR
    order = FIRST
    initial_condition = 1
  [../]
[]

[ScalarKernels]
  [./dn]
    type = ODETimeDerivative
    variable = n
  [../]
  [./ode1]
    type = ParsedODEKernel
    expression = '-n'
    variable = n
    # implicit = false
  [../]
[]

[Executioner]
  type = Transient
  [./TimeIntegrator]
    # type = ImplicitEuler
    # type = BDF2
    type = CrankNicolson
    # type = ImplicitMidpoint
    # type = LStableDirk2
    # type = LStableDirk3
    # type = LStableDirk4
    # type = AStableDirk4
    #
    # Explicit methods
    # type = ExplicitEuler
    # type = ExplicitMidpoint
    # type = Heun
    # type = Ralston
  [../]
  start_time = 0
  end_time = 1
  dt = 0.001
  dtmin = 0.001 # Don't allow timestep cutting
  solve_type = 'PJFNK'
  nl_max_its = 2
  nl_abs_tol = 1.e-12 # This is an ODE, so nl_abs_tol makes sense.
[]

[Functions]
  [./exact_solution]
    type = ParsedFunction
    expression = exp(t)
  [../]
[]

[Postprocessors]
  [./error_n]
    # Post processor that computes the difference between the computed
    # and exact solutions.  For the exact solution used here, the
    # error at the final time should converge at O(dt^p), where p is
    # the order of the method.
    type = ScalarL2Error
    variable = n
    function = exact_solution
    # final is not currently supported for Postprocessor execute_on...
    # execute_on = 'final'
  [../]
[]

[Outputs]
  csv = true
[]

Description
Input Parameters
Input Files